States of an arm of the inverter with three levels.
\r\n\tAnimal food additives are products used in animal nutrition for purposes of improving the quality of feed or to improve the animal’s performance and health. Other additives can be used to enhance digestibility or even flavour of feed materials. In addition, feed additives are known which improve the quality of compound feed production; consequently e.g. they improve the quality of the granulated mixed diet.
\r\n\r\n\tGenerally feed additives could be divided into five groups:
\r\n\t1.Technological additives which influence the technological aspects of the diet to improve its handling or hygiene characteristics.
\r\n\t2. Sensory additives which improve the palatability of a diet by stimulating appetite, usually through the effect these products have on the flavour or colour.
\r\n\t3. Nutritional additives, such additives are specific nutrient(s) required by the animal for optimal production.
\r\n\t4.Zootechnical additives which improve the nutrient status of the animal, not by providing specific nutrients, but by enabling more efficient use of the nutrients present in the diet, in other words, it increases the efficiency of production.
\r\n\t5. In poultry nutrition: Coccidiostats and Histomonostats which widely used to control intestinal health of poultry through direct effects on the parasitic organism concerned.
\r\n\tThe aim of the book is to present the impact of the most important feed additives on the animal production, to demonstrate their mode of action, to show their effect on intermediate metabolism and heath status of livestock and to suggest how to use the different feed additives in animal nutrition to produce high quality and safety animal origin foodstuffs for human consumer.
",isbn:"978-1-83969-404-2",printIsbn:"978-1-83969-403-5",pdfIsbn:"978-1-83969-405-9",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"8ffe43a82ac48b309abc3632bbf3efd0",bookSignature:"Prof. László Babinszky",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10496.jpg",keywords:"Technological Feed Additives, Feed Industry, Quality of Compound Feed, Non-Antibiotic Growth Promoter, Product Quality, Additive Enzymes, Digestibility of Nutrients, NSP Enzymes, Farm Animals, Livestock, Immunity, Microbiome",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 24th 2020",dateEndSecondStepPublish:"December 22nd 2020",dateEndThirdStepPublish:"February 20th 2021",dateEndFourthStepPublish:"May 11th 2021",dateEndFifthStepPublish:"July 10th 2021",remainingDaysToSecondStep:"2 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Professor Emeritus from the University of Debrecen, Hungary who authored 297 publications (papers, book chapters) and edited 3 books. Member of various committees and chairman of the World Conference of Innovative Animal Nutrition and Feeding (WIANF).",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"53998",title:"Prof.",name:"László",middleName:null,surname:"Babinszky",slug:"laszlo-babinszky",fullName:"László Babinszky",profilePictureURL:"https://mts.intechopen.com/storage/users/53998/images/system/53998.jpg",biography:"László Babinszky is Professor Emeritus of animal nutrition at the University of Debrecen, Hungary. From 1984 to 1985 he worked at the Agricultural University in Wageningen and in the Institute for Livestock Feeding and Nutrition in Lelystad (the Netherlands). He also worked at the Agricultural University of Vienna in the Institute for Animal Breeding and Nutrition (Austria) and in the Oscar Kellner Research Institute in Rostock (Germany). From 1988 to 1992, he worked in the Department of Animal Nutrition (Agricultural University in Wageningen). In 1992 he obtained a PhD degree in animal nutrition from the University of Wageningen.He has authored 297 publications (papers, book chapters). He edited 3 books and 14 international conference proceedings. His total number of citation is 407. \r\nHe is member of various committees e.g.: American Society of Animal Science (ASAS, USA); the editorial board of the Acta Agriculturae Scandinavica, Section A- Animal Science (Norway); KRMIVA, Journal of Animal Nutrition (Croatia), Austin Food Sciences (NJ, USA), E-Cronicon Nutrition (UK), SciTz Nutrition and Food Science (DE, USA), Journal of Medical Chemistry and Toxicology (NJ, USA), Current Research in Food Technology and Nutritional Sciences (USA). 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Within the classical RAS, angiotensinogen (AGT) secreted by liver is enzymatically cleaved to angiotensin (ANG) I by kidney-derived renin. ANG I is, hereafter, cleaved by angiotensin-converting enzyme (ACE) to generate the effector hormone ANG II, which exerts various biological actions through its receptors, ANG II type 1 receptor (AT1) and ANG II type 2 receptor (AT2). The initial reaction between the enzyme renin and the substrate AGT is the rate-limiting step of the RAS [1].
