Sakti Prasad Ghoshal

Biogeography based optimization for multi-constraint optimal power flow with emission and non-smooth cost function

This paper presents biogeography based optimization (BBO) technique for solving constrained optimal power flow problems in power systems, considering valve point nonlinearities of generators. In this paper, the proposed algorithm has been tested in 9-bus and IEEE 30-bus systems under various simulated conditions. A comparison of simulation results reveals optimization efficacy of the proposed scheme over evolutionary programming (EP), genetic algorithm (GA), particle swarm optimization (PSO), mixed-integer particle swarm optimization (MIPSO) and sequential quadratic programming (SQP) used in MATPOWER for the global optimization of multi-constraint OPF problems.

Bio-inspired fuzzy logic based tuning of power system stabilizer

In this paper, bacteria foraging optimization (BFO) - a bio-inspired technique, is utilized to tune the parameters of both single-input and dual-input power system stabilizers (PSSs). Conventional PSS (CPSS) and the three dual-input IEEE PSSs (PSS2B, PSS3B, and PSS4B) are optimally tuned to obtain the optimal transient performances. A comparative performance study of these four variants of PSSs is also made. It is revealed that the transient performance of dual-input PSS is better than single-input PSS. It is, further, explored that among dual-input PSSs, PSS3B offers superior transient performance. A comparison between the results of the BFO and that of genetic algorithm (GA) is conducted in this study. The comparison reveals that BFO is more effective than GA in finding the optimal transient performance. For on-line, off-nominal operating conditions Sugeno fuzzy logic (SFL) based approach is adopted. On real time measurements of system operating conditions, SFL adaptively and very fast yields on-line, off-nominal optimal stabilizer parameters.

Biogeography-based Optimization for Economic Load Dispatch Problems

Abstract This article presents a biogeography-based optimization technique for solving constrained economic dispatch problems in a power system, considering the non-linear characteristics of generators such as valve point loading, ramp rate limits, prohibited operating zones, and multiple fuels cost functions. In this article, the proposed algorithm has been tested in different systems under various simulated conditions. A comparison of simulation results reveals optimization efficacy of the proposed scheme over the genetic algorithm, particle swarm optimization, the Hopfield model, etc., for the global optimization of multi-objective constrained economic load dispatch problems.

Multi-objective Optimal Power Flow Using Biogeography-based Optimization

Abstract This article presents a novel biogeography-based optimization algorithm for solving constrained optimal power flow problems in power systems, considering valve point non-linearities of generators. In this article, the feasibility of the proposed algorithm is demonstrated for 9-bus, 26-bus, and IEEE 118-bus systems with three different objective functions, and it is compared to other well-established population-based optimization techniques. A comparison of simulation results reveals better solution quality and computational efficiency of the proposed algorithm over evolutionary programming, genetic algorithm, and mixed-integer particle swarm optimization for the global optimization of multi-objective constrained optimal power flow problems.

A Novel Particle Swarm Optimization Based Optimal Design of Three-Ring Concentric Circular Antenna Array

In this paper the maximum sidelobe level (SLL) reductions without and with central element feeding in various designs of three-ring concentric circular antenna arrays (CCAA) are examined using a novel particle swarm optimization algorithm (NPSO) to finally determine the global optimal CCAA design. Real coded Genetic Algorithm (RGA) is also employed for comparative optimization but it proves to be suboptimal. The present paper assumes non-uniform excitations and uniform spacing of excitation elements in each three-ring CCAA design. Among the various CCAA designs, the three-ring CCAA containing central element and 4, 6 and 8 elements in three successive concentric rings proves to be such global optimal design with global minimum SLL (-19.69dB) determined by NPSO.

