Modern power system becomes a complex system consisted with various load and power stations. Therefore, it may spread into some areas of power system in neighborhood, and so a load frequency control (LFC) is a necessity device to regulate the frequency of the power system by distributing the load to the generating units and controlling tie-line power interchange between areas. The integration of renewable energy sources (RES) into a power grid has presented important issues about stability and security of power system. In such conditions, the use of conventional LFC may not be sufficient to protect the power system against the power changes. In this chapter, an adaptive LFC controller is proposed using the least square method (LSM). The controller adopts an internal model control (IMC) structure in two scenarios, i.e., static controller gain with adaptive internal model and both the adaptive controller gain and adaptive internal model. A two-area power system is used to test and to validate both performance and the effectiveness of this controller through some case studies.
- adaptive controller
- power system stability
Demand load in a power system is continuously varied by the time and the change of the active and reactive power demand has introduced generator-load mismatching power. When the load increases, it will slow down the rotor speed which result drop of the frequency. On the other hand, when the load is reduced, the load frequency will rise. The change of load frequency will directly affect electrical motor performance, further interfering the protection of the system .
Smart grid technology is well developed, and it currently has been widely used in the power system operation due to the need for integrating renewable energy to the existing power grid. Penetration of the renewable energy resources such as solar generations and wind turbines to the power grid has introduced significant issues of stability and security of the power system. However, frequency stability is a major issue in the power system operation due to continuous output change of the renewables. Therefore a load frequency control (LFC) is an essential device to back up the automatic generation control (AGC) to keep the frequency stable during power system operation .
The frequency controllers in a power system consist of AGC and LFC systems as primary control and secondary control. An AGC will respond to small changes of frequency in a generator, and an LFC will have to regulate load frequency in a large area of the power system and large change in load frequency which is typically in between ±5 and 6% of the frequency bias [2, 3, 4, 5].
In this chapter, adaptive LFC controllers are introduced, where an adaptive IMC model that is repeatedly updated by the least square method (LSM) in real-time operation is proposed. It is shown that the target model is successfully identified and therefore that the proposed LFC controller scheme effectively keeps the system frequency at a desired set point. The effectiveness of the proposed controller is demonstrated by the simulations using a standard LFC model representing two-area interconnected power system.
2. Power system model
2.1 Mathematical modelling of generator
Eq. (1) is well known as a swing equation that describes the rotor dynamics and hence is known as the swing Equation [6, 7]. The internal EMF angle
For small perturbation, the swing equation of a synchronous machine will be formed in Eqs. (2) and (3) for its small deviation in speed. Therefore, Laplace transformation of Eq. (3) is shown in Eqs. (4) [6, 7]:
2.2 Mathematical modelling of load
The load on a power system consists of a variety of electrical devices which is a resistive or inductive load. The equipment used for lighting purposes and heating are basically resistive in nature, and this kind of load is independent to frequency. On the other hand, rotating devices, such as fans and pumps, are basically a composite of the resistive and inductive components which are dependent to the frequency changes. The speed-load characteristic of the composite load is given by [6, 7]:
2.3 Mathematical modelling for prime mover
The electrical energy is generated inside a power generation by converting the other kind of energy sources by means of a prime mover. The prime mover may be diesel machines, hydraulic turbines at waterfalls, or steam turbines. The model for the turbine relates the changes in mechanical power output Δ
Figure 3 expresses the prime mover block diagram, where
2.4 Mathematical modelling for governor
The electrical power will exceed the mechanical power input when the electrical load is suddenly increased. This condition will result in the extraction from the rotating energy of the turbine. Then the kinetic energy stored in the machine is reduced and slows down the speed of prime mover. Therefore, to compensate the reduced speed, the governor sends a command to supply more volumes of water or steam or gas to increase the prime mover speed.
The quantity of Δ
Finally, Figure 6 summarizes a combining of all of the block diagrams from earlier block diagrams for a single area system.
The tie-line power change
Due to no direct connection between input and output variables, the feed forward matrix D is removed from the model. Therefore the system matrices of a LFC system are written in Eqs. (13)–(16) [3, 4, 9]:
2.5 System response of power change
If there is some change in mechanical power input
The quantity of
It can be seen from Eq. (20) that there are possibilities of roots in s-plane when
3. Controller structure
3.1 Model predictive control
Model predictive control (MPC) is an advance optimal control in the field of control systems engineering. In an MPC, the optimal trajectory movement is given by properly choosing the MPC gain so that the control errors can be minimized.