As the RAS is a hormonal cascade that is thought to act as a master controller of blood pressure and fluid balance within the body [2], the systematic RAS has been an important target of antihypertensive medications. There are several groups of drugs in this category that affect different parts of the RAS axis, including ACE inhibitors (ACEI) and angiotensin receptor blockers (ARB), both of which are widely used for anti-hypertension treatment [3]. Additionally, Aliskiren, the first orally active direct renin inhibitor approved for clinical use, is a small molecule competitive inhibitor that specifically inhibits the enzymatic activity of renin [4, 5], consequently it could effectively suppress the rate-limiting step within RAS cascade to reduce the production of ANG II. The recent evidences have shown the effective blood pressure control of Aliskiren, generally well tolerated as monotherapy or in combination with other antihypertensive drugs [3, 6].
From an evolutionary point of view, it is cost-effective to have a common system to potentiate and effect the actions of the regulating hormones. Interestingly, the RAS is also found in primitive animals without a closed circulatory system, which indicates that the system is far more than a mediator of vasoconstriction [7]. It is now evident that the components of RAS, in addition to the classical pathway, are produced and acting locally in multiple tissues, a concept known as tissue RAS [7]. The local effects of tissue RAS are diverse and depend on the specific tissues involved.
The functional tissue RAS is postulated to participate in various physiological and pathological processes such as insulin secretion [8], glomerular sclerosis [9], renal inflammation [10], atherosclerosis [11], cardiac hypertrophy [12], brain ischemia [13] and follicular development and endometrial cancer in female reproductive tract [14]. A growing body of studies has demonstrated that the diabetic complications, such as cardiovascular disease [15], nephropathy [16] and retinopathy [17], are caused by the high activity of tissue RAS and the increased production of ANG II in local tissues, and the clinical practice has revealed that these pathological alterations associated with diabetes were significantly improved in response to the treatment with RAS inhibitors [15-17]. Additionally, hyperglycemia, obesity, hypertension, and cortisol, well-known risk factors of metabolic disease, are all stimulators on tissue RAS, whereas glucagon-like peptide-1, vitamin D, and aerobic exercise could, to some extent, prevent metabolic disease through inhibiting tissue RAS [7]. Thus, the factors and drugs suppressing tissue RAS activity have potential in improving RAS-involved tissue injuries.
Recent in vivo studies showed that the components of RAS, such as renin, ACE, and ANG II receptors, were expressed in the local milieu of bone [1, 18-20], and in vitro study identified the expression of ANG II receptors in primary osteoblasts derived from newborn mouse calvaria [1], indicating the components of RAS are expressed locally in bone microenvironment. Our further animal studies demonstrated that the local RAS in bone was involved in age-related osteoporosis of aging mice [21], and bone deteriorations of mice with either obstructive nephropathy [22] or type 1 diabetes [23]. Other groups elucidated the involvement of skeletal RAS in the process of fracture healing in a mouse femur fracture model [24], and the steroid-induced osteonecrosis in rabbits [20] as well as the development of postmenopausal osteoporosis in ovariectomized (OVX) animal models [25, 26] and glucocorticoid-induced osteoporosis [19]. Therefore, it concludes that the local RAS exists in bone tissue and plays an important role in local bone metabolism.
ANG II has been postulated to be able to act upon the cells involved in bone metabolism through receptors located in osteoblasts and osteoclasts or regulate blood flow in bone marrow capillaries. At the end of last century, the studies showed that ANG II stimulated DNA and collagen synthesis and decreased alkaline phosphatase (ALP) activity in bone cell populations derived from calvariae of fetal rat [27] and newborn rat [28]. Similar effects of ANG II were observed in osteoblastic ROS17/2.8 cells [29] and human adult bone cells obtained by collagenase digestion from trabecular bone [27]. The clonal cell analysis, autoradiographic studies, and receptor subtype analysis suggested that ANG II might be intimately involved in the proliferation of the osteoblast-rich populations of cells through the AT1 receptor [27, 28], which also plays one of the essential roles in bone metabolism as a mechanoreceptor of osteoblasts [30].
