PV system specifications of each sensor.
\r\n\t
",isbn:"978-1-83968-924-6",printIsbn:"978-1-83968-923-9",pdfIsbn:"978-1-83968-925-3",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"ea4ec0d6ee01b88e264178886e3210ed",bookSignature:"Dr. Hiran Wimal Amarasekera",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/9500.jpg",keywords:"Bone Tumors, Oncology, Childhood Tumors, Cancer, Risk Factors, Modern Management, Benign Lesions, Tumor-Like Conditions, Immunology, Histochemistry, Cell Oncology, Tumor Markers",numberOfDownloads:308,numberOfWosCitations:0,numberOfCrossrefCitations:1,numberOfDimensionsCitations:1,numberOfTotalCitations:2,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 28th 2020",dateEndSecondStepPublish:"October 26th 2020",dateEndThirdStepPublish:"December 25th 2020",dateEndFourthStepPublish:"March 15th 2021",dateEndFifthStepPublish:"May 14th 2021",remainingDaysToSecondStep:"3 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Consultant Orthopaedic Surgeon from Sri Lanka currently working in University Hospitals of Coventry and Warwickshire, UK, trained at the National Hospital of Sri Lanka, at the Oldchurch Hospital in Essex UK and The Avenue Hospital Melbourne, Australia and University Hospitals of Coventry and Warwickshire, UK, obtained the FRCS from Royal College of Surgeons of Edinburgh, Scotland.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"67634",title:"Dr.",name:"Hiran",middleName:"Wimal",surname:"Amarasekera",slug:"hiran-amarasekera",fullName:"Hiran Amarasekera",profilePictureURL:"https://mts.intechopen.com/storage/users/67634/images/system/67634.jpg",biography:"Hiran Amarasekera is a Consultant Orthopaedic Surgeon from Sri Lanka currently working in University Hospitals of Coventry and Warwickshire, the UK as a hip preservation fellow. \r\nHis special interests include young adult hip and knee problems, sports injuries, Hip and knee arthroplasty, and complex arthroscopic procedures. \r\nHe completed the MBBS from Kasturba medical college Manipal, India and did his postgraduate in Trauma and Orthopaedics at the Post-graduate Institute of the Medicine University of Colombo obtained the MS. \r\nHe was initially trained at the National Hospital of Sri Lanka and then completed the further training at the Oldchurch Hospital in Essex UK and The Avenue Hospital Melbourne, Australia and University Hospitals of Coventry and Warwickshire, UK.\r\nHe obtained the FRCS from Royal College of Surgeons of Edinburgh in 2003 and was elected a fellow of Sri Lanka College of surgeons (FCSSL) 2012. \r\nHe has a keen interest in academia and research. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"48885",title:"High-Speed Single-Photon Detection with Avalanche Photodiodes in the Near Infrared",doi:"10.5772/60481",slug:"high-speed-single-photon-detection-with-avalanche-photodiodes-in-the-near-infrared",body:'Single-photon detectors (SPDs), which are sufficiently sensitive to register single-photon clicks, are widely used in numerous fields of great importance, such as positron emission tomography, optical time domain reflectometry, astronomy and deep-space communication, and biological imaging [1-6]. SPDs are extraordinarily essential not only in fundamental research of quantum physics [7, 8], but also in practical quantum information processing techniques [9-11]. As one of the most commercially successful quantum information applications, quantum key distribution (QKD), which makes it possible for two distant parties to share secret keys via telecommunication, has rapidly progressed since its initial proposal in 1984 [12–16]. In order to achieve efficient QKD of long distances, GHz-clocked QKD systems have been developed. In these schemes, high-speed SPDs of high detection efficiency and low noise at the telecommunication wavelengths are necessary to guarantee the performance of high-speed QKDs [17-18]. Moreover, as for another revolutionary quantum information application, linear optical quantum computing (LOQC), which is a scalable paradigm for quantum information processing and computation, remains difficult to achieve [19-20]. Many efforts have been made worldwide toward this goal. A major factor that limits the advance of LOQC is the performances of the optical components such as SPDs. Significant improvements are still needed in terms of their detection efficiency, error counts, and ability to resolve photon numbers.
Thus far, many concepts and techniques have been proposed to realize high-performance single-photon detection. For instance, single-photon avalanche photodiodes (SPADs), SPDs based on frequency up-conversion, visible-light photon counters, superconducting transition-edge sensors, superconducting nanowire SPDs (SNSPDs), and SPDs based on quantum dots and semiconductor defects, differ in terms of spectral response, quantum efficiency, dark count and afterpulse noise, signal-to-noise ratio, timing jitter, and photon-number-resolving capability, providing optimal choices for various specific applications [21-28]. In consideration of high-speed quantum information processing applications, SPADs, frequency up-conversion technique, and SNSPDs are competitive choices [29-31]. Although SNSPDs possess the characteristic of ultra-low noise and timing jitter, which enables hundreds-of-kilometers QKD system, the requirement of cryogenic cooling system is one of the obvious drawbacks for practical applications. In this chapter, we focus on the recent development of high-speed single-photon detection based on InGaAs/InP SPADs and frequency up-conversion in the near infrared. Since the basic principle of the frequency up-conversion is to translate a near-infrared photon to the visible regime and then detect the photon with silicon avalanche photodiodes, the content presented in the following sections is concluded to be high-speed single-photon detection with avalanche photodiodes in the near infrared.
For their compact structure and low-power consumption, InGaAs/InP SPADs have been used intensively in practical applications at the near-infrared wavelengths, especially in QKD and laser ranging and imaging systems [32-34]. The avalanche photodiode (APD) is reverse-biased above the breakdown voltage (which is called Geiger operation mode), and carriers generated by a single-photon absorption could trigger a detectable macroscopic current after the avalanche gain. To make use of this avalanche propagation progress adequately, the avalanche should be stopped and the APD reset with a peripheral circuit. Generally, InGaAs/InP SPADs are operated in gated Geiger mode that employs gating pulse to determine the switching of the APD’s bias voltage between overvoltage and undervoltage. In this operation mode, the dark counts of InGaAs/InP SPADs would be reduced effectively [35-37]. However, since the APD is a capacitive device, the spike noise produced by the gating pulses charging and discharging on the APD’s capacitance is an inevitable problem. The weak photon-induced avalanche signals are buried in the spike noise, making the key technique to improve the performance of InGaAs/InP SPADs lie in efficient discrimination of avalanche signals from the spike noise. Furthermore, with the increase of the working repetition frequency, the afterpulsing effect becomes more and more serious, greatly affecting the performance of InGaAs/InP SPADs. The afterpulses are the error counts induced by the release of carriers trapped by defects in the multiplication region during an earlier avalanche event. To solve this issue, the avalanche gain should be decreased correspondingly, unavoidably resulting in the increase of the difficulty in the extraction of the valid avalanche signals.