The objective of the predictive control is to compute the manipulated variable
At a given discrete time
where N, Q, and R are the error weight matrices of the control action, output system, and rate of change in control action, respectively. Δ
In order to have optimal result, prediction horizon and control horizon have to be set properly so that the MPC controller can work in high performance. On the other hand, absence of doing this will cause the MPC to lose optimal action that will result in a high overshoot response.
In a short time control horizon,
Then the model will be converted to an augmented model so that later the quadratic programming problem with respect to ∆
Then the objective function could be written as:
Therefore, Eq. (31) could be solved efficiently by a quadratic programming.
3.2 Internal model control (IMC)
An IMC controller structure can be seen on Figure 10 where an internal model is used parallel with the plant. This internal model, which is also called efferent model, used plant model to estimate plant output. It is known that to find the internal model as same as plant model is difficult due to plant dynamics that is not well captured in the modeling part.
In an IMC controller, an efferent model is expected to correct the actual output before it is feedback to the controller. This is the difference between MPC controllers with classical controllers. In order to get maximum performance, the efferent model may have input and output relation as close to the real plant model so that the only feedback signal is the disturbance.
As a type of controller, an IMC can control a plant directly. This controller can also be used to tune another controller if it is difficult to be the best parameter for this controller. Based on the IMC structure, this main controller can be any type of controller so that this IMC can be combined with any type of controllers . The control law for an IMC control is written in Eq. (32) to Eq. (34) [13, 14]:
The difference between classical controller and an IMC is that an IMC will correct the actual output before it is fed back. Since an IMC uses the efferent model, the model should be a perfect model to have the highest control performance. The way to provide the model in an IMC can be in forward model, inverse models, combination of both forward and inverse models, or adaptive model.
3.3 Adaptive model structure
Due to highly nonlinearity in a power system, the classical model of power system may not be accurate to configure the real power system. Therefore the other objective of this paper is to find simple model of a load frequency of power system using the least square method (LSM). In order to capture the essential dynamics of power system, a first-order lag model is adopted in this chapter as follows:
The power system model, including governor, turbine, and rotating mass and load model as in Figure 7 will be replaced by the first-order lag model in Eq. (35). Therefore it has become a simple model as formulated in Eq. (36):
Expanding Eq. (35) gives
For discrete time system, the above equation can be described in a matrix form as follows:
Taking the derivative for the
Minimizing the derivative by setting it to zero gives
The least square solution for the discrete time system of the power system model is shown in Eq. (46).
where is the pseudo inverse of the
3.4 Model simplification
In order to adopt the least square solution into the internal model of the LFC system, the complex system of the LFC is likely to be reduced into a first-order model. After the disturbance is entering the LFC system, generations are expected to change as fast as possible to meet the load demand. This expectation may not be achieved due to slow response of governor and turbine operations. For these purposes, both turbine and generator responses are neglected to derive a simple expression for the time response.
Typically in a LFC system, power system time constant is relatively higher than governor and turbine time constant as described in Eq. (48). Therefore both governor and turbine time constant are negligible, and by adjusting
The first-order system effectively reduced the unexpected response. An example of system responses of first-order and third-order systems is given in Figure 12.
The dynamics response of a frequency control means how the frequency is immediately corrected after the disturbance and before the system reaches new steady-state condition . In this case, a static first-order model may not solve the LFC problem since the disturbance is fluctuated by the time. Therefore an adaptive model of first-order system is suggested to be used in solving this problem.
3.5 Computational procedure
Power system model is expressed in a first-order lag system. The way to build the model can be a combination of either inertia constant and damping or a gain and time constant. In this case, the second combination will be used to provide the power system model using the least square method. The power system equation can be written as follows.
Block diagram of the adaptive LFC system is shown in Figure 13 as rewritten from . The adaptive internal model is utilized to provide an update model of the power system, and the model is generated by using a LSM method. The proposed adaptive LFC controller uses MPC controller as its main controller combining with an adaptive efferent model as the internal model.