When investigating the direct effects of ANG II on matured osteoblasts, the results revealed that ANG II inhibited the expression of mRNA for osteocalcin, which is a protein that is specifically expressed during maturation of osteoblastic cells, decreased the activity of ALP, the number and the total area of mineralized nodules as well as reduced the accumulation of calcium in cells and the matrix layer [31]. Besides, the ANG II-involved impairment of bone formation may be attributed that it altered the expression of Cbfa1 by activating the cAMP signaling pathway and subsequently reduced osteoblast number and osteoblastic function [32]. SOST, which encodes sclerostin, is a secretory product of osteocytes that counters Wnt signaling, thereby negatively regulates bone formation [33]. The AT1-involved inhibition on bone formation was highly correlated with its regulation on downstream factor SOST, as the decreased SOST expression in osteocytes was observed in AT1-deficient mice [34]. Furthermore, the treatment with ANG II strikingly increased the expressions of matrix metalloproteinase (MMP)-3 and-13 through MAPK signaling pathways via the AT1 in osteoblastic ROS17/2.8 cells, suggesting that ANG II stimulated the degradation process that occurs during extracellular matrix (ECM) turnover in osteoid by increasing the production of MMP-3 and-13 in osteoblasts [29]. Additionally, ANG II induced mitochondrial dysfunction and promoted apoptosis via JNK signaling pathway in primary mouse calvaria osteoblast [35]. Taken together, the target genes including Cbfa1, SOST, MMP-3 and MMP-13, and the signaling pathways like MAPK and JNK are involved in the mediation of ANG II on osteoblastic function and bone formation.
Osteoblast modulates osteoclast differentiation by producing both positive and negative regulators, most notably receptor activator of NF-κB ligand (RANKL) and osteoprotegerin (OPG), respectively [36]. Of note, it has been recently found that ANG II could induce the differentiation of bone marrow mononuclear cells to multinuclear cells and the number of multinuclear cells in osteoclasts as well as increase tartrate-resistant acid phosphatase (TRAP)-positive multinuclear osteoclasts due to its stimulation on the expression of osteoclastogenesis-supporting cytokine, RANKL in osteoblasts, leading to the activation of osteoclasts [1, 25], whereas these effects were completely blocked by either ANG II type 1 receptor blockade (olmesartan) or mitogen-activated protein kinase kinase inhibitors (U0126) [25]. Importantly, ANG II itself had no capacity to induce osteoclast differentiation and did not potentiate osteoclast formation triggered by RANKL, while it stimulated the formation of osteoclasts in the co-culture system of primary osteoblasts and bone marrow macrophages in a dose-dependent manner [1, 32]. Taken together, these results suggested that ANG II stimulates osteoclastogenesis by acting on osteoblastic cells (i.e., ‘the soil cells’), but not through a direct action on hematopoietic ‘seed cells’ [1].
It was found that the TRAP activity and the TRAP-positive stained area were significantly increased in the tibia of OVX rats with systemic administration of ANG II at a subpresser dose (200 ng/kg/min), and the treatment with ANG II significantly induced the ovariectomy-induced increase in urinary level of deoxypyridinoline [25]. The ratio of ALP to TRAP was significantly decreased in the tibia of OVX rats upon to ANG II treatment. These results suggested that ANG II accelerated the turnover of bone metabolism, which is similar to the typical pattern in elderly postmenopausal women who are at high risk for osteoporosis [25]. Of importance, the bone density as assessed by double energy X ray absorptiometry (DEXA) was significantly decreased in the tibia of OVX rats by ANG II. These results suggested that ANG II directly accelerated estrogen deficiency-induced osteoporosis.
Previous studies have focused on the ANG II–AT1 interactions since these are the best described and considered the most important. However, the system is complex and several other components probably play significant roles as well [7]. Several publications raise the possibility that AT1 and AT2 carry out negative cross-talk within fibroblasts and vascular endothelial cells with respect to each other’s signaling pathways and responses [37]. This may be of particular importance when the AT1 are pharmacologically blocked.