Recently, some artful techniques, such as sinusoidal gating, self-differencing, and the combination of both, have been demonstrated to suppress the spike noise efficiently while increase the gating repetition rate over 1 GHz [38-42]. In a sinusoidal gating circuit, the spike noise produced by the applied sinusoidal gates was well cancelled by the specific electric band-elimination filters, considering that the APD’s capacitive response exhibits a relatively simple frequency spectrum. The InGaAs/InP SPADs that made use of this technique could be operated at 1.5 GHz with the detection efficiency of 10.8% [38]. Comparatively, the spike noise in the self-differencing scheme was eliminated by comparing the output of the APD with that delayed by one gating cycle. This type of SPAD was able to work at 1.25 GHz with the detection efficiency of 10.9% and dark count rate of 2.34×10-6 per gate [40]. Besides, the technique of harmonic subtraction was put forward to achieve high detection efficiency and low afterpulse probability for high-speed single-photon detection [43]. With this method, the detection efficiency of InGaAs/InP SPADs could reach ~ 50% with afterpulse probability below 3.5×10-4 per gate at 1.25 GHz. Although, all these approaches performed admirably, they are far from mature. There is still room for further enhancement in the respects of dark counts, maximum count rate, and timing jitter.
On the other hand, given the mature silicon SPD with high performance, the frequency up-conversion single-photon detection has shown great potential for many applications. Its basic principle is to translate a near-infrared photon to the visible regime, avoiding the disadvantages of InGaAs/InP APDs [44-46]. This nonlinear optics process requires a large nonlinearity of the nonlinear media and a strong pump field to realize the complete quantum conversion. Generally, the strong pump could be achieved by an external cavity or intracavity enhancement or a waveguide confinement, inevitably bringing about severe background noise because of the parasitic nonlinear interactions. Synchronized single-photon frequency up-conversion was presented to lower the noise. For the improvement of the conversion efficiency, the specific control of the synchronized pulses was required. Recently, efficient single-photon frequency up-conversion detection system operating at tens of MHz has been realized based on the all-optical synchronized fiber lasers, promising its applications in high-speed QKDs.
In this chapter, our recent developments and achievements in high-speed single-photon detection based on InGaAs/InP SPAD and frequency up-conversion single-photon detection were introduced in detail. In Section II, we present the experimental demonstration on some innovative schemes for InGaAs/InP SPAD, such as the optically self-differencing, the low-pass filtering with ultrashort pulses, and the creative combination of the self-differencing and low-pass filtering, to increase the working repetition frequency of the gated SPAD over 1 GHz. Meanwhile, other properties of the SPAD, for instance, the detection efficiency, the timing jitter, and the maximum counts, have been improved as well. Furthermore, a compact synchronized fiber laser system for highly efficient single-photon frequency up-conversion is illustrated in Section III, realizing high conversion efficiency with low background counts. All these high-performance single-photon detectors provide essential facility for high-speed quantum information applications. In Section IV, we discuss the advantage of the high-speed single-photon detection in some applications, such as laser ranging and imaging, quantum key distribution, and so on. Finally, we conclude the chapter in the last section by emphasizing the importance of the high-speed single-photon detection for quantum information applications.
As mentioned in the introduction, the suppression ratio of the spike noise, which is generated by the capacitive response of the APD to the gating pulses, is quite critical to the performance of InGaAs/InP SPAD operated in gated Geiger mode. In this section, we present several methods to remove spike noise and acquire avalanche signals, achieving high-speed SPAD with excellent properties.
Since the spike noise is caused by the capacitance characteristics of the APD, the capacitance-balancing technique employs a capacitor to imitate the APD’s response. As shown in Fig. 1 (a), the InGaAs/InP APD was connected in parallel with a complementary capacitor. The output signals of the APD and capacitor were connected to the 0 and π inputs of the magic-T network (MTNT), respectively. The MTNT was used as a subtracter here, subtracting the two spike noises from the APD and the capacitor. A tunable capacitor was chosen for the perfect matching between the APD and the complementary capacitor. Then, at the output of the circuit, only the avalanche signal was extracted and acquired by an oscilloscope after amplification by an RF amplifier.
(a) Schematic setup of capacitance-balancing InGaAs/InP SPAD. MTNT: a so-called magic-T network consisted with a broadband transformer; Amp: an RF amplifier; Osc: a high bandwidth oscilloscope. (b) Dark count rate of capacitance-balancing InGaAs/InP SPAD as a function of detection efficiency at 100 MHz.
In the capacitance-balancing scheme, the repetition frequency of the gating pulses could be tuned continuously on a large scale. Moreover, the capacitance-balancing InGaAs/InP SPADs are suitable for the applications requiring multi-channel timing acquisition, such as the time-code quantum key distribution. We just need to adjust the tunable capacitor to ensure the suppression ratios of the spike noise. A train of double gating pulses was employed to illustrate the capability of multi-channel detection. By changing the time interval between the double gating pulses, we could obtain that the capacitance balancing technique remained applicable in the single-photon detection with the gating repetition rate no higher than 260 MHz [47].
Here, we examined the performance of the SPADs at 100 MHz. The amplitude of the gating pulses was 4 V with the duration of ~ 1 ns, while the DC bias applied on the APD was varied to obtain different detection efficiencies. The operation temperature of the InGaAs/InP APD was set at -50℃. And a 1550-nm pulsed laser at 10 MHz with full width at half maximum (FWHM) of ~35 ps was attenuated to contain 0.1 photon per pulse before coupling into the APD fiber pigtail as the photon source. The laser pulse was synchronously triggered with the gating pulse, while their delay was adjusted to gain the highest detection efficiency for optimized operation. Figure 1 (b) exhibited the dark count rates as a function of the detection efficiency. The dark count rate increased with the detection efficiency, and we could figure out that the dark count rate was approximately 4.6×10-6 with the efficiency of 20%, indicating this SPAD performed well at 100 MHz.
Unlike the double-APD balancing [48], the capacitance-balancing technology using a capacitor instead to imitate the APD was much more economic and practical. The suppression ratio of the spike noise was ~ 19 dB, limiting the working speed. We believe that the capacitance-balancing SPAD would be able to be operated at a higher speed with the advance of semiconductor techniques.
