Artificial Intelligent Systems and Machine Learning

Kamal K. Mandal ¹, Debashis Jana ²

Affiliations
1 Department of Power Engineering, Jadavpur University, Salt Lake Campus, Kolkata-700098, IN
2 Department of Electrical Engineering, Camellia Institute of Engineering, Kolkata-700129, IN

Abstract

Reactive power planning is one of the important tasks in the satisfactory operation of power systems. Many evolutionary techniques such as particle swarm optimization (PSO), differential evolution have been applied to solve these problems and found to perform in a better way in comparison with conventional optimization methods. But often these methods converge to a sub-optimal solution prematurely. This paper presents a novel improved self adaptive particle swarm optimization (NISAPSO) technique for optimal reactive power compensation to avoid premature convergence. First, the inertia weight is made self adaptive in terms of fitness and particle numbers. Further, time varying acceleration coefficients (TVSC) are used for better performance. The performance of the proposed method is demonstrated on two test systems. The results obtained by the proposed method are compared with other methods. The results show that the proposed technique is capable of producing comparable results.

Keywords

Reactive Power Planning (RPP), Loss Reduction, Voltage Profile, Novel Self Adaptive Improved Particle Swarm Optimization Technique (NSAPSO).

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