Differential Evolution Approach for Reactive Power Optimization of Nigerian Grid System

Bakare, G.A.; Krost, G.; Venayagamoorthy, G.K.; Aliyu, Usman

doi:10.1109/pes.2007.386015

Cited by 28 publications

(12 citation statements)

References 10 publications

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“…These techniques could not explain the complex objective functions which are not differentiable, mostly with complicated constraints, as today's ORPF problem is not a mathematically convex problem. Due to these reason, many evolutionary algorithms (EAs), such as genetic algorithm (GA) [1][2][3], evolutionary programming (EP) [4][5], particle swarm optimization (PSO) [6][7][8][9][10], differential evolution (DE) [11][12][13], seeker optimization algorithm (SOA) [14][15], biogeography based optimization (BBO) [16], Gravitational Search Algorithm (GSA) [17], opposition based gravitational search algorithm (OGSA) [18], teaching learning based optimization (TLBO) [22], quasi-oppositional teaching learning based optimization (QOTLBO) [22], etc. are being applied for solving different complex ORPF problems to overcome some of the drawbacks of conventional classical mathematical techniques.…”

Section: Introductionmentioning

confidence: 99%

Optimal reactive power flow using non dominated sorting multi objective gravitational search algorithm

Bhowmik

Chakraborty

2014

2014 11th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technolo

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This paper presents application of non dominated sorting multi objective gravitational search algorithm (NSMOGSA) for solving optimal reactive power flow (ORPF) problems. ORPF problem is formulated as a non-linear constrained multi-objective optimization problem where different objectives and various constraints have been considered into the formulation. Fast elitist non-dominated sorting and crowding distance have been used to locate and manage the Pareto optimal front. An external archive of the Pareto optimal solutions are also used to provide some elitism. To show the efficacy of the proposed algorithm, it has been tested on a standard IEEE 30-bus system with three different individual objectives that reflect minimization of active power loss, bus voltage deviation and enhancement of voltage stability. The performance of NSMOGSA is compared with the results found by other meta-heuristic techniques reported in the recent literature. Numerical results demonstrate the tangible superiority of the proposed method in achieving the optimum ORPF solution.

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Section: Introductionmentioning

confidence: 99%

Optimal reactive power flow using non dominated sorting multi objective gravitational search algorithm

Bhowmik

Chakraborty

2014

2014 11th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technolo

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“…It has demonstrated its robustness and effectiveness in a variety of applications, such as neural network learning and infinite impulse response filter design. Is an evolutionary computation technique which has been found to be robust in solving continuous nonlinear optimization problems [3]. The DE algorithm is simple in concept, easy to implement and computationally efficient.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary techniques, a sensitivity-based approach for handling discrete variables in reactive power planning

Ionescu

Bulac

Eremia

et al. 2012

2012 IEEE 15th International Conference on Harmonics and Quality of Power

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The paper proposes an application of an optimization evolutionary technique, improved Differential Evolution, to reactive power planning. This problem could be formulated mathematically as a nonlinear, non smooth, mixed integer, multi-objective optimization problem. The main objective deals with the minimization of the real power losses that result in a better reactive power dispatch and improving the voltage profile across the system and keeping the control variables in their operational limits. Here we use an approach to deal with discrete variables, Mixed Integer Hybrid Differential Evolution (MIHDE), a modified Differential Evolution algorithm.The proposed approach has been tested in a modified version of two electric networks, the Nordic 32, 60 bus and IEEE 118 bus system.

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“…The most traditional techniques used for VAR optimization are as follows: nonlinear programming, success linear programming, mixed integer programming, Newton and quadratic techniques [3,4]. The high nonlinearity characteristic of reactive power control causes the search space of this problem to have several local minima.…”

Section: Introductionmentioning

confidence: 99%

“…For this reason, recently, new methods based on artificial intelligence (AI) or Evolutionary algorithms (EAs) have been used. These techniques include artificial neural network (ANN), tabu search (TS), simulated annealing (SA), expert system (ES), genetic algorithms (GAs), differential evolution (DE), evolutionary programming (EP), particle swarm optimization (PSO), etc [3,4]. However, PSO was revealed to have many advantages over other similar AI methods [4,5] and is even considered to be the best [6].…”

Section: Introductionmentioning

confidence: 99%

Optimal reactive power control in transmission network with a large wind farm connection

et al. 2011

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In a weak network where the reactive power capability is not able to satisfy the demand of wind farm based on squirrel-cage induction generator (SCIG), flexible AC transmission system such as static VAR compensator (SVC) is used. Traditionally, the SVC device and other network reactive power facilities are used in optimal way by the network operator, for optimal voltage profile and loss minimization, during different steady-state operations caused by wind resource changes. In this paper, the SVC reactive power reserve (SVC-RPR) is added to the problem as a third objective function to be maximized with the purpose of further compensation usage during dynamic operation. Particle swarm optimization (PSO) is used to optimize the search space of this multi-objective problem. The reactive power optimization scheme is tested in a MATLAB/R2010a based simulation model of Wale & Hale 6-bus system with wind farm integration. It is has been found that the conflict between the three objective functions causes the difficulty of achieving a sufficient SVC-RPR during high wind farm power generation, unless the SVC rating is designed in such away to be higher than the reactive power required by the total number of SCIG at full load.

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Differential Evolution Approach for Reactive Power Optimization of Nigerian Grid System

Cited by 28 publications

References 10 publications

Optimal reactive power flow using non dominated sorting multi objective gravitational search algorithm

Optimal reactive power flow using non dominated sorting multi objective gravitational search algorithm

Evolutionary techniques, a sensitivity-based approach for handling discrete variables in reactive power planning

Optimal reactive power control in transmission network with a large wind farm connection

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