Optimal Reactive Power Dispatch Considering Flexible AC Transmission System Devices Using Biogeography-based Optimization

Abstract This study presents biogeography-based optimization to solve the optimal reactive power dispatch problem incorporating a flexible AC transmission system. The purpose of optimal reactive power dispatch is to provide a solution that improves voltage profile and reduces transmission loss for every practical power system. The proposed biogeography-based optimization algorithm is implemented and tested on the IEEE 30-bus system with multiple flexible AC transmission system devices, such as a thyristor control series compensator and a thyristor control phase shifter. The proposed approach results have been compared to those of particle swarm optimization with inertia weight approach, real-coded genetic algorithm, and differential evolution. The comparison of the results with other methods shows the superiority of the proposed biogeography-based optimization.

Particle swarm optimization with an aging leader and challengers algorithm for the solution of optimal power flow problem

Graphical abstract for application of ALC-PSO algorithm for solution of OPF problem. ALC-PSO is applied for the solution of OPF problem of power systems.OPF is formulated as a non-linear constrained optimization problem.The study is implemented on two IEEE standard test power systems.The presented results demonstrate the potential of the proposed approach. Solution of optimal power flow (OPF) problem aims to optimize a selected objective function such as fuel cost, active power loss, total voltage deviation (TVD) etc. via optimal adjustment of the power system control variables while at the same time satisfying various equality and inequality constraints. In the present work, a particle swarm optimization with an aging leader and challengers (ALC-PSO) is applied for the solution of the OPF problem of power systems. The proposed approach is examined and tested on modified IEEE 30-bus and IEEE 118-bus test power system with different objectives that reflect minimization of fuel cost or active power loss or TVD. The simulation results demonstrate the effectiveness of the proposed approach compared with other evolutionary optimization techniques surfaced in recent state-of-the-art literature. Statistical analysis, presented in this paper, indicates the robustness of the proposed ALC-PSO algorithm.

A new design method using opposition-based BAT algorithm for IIR system identification problem

BAT algorithm BA is a meta-heuristic algorithm, based on the echolocation behaviour of bats. In this paper, optimal set of filter coefficients is searched by the modified optimisation methodology called opposition-based BAT algorithm OBA for infinite impulse response IIR system identification problem. Opposition based numbering concept is embedded into the primary foundation of BA metaphorically to enhance the convergence speed and performance for finding better near-global optimal solution. Detailed and balanced search in multidimensional problem space is accomplished with judiciously chosen control parameters of OBA technique. When tested against standard benchmark examples, for same and reduced order models, the simulation results establish the OBA as a more competent candidate to other evolutionary algorithms as real coded genetic algorithm RGA, differential evolution DE and particle swarm optimisation PSO in terms of accuracy and convergence speed.

Digital FIR filter design using fitness based hybrid adaptive differential evolution with particle swarm optimization

This paper presents an efficient way of designing linear phase finite impulse response (FIR) low pass and high pass filters using a novel algorithm ADEPSO. ADEPSO is hybrid of fitness based adaptive differential evolution (ADE) and particle swarm optimization (PSO). DE is a simple and robust evolutionary algorithm but sometimes causes instability problem; PSO is also a simple, population based robust evolutionary algorithm but has the problem of sub-optimality. ADEPSO has overcome the above individual disadvantages faced by both the algorithms and is used for the design of linear phase low pass and high pass FIR filters. The simulation results show that the ADEPSO outperforms PSO, ADE, and DE in combination with PSO not only in magnitude response but also in the convergence speed and thus proves itself to be a promising candidate for designing the FIR filters.

Solution of economic dispatch problems by seeker optimization algorithm

Seeker optimization algorithm (SOA), a novel heuristic population-based search algorithm, is utilized in this paper to solve different economic dispatch (ED) problems of thermal power units. In the SOA, the act of human searching capability and understanding are exploited for the purpose of optimization. In this algorithm, the search direction is based on empirical gradient by evaluating the response to the position changes and the step length is based on uncertainty reasoning by using a simple fuzzy rule. The effectiveness of the algorithm has been tested on four different small, as well as, large scale test power systems to solve the ED problems. The outcome of the present work is to establish the SOA as a promising alternative approach to solve the ED problems in practical power systems. Both the near-optimality of the solution and the convergence speed of the algorithm are promising. The results obtained are compared with those published in the recent literatures.