The self-differencing technique first proposed by Z. L. Yuan et al. has shown a great improvement of the detection speed. Recently, the InGaAs/InP SPAD using this method has been shown to perform remarkably with the detection efficiency of 25% and dark count rate of 5.9×10-5 per gate at 1 GHz without Peltier cooling [49]. Unlike the traditional self-differencing circuits, we added a tunable phase shifter and attenuator for better suppression of the spike noise, as demonstrated in Fig. 2 (a). The gating pulses were superposed on the reversely biased InGaAs/InP APD. The output signal of the APD was sent to a 50/50 power splitter (DC to 3 GHz), being divided into two identical components. Then one component was delayed by one gating period through the tunable phase shifter, and the tunable attenuator in the other arm was used to guarantee equal amplitudes.
(a) Experimental setup of the self-differencing InGaAs/InP SPAD. PS: tunable phase shifter, Attn: tunable attenuator, Amp: RF amplifier. (b) Dark count rate and afterpulse probability as a function of the detection efficiency at –40℃.
Afterward the two components were combined by a differencer (DC to 3 GHz) before amplification. The output of the circuit was the amplified difference between the two split components, shifted relatively by one gating period. The tunable phase shifter and attenuator precisely controlled the split two components, ensuring the avalanche signal extracted with spike noise suppressed greatly. Furthermore, with the tunable phase shifter, the working repetition frequency of the self-differencing SPAD could be adjusted continuously with ease. And the adjustment range was determined by that of the phase shifter.
The 200-MHz gating pulses with amplitude of 5 V and duration of ~ 1 ns were used to characterize the performance of this InGaAs/InP SPAD. And the InGaAs/InP APD was cooled to be -40℃. Figure 2 (b) displayed the dark count rate and afterpulse probability as a function of detection efficiency. The reverse bias voltage applied on the APD was changed to obtain different efficiencies. The laser source was attenuated to contain 0.1 photons per pulse on average. The detection efficiency was corrected for Poissonian statistics of the photon numbers by the formula
where η was the detection efficiency, RO was the overall counting rate, RL was the repetition rate of the laser pulse, PE was the error counting probability, and μ was the average photon per pulse. In the experimental measurement, the dark count rate was measured with the laser off. It increased gradually with the detection efficiency.
The afterpulse probability, defined as the ratio of the total afterpulse counts to the photon counts, can be calculated from
where IPh and INI were the count rate per gate at the illuminated and nonilluminated gates, respectively, while ID was the dark count rate for each gate. R was the ratio of the repetition frequency of the gating pulse to that of the laser pulse. Here we took R=20 for measuring the afterpulse. The afterpulse probability increased with the detection efficiency and began to increase dramatically when the detection efficiency reached 16.7%, greatly impacting the performance of the SPAD. When the detection efficiency was 10.1%, the afterpulse probability was just 2.9% and the corresponding dark count rate was 9.0×10-6 per gate.
DC bias voltage applied on APD and dark count rate as a function of detection efficiency at three different temperatures.
The operation temperature of the InGaAs/InP APD was vital to the SPAD. We Peltier-cooled the APD to work at three different temperatures and tested the parameters of the SPAD. Figure 3 showed the dc voltage and dark count rate as a function of the dc bias voltage. The detection efficiency increased with the voltage. Meanwhile the rising slopes were almost the same at the three temperatures. To achieve the same detection efficiency, we should apply higher voltage at higher temperature. Since the gating pulses were identical, it could be figured out that the breakdown voltage increased with the temperature of the APD, leading to a great influence on the SPAD. Meanwhile, the dark count rate increased with the detection efficiency, while it was higher at the same efficiency at higher temperature. However, cooling the APD to lower temperatures consumes more energy. Therefore, we should choose an appropriate temperature in practical applications.
Furthermore, the InGaAs/InP SPAD using the cascade of self-differencing circuits was demonstrated [50]. By introducing a second self-differencing circuit, the suppression ratio was enhanced up to ~18 dB, making the SPAD more suitable for high-speed applications. Considering the twice splitting of the valid avalanche signal, the signal-to-noise ratio (SNR) was merely improved by ~10 dB.
Schematic setup of the optically self-balancing SPAD. AMP: RF amplifier; LD: distributed-feedback laser diode at 1550 nm; Attn: tunable optical attenuator; and PD1; 2: pin photodiodes.
We also proposed the optically self-balancing technique, as exhibited in Fig. 4 [51-53]. The output of the InGaAs/InP APD was amplified to trigger a laser diode at 1550 nm. Then, an erbium-doped fiber amplifier (EDFA) was used to magnify the transformed optical signal. Afterward, the splitting and the relative delay of the signal were processed through the optical devices. Finally, the avalanche signal was extracted and transformed to the electronic signal by two pin photodiode. Compared to the electronic self-differential technique, the optical method used stable and precisely controllable optical signals, providing immunity to the electromagnetic field of the surrounding circuits. Besides, the impedance matching was not necessary to be considered in this scheme. A 31-dB suppression of the spike noise was obtained, allowing the study on the photon-number resolving dynamics of the InGaAs/InP avalanche photodiode. The detection efficiency reached 22.4% while the afterpulse probability was controlled as low as 2.4% at 25 MHz. However, the transformation between the electronic and optical signal was more and more complicated with the advance of the repetition frequency of the gating pulses superposed on the APD, limiting its applications in high-speed single-photon detection.
The core concepts of the capacitance-balancing and self-differencing techniques are to produce a mimic signal of the spike noise and obtain the valid avalanche signal by making the two signals subtract each other. The performance of the SPAD using those two techniques would be enhanced by improving the similarity of the two signals. In contrast, the technique presented in the next section is to eliminate the spike noise directly by corresponding filters.
N. Namekata et al. first put forward the sinusoidal gating technique, employing the sinusoidal gates to control the bias voltage of the InGaAs/InP APD. The frequency distribution of the capacitive response of the APD to the sinusoidal gates was relatively simple, mainly concentrating at the repetition frequency of the gates and its harmonic frequencies. Notch filters were used to eliminate the spike noise and obtain the avalanche signal. The scheme suppressed the spike noise robustly and conveniently. By this means, the detection efficiency of the SPAD reached 10.5% with a dark count rate of 6.1×10−7 per gate and afterpulse probability of 3.4% at 2 GHz [39]. However, due to the distortion of the avalanche signal caused by the notch filters, the timing jitter of this SPAD was as large as 180 ps, limiting its applications in high-speed QKD systems or ultra-sensitive long-distance laser ranging. To solve this problem, we proposed the low-pass filtering technology, maintaining the suppression ratio of the spike noise while reducing the timing jitter.
Schematic setup of the low-pass filtering SPAD using sinusoidal gates and ultrashort gating pulses. SG: signal generator; BPF: band-pass filter; HPF: high-pass filter; LPF: low-pass filter.
