Load Frequency Controller Design for Interconnected Electric Power System

Transcription

1 Load Frequency Controller Design for Interconnected Electric Power System M. A. Tammam** M. A. S. Aboelela* M. A. Moustafa* A. E. A. Seif* * Department of Electrical Power and Machines, Faculty of Engineering, Cairo University, Egypt **Invensys Process Systems, Cairo, Egypt Correspondence aboelelamagdy@yahoo.com Abstract: This paper studies control of load frequency in single area power system with PID controller. In this study, PID parameters are improved using the genetic algorithm technique. The proposed controller compared with a conventional PID controllers tuned by Ziegler-Nicholas technique, Particle Swarm Optimization. The overshoots and settling times with the proposed Genetic-PID controller are better than the outputs of the conventional PID controllers. The effectiveness of the proposed scheme is confirmed via extensive study using MATLAB-Simulink software. Keywords : Load Frequency Control, Single Area Power System, PID Controller, Genetic Algorithm. I. INTRODUCTION Frequency is a key stability criterion in power systems. To provide the stability, active power balance and constant frequency are required. Frequency depends on active power balance. If any change occurs in active power demand/generation in power systems, frequency cannot be hold in its rated value. So oscillations increase in both power and frequency. Thus, system subjects to a serious instability problem. To improve the stability of the power networks, it is necessary to design Load Frequency Control (LFC) systems that control the power generation and active power. Because of the relationship between active power and frequency, three level automatic generation controls have been proposed by power system researchers [1]. Generally, ordinary LFC systems are designed with Proportional-Integral (PI) controllers. However, since the I control parameters are usually tuned; it is incapable of obtaining good dynamic performance for various load and system changes. Many studies have been carried out in the past on this important issue in power systems, which is the load frequency control. As stated in some literature [2], some control strategies have been suggested based on the conventional linear control theory. These controllers may be inappropriate in some operating conditions. This could be due to the complexity of the power systems such as nonlinear load characteristics and variable operating points. In this study, Genetic Algorithm (GA)is used to determine the parameters of a PID controller according to the system dynamics. In the integral controller, if the integral gain is very high, undesirable and unacceptable large overshoots will be occurred. However, adjusting the maximum and minimum values of proportional ( ), integral ( ) and integral ( ) gains respectively, the outputs of the system (voltage, frequency) could be improved. In this simulation study, a single area power system is chosen and load frequency control of this system is made by genetic based PID controller. The overshoots and settling times with the proposed Genetic-PID controller are better than the outputs of the conventional PID controllers tuned by Ziegler-Nicholas technique, Particle Swarm Optimization. I. OVERVIEW ON GENETIC ALGORITHM The Genetic Algorithm (GA) is an optimization and search technique based on the principles of genetics and natural selection. The GA allows a population composed of many individuals to evolve under specified selection rules to a state that maximizes the fitness (i.e., minimizes the cost function), many versions of evolutionary programming have been tried with varying degrees of success. Some of the advantages of a GA include [3, 4]: Optimization with continuous or discrete variables. Doesn t require derivative information. Simultaneously searches from a wide sampling of the cost surface. Optimization of variables with extremely complex cost surfaces (they can jump out of a local minimum). May encode the variables so that the optimization is done with the encoded variables. Works with numerically generated data, experimental data, or analytical functions. These advantages are interesting and produce surprising results when traditional optimization approaches fail miserably. The basic operating principles of GAs are based on the principles of natural evolution. There are many variations of the genetic algorithms but the basic form is simple genetic algorithm (SGA). This algorithm works with a set of population of candidate solution represented as strings. The initial population consists of randomly generated individuals. In every iteration of the algorithm, fitness of each individual in current population is computed. The population is then transformed in stages to yield a new current population for next iteration. The transformation is usually done in three stages by simply applying the following genetic operators: (1) selection, (2) crossover, and (3) mutation. In the first stage

