Dipayan Guha

Load frequency control of interconnected power system using grey wolf optimization

Abstract In this article an attempt has been made to solve load frequency control (LFC) problem in an interconnected power system network equipped with classical PI/PID controller using grey wolf optimization (GWO) technique. Initially, proposed algorithm is used for two-area interconnected non-reheat thermal-thermal power system and then the study is extended to three other realistic power systems, viz. (i) two-area multi-units hydro-thermal, (ii) two-area multi-sources power system having thermal, hydro and gas power plants and (iii) three-unequal-area all thermal power system for better validation of the effectiveness of proposed algorithm. The generation rate constraint (GRC) of the steam turbine is included in the system modeling and dynamic stability of aforesaid systems is investigated in the presence of GRC. The controller gains are optimized by using GWO algorithm employing integral time multiplied absolute error (ITAE) based fitness function. Performance of the proposed GWO algorithm has been compared with comprehensive learning particle swarm optimization (CLPSO), ensemble of mutation and crossover strategies and parameters in differential evolution (EPSDE) and other similar meta-heuristic optimization techniques available in literature for similar test system. Moreover, to demonstrate the robustness of proposed GWO algorithm, sensitivity analysis is performed by varying the operating loading conditions and system parameters in the range of ± 50 % . Simulation results show that GWO has better tuning capability than CLPSO, EPSDE and other similar population-based optimization techniques.

Optimal Design of Superconducting Magnetic Energy Storage Based Multi-area Hydro-Thermal System Using Biogeography Based Optimization

This article proposes automatic generation control (AGC) of an interconnected three equal and unequal hydro-thermal system with DB non-linearity. Moreover, the self tuning control scheme of superconducting magnetic energy storage unit (SMES) is performed to investigate the performances of AGC problem. Dynamic responses of SMES connected AGC are compared with that of integral (I) and proportional-integral-derivative (PID) controlled AGC. Frequency deviation signal is used as an input to SMES. Integral square error approach with Biogeography based optimization algorithm is used to find optimum values of controller parameters. 1% step load perturbation in either area is considered for simulation study. Simulation study exhibits significant effect of designed SMES based controller on the dynamic performances of an interconnected power system with sudden load perturbation.

Quasi-oppositional symbiotic organism search algorithm applied to load frequency control

Abstract The present work approaches a relatively new optimization scheme called “quasi-oppositional symbiotic organism search (QOSOS) algorithm”, for the first time, to find an optimal and effective solution for load frequency control (LFC) problem of the power system. The symbiotic organism search (SOS) algorithm works on the effect of symbiotic interaction strategies adopted by an organism to survive and propagate in the ecosystem. To avoid the suboptimal solution and to accelerate the convergence speed, the theory of quasi-oppositional based learning (Q-OBL) is integrated with original SOS and used to solve the LFC problem. To demonstrate the effectiveness of QOSOS algorithm, two-area interconnected power system with nonlinearity effect of governor dead band and generation rate constraint is considered at the first instant, followed by the four-area power system showing the consequence of load perturbation. The structural simplicity, robust performance and acceptability of well-popular proportional-integral-derivative (PID) controller enforce to implement it as a secondary controller for the present analysis. The success of QOSOS algorithm is established by comparing the dynamic performances of concerned power system with those obtained by some recently published algorithms available in the literature. Furthermore, the robustness and sensitivity are analyzed for the concerned power system to judge the efficacy of the proposed QOSOS approach.

Study of differential search algorithm based automatic generation control of an interconnected thermal-thermal system with governor dead-band

Display Omitted This paper presents differential search algorithm to solve load frequency control problem.Proposed algorithms are implemented on three types of interconnected power systems.Results obtained using differential search algorithm is compared with CLPSO, EPSDE, SHADE, and other reported algorithms.Sensitivity analysis is performed to investigate the robustness of the proposed controllers.The study has been extended to more realistic domain by taking nonlinearities of the power system. An attempt has been made to the effective application of a recently introduced, powerful optimization technique called differential search algorithm (DSA), for the first time to solve load frequency control (LFC) problem in power system. In this paper, initially, DSA optimized classical PI/PIDF controller is implemented to an identical two-area thermal-thermal power system and then the study is extended to two more realistic power systems which are widely used in the literature. To assess the usefulness of DSA, three enhanced competitive algorithms namely comprehensive learning particle swarm optimization (CLPSO), ensemble of mutation and crossover strategies and parameters in differential evolution (EPSDE), and success history based DE (SHADE) are studied in this paper. Moreover, the superiority of proposed DSA optimized PI/PID/PIDF controller is validated by an extensive comparative analysis with some recently published meta-heuristic algorithms such as firefly algorithm (FA), bacteria foraging optimization algorithm (BFOA), genetic algorithm (GA), craziness based particle swarm optimization (CRPSO), differential evolution (DE), teaching-learning based optimization (TLBO), particle swarm optimization (PSO), and quasi-oppositional harmony search algorithm (QOHSA). A case of robustness and sensitivity analysis has been performed for the concerned test system under parametric uncertainty and random load perturbation. Furthermore, to demonstrate the efficacy of proposed DSA, the system nonlinearities like reheater of the steam turbine and governor dead band are included in the system modeling. The extensive results presented in this article demonstrate that proposed DSA can effectively improve system dynamics and may be applied to real-time LFC problem.