Figure 5 illustrated the low-pass filtering technology. Before being applied on the APD, the sinusoidal gates passed through a band-pass filter to eliminate the sideband noise and harmonic noise. We used 1.5-GHz sinusoidal waves to examine the performance of the low-pass filtering InGaAs/InP SPAD. The output of the APD was filtered by the low-pass filtering cutting off at 1 GHz with the attenuation higher than 40 dB at 1.5 GHz. Since the spectrum of the avalanche signal distributed mostly at low frequency under 1 GHz, while that of the spike noise concentrated at 1.5 GHz and its harmonic frequencies, we could acquire the avalanche signal by the low-pass filter. To obtain higher SNR, we could employ one more low-pass filter.
(a) Dark count rate and afterpulse probability of InGaAs/InP SPAD using low-pass filtering technique with sinusoidal gates as a function of the detection efficiency. (b) Count rate dependent on the laser pulse delay in sinusoidally gated SPAD. (c) Dark count rate and afterpulse probability of low-pass filtering SPAD with ultrashort gates as a function of the detection efficiency. (d) Count rate dependent on the laser pulse delay in SPAD with ultrashort gates.
The operation temperature of the APD was set at -30℃. The laser source was attenuated to contain one photon per pulse on average to shorten the time of data acquisition, and synchronously triggered with the sine wave frequency. The amplitude of the amplified sinusoidal gating waves was fixed at 6 V. Figure 6 (a) illustrated the performance of the SPAD. The dark count rate and afterpulse probability increased with the detection efficiency. While the efficiency exceeded ~27%, the afterpulse probability rose sharply, limiting further increase of the detection efficiency. By this approach, the detection efficiency reached 13.0% with a dark-count rate of 1.5×10−5 per gate and afterpulse probability of 1.1%. While the detection efficiency was kept at 10.0%, we adjusted the delay between the laser and sinusoidal gate to get the effective gating width superposed on the APD, as charted in Fig. 6 (b). It was measured to be approximately 200 ps.
Unlike the traditional sinusoidal gating technique, the low-pass filtering was also appropriate for ultrashort gating pulses. We used 1.5-GHz ultrashort gating pulses to characterize the SPAD. The ultrashort pulses were filtered by a high-pass filter (HPF) cutting off at 1 GHz, canceling the noise at low frequencies and ensuring the final SNR of the SPAD. The transmit performance of the HPF remained excellent up to 6 GHz, maintaining the waveform of the ultrashort pulses. The performance of the SPAD was illustrated in Fig. 6 (c). We could find out that the afterpulse probability did not increase obviously until the detection efficiency reached ~ 35%. At the detection efficiency of 35%, the afterpulse probability of the SPAD using ultrashort gating pulses was 9.3% with dark count rate of 6.2×10−5 per gate. In comparison, the afterpulse probability of the SPAD using sinusoidal gating pulses was 19.3% with dark count rate of 8.3×10−5 per gate. From Fig. 6 (d), it could be noted that the FWHM of the effective gating pulses applied on the APD was 140 ps, less than that in Fig. 6 (b). Since the schematic setup of the two SPADs were exactly the same except the gating signals, we can deduce that ultrashort gating pulses of smaller gating widths improved the SPAD. For a better performance, we could further decrease the gating width.
The timing jitter of the 1.5-GHz SPAD was measured to be 68 ps with a time-correlated single-photon counting (TCSPC) setup (PicoQuant GmbH, PicoHarp 300, Germany). It has been improved a lot by using the low-pass filtering technique, due to the integrity of the avalanche signal preserved well with the low-pass filter. This technology was extraordinarily suitable for high-speed single-photon detection, on account that we could choose low-pass filters cutting off at higher frequencies for better preservation of the avalanche signal. With such a low timing jitter and convenient structure, this type of high-speed SPAD has be widely used in laser ranging systems with high resolution at the near-infrared wavelengths.
As mentioned in the previous section, the self-differencing and sinusoidal gating techniques offered effective methods for high-speed single-photon detection. To further advance the suppression ratio of the spike noise, there were schemes to combine the two techniques. However, the SNR was not improved as much, due to the splitting of the avalanche signal in the self-differencing circuit. Here, we introduced some combining techniques to take full advantage of both techniques, achieving high-performance GHz InGaAs/InP SPAD.
Figure 7 (a) exhibited the experimental setup of the SPAD using the combining technique. The sinusoidal wave, which came out from the signal generator, was divided into two parts. One part was amplified to serve as the gating signal superposed on the APD. Here, we set the repetition frequency of the sinusoidal signal to be 1 GHz to characterize the SPAD with this scheme. The output of the APD was filtered by a low-pass filter (LPF1) that cut off at 700 MHz with attenuation higher than 40 dB at 1 GHz. Then it was connected to the power combiner, combined with the other part of the 1-GHz sinusoidal signal. The spectrum of the filtered spike noise concentrated at 1 GHz. The phase shifter was used to make the phase difference between the two signal 180°, and the tunable attenuator was employed to ensure the amplitudes equal. Therefore, we could further eliminate the spike noise and get the avalanche signal, improving the suppression ratio of the spike noise by 21 dB. The low-pass filter (LPF2) cutting off at 1.5 GHz was employed to cancel the electronic noise of high frequency of the cascade RF amplifiers. By this means, the SNR of the SPAD could be advanced with the performance of the timing jitter maintained.
We employed a TCSPC with the resolution of 4 ps to test the timing jitter of the SPAD. As shown in Fig. 7 (b), the time histogram of detection events was recorded. The count peak in the illuminated gating pulse was much higher than the other peaks. The residual peaks after the maximum peak, which might be induced by the oscillation, could be neglected by introducing a proper dead time. Here, a 10-ns dead time was applied. The time interval between the peaks was ~1 ns, matching with the 1-GHz repetition frequency of the sinusoidal gating. And the timing jitter of the avalanche signal showed an FWHM of 60 ps, which was extremely low for sinusoidally gated InGaAs/InP SPAD.
(a) Experimental setup of sinusoidally gated SPAD using low-pass filtered self-differencing technique. SG: signal generator; HP-AMP: high-power amplifier; BPF: band-pass filter; Attn: variable attenuator; LPF1, 2: low-pass filter; RF-AMP1, 2: RF amplifier. (b) Time histogram of detection events recorded by the TCSPC. Inset: Photon detection rate as a function of photon flux.
