Sidhartha Panda

Comparison of particle swarm optimization and genetic algorithm for FACTS-based controller design

Recently, genetic algorithms (GA) and particle swarm optimization (PSO) technique have attracted considerable attention among various modern heuristic optimization techniques. The GA has been popular in academia and the industry mainly because of its intuitiveness, ease of implementation, and the ability to effectively solve highly non-linear, mixed integer optimization problems that are typical of complex engineering systems. PSO technique is a relatively recent heuristic search method whose mechanics are inspired by the swarming or collaborative behavior of biological populations. Since the two approaches are supposed to find a solution to a given objective function but employ different strategies and computational effort, it is appropriate to compare their performance. This paper presents the application and performance comparison of PSO and GA optimization techniques, for flexible ac transmission system (FACTS)-based controller design. The design objective is to enhance the power system stability. The design problem of the FACTS-based controller is formulated as an optimization problem and both PSO and GA optimization techniques are employed to search for optimal controller parameters. The performance of both optimization techniques in terms of computational effort, computational time and convergence rate is compared. Further, the optimized controllers are tested on a weakly connected power system subjected to different disturbances over a wide range of loading conditions and parameter variations and their performance is compared with the conventional power system stabilizer (CPSS). The eigenvalue analysis and non-linear simulation results are presented and compared to show the effectiveness of both the techniques in designing a FACTS-based controller, to enhance power system stability.

Simulation study for automatic generation control of a multi-area power system by ANFIS approach

This paper deals with the application of artificial neural network (ANN) based ANFIS approach to automatic generation control (AGC) of a three unequal area hydrothermal system. The proposed ANFIS controller combines the advantages of fuzzy controller as well as quick response and adaptability nature of ANN. Appropriate generation rate constraints (GRC) have been considered for the thermal and hydro plants. The hydro area is considered with an electric governor and thermal area is considered with reheat turbine. The design objective is to improve the frequency and tie-line power deviations of the interconnected system. 1% step load perturbation has been considered occurring either in any individual area or occurring simultaneously in all the areas. It is a maiden application of ANFIS approach to a three unequal area hydrothermal system with GRC considering perturbation in a single area as well as in all areas. The performance of the ANFIS controller is compared with the results of integral squared error (ISE) criterion based integral controller published previously. Simulation results are presented to show the improved performance of ANFIS controller in comparison with the conventional integral controller. The results indicate that the controllers exhibit better performance. In fact, ANFIS approach satisfies the load frequency control requirements with a reasonable dynamic response.

Hybrid BFOA-PSO algorithm for automatic generation control of linear and nonlinear interconnected power systems

In the bacteria foraging optimization algorithm (BFAO), the chemotactic process is randomly set, imposing that the bacteria swarm together and keep a safe distance from each other. In hybrid bacteria foraging optimization algorithm and particle swarm optimization (hBFOA-PSO) algorithm the principle of swarming is introduced in the framework of BFAO. The hBFOA-PSO algorithm is based on the adjustment of each bacterium position according to the neighborhood environment. In this paper, the effectiveness of the hBFOA-PSO algorithm has been tested for automatic generation control (AGC) of an interconnected power system. A widely used linear model of two area non-reheat thermal system equipped with proportional-integral (PI) controller is considered initially for the design and analysis purpose. At first, a conventional integral time multiply absolute error (ITAE) based objective function is considered and the performance of hBFOA-PSO algorithm is compared with PSO, BFOA and GA. Further a modified objective function using ITAE, damping ratio of dominant eigenvalues and settling time with appropriate weight coefficients is proposed to increase the performance of the controller. Further, robustness analysis is carried out by varying the operating load condition and time constants of speed governor, turbine, tie-line power in the range of +50% to -50% as well as size and position of step load perturbation to demonstrate the robustness of the proposed hBFOA-PSO optimized PI controller. The proposed approach is also extended to a non-linear power system model by considering the effect of governor dead band non-linearity and the superiority of the proposed approach is shown by comparing the results of craziness based particle swarm optimization (CRAZYPSO) approach for the identical interconnected power system. Finally, the study is extended to a three area system considering both thermal and hydro units with different PI coefficients and comparison between ANFIS and proposed approach has been provided.

Optimal location and controller design of STATCOM for power system stability improvement using PSO

The optimal location of a static synchronous compensator (STATCOM) and its coordinated design with power system stabilizers (PSSs) for power system stability improvement are presented in this paper. First, the location of STATCOM to improve transient stability is formulated as an optimization problem and particle swarm optimization (PSO) is employed to search for its optimal location. Then, coordinated design problem of STATCOM-based controller with multiple PSS is formulated as an optimization problem and optimal controller parameters are obtained using PSO. A two-area test system is used to show the effectiveness of the proposed approach for determining the optimal location and controller parameters for power system stability improvement. The nonlinear simulation results show that optimally located STATCOM improves the transient stability and coordinated design of STATCOM-based controller and PSSs improve greatly the system damping. Finally, the coordinated design problem is extended to a four-machine two-area system and the results show that the inter-area and local modes of oscillations are well damped with the proposed PSO-optimized controllers.