Asaba et al. determined the relative contribution of the two receptors for transducing the osteoclastogenesis-supporting function of ANG II in osteoblasts by knocking down the expression of each of the receptors with siRNA in primary osteoblasts in culture [1]. In AT1-knockdown osteoblasts, the stimulatory effect of ANG II on osteoclast formation was somewhat enhanced. In AT2-knockdown osteoblasts, in contrast, the osteoclastogenic potential was markedly attenuated [1], which was consistent with that AT2 deficiency increased bone mass of distal metaphyseal regions of femoral in mice as well as the treatment with AT2 blocker PD123319 suppressed ANG II-induced increase in the number of osteoclasts in organ cultures of bone [18]. Asaba et al.’s study suggested that the action of ANG II on osteoblasts in terms of stimulating osteoclastogenesis was mainly mediated through the AT2 and AT1 might exert an inhibitory effect on AT2. These findings in osteoblasts are consistent with the notion that the functions of AT1 and AT2 are in many cases counter-regulatory to each other [7]. However, they are contrary to the conclusion that AT2 is the protective arm of RAS and counterbalances pathological processes and enable recovery from disease [38]. Thus, further studies are needed to dissect the signaling pathways downstream of each receptor in osteoblasts.
Osteoporosis, hypertension, diabetes are major chronic diseases in older subjects and the latter two are well known to be high risk factors for osteoporosis. As ACE inhibitors are usually prescribed for hypertension, cardiac failure, and diabetic nephropathy [23, 39], it is important to know the prospective effects of ACEI on bones of these patients taking ACEI treatment.
Previously, most of clinical studies demonstrated that patients treated with ACEI showed an increased bone mineral density (BMD) and a reduced fracture risk [40-45]. The menopausal and hypertensive women who followed treatment with ACEI fosinopril did not present the physiological loss of bone mass that affected to menopausal women without treatment [43]. A large case–control analysis carried out in the UK, suggested a possibly decreased fracture risk associated with longer-term use of ACE inhibitors [40], and in an open prospective study including 134 patients with low to moderate hypertension and stable BMD, the plasma calcium and 25-hydroxyvitamin D levels were both increased in patients treated with the ACEI quinapril [41]. It also significantly increased BMD of lumbar spine in female subjects with ACE DD genotype, which could induce a higher level of ANG II [41]. The research group from Hong Kong performed two large scale cohort studies which investigated the risk factors for osteoporotic fractures in Hong Kong-dwelling elderly Chinese, and their data concluded that male ACEI users had higher BMD at the total hip, female neck and lumbar spine than non-users. Likewise, female ACEI users also had higher BMD than non-users, although only significant at the femoral neck [42].
While, in the contrary to the above mentioned beneficial effects of ACEI on bone health, the recent emerging evidences indicated that ACEI use did not change the rate and risk of fracture [46], and even led to greater bone loss [39, 47, 48]. The same research group from Hong Kong recently also stated that female continuous users of ACEI had increased bone loss both in total hip and femur neck [47]. A large sample size study in American men also supported this theory by showing that ACEI use was associated with increased bone loss [39], moreover, another prospective study-a cohort study of atomic bomb survivors in Japan, demonstrated that ACEI use was associated with increased bone loss of femoral neck in older Japanese [48].
Similarly in animal studies, the use of ACEI enalapril (10-20 mg/kg, i.g.) did not show positive effects on bone function of OVX mice [49] or OVX spontaneously hypertensive rats (SHR) [50], and the administration of enalapril (0.4 mg/kg, i.p.) in a dose recommended for the treatment of hypertension did not cause significant changes in bone density, the ash and mineral content or morphometric parameters of the femur in female Wistar rats [51]. Another ACEI moexipril, when given alone at oral dose of 10 mg/kg, had no effect on the cancellous bone site in either OVX or sham-operated rats and did not hamper the osteoprotective effects of 17beta-estradiol [52]. Even though the treatment of Tsukuba hypertensive mouse with enalapril improved osteoporosis [1] as well as the OVX rats in response to the treatment with ACEI captopril (1 or 5 mg/kg) showed the increased trabecular area of lumbar vertebrae (L4) and the improved biomechanical properties by increasing L5 break stress and elastic modulus [26], our recent published article elucidated that the treatment with captopril (10 mg/kg, i.g.) significantly elevated serum level of TRAP 5b, and had a trend to decrease BMD of trabecular bone and damage micro-architecture of proximal tibial head and distal femoral end in type 1 diabetic mice [23].