Application of Krill Herd Algorithm for Optimum Design of Load Frequency Controller for Multi-Area Power System Network with Generation Rate Constraint

In this paper, a novel biologically inspired algorithm, namely krill herd algorithm (KHA), is proposed for solving load frequency control (LFC) problem in power system. The KHA is based on the simulations of herding behavior of individual krill. Three unequal interconnected reheat thermal power plants equipped with different classical controllers are considered for simulation study and their optimum settings are determined using KHA employing integral square error-based fitness function. The appropriate value of generation rate constraint (GRC) of the steam turbine is included in the study to confirm the effectiveness of proposed method. Performances of several classical controllers are compared with their nominal results and some other recently published algorithms. Additionally, two-stage lag–lead compensator with superconducting magnetic coil is designed to improve the existing results in coordination with LFC. Finally, random pulse load perturbation is given to the system to identify the robustness of proposed controller.

Oppositional biogeography-based optimisation applied to SMES and TCSC-based load frequency control with generation rate constraints and time delay

This article introduces biogeography-based optimisation (BBO) and oppositional BBO for solving load frequency control (LFC) in power system. Two widely employed test systems, viz. two-area multi-unit thermal-thermal and three-area thermal power plant, are considered for analysis. To make the study realistic, inherent power system nonlinearities like time delay, generation rate constraint and reheater are included. 2nd order Pade approximation is used to find linear model of time delay. To provide additional damping to frequency and tie-line power oscillations caused by sudden load perturbation, optimal thyristor controlled series compensator (TCSC) and superconducting magnetic energy storage (SMES) are design in coordination to LFC for the concerned power systems. An extensive comparative analysis is performed to established superiority of proposed method over other recently published algorithms. Simulation results show that coordinated LFC-TCSC-SMES controller yields greater dynamic performance compared to other. Finally, sensitivity analysis is performed to demonstrate the robustness of the proposed controller.

Application of Modified Biogeography Based Optimization in AGC of an Interconnected Multi-Unit Multi-Source AC-DC Linked Power System

An attempt has been made for the effective application of biogeography based optimization and its modified version to solve load frequency control (LFC) problem. Two-area interconnected multi-unit multi-source power system having thermal, hydro and gas power plant without and with AC-DC link is considered for study. Proportional-integral-derivative controller is used as secondary controller in LFC system and its gains are tuned by proposed algorithms through minimization of integral time absolute error based objective function. The results confirm the effectiveness of proposed algorithms after comparing results with other evolutionary algorithms like differential evolution (DE), teaching learning based optimization (TLBO) for the similar test system. The robustness of proposed algorithm is checked with different objective functions like integral square error, integral absolute error, integral time square error criterions and under different loading conditions. Critical analysis of results reveals that proposed method gives better performance than that obtained with DE, TLBO.

Optimal solutions of load frequency control problem using oppositional krill herd algorithm

An attempt has been made in this article for effective solution of load frequency control (LFC) problem in an interconnected power system using a meta-heuristic optimization technique, called oppositional krill herd algorithm (OKHA). Krill herd algorithm (KHA) is simply mimics the life cycle of krill in an oceans and designed based on the solution of herding behavior of individual krill. A two-area multi-unit all hydro power plants equipped with a classical integral controller is considered for design and analysis. The interconnected hydro-hydro power plant is inherently an unstable system due the non-minimum phase characteristic of hydro turbine. Thus to retrieve the stability, different frequency stabilizers like superconducting magnetic energy storage, thyristor controlled phase shifter, and static synchronous series compensator in coordination with LFC are intended using OKHA paying integral square error based fitness function. Time domain simulation results obtained with OKHA are compared with original KHA and some recently published optimization schemes for the similar test system. Additionally, the robustness of the designed controller is validated by investigating the dynamic responses of test system with random load perturbation. Simulation results confirmed that OKHA outperforms other optimization techniques in terms of solution quality and computational efficiency.

Study of dynamic responses of an interconnected two-area all thermal power system with governor and boiler nonlinearities using BBO

This paper aims to investigate the application of biogeography based optimization (BBO) technique to load frequency control (LFC) problem for improving power system dynamics with governor dead band and boiler dynamics type of nonlinearities. Two-area all thermal types interconnected power system network is considered for design and analysis purpose. The thermal areas are equipped with single and double reheat turbine, simultaneously. The designed problem is considered as an optimization problem and BBO algorithm is applied to search optimal gains of classical PID-controller which is used in AGC problem. The behavior of the test system is also investigated with the analysis towards the different cost functions such as integral square error (ISE) and integral time absolute error (ITAE). Small load perturbation (SLP) is considered in area-1 for studying the dynamic behaviors of the designed power system unit. To validate the effectiveness and superiority of proposed controller, the performances are compared with the results obtained by differential evaluation (DE) and particle swarm optimization (PSO) techniques. Simulation study exhibits significant effect of the designed controller on the dynamics of concerned power system network.

Optimal tuning of 3 degree-of-freedom proportional-integral-derivative controller for hybrid distributed power system using dragonfly algorithm

Abstract The objective of this paper is to study the dynamic stability of a hybrid energy distributed power system (HEDPS) subject to load and wind power variations. A three degree-of-freedom (3-DOF) proportional-integral-derivative (PID) controller is designed and implemented in the HEDPS to stabilize frequency and power fluctuations after the perturbation. For enhancing system dynamics, the parameters of the 3-DOF PID controller are optimized by using dragonfly algorithm (DA). The results are compared with the results obtained by Zeigler–Nichols tuning and some other well-known meta-heuristic algorithms. The efficacy of proposed DA over different reported algorithms is established in terms of convergence rate, minimum fitness value and dynamic performance of the system. The robustness of the 3-DOF PID-controller is ascertained with time-varying step load perturbation, random wind power perturbation, and under system parameter variation. The robust performance of proposed DA has also been established by performing statistical analysis.