Design and performance analysis of PID controller for an automatic voltage regulator system using simplified particle swarm optimization

Abstract This paper presents the design and performance analysis of Proportional Integral Derivate (PID) controller for an Automatic Voltage Regulator (AVR) system using recently proposed simplified Particle Swarm Optimization (PSO) also called Many Optimizing Liaisons (MOL) algorithm. MOL simplifies the original PSO by randomly choosing the particle to update, instead of iterating over the entire swarm thus eliminating the particles best known position and making it easier to tune the behavioral parameters. The design problem of the proposed PID controller is formulated as an optimization problem and MOL algorithm is employed to search for the optimal controller parameters. For the performance analysis, different analysis methods such as transient response analysis, root locus analysis and bode analysis are performed. The superiority of the proposed approach is shown by comparing the results with some recently published modern heuristic optimization algorithms such as Artificial Bee Colony (ABC) algorithm, Particle Swarm Optimization (PSO) algorithm and Differential Evolution (DE) algorithm. Further, robustness analysis of the AVR system tuned by MOL algorithm is performed by varying the time constants of amplifier, exciter, generator and sensor in the range of −50% to +50% in steps of 25%. The analysis results reveal that the proposed MOL based PID controller for the AVR system performs better than the other similar recently reported population based optimization algorithms.

Teaching-learning based optimization algorithm based fuzzy-PID controller for automatic generation control of multi-area power system

Fuzzy-PID controller is proposed for AGC of multi-area power system.TLBO algorithm is applied to optimize the parameters of fuzzy-PID controller.The superiority of proposed approach over LCOA, GA, PS and SA based PID controller is shown.Robustness analysis is performed under wide changes in system parameters and disturbance. This paper deals with the design of a novel fuzzy proportional-integral-derivative (PID) controller for automatic generation control (AGC) of a two unequal area interconnected thermal system. For the first time teaching-learning based optimization (TLBO) algorithm is applied in this area to obtain the parameters of the proposed fuzzy-PID controller. The design problem is formulated as an optimization problem and TLBO is employed to optimize the parameters of the fuzzy-PID controller. The superiority of proposed approach is demonstrated by comparing the results with some of the recently published approaches such as Lozi map based chaotic optimization algorithm (LCOA), genetic algorithm (GA), pattern search (PS) and simulated algorithm (SA) based PID controller for the same system under study employing the same objective function. It is observed that TLBO optimized fuzzy-PID controller gives better dynamic performance in terms of settling time, overshoot and undershoot in frequency and tie-line power deviation as compared to LCOA, GA, PS and SA based PID controllers. Further, robustness of the system is studied by varying all the system parameters from -50% to +50% in step of 25%. Analysis also reveals that TLBO optimized fuzzy-PID controller gains are quite robust and need not be reset for wide variation in system parameters.

Power-system Stability Improvement by PSO Optimized SSSC-based Damping Controller

Abstract Power-system stability improvement by a static synchronous series compensator (SSSC)-based damping controller is thoroughly investigated in this article. The design problem of the proposed controller is formulated as an optimization problem, and the particle swarm optimization technique is employed to search for the optimal controller parameters. By minimizing a time-domain-based objective function, in which the deviation in the oscillatory rotor speed of the generator is involved, stability performance of the system is improved. The performance of the proposed controller is evaluated under different disturbances for both a single-machine infinite-bus power system and a multi-machine power system. Results are presented to show the effectiveness of the proposed controller. It is observed that the proposed SSSC-based controller provides efficient damping to power-system oscillations and greatly improves the system voltage profile under various severe disturbances. Furthermore, the simulation results show that in a multi-machine power system, the modal oscillations are effectively damped by the proposed SSSC controller.

Differential evolution algorithm for SSSC-based damping controller design considering time delay

Abstract Power-system stability improvement by a static synchronous series compensator (SSSC)-based damping controller is thoroughly investigated in this paper. Both local and remote signals with associated time delays are considered in the present study. The design problem of the proposed controller is formulated as an optimization problem, and differential evolution (DE) algorithm is employed to search for the optimal controller parameters. The performances of the proposed controllers are evaluated under different disturbances for both single-machine infinite-bus power system and multi-machine power system. The performance of the proposed controllers with variations in the signal transmission delays has also been investigated. Simulation results are presented and compared with a recently published modern heuristic optimization technique under various disturbances to show the effectiveness and robustness of the proposed approach. The performances of the proposed controllers are also evaluated under N −2 contingency situation.

Design and analysis of SSSC-based supplementary damping controller

Abstract Power-system stability improvement by a static synchronous series compensator (SSSC)-based damping controller is thoroughly investigated in this paper. The design problem of the proposed controller is formulated as an optimization problem, and real coded genetic algorithm (RCGA) is employed to search for the optimal controller parameters. Both local and remote signals with associated time delays are considered in the present study and a comparison has been made between the two signals. The performances of the proposed controllers are evaluated under different disturbances for both single-machine infinite-bus power system and multi-machine power system. Simulation results are presented and compared with a recently published modern heuristic optimization technique under various disturbances to show the effectiveness and robustness of the proposed approach.

Differential evolutionary algorithm for TCSC-based controller design

Abstract In this paper, a systematic procedure for modelling, simulation and optimal tuning the parameters of a thyristor controlled series compensator (TCSC) controller, for the power system stability enhancement is presented. The design problem of the proposed controller is formulated as an optimization problem and differential evolution (DE) is employed to search for optimal controller parameters. A detailed analysis on the selection of objective function and controller structure on the effectiveness of the TCSC controller is carried out and simulation results are presented. The dynamic performance TCSC controller under various loading and disturbance conditions are analyzed and compared. Finally, the proposed design approach is extended to a multi-machine power system for simultaneous design of multiple and multi-type controllers.