Based on the facts that ANG II locally in bone tissue has detrimental effects to bone function and ACE is the major enzyme producing ANG II, it is surprising that ACEI could not improve even accelerate bone loss in both humans and animals. Since the modest changes in ACE level affect the levels of its substrates much more than its products, indicating that relatively small changes in the levels of ACE affect kinin level more than ANG II level [53], a possible reason comes from the regulation of ACE on kinin-kallikrein system within which bradykinin can stimulate bone resorption and reduce BMD [47]. Another possible explanation we should consider is that although short-term ACEI therapy was associated with decreased ANG II level, there were some evidences that long-term ACE inhibition resulted in a return of ANG II towards baseline level, so-called ‘ACE escape’ [47]. The complete mechanisms have not yet been fully demonstrated. More research needs to be carried out to clarify the influence of the ACEI treatment on bone health as this might be of clinical relevance when antihypertensive therapy is initiated, particularly in hypertensive women who typically suffer from a concomitant rapid onset of osteoporosis after menopause [52].
The clinical profiles of users of ACEI and ARB were very similar. In the USA, ARB was usually prescribed when ACEI was not tolerated, thus explaining the smaller number of ARB users [39] and limited human studies of ARB and BMD or fracture risk in the literatures [46]. The completed studies on the affections of ARB on bone function in human and animals have shown contradictory results.
The recent population-based, retrospective cohort study with propensity score-matching using administrative databases in Ontario, Canada to examine the risk of osteoporosis-related fractures in hypertensive elderly patients treated with ARBs versus ACEIs, showed that there was no significant difference between the effects of ARBs and ACE inhibitors on hip and other osteoporotic fractures [54]. A large cohort study on Medicare beneficiaries with a diagnosis of hypertension initiating single-drug therapy for anti-hypertension treatment suggested the increasingly protective effect of ARB on relative fracture risk over time [46]. While, the study with large sample size of community-dwelling older adults from six different geographic regions demonstrated that the use of ARBs did not have any significant overall effect on bone loss in older men [39].
The contradictory results about the actions of ARBs on bone metabolism were also shown among animal studies. The treatment with telmisartan, olmesartan, and losartan, could reduce bone loss of OVX mice [49], attenuate the ovariectomy-induced decrease in BMD [25], and increase bone strength, mass and trabecular connections of OVX rats femur [55, 56], respectively. Moreover, telmisartan partially protected from thiazolidinedione-induced bone loss by actively blocking thiazolidinedione-induced anti-osteoblastic activity via maintaining PPARγ serine 112 phosphorylation [57], and promoted fracture healing in a mice model [58]. However, some studies reported ARBs did not cause significant changes of bone properties in normal female rats [51], type 2 diabetic mice [57], OVX rats [59] or orchiectomized rats [60]. Importantly, it was noted that in some animal models ARBs may lead to more bone injuries [1, 61]. The treatment of transgenic Tsukuba hypertensive mouse with losartan resulted in exacerbation of the low bone mass phenotype [1]. The study in our group demonstrated a trend of losartan to promote the loss of bone mass and the deteriorations of trabecular bone micro-architecture in type 1 diabetic mice due to the compensatory stimulation of bone RAS activation as shown by the up-regulation of renin and ANG II expression in bone tissue [61].
It has been argued that neither ACEIs nor ARBs completely block the RAS cascade due to the disruption of the feedback inhibition of renin production [62]. The increase in renin activity stimulates the conversion of ANG I and ultimately ANG II, which largely limits the efficacy of RAS inhibition [63]. The increased renin can also act through the prorenin/renin receptor, which may cause tissue damages independent of ANG II [64]. Thus, as compared to single treatment with RAS inhibitors, whether combining renin inhibitor, like Aliskiren, with ARB or ACEI could generate better therapeutic effects on tissue injuries, such as osteoporosis, should be further clarified.
As discussed in this chapter, RAS locally plays a key role in the modulation of bone metabolism. However, over the past 10 years, several studies have presented evidences for the existence of a new arm of the RAS, namely the ACE2/ANG-(1–7)/Mas axis [65]. The identification of the ACE homolog, ACE2 as a key ANG-(1–7)-forming enzyme, unravels the existence of a distinct enzymatic pathway for the production of ANG-(1-7), which has a broad range of effects in different organs and tissues that goes beyond its initially described cardiovascular and renal actions [66]. This heptapeptide exerts its actions through binding to a G protein-coupled receptor Mas, distinct from AT1 and AT2 [67]. It is now accepted that the ACE2/ANG-(1–7)/Mas axis is able to counteract most of the deleterious actions of the ACE/ANG II/AT1 axis, especially in pathological conditions [68] such as cardiac dysfunction, increased blood pressure, decreased baroreflex function, endothelial dysfunction, reduced reproductive function, increased thrombogenesis [66]. Thus, how the cross-talk and the interaction between the dual axis systems of RAS contribute to the maintainance of bone metabolism needs to be further investigated and elucidated for better understanding the molecular mechanism of bone metabolic diseases.