2 selection operator is applied as many times as there are individuals in the population. In this stage every individual is replicated with a probability proportional to its relative fitness in the population. In the next stage, the crossover operator is applied. Two individuals (parents) are chosen and combined to produce two new individuals. The combination is done by choosing at random a cutting point at which each of parents is divided into two parts; these are exchanged to form the two offspring which replace their parents in the population. In the final stage, the mutation operator changes the values in a randomly chosen location on an individual. The algorithm terminates after a fixed number of iterations and the best individual generated during the run is taken as the solution. II. PROBLEM FORMULATION Non-reheat type single area thermal generating system represents by block diagram of closed loop controlled system model. As shown in Fig. 2, is the system frequency (Hz), is regulation constant (Hz/unit), is speed governor time constant (sec), is turbine time constant (sec), is Inertia Constant (s) and is area parameter (Mw/Hz). III. INTRODUCTION Frequency is a key stability criterion in power systems. To provide the stability, active power balance and constant frequency are required. Frequency depends on active power balance. If any change occurs in active power demand/generation in power systems, frequency cannot be hold in its rated value. So oscillations increase in both power and frequency. Thus, system subjects to a serious instability problem. To improve the stability of the power networks, it is necessary to design Load Frequency Control (LFC) systems that control the power generation and active power. Because of the relationship between active power and frequency, three level automatic generation controls have been proposed by power system researchers [1]. Generally, ordinary LFC systems are designed with Proportional-Integral (PI) controllers. However, since the I control parameters are usually tuned; it is incapable of obtaining good dynamic performance for various load and system changes. Many studies have been carried out in the past on this important issue in power systems, which is the load frequency control. As stated in some literature [2], some control strategies have been suggested based on the conventional linear control theory. These controllers may be inappropriate in some operating conditions. This could be due to the complexity of the power systems such as nonlinear load characteristics and variable operating points. In this study, Genetic Algorithm (GA) is used to determine the parameters of a PID controller according to the system dynamics. In the integral controller, if the integral gain is very high, undesirable and unacceptable large overshoots will be occurred. However, adjusting the maximum and minimum values of proportional ( ), integral ( ) and integral ( ) gains respectively, the outputs of the system (voltage, frequency) could be improved. In this simulation study, a single area power system is chosen and load frequency control of this system is made by genetic based PID controller. The overshoots and settling times with the proposed Genetic-PID controller are better than the outputs of the conventional PID controllers tuned by Ziegler- Nicholas technique, Particle Swarm Optimization. IV. OVERVIEW ON GENETIC ALGORITHM The Genetic Algorithm (GA) is an optimization and search technique based on the principles of genetics and natural selection. The GA allows a population composed of many individuals to evolve under specified selection rules to a state that maximizes the fitness (i.e., minimizes the cost function), many versions of evolutionary programming have been tried with varying degrees of success. Some of the advantages of a GA include [3, 4]: Optimization with continuous or discrete variables. Doesn t require derivative information. Simultaneously searches from a wide sampling of the cost surface. Optimization of variables with extremely complex cost surfaces (they can jump out of a local minimum). May encode the variables so that the optimization is done with the encoded variables. Works with numerically generated data, experimental data, or analytical functions. These advantages are interesting and produce surprising results when traditional optimization approaches fail miserably. The basic operating principles of GAs are based on the principles of natural evolution. There are many variations of the genetic algorithms but the basic form is simple genetic algorithm (SGA). This algorithm works with a set of population of candidate solution represented as strings. The initial population consists of randomly generated individuals. In every iteration of the algorithm, fitness of each individual in current population is computed. The population is then transformed in stages to yield a new current population for next iteration. The transformation is usually done in three stages by simply applying the following genetic operators: (1) selection, (2) crossover, and (3) mutation. In the first stage selection operator is applied as many times as there are individuals in the population. In this stage every individual is replicated with a probability proportional to its relative fitness in the population. In the next stage, the crossover operator is applied. Two individuals (parents) are chosen and combined to produce two new individuals. The combination is done by choosing at random a cutting point at which each of parents is divided into two parts; these are exchanged to form the two offspring which replace their parents in the population. In the final stage, the mutation operator changes the values in a