With the 10-ns dead time, we could effectively reduce the error counts. However, it would place a limitation for the maximum counts. We measured the linearity and maximum count rate of the SPAD with a continuous wave laser at 1550 nm to illuminate the APD. As charted in the inset of Fig. 8(b), the photon count rate increased linearly as a function of the photon flux while the counting rate was below 80 MHz. Finally the SPAD was saturated at 95 MHz. The SPAD with such a low timing jitter and high maximum counts provided possibilities for the achievement of high-speed QKD.
Schematic setup of SPAD using the combining technique with ultrashort gates. HPF: high-pass filter; Attn: tunable attenuator; PS: phase shifter; PC: power combiner; LPF: low-pass filter.
As mentioned in the previous section, the performance of the SPAD using the ultrashort gating pulses with shorter duration was even more excellent. We proposed a combining method more appropriate for ultrashort gates, as demonstrated in Fig. 8. In consideration of the distribution of the spike noise, the sinusoidal waves at the repetition rates of f and 2f were added by a power combiner to mimic the spike noise. The differential signal of the output of the APD and the mimic one was gained and then filtered by a low-pass filter whose cutting-off frequency could be set between 2f and 3f. By this means, the integrity of the avalanche signal could be maintained to the best of the capability of the filtering technique, further reducing the timing jitter of the SPAD. Moreover, the tunable attenuator and phase shifter were used here to guarantee the waveform of the synthetic signal resembles the spike noise as closely as possible. The similarity would advance with the increase of the gating repetition rate, making the scheme quite suitable for high-speed single-photon detection.
The combining technique illustrated earlier took advantages of self-differencing and low-pass filtering technique, achieving high-speed single-photon detection with high suppression ratio and low-timing jitter. With the blossom of the quantum information applications, the SPAD using this technology would be implemented more and more widely.
Frequency conversion plays quite an important role in nonlinear optical signal processing. Infrared single-photon up-conversion based on sum frequency generation was put forward to realize single-photon detection at the infrared wavelengths with existing high-performance Si APDs. Recently, the technique has been successfully used in various applications, including infrared imaging, QKD, and infrared ultra-sensitive spectroscopy [54-56]. More and more interest has been focused on proposing novel schemes for achieving single-photon frequency up-conversion with high efficiency and low noise.
The nonlinear optical media of large nonlinearity and a sufficiently strong pump field were necessary to enforce the complete quantum conversion. Generally, periodically poled lithium niobate (PPLN) crystal was used as the nonlinear media for nonlinear interaction, considering its relatively large effective nonlinear coefficient and long interaction length. For the requisite strong pump, schemes using an external cavity or intracavity enhancement or a waveguide confinement have been proposed. With such high-intensity pump, frequency up-conversion has been carried out with almost 100% conversion efficiency. However, a strong pump field would unavoidably bring about severe background noise. To solve this problem, synchronized single-photon frequency up-conversion was presented. In the scheme, each signal photon was synchronized to a pump pulse, achieving high efficiencies of frequency up-conversion with quite low noise.
Experimental setup of the synchronous single-photon frequency up-conversion detection. EDFL: erbium-doped fiber laser; YDFL: ytterbium-doped fiber laser; YDFA, ytterbium-doped fiber amplifier; FBG: fiber Bragg grating; Col1, 2, 3: collimator; BS: beam splitter; DM: dichroic mirror; GP: Glan prism; L1, 2, 3: lens; PPLN, periodically poled lithium niobate crystal; BP: optical band-pass filter.
Figure 9 exhibited the experimental setup of the synchronous single-photon frequency up-conversion detection. The signal and pump sources were synchronized in master–slave configuration. The repetition frequency of the synchronized system was about 20.3 MHz. We optimized the spectrum and pulse duration to improve the frequency up-conversion system by managing the intracavity dispersion of the two lasers. The narrow spectrum was required to match with the bandwidth of the PPLN crystal. And the pulse duration of the pump should be a little longer than that of the signal to include all the signal photons within the pump envelop, ensuring the final conversion efficiency.
(a) (b) Spectrum and duration of the signal source. The pulse duration was measured by two-photon absorption. (c) (d) Spectrum and duration of the pump source. The pulse duration was measured by self-correlation.
Figure 10 gave the typical spectra and durations of the signal and pump source. The signal source was provided by an Er-doped NPR locking fiber laser (EDFL), whose output centered at 1562.1 nm with a full width at half maximum (FWHM) of 1.0 nm. The pulse duration was measured to be 1.3 ps by two-photon absorption. The pump source was generated by using an Yb-doped NPR locking fiber laser (YDFL), centering at 1041.1 with an FWHM of 0.7 nm. By self-correlation, the pulse duration was measured to be 34.3 ps. Finally, the 1562.1-nm signal source was up-converted through sum-frequency generation and the up-converted photons at 624 nm were detected by using a standard Si-APD based single-photon detector. The quantum conversion efficiency of infrared photons was up to ~80% with the corresponding background noise of ~300 counts per second. Compared with the background counts of CW pumping, the background was about two orders smaller in synchronously pulsed pumping scheme. However, for satisfying the applications of GHz QKD, the single-photon frequency up-conversion technique requires enormous development.
Quantum key distribution (QKD) has nowadays been demonstrated as a cryptographic approach to provide absolute security between the sender (Alice) and the receiver (Bob), according to the fundamental laws of quantum mechanics. Fiber-based systems have been implemented in prototype QKD experiments, with practical stabilities in long-distance telecom fibers. The separation distance between Alice and Bob has achieved tens of kilometers in field trials. However, despite these significant advances over recent years QKD’s primary challenge is still to obtain higher bit rates over longer distances. The major factors that limit the performance of the QKD are due to the immaturity of single-photon detectors at telecom-wavelengths. The probability of detection decreasing at long distance because of the high losses, while on the other hand, the noise rate of the detector being constant leads to a too high error rate above a certain distance, making it no longer possible to exchange secret keys. Therefore low-noise detectors are essential for long-distance QKD. It could be figured out that with the high-speed SPADs mentioned in the previous section, the performance of QKD systems would be further improved.
Among the fiber-based QKD systems, the polarization-encoding and phase-encoding methods are most widely implemented [57, 58]. Figure 11 showed the schematic setup of polarization-encoding QKD system based on the BB84 four-state protocol. The single-photon signals generated from attenuated laser pulses. Each laser diode could produce only one state of the BB84 protocol. The single-photon signals transmitted through the quantum channel after attenuation and reached Bob’s side. As Bob tried to decode the polarization information, he randomly chose HV base or QR base to measure to polarization. Four single-photon detectors (SPD1∼ SPD4) were used to detect the single-photon signals. However, in the polarization-encoding QKD system, the polarization states must be aligned and kept aligned due to the imperfection of the optical fiber and the disturbance of the environment. Thus, the polarization real-time control was needed, which presented the main difficulty for the implementation of polarization-encoding QKD system.