This work was sponsored by National Natural Science Foundation of China (No. 81202894).
The use of a conventional two-level inverter in the field of high power applications is not appropriate because it requires electronic components capable of withstanding high reverse voltage and high current. Another disadvantage of this inverter is the problem of magnetic interference caused by the abrupt change of the output voltage of the inverter from zero to high value [1].
With the appearance of the structures of the multilevel inverters proposed for the first time by [2], the research was able to face the handicaps presented by the classical structure. The goal of this research focus is to improve the quality of the output voltage, as well as to overcome the problems associated with two-level inverters. There are several topologies of multilevel inverters such as floating-diode, floating-capacitor, and cascaded inverters [3]. These structures make it possible to generate an output voltage of several levels.
Diode-clamped inverter (DCI) is the one that attracts the most attention because of the simplicity of its structure compared to the floating capacity inverter; in fact we do not need to use capacities for each phase, which eliminates the risks of parasitic resonances [4]. In this structure, diodes called floating diodes are associated with each phase, which serves to apply the different voltage levels of the DC source.
In high power, AC machines powered by static inverters find more and more applications. But the constraints on the power components limit the switching frequency and therefore the performance. To enable the use of higher switching frequency components, the power must be divided. To do this, one of the solutions is to use multiphase machines thanks to their advantages, such as the power segmentation and the minimization of the ripples of the torque (elimination of the harmonic torque of rank six). One of the most common examples of multiphase machines is the double-star induction machine (DSIM) [5].
To improve the decoupling between the flux and the torque, a so-called direct torque control (DTC) control technique has been applied.
The conventional direct torque control (DTCc) is proposed by Takahashi and Depenbrock in 1985 [2], and several studies allowed to apply this control technique on multiphase machines. As for each control, the DTC has advantages and disadvantages, and among these advantages, the stator resistance is theoretically the only parameter of the machine that intervenes in the control. This is essential for estimating the stator flux vector [6]. From this purely theoretical point of view, one can thus consider a great robustness compared to the other parameters of the machine; the block PWM is usually deleted [7].
Despite these advantages, this control also has significant disadvantages, the problem of instability such as the lack of control of the generator of acoustic noise at the machine. In addition, the use of hysteresis tapes is the cause of electromagnetic torque ripples and noise in the machine. To solve these drawbacks, in the framework of this work, we try to apply the multilevel direct torque control for DSIM and to develop a new control method such as artificial neural networks that replaces the switching tables [8].
This chapter is organized as follows: the DSIM model will be presented in the next section. The three-level and the five-level inverter modeling is described in the third and fourth section. The control method by DTC based on artificial neural networks (DTC-ANN) will be discussed in the fifth section. Moreover, in the sixth section, the simulation results are presented. Finally, a general conclusion summarizes this work.
In the conventional configuration, two identical three-phase windings share the same stator and are shifted by an electric angle of 30°. The rotor structure remains identical to that of a three-phase machine [9].
The model of machine DSIM is nonlinear. The DSIM model fed by voltage inverter is given by the following equations [10]:
where:
Matrixes A and B are given by
where
Figure 1 shows the structure of the three-level floating-diode inverter introduced by A. Nabae and H. Akagi in 1981 [11] (Table 1).
Three-phase inverter with floating diodes (k = 1 is the first inverter, and k = 2 is the second inverter).
Switching states | State of the switches of an arm | Output voltage | |||
---|---|---|---|---|---|
Txk1 | Txk2 | Txk3 | Txk4 | ||
2 | 1 | 1 | 0 | 0 | vc2 |
1 | 0 | 1 | 1 | 0 | 0 |
0 | 0 | 0 | 1 | 1 | −vc1 |
States of an arm of the inverter with three levels.