In order to reach the best controller solution, the simulation will be done using the given algorithms. IMC type 1 includes the adaptive internal model, while in IMC type 2, controller gain
IMC with adaptive model (type 1)
IMC with adaptive model and controller (type 2)
|1. set disturbances and noises|
2. configure presetting model
7. calculate ∆
8. upgrade state matrix ∆
11. change internal model
13. calculate model output ∆
14. correct signal ∆
|1. set disturbances and noises|
2. configure presetting model
7. calculate ∆
8. upgrade state matrix ∆
11. change internal model
12. define new MPC gain
14. calculate model output ∆
15. correct signal ∆
4. Controller test
The investigated power system consists of a two-area power system which is modified from a three-area poser system in [2, 3, 4, 9, 18]. The controller performance is tested by comparing a classical MPC controller and the proposed adaptive IMC controller. System configuration is based on Figure 7 where the system parameter is shown in Table 1.
The proposed adaptive LFC controller type 1 and type 2 will be computed as follows. The gain
The main controller for the proposed controller is an MPC controller which at initial condition is set as the same as the existing controller. Prediction horizon
Three different cases are performed in simulations under disturbance setting as in Table 2. The disturbances are load changes which are imposed by white noises. The random disturbance is white noises with a maximum magnitude about 0.05 pu, while the step disturbance is a sudden change of load. The step disturbance is assumed as the action of economic load dispatching which is applied at t = 40 for case II and case III in addition to the white noise.
A. Case 1.
Simulation results are shown in Figure 15 for Δ
B. Case 2.
A better performance is observed. It is noted that although the adaptive controllers are a much simpler configuration compared with the existing controller, the control performance is even better. This is an advantageous feature of the proposed method.
C. Case 3.
In this case, step disturbance about −0.1 pu. which is applied at
The similar performance is shown. It is noted that the adaptive controllers can handle incoming disturbance and also released disturbance with even better performance compared to the existing controller.
Figures 21–24 show how the internal model parameters are identified. It is observed from Figures 21 and 23 that initial gain
LFC system based of IMC-1 is equivalent to the existing MPC controller in some cases, while the performance of IMC type 2 is even better in all areas and all cases. Tables 3 and 4 list measured values of the overshoot for the step disturbance and the standard deviations of both frequency and prime mover responses for all cases. It is seen from the tables that the performance of the proposed adaptive LFC controllers is equivalent or better than the conventional MPC controller. This implies that the adaptive LFC controllers can successfully identify the target model and handle the power system disturbances. In the same way, the controllers keep the system conditions successfully at the set points.
|Case 1||Case 2||Case 3|
|Area 1||Area 2||Area 1||Area 2||Area 1||Area 2|
|Case 1||Case 2||Case 3|
|Area 1||Area 2||Area 1||Area 2||Area 1||Area 2|
The simulations are carried out on PC with Intel Core i7 1.8 GHz CPU and 4 GB RAM using MATLAB 2016a under Windows 10. CPU times for the computation of controllers are listed in Table 5. It is observed that the adaptive LFC controllers are slower than the conventional method.
In today’s power system condition, where smart grid and energy management system are applied, the power system dynamics is varying. Therefore an adaptive model would be an advantage and is required for the better performance of LFC system.
It is observed that time consuming is increased together with the improvement of the controller performances. Therefore the IMC type 2 controller needs more time to compute and update both internal model and controller gain while giving a better performance. On the other hand this time consuming is the order of millisecond and so it is acceptable for an LFC system which in general operated in the order of second. Therefore the proposed controller has bit complexities in hardware for computing the model and updating the controller since its consuming time is about 40% higher than the existing MPC controller.
This chapter presents adaptive LFC methods based on IMC controller structure, where the internal model is adaptively updated online in IMC type 1, while both internal model and MPC controller gain are restructured in IMC type 2 by using the least square method. The performance of the controller is fair in handling load disturbances in spite of relatively slow control cycle with ramp rate constraints of actual systems.
Simulation results show that the gain and time constant of the internal model have been adaptively changed. This change has guaranteed the better performance of the proposed controller. Based on the system responses, the adaptive IMC controller type 1 has responses similar to the existing MPC controller, while the adaptive IMC controller type 2 has shown its superiority compared to MPC controller and IMC type 1 controller. In contrary, consuming time becomes larger by the enhancement of the controller performance.
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