Schematic setup of polarization-coding QKD system. ATT: attenuator, BS: beam splitter, PC: polarization controller, PBS: polarization maintaining beam splitter, SPD: single-photon detector, LD: laser diode.
To examine the applications of the high-speed SPDs in QKD systems, we instead used 1.25-GHz sinusoidally gated InGaAs/InP SPADs to investigate the characteristics of a 1.25 GHz light source at 1550 nm. The laser pulse with the duration of ~20 ps was attenuated to contain 0.01 photon per pulse on average. The dark count rate of the 1.25-GHz SPAD using the low-pass filtering technique was 8×10−6 per gate at the detection efficiency of 10%. The afterpulse probability was ~3.5% with the 10-ns dead time. Figure 12 illustrated the counting rate recorded by the SPAD by changing the delay between the light source and the detector. The extinction ratio of the light pulse was up to 33 dB with the efficient gate width of ∼ 140 ps. It could be noted that this SPAD could efficiently achieve high-speed single-photon detection with low error counts, promising its applications in high-speed QKD systems.
Counting rate depends on the pulse laser delay.
In this chapter, we mainly introduced several techniques, such as capacitance-balancing, self-differencing, low-pass filtering, and the combination techniques, to achieve high-speed single-photon detection based on InGaAs/InP SPAD. The spike noise produced by the capacitive response of the APD was well detached, maintaining high efficiency and reducing the error counts correspondingly at GHz working repetition frequency. Furthermore, the frequency up-conversion technique was used to realize infrared single-photon detection with high conversion efficiency and low background noise at ~ 20 MHz. The advance of single-photon detectors highly supported the development of QKD systems, because both the key generation rate and the key distribution distance were mainly limited by the performance of SPDs thus far.
This work was supported by the National Natural Science Fund of China (Grant No. 61127014) and the National Key Scientific Instrument Project (Grant No. 2012YQ150092), General Financial Grant from the China Postdoctoral Science Foundation (Grant No. 2014M560347), and the Hujiang Foundation of China (B14002/D14002).
Qatar’s rapid development over the past decade led to a remarkable growth on its economy and population. Hence, increasing the demands on food, water, electronics and services. All of which relies on electricity to power the industries such as desalinization plants, farms, commercial infrastructures, semiconductor factories and more. According to the Qatar Water and Electricity Corporation or QWEC, a foremost power generation plant in the country stated that the electricity demand in the country is increasing at an estimated yearly average growth rate of 6–7% in the coming years [1]. In order to address the increasing electricity demand, the state is considering a new energy strategy that would foster sustainability, but also contribute to the reduction of the greenhouse gas emission levels. Fortunately, the gulf region where the country resides, experiences 6 kWh/m2/day amounting to 4449 h/year where 70% comes from sunshine, thus, focusing on optimization of energy extraction from sunlight is a viable solution [2]. In fact, renewable energy sources such as those from photovoltaic cell (PV) plants are estimated to contribute 11% to the global demand by 2050 according to the International Energy Agency (IEA) [3].
\nAnother possible source of renewable energy in Qatar can be harnessed from wind turbines. An assessment on wind energy potential in Qatar conducted by Qatar Petroleum [4] revealed that Qatar may employ use of small and medium wind turbines since 80% of the time wind speed over the country exceeds the critical speed of 3 m/s with annual mean speed over land and offshore of 4.3 and 5.7 m/s, respectively. It was estimated that 150 W/m2 may be harnessed from a 5 m/s wind speed but the power generated from wind turbines may be 8% less compared to the gas fired electricity. The cost projected for an offshore wind turbine is 10% less than the gas-based counterpart. Although wind turbines sound promising as a potential source of renewable energy, it does present several disadvantages compared to PV plants such as: annual maintenance on the turbine’s gear box in contrast to minimal maintenance for the PV, loud noise during operation for nearby inhabitants, and smaller life span of 20–25 years compared to 30 year life span of PV [5]. Qatar does not have immediate plans for installing wind turbines yet, instead it has been focusing on solar energy by allocating US $1 billion investment for the project which includes desalinization plants and a 200 MW power plant by Kahramaa [4]. With the upcoming 2022 FIFA cup, the country aims to be the first carbon neutral world cup utilizing solar energy to power air conditioning and fan zones. Since the state is leaning towards utilizing mostly solar energy to help power its industry, this study was conducted to primarily focus on PV alternative that was designed specifically for Qatar’s environment to test and understand its performance through measurment, prediction and analysis that should provide possible references for its solar industry.
\nLarge-scale PV farms are usually situated where maximum solar energy conversion can be generated which are either semi-arid lands or a desert. However, soaring temperatures reaching 50°C or more, high humidity and heavy sandstorms are some examples of environmental factors that may significantly reduce the efficiency in power generation of the PV systems. These issues are region-specific and may differ from one place to another even within the Gulf region, Hence, it is significant to investigate the modern PV technology under these harsh conditions that are specifically present in Qatar so that performance could be strongly correlated to it [6]. One apparent benefit from this is that the uncertainty of PV performance will be greatly reduced leading to a more predictable and profitable solar megaprojects that are planned to be constructed in the area [7, 8, 9]. The results could also cater to the interests of the manufacturers, researchers and technology enthusiasts in order to develop or innovate solutions.
\nEfficient energy management is among the benefits from understanding PV performance since some modern communities now use hybrid systems where they integrate renewable sources of energy such as solar PV to determine how it behaves in such systems. In [10], the authors discussed modeling and optimization of urban integrated energy systems to provide an energy plan or policy for a better energy efficiency aiming to mitigate energy crisis experienced in urban communities. In addition, Menetti et al. [11] proposed an efficient energy management that effectively use energy storage systems for renewable energy sources and the electric grid to reduce energy exchanged and power peaks on the grid. The data from the monitoring system becomes a necessary tool for conducting important analysis on the system for a region such as [12] to determine its costs and profit throughout its operation to assess its financial sustenance and feasibility for its possible application to other regions. In addition, it would also aid in contributing to the continuing development of efficient operations in industries to yield better results through exergy and energy analysis such as in [13, 14] and techno-economic analysis in [15, 16]. With increasing amount of studies being conducted centered on renewable energy especially on solar energy and PV, this study will prove useful to the scientific community and may serve as a significant reference to the ones conducted similarly in Qatar.
