Binod Kumar Sahu

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.

Tuning and Assessment of Proportional–Integral–Derivative Controller for an Automatic Voltage Regulator System Employing Local Unimodal Sampling Algorithm

Abstract—This article presents an approach for obtaining proportional–integral–derivative controller parameters for an automatic voltage regulator system based on a local unimodal sampling optimization algorithm. A conventional integral time of squared error objective function and modified objective functions in terms of integral time of absolute error, integral of absolute error, integral of squared error, peak overshoot, and settling time with appropriate weighting factors are employed to tune the controller parameters. Different objective functions are employed to obtain optimized proportional–integral–derivative controller gains. Superiority of proposed technique over some recently published modern heuristic optimization techniques, such as artificial bee colony algorithm, particle swarm optimization algorithm, and differential evolution algorithm, for the same automatic voltage regulator system is demonstrated. Simulation results reveal that the proposed proportional–integral–derivative controlled automatic voltage regulator system tuned by the local unimodal sampling algorithm with modified objective function exhibits better performance in terms of settling time, peak overshoot, and stability. The robustness of the system tuned by the proposed algorithm is also studied satisfactorily by varying the time constants of the automatic voltage regulator system in the range of –50% to +50% in steps of 25%.

Performance analysis of automatic generation control of a two area interconnected thermal system with nonlinear governor using PSO and DE algorithm

This paper deals with design and analysis of automatic generation control (AGC) of a two area interconnected thermal power system using proportional-integral-derivative (PID) controller. In order to get realistic approach, both the areas are considered with reheat and governor with dead band which makes the system non-linear. The controller parameters are tuned by using particle swarm optimization (PSO) and differential evolution (DE) algorithms. Dynamic response of the PID controlled AGC tuned by both the algorithms are compared by applying 1% step load perturbation (SLP) in area-1.The cost function is derived by taking the area control errors (AEC) of both the areas. The system is checked for robustness by varying the parameters of the governor, turbine and reheat from -50% to +50% in steps of 25%. Based on analysis, facts & figures the system is found to be robust and performs better when tuned by the PSO algorithm.

Robust analysis and design of PID controlled AVR system using Pattern Search algorithm

This paper has been presented in keeping the view of optimal design of a Proportional-Integral-Derivative (PID) controller based on Pattern Search (PS) Optimization algorithm. In order to determine the optimum parameters of the PID controller for an automatic voltage regulator (AVR) system in a 3-ph generator this meta-heuristic algorithm is used. In this paper, the objective function is expressed as a function of Integral Time Absolute Error (ITAE), damping ratio, settling time, peak time and the peak value of the amplitude of the wave. The proposed method is found to be better than Artificial Bee Colony (ABC) algorithm with objective function as Integral Time Square Error (ITSE). It has been observed that different parameters of the AVR system such as settling time, peak time, rise time, oscillations and overshoot improve drastically. The AVR systems results were analyzed by different methods like of transient analysis, root locus analysis and bode analysis. Moreover, the results obtained by the simulation derive that the AVR tuned by Pattern Search algorithm are highly better and robust.

Design and comparative performance analysis of PID controlled automatic voltage regulator tuned by many optimizing liaisons

This paper deals with the design of Proportional, Integral, and Derivative (PID) controller to an Automatic Voltage Regulator (AVR) tuned by recently developed Simplified Particle Swarm Optimization algorithm so 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 behavioural parameters. The proposed method is compared with the earlier used PSO algorithm. For performance studies; Transient response analysis, Bode plot analysis and Root locus analysis are explained in details. The robustness analysis is done by varying the time constants of amplifier, exciter, generator & sensor in the range of -50% to + 50% with a step size of 25% respectively. The results of these analyses using the MOL algorithm are found to be better with respect to the analysis of the PID controller using PSO algorithm.

Automatic voltage regulator using TLBO algorithm optimized PID controller

This paper deals with design and analysis of AVR using proportional-integral-derivative (PID) controller optimized by Teaching Learning Based Optimization (TLBO) algorithm The optimum gain of the controller for the proposed model is obtained with objective function as Integral Time Absolute Error (ITAE). The performance of the system is found to be better in every aspect in terms of the settling point, rise point & peak overshoot. By changing the different components of AVR (sensor, generator, exciter & amplifier) by ±50 % robustness analysis is performed.

Application of TLBO algorithm to study the performance of automatic generation control of a two-area multi-units interconnected power system

This paper deals with design and performance analysis of automatic generation control (AGC) of a two area six unit interconnected power system. A single input Fuzzy Proportional Integral Derivative (FPID) controller is used to study the transient response of the proposed system. Area-1 consists of two thermal generating units with reheat turbines along with a diesel unit and area-2 has two thermal generating units with non-reheat turbines with a diesel unit. The gains of FPID controller is optimized by using two novel Teaching Learning Based Optimisation (TLBO) & hybrid Differential Evolution-Particle Swarm Optimization (DEPSO) technique. Step load perturbation (SLP) of 1 % is applied in area1 to study the performance of two algorithms in terms of undershoot, overshoot & settling time. Finally it is observed that the FPID controller optimised TLBO algorithm performs better than the other one.

Application of Hybrid Differential Evolution–Grey Wolf Optimization Algorithm for Automatic Generation Control of a Multi-Source Interconnected Power System Using Optimal Fuzzy–PID Controller

Abstract This paper deals with an optimal hybrid fuzzy-Proportional Integral Derivative (fuzzy-PID) controller optimized by hybrid differential evolution–Grey Wolf optimization algorithm for automatic generation control of an interconnected multi-source power system. Here a two area system is considered; each area is provided with three types of sources namely a thermal unit with reheat turbine, a hydro unit and a gas unit. The dynamic performance of the system is analyzed under two cases: with AC tie-line and with AC-DC tie-line. The efficiency and effectiveness of the proposed controller is substantiated equally in the two cases. The sturdiness of the system is proved by varying the values of the system parameters. The supremacy of the recommended work is additionally ascertained by comparison with the recently published results like differential evolution optimized PID Controller and hybrid Local Unimodal Sampling-Teaching Learning based Optimization (LUS-TLBO) optimized fuzzy-PID controller. The dynamic performance of the system is observed in terms of settling time, peak overshoot and peak undershoot. Finally the analysis is extended by applying the proposed control technique in two different models namely (i) A three area unequal thermal system considering proper generation rate constraints (GRC) and (ii) A three area hydro-thermal system with mechanical hydro governor. These test results reveal the adaptability of the proposed method in multi-area interconnected power system.

Automatic generation control of multi-area thermal power system using TLBO algorithm optimized fuzzy-PID controller

This paper deals with design and performance scrutiny of automatic generation control (AGC) of a multi area interconnected power system. Here three thermal systems with equal ratings have been considered. Single input Fuzzy proportional integral-derivative (FPID) controller is used to study the transient response of the proposed system. In order to get realistic approach turbine time constant along with the governor dead band are used in all the three areas. The gains of FPID controller is optimized by using Teaching Learning Based Optimization (TLBO) technique & Particle Swarm Optimization (PSO). Step load perturbation (SLP) of 1 % is applied in area 1 to study the performance of the proposed controller. Finally it is observed that TLBO algorithm yields better dynamic performance.