The three symmetrical arms consist of four fully controllable switches. These switches must not be opened or closed simultaneously, in order to avoid short circuiting of the DC source at the input of the inverter. Each switch is composed of an antiparallel transistor with a diode. The floating diodes ensure the application of the different voltage levels at the output of each arm. The DC input voltage is divided into two equal parts by using two capacitors. Each capacitor must be sized for a voltage equal to vdc/2 [12].
The switching function of each switch Txki (k = 1, 2, i = 1 ... 4, x = a, b, and c) is defined as follows:
The controls of the switches of the lower half-arms are complementary to those of the upper half-arms:
For each arm, we define three connection functions:
The output voltages with respect to the neutral point of the DC source are expressed by
Currently the diode-clamped inverter is the one that attracts the most attention, given the simplicity of its structure compared to floating capacity inverters and cascading. In fact, compared to the inverter with floating capacities, it is not necessary to use capacities for each phase, which eliminates the risks of parasitic resonances.
The main advantage lies in a considerable reduction in switching losses and its ability to control harmonic content [13].
Figure 2 shows the structure of the inverter with five levels, each of the three arms of the inverter consists of eight controlled switches and six floating diodes. The controlled switches are unidirectional in voltage and bidirectional current; it is conventional associations of a transistor and an antiparallel diode.
Diagram of the five-level inverter with NPC structure.
These switches must not be opened or closed simultaneously, in order to avoid a short circuit of the DC source in the input. The floating diodes (six per arm) ensure the application of the different voltage levels at the output of each arm. The DC input voltage is divided into four equal parts using four capacitors [14].
The DC input bus is composed of four capacitors (C1, C2, C3, and C4), making it possible to create a set of three capacitive middle points. The total voltage of the DC bus is vdc; under normal operating conditions, this is uniformly distributed over the four capacitors, which then have a voltage vdc/4 at their terminals [15] (Table 2).
Switching states | State of the switches of an arm | Output voltage | |||||||
---|---|---|---|---|---|---|---|---|---|
Txk1 | Txk2 | Txk3 | Txk4 | Txk5 | Txk6 | Txk7 | Txk8 | ||
4 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | vc3 + vc4 |
3 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | vc3 |
2 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 |
1 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | −vc2 |
0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | −(vc1+ vc2) |
States of an arm of the inverter with five levels.
For each switch Txki (k = 1, 2, i = 1 ... 8, x = a, b, and c), a switching function is defined as follows:
The switch control of the lower half-arms is complementary to those of the upper half-arms:
We define five connection functions, each associated with one of the five states of the arm:
The potentials of nodes a, b, and c of the three-phase inverter at five levels with respect to the point o are given by the following system:
The direct torque control of a DSIM is based on the direct determination of the control sequence applied to the switches of a voltage inverter. This choice is based generally on the use of hysteresis comparators whose function is to control the state of the system, namely, the amplitude of the stator flux and the electromagnetic torque [16].
In the structure of the DTC, the voltage model is commonly used. Thus, the amplitude of the stator flux is estimated from its components following the axes (α, β):
The stator flux module is given by
The angle θs is given by
This method of estimating the stator flux has the advantage of simplicity and accuracy, particularly at medium and high speeds where the ohmic voltage drop becomes negligible [17].
The electromagnetic torque can be estimated from the estimated magnitudes of the stator flux and the measured magnitudes of the line currents, by the following equation:
The human brain is able to adapt, learn, and decide, and it is on this fact that researchers have been interested in understanding its operating principle and being able to apply it to the field of computer science.
Among the disadvantages of DTC control, a slow response for small changes in stator flux and electromagnetic torque, size, and complexity of switching tables when the number of levels of inverters is high. In order to improve the performance of the DTC control, many contributions have been made in the DTC control based on artificial neural networks [18].
In this application, our goal is to replace switching tables with artificial neural networks.
The multilayer architecture was chosen to be applied to multilevel DTC control. This network, which can be multiplexed for each controller output, has acceptable performance in many industrial applications [19]. The neural network contains three layers: input layer, hidden layers, and output layer. Each layer consists of several neurons. The number of neurons in the output and the layers depends on the number of input and output variables chosen. The number of hidden layers and the number of neurons in each one depend on the dynamics of the system and the desired degree of accuracy.