\nSeveral similar investigations in Qatar with same line of inquiries [17, 18, 19, 20, 21, 22, 23, 24, 25, 26] were conducted but none has been able to provide a cost-effective yet reliable system that satisfies the requirement for accessing, monitoring and predicting PV yield. Another major concern is the data acquisition system (DAS); most available commercial DAS tend to be costly when implemented for large solar PV plants. In addition, commercial DAS are inflexible for reconfigurations and modifications for various scenarios, thus, limiting its use. Furthermore, numerous efforts have been conducted in designing and implementing PV monitoring systems that utilize several sensors and data acquisition [27]. The system in [28] included an off-shelf component of Agilent 24902A, wherein the data were transmitted to the wired general purpose instrumentation bus to a computer that is running a LabVIEW™ program to determine the impact of solar irradiance and ambient temperature. Haba [29] developed a designated monitoring system for several PV panels that utilizes three gateways intended for weather station, current and voltage readings and storm detection which were then sent and hosted to online cloud specifically freeboard.io. A readily available commercial DAS was used for investigating the impact of module temperature and solar irradiance on PV efficiency and transmits to a server through the use of GPIB bus and cloud service [30]. Study [31] used a system consisting of LM35 temperature sensor and LDRs (light dependent resistors) for measuring ambient temperature and solar irradiance of PV module, respectively. The data is then transmitted to the computer wirelessly via Wi-Fi by connecting the microcontroller with EGSR7150 modem through its serial interface.
\nForecasting of PV performance were recently introduced to improve the quality of the systems such as providing dispatch management, control operations, power ramp and flicker prediction on hourly basis; and load consumption and production monitoring on daily basis [32]. Parametric models were also utilized for forecasting which are mostly affected by the execution of the component models and factors that are not readily available, thus, affects the accuracy of the system [33]. Recently, ML was introduced to overcome the above drawbacks; which is driven by the interactions between the input and output variables according to the data. Several studies were already conducted like in [34] were they determined the solar potential from rooftops in Switzerland by utilizing ML. Li et al. [35] used ML to predict solar irradiance to precisely determine the PV output utilizing Markov model and regression. Most of these forecasts were conducted on a specific environment, hence it would not be able to provide the same accruacy when used in another locations that exhibits different environmetal parameters like in Doha were it experiences unique intense heat and heavy dust storms that lasts year long. Therefore, we planned to deliberately harness ML for predicting the performance of PV systems from the various environmetal parameters that are present in Doha along the year for viability and bankability of PV energy source.
\nThis study describes the development of an in-house customized DAS system that is viable for monitoring PV systems under Qatar’s climate and which comprises of two parts: hardware and software. Also, the study is enhanced by describing the calibration tools that are necessary in such studies. The remainder of the study is as follows: Section 2 describes the hardware and signal acquisition. Section 3 depicts the ML used for the data gathered throughout the duration of the study. Section 4 discusses the results from the developed system and the ML results. Finally, the conclusion and future work is provided in Section 5.
\nThe hardware and signal acquisition system were installed in the Solar Lab facility under the College of Engineering, Qatar University. The ground floor of the solar lab facility houses computer workstation and wireless access point while its rooftop emulates the PV panel remote site where PV panels and data acquisition hardware system are mounted along with all environmental sensors and transducers. Qatar, having an arid environment with extreme ambient temperature easily surpassing 38°C during summer and often approaches 50°C with a humidity of 90% [36].
\nThe authors developed an in-house and customized DAS that acquires six environmental parameters and two electrical parameters enhanced by analog filters with gain and offset adjustments for calibration purposes. The in-house DAS was designed to allow flexibility in order to construct a customized signal conditioning circuit suitable for each sensor that are deemed appropropriate for the range of parameter values in an arid environment. The selected sensors along with the signal conditioning circuit and topology were chosen in order to implement a robust DAS that is appropriate to Doha’s harsh weather condition.
\n\nFigure 1 depicts the overall data acquisition framework. Data acquisition starts from the PV panel remote site where the PV panels are installed to ensure maximum exposure to sun’s irradiance, free from shadows due to obstructions. Selection of azimuth and tilt angle of PV panels are also important mounting details that needs to be considered. Two polycrystalline PV panels connected in series were installed in the remote site where the electrical and environmental parameters are needed to be monitored periodically in a specified sequence of steps as shown in the generalized flowchart in Figure 2. Periodic acquisition are normally spaced 15 minutes apart to ensure seamless wireless transmission between the PV panel remote site to the research lab site due to the considering the response time of the hardware. Information collected in the research lab site are stored locally and to the file hosting service of Dropbox™ along with the visualization facility of ThingSpeak™ through and iCloud™ server.
\nOverall data acquisition system.
Generalized flowchart of the PV monitoring system.
A detailed illustration of the connection diagram exhibiting important components of the PV panel remote site is shown in Figure 3. Six environmental and two electrical parameters, namely; (1) ambient temperature, (2) irradiance level, (3) wind speed, (4) surface temperature, (5) relative humidity, (6) dust levels, along with PV voltage and current are carefully studied and chosen by the authors in [37, 38] in order to provide highest probable impact contributing to the correlation to PV panel performance and efficiency, thus, allowing higher reliability when applying ML algorithms in [37, 38, 39]. The system specifications of each sensor are enumerated in Table 1 that includes actual part number of the off-the-shelf sensors along with the manufacturer and range of operation. The details of DAS design and operation were presented by the authors in [37, 38, 39, 40].
\nSystem set-up of the PV panel remote site.
Parameter to be measured | \nDiscrete sensor | \nManufacturer | \nMeasurement range | \n
---|---|---|---|
Ambient temperature | \nLM35 | \nNational Instruments | \n0–70°C | \n
Irradiance | \nPyranometer SP-110 | \nApogee | \n0 \n | \n
Wind speed | \nType 485 Wind sensor | \nQS-FS | \n0 \n | \n
Surface temperature | \nPlatinum RTD PT100 | \nFarnell | \n0–100°C | \n
Humidity | \nHIH-4000-003 | \nHoneywell | \n0–100% | \n
Dust level | \nGP2Y1010AU0F Optical Sensor | \nSharp | \n0 \n | \n
Voltage | \nVoltage transducer LV 25-P | \nLEM | \n0 V to 40 V | \n
Current | \nHall effect current transducer LA 100-P | \nLEM | \n0A to 5A | \n
PV system specifications of each sensor.
\nFigure 4 exhibits the simplified connection of various elements to process the required signal for redundant storage and visualization in the research lab set-up. The computer workstation uses LabVIEW™ program to process data that allows visualization of recently acquired data as depicted in Figure 5.
\nSystem set-up of the research lab site.
Sample visualization of collected data using LabVIEW™ in the computer workstation.