3 randomly chosen location on an individual. The algorithm terminates after a fixed number of iterations and the best individual generated during the run is taken as the solution. V. PROBLEM FORMULATION model. As shown in Error! Reference source not found., is the system frequency (Hz), is regulation constant (Hz/unit), is speed governor time constant (sec), is turbine time constant (sec), is Inertia Constant (s) and is area parameter (Mw/Hz). Non-reheat type single area thermal generating system represents by block diagram of closed loop controlled system Fig.1 One Area Power Generation Model Basically, electric power system components are non-linear; therefore a linearization around a nominal operating point is usually performed to get a linear system model which is used in the controller design process. The operating conditions of power systems are continuously varying. Accordingly, the real plant usually differs from the assumed one. Therefore, classical algorithms to design a Load Frequency Control using an assumed plant may not ensure the stability of the overall real system. For the single area non-reheat thermal system considered in this study, the conventional Proportional integral (PI) controller was replaced by a PID controller with the following structure: ( ) ( ) (4) ( ) ( ) ( ( ( )) ) (5) Based on this performance index (ISE) optimization problem can be stated as: Subjected to: ( ) (1) Where: is proportional gain, and are integral and derivative time constants, respectively. : PID controller parameters. In this simulation, the objective is to minimize the cost function. For this reason the objective function is chosen as the Integral Square Error (ISE). The ISE squares the error to remove negative error components. ( ) (2) The minimization fitness function becomes (3) Fig.2 Single Area Power System with Genetic based PID Controller The nominal system parameters are The control signal for the conventional PID controller can be given in the following equation., VI.,, RESULTS AND DISCUSSIONS

4 By using Genetic Algorithm technique in conjunction with equation (1)-(5), optimal controller parameters were obtained as shown in Table 1. TABLE 1 PID Controller Parameters using Genetic Algorithm Technique. PID parameter values Performance of the Genetic based PID controller was compared with the PSO based self tuning PID controller, the conventionally tuned PID controller (Ziegler-Nichols method), and conventional PI controller. Fig.3 shows time response with the conventionally tuned PID controller (Ziegler-Nichols method) and the conventional PI controller. Fig.4 shows time response with PSO based PID controller; System was simulated for 20 seconds with step change of 0.01 p.u. Fig.4 Frequency Deviation of Single Area Power System Compared to PSO-PID Fig.5 shows the time domain performance of the system under the proposed GA-PID controller with step change of 0.05 p.u. At the simulation, the Genetic Algorithm was run for 1000 generations with a population size of 100; As seen in the time response, the Genetic tuned controller gives better performance in terms of overshoot and setting time this shows the efficacy of the A Genetic tuned PID controller over the performance of the PSO based Controller, the conventionally tuned PID controller (Ziegler-Nichols method) and conventional PI controller. Controller Settling Time Peak Overshoot Proposed Genetic-PID PSO based PID Ziegler-Nichols PID Conventional PI Fig.5 Frequency Deviation of Single Area Power System with = 0.05 p.u VII. CONCLUSION In this proposed study, a new Genetic Algorithm based PID has been investigated for automatic load frequency control of a single area power system. For this purpose, first, more adaptive tuning mechanism for the PID controller parameters is obtained. It has been shown that the proposed control algorithm is effective and provides significant improvement in system performance. Therefore, the proposed Genetic-PID controller is recommended to generate good quality and reliable electric energy. In addition, the proposed controller is very simple and easy to implement since it does not require many information about system parameters. Comparison of the proposed Genetic-PID controller with conventional PID controllers was presented. Fig.3 Frequency Deviation of Single Area Power System Compared to Conventional PID, PI REFERENCES [1] Bevrani Hassan, "Robust Power System Frequency Control", Brisbane, Australia : Springer Science + Business Media, LLC, [2] H. Shayeghi a H.A. Shayanfar b, A. Jalili, "Load Frequency Control Strategies - A State Of The Art Survey for The Researcher", pp

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