Figure 3 shows the structure of the neural network applied to the multilevel DTC control of the DSIM. It is a network with three neurons in the input layer, whose inputs are flow error (Ef), torque error (Ec), and flow position angle (Z) [20]. For the three-level inverter, there are 12 neurons in the hidden layer and 06 neurons in the output, and for the five-level inverter, there are 24 neurons in the hidden layer and 12 neurons in the output. Figure 4 shows the chosen architecture.
Neural network structure applied to the multilevel DTC control. (a) for three-level DTC, (b) for five-level DTC.
Selection table based on neuron network.
In order to test the static and dynamic performance of the control, the DSIM is accelerated from standstill to reference speed 100 rad/s. The machine is applied with a load torque of 11 Nm. Finally, the direction of rotation of the machine is reversed from 100 rad/s to −100 rad/s at time t = 2 s. Figures 5 and 6 show the simulation results of the three- and five-level DTC control for DSIM.
Simulation results of real and estimated speed, torque, flux, and current of three-level DTC-ANN.
Simulation results of real and estimated speed, torque, flux, and current of five-level DTC-ANN.
Simulation results of speed, stator flux, torque, stator current, and stator voltage show the good performance of the three- and five-level DTC-ANN control of DSIM (speed, stability, and precision).
We note that the speed follows its reference value. The electromagnetic torque stabilizes at the value of the nominal torque after a transient regime with rapid response and without exceeding before stabilizing at the value of the applied load torque.
Figure 6 shows that the five-level DTC-ANN control reduces the ripple of the electromagnetic torque, the stator flux, and the THD value compared to that of the three-level DTC-ANN. On the other hand, we note that the speed reaches its reference without exceeding for the two control types. Moreover, the couple follows the load torque. The dynamics of the stator flux are not affected by the application of these load instructions.
The use of multilevel inverter at five levels causes a decrease in the current ripple at the steady state that is to say low peaks than that of the three-level control. However, the results of the simulations shows a good dynamic characteristic of the stator flux in the transient regime for five-level DTC-ANN compared to the three-level DTC-ANN with static errors that are virtually null in both cases of control DTC proposed.
Figures 7 and 8 show the simulation results of the three-level and five-level DTC-ANN control for low-speed operation. DSIM is accelerated from standstill to a low reference speed of 10 rad/s, at time t = 0.5 s; the DSIM is accelerated again to a reference speed of 100 rad/s. The machine is loaded with a nominal load of 11 Nm. Finally, a reversal of the direction of rotation of the machine from 100 rad/s to −10 rad/s is performed at time t = 2 s.
Simulation results of three-level DTC-ANN for low-speed operation.
Simulation results of five-level DTC-ANN for low-speed operation.
The simulation results show that low-speed operation does not affect the performance of the proposed drive. Indeed, the good reference speed tracking is ensured, with advantages brought by the use of five-level DTC-ANN control, the minimization of torque ripple, and stator flux, which is confirmed by the simulation results.
In order to know the best type control of DSIM, a comparative study is essential between the two types (three-level DTC-ANN and five-level DTC-ANN). The following table shows the comparison between the two types (Table 3).
THD (%) | Ripples of torque | Ripples of flux | |
---|---|---|---|
Three-level DTC | 18.73 | Good | Good |
Five-level DTC | 12.82 | Very good | Very good |
The comparison between three-level and five-level DTC-ANN.
In this chapter, we presented two types of DTC control (three-level DTC-ANN and five-level DTC-ANN) of a DSIM fed by two NPC voltage inverters, and the technique of neural networks was applied to the DTC control. The main advantage of this control is to allow control of the flux and torque of the machine without the need to use a mechanical sensor. The direct torque control strategy is an effective and simple way to control an induction machine. In order to improve the performance of the DSIM (torque ripple reductions, flux, response time, and the THD value of the stator current), simulation tests of the control by variation and inversely of the speed have been presented; the results obtained show that the five-level DTC-ANN control with speed control is very efficient. This shows the effectiveness of the proposed strategy.
Pn = 4.5 Kw
In = 6 A
Rr = 2.12 Ω
Lr = 0.006 H
Rs1 = Rs2 = 1.86 Ω
Ls1 = Ls2 = 0.011 H
Lm = 0.3672 H
J = 0.065 kg.m2
kf = 0.001 Nm/rad.
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