S.S. Thakur

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.

Robust dynamic state estimation of power systems based on M-Estimation and realistic modeling of system dynamics

This paper presents a new scheme of dynamic state estimation, utilizing a statistical approach called the M-Estimation to resolve the filtering problem robustly. In the prediction step, realistic treatment of system dynamics based on nodal analysis produces an efficient state prediction method. The proposed Robust Realistic Dynamic State Estimation (RRDSE) has been tested on 5-bus, 14-bus, and 30-bus test systems and the results are presented. The error analysis presented reveals the superiority of the proposed RRDSE particularly when the system measurements are under bad-data condition.

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.

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.

A new approach to dynamic state estimation of power systems

This paper presents a new method of dynamic state estimation (DSE) based on Kalman filter, rather than extended Kalman filter. The complex line flow measurements are used to obtain the complex element voltages of the power system, which are then treated as the effective system measurements at the filtering stage. Since, the complex element voltages are linearly related to the complex bus voltages, use of the Kalman filter provides more accurate and faster estimation of the unknown complex bus voltages. At the prediction stage, Holts two parameter exponential smoothing technique has been adopted for its reliable performance. The proposed new dynamic state estimation (NDSE) has been tested on 5-bus, 14-bus and 30-bus test systems and the numerical results are presented. The performance of the proposed NDSE has been compared with the existing DSE methods for various simulated test conditions. The error analysis of the methods studied reveals the superiority of the proposed NDSE method particularly under sudden change in operating conditions of the system.

Biogeography Based Optimization to solve economic load dispatch considering valve point effects

This paper presents Biogeography Based Optimization (BBO) technique for solving constrained economic dispatch problems in power system, Considering valve point nonlinearities of generators. In this paper, two ELD problems of different characteristics have been used to investigate the effectiveness of the proposed algorithm A comparison of simulation results reveals that the proposed algorithm is better than, or at least comparable to other well established algorithms in terms of the quality of the solution.

Biogeography Based Optimization technique applied to multi-constraints economic load dispatch problems

This paper presents Biogeography-Based Optimization (BBO) technique for solving constrained economic dispatch problems in power system. Many nonlinear characteristics of generators, like valve point loading, ramp rate limits, prohibited zone, and multiple fuels cost functions are considered. Two Economic Load Dispatch (ELD) problems with different characteristics are applied and compared its solution quality and computation efficiency to Genetic algorithm (GA), Particle swarm optimization (PSO), and other optimization techniques. The simulation results show that the proposed algorithm outperforms previous optimization methods.

Constrained optimal power flow using Craziness Based Particle Swarm Optimization considering valve point loading and prohibited operating zone

This paper presents Craziness Based Particle Swarm Optimization (CRPSO) technique for solving constrained optimal power flow problems in power systems, considering nonlinearities like valve point loading and prohibited operating zones of generators. In this paper, the proposed algorithm has been tested in 26-bus system under various simulated conditions and its solutions are compared to those of simple genetic algorithm (SGA) and real coded mixed integer genetic algorithm (MIGA). The simulation results show that the proposed algorithm is a very promising evolutionary optimization technique for the global optimization of constrained optimal power flow problem.

Optimal power flow using biogeography based optimisation

This paper presents a novel biogeography based optimisation (BBO) algorithm for solving constrained optimal power flow (OPF) problems in power system. In this paper, the feasibility of the proposed algorithm is demonstrated for IEEE 30-bus and IEEE 118-bus systems with three different objective functions and it is compared to other population based optimisation techniques. A comparison of simulation results reveals better solution quality and computation efficiency of the proposed algorithm over hybrid particle swarm optimisation with constriction factor approach (PSOCFA), hybrid particle swarm optimisation with inertia weight approach (PSOIWA), real coded genetic algorithm (RGA) and differential evolution (DE) for the global optimisation of constrained OPF problems.