ML is the process of training a system to automatically predict output from given inputs. The system is trained using available set of inputs and their respective outputs. The concept of ML is useful in biomedical applications [41, 42], power prediction [43] and in general for any data processing and analysis studies. ML will be used to learn from the large amount of monitoring data collected from the setup discussed in the previous section and this phase is the training phase. During the training phase a part of the input data used for training is kept for validation purposes of the trained network. The validation accuracy is a metric used to determine how good or bad a trained ML network is. This ML trained network is then used for testing some data, which was unknown to the ML network, and is used to check if the ML trained network can actually predict the output correctly. The best performing ML network can later be used to predict the PV performance in the future based on the environmental and electrical inputs. The various stages that are involved in the ML are shown in Figure 6 and will also be discussed in details in the sub sections below.
\nStages involved in ML training and testing phase.
It is always important to make sure that the data given to the ML network for training is correctly formatted, making sure all outliers in the data or data which are incorrect and not trustable are removed. The data should be made in a format which is acceptable to the ML network in whichever platform it is being operated on. The ML Toolbox in Matlab 2019a version was used in the study. There are many other popular ML platforms available such as TensorFlow, Keras, Shogun, and RapidMiner.
\nOnce the data (input and output) for the training and testing purpose is ready, it is important to select the inputs that can help in predicting the output better. Sometimes giving more input or options to help in prediction can lead to overfitting problem. Overfitting is an issue where a ML network is trained to work the best for only the trained dataset and predicts mostly wrong outputs in the testing phase. This process of selecting the input data that can increase the testing accuracy is called feature selection. Selection of features is the process of selecting a subset of relevant, high-quality and non-redundant features to create learning models with better accuracy [44, 45]. Well known feature selection techniques – Correlation feature selection (CFS) and Relief feature selection (ReliefF) was used in this study. CFS technique selects feature sub-sets based on correlation-based heuristic evaluation function and ReliefF is an instance-based algorithm that assigns a relevance weight to each feature that reflects its ability to differentiate class values [43].
\nOnce the data that will be given as input to the ML training phase is selected, then there are several ML techniques that can be used to see which techniques help in reaching better performance. The techniques used in this study can be broadly classified into two categories: Classical ML Technique and Artificial Neural Network. These techniques are compared in the performance in prediction during the testing phase and the best performing technique is archived for future use.
\nSeveral simple and popular regression and prediction models are stated in this work to estimate the PV output power. These are namely Simple Linear Regression [46], Gaussian Process Regression (GPR) [47] from the regression learner, and M5P regression tree [37, 48]. Simple linear regression model has a linear relationship between the output response and the input parameters. GPR involves a Gaussian process using lazy learning and a measure of the point similarity (kernel function) to predict the value from the training data for an unseen point. The M5P regression tree uses algorithm which contains if and else statements [48, 49] . In other words, predicted power will be the result of “if… then…else…” statements.
\nArtificial Neural Network (ANN) (Figure 7) can be thought of a replication of how the human nervous system works, but as it is artificial thus it gets its name [50]. ANN has three major layers: (1) Input Layer, Output Layer and the Hidden Layer. The input layer are the artificial neurons where the actual learning happens and is also the layer where the input is fed. Each neuron in this layer has specific weights, which are details used to solve a specific problem. These weighted summed inputs are used in the hidden layers or in the transfer functions. Transfer functions are then inputs to activation function which tries to predict the output or provides the error back to the network as a feedback. This feedback acts as learning for the input layers again to try providing inputs to the activation function to help in better prediction.
\nANN architecture and its main components.
There are several Training Algorithms (TA) available in the Matlab implementation of ANN and each of them have their advantages and disadvantages and each application can have a specific TA giving better results than the others due to the nature of the data. It is always important to explore various combinations of number of hidden layers and training functions to find the best combination that predicts the PV power most accurately, as shown in Figure 8. The algorithm first varies the training algorithms, then the number of hidden layers and then does many tries using the combination. During each trial the algorithm stores the network with best performance for testing purpose. The final best network is used for predicting the PV power using the input variables.
\nMethod to find the best ANN to predict PV power.
\nFigure 9 summarizes the network settings for the ANN based PV power prediction. The optimum number of hidden layers providing the best model were different for all features (60), CFS technique (260) and ReliefF technique (180) and were found using the algorithm stated in Figure 8.
\nDetails of the ANN.
In order to compare between the various categories, techniques of ML and also the various feature selection techniques the below statistical parameters were used as performance metrics [51].
\n\n\n
The prototype system (setup shown in Figures 1 and 3) was used for collecting the PV and environmental parameters and PV power output data from the period November 2014 until October 2016. Summary of the PV and environmental parameters and the data used for deriving the predictive model of the PV power is shown in Table 2.
\nDetails of the environment parameters used for the predictive model.
Selected features vector.
\nTable 3 summarizes the parameters selected based on the feature selection techniques CFS and relief F.
\n\nTable 4 summarizes the performance of the different classical ML techniques with the different feature selection techniques. It shows both the Training and Testing Phase performance metrics. It can be clearly seen the best performance is the CFS feature selection technique using the GPR algorithm with RMSE of 12.7144 watts compared to the maximum power of 114.2017 watts generated from the setup, as shown in Table 2.
\nPerformance comparison between the various regression techniques.
\nTable 5 summarizes the performance of the ANN best trained network found using the algorithm in Figure 8 and with the different feature selection techniques. It can be clearly seen that the ANN trained network outperforms the techniques in the classical ML techniques. In ANN, without feature selection techniques provides the best testing performance with RMSE of 5.48 watts compared to the maximum power of 114.20 watts generated from the setup, as shown in Table 2.
\nPerformance comparison between the various ANN techniques.
A customized PV system was developed at Qatar University to monitor, analyze and evaluate the performance of PV using various weather factors. The study also showed details of how the data collected could be used for training different ML algorithms which were compared using different statistical analytical tools. Several feature selection techniques were also used to avoid the problem of overfitting. Comparison between the different ML techniques and different feature selection techniques helped in concluding an ANN model to be used for predicting PV performance using different environment and electrical parameters. The paper also showed the opportunity of tuning the ANN by varying the number of hidden layers and changing the training algorithm. This study describes the development of an in-house customized DAS system that is viable for monitoring PV systems under Qatar’s climate and which comprises of two parts: hardware and software. Also, the study is enhanced by describing the calibration tools that are necessary in such studies. The remainder of the study is as follows: Section 2 describes the hardware and signal acquisition. Section 3 depicts the ML used for the data gathered throughout the duration of the study. Section 4 discusses the results from the developed system and the ML results. Finally, the conclusion and future work is provided in Section 5.
\nThe authors would like to thank Qatar University for the financial, technical, and administrative support, without which this work would have not been achieved.
\nThe authors declare no conflict of interest.
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