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Computational Analysis of Different Artificial Intelligence Based Optimization Techniques for Optimal Power Flow and Economic Load Dispatch Problem | Lokhande | INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY

Computational Analysis of Different Artificial Intelligence Based Optimization Techniques for Optimal Power Flow and Economic Load Dispatch Problem

Netra M Lokhande, Debirupa Hore

Abstract


The purpose of this paper is to present a computational Analysis of various Artificial Intelligence based optimization Techniques used to solve OPF problems. The various Artificial Intelligence methods such as Genetic Algorithm(GA), Particle Swarm Optimization(PSO), Bacterial Foraging Optimization(BFO), ANN are studied and analyzed in detail. The objective of an Optimal Power Flow (OPF) algorithm is to find steady state operation point which minimizes generation cost and transmission loss etc. or maximizes social welfare, load ability etc. while maintaining an acceptable system performance in terms of limits on generators’ real and reactive powers, power flow limits, output of various compensating devices etc. Traditionally, Classical optimization methods were used effectively to solve optimal power flow. But, recently due to the incorporation of FACTS devices and deregulation of power sector the traditional concepts and practices of power systems are superimposed by an economic market management and hence OPF have become more complex. So, in recent years, Artificial Intelligence (AI) methods have been emerged which can solve highly complex OPF problems at faster rate.


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References


] Rasoul Rahmani, Mohd Fauzi Othman, Rubiyah Yusof, Marzuki Khalid ”Solving economic dispatch problem using particle swarm optimization by an evolutionary technique for initializing particles” Journal of Theoretical and Applied Information Technology. Vol. 46 No.2 pp 526-536 December 2012

. Kwang Y. Lee, Jong-Bae Park, “Application of Particle Swarm Optimization to Economic Dispatch Problem: Advantages and Disadvantages” 2006 IEEE pp 188-193.

. Naveen Kumar, K.P.Singh Parmar, Surender Dahiya “A Genetic Algorithm Approach for the Solution of Economic Load

Dispatch Problem” International Journal on Computer Science and Engineering (IJCSE), Vol. 4 No. 06 June 2012 pp 106-1068.

. J. Nanda*, R. Badri Narayanan “Application of genetic algorithm to economic load dispatch with Lineflow constraints” November 2001 pp 723-729

.Amita Mahor, Vishnu Prasad, Saroj Rangnekar “Economic dispatch using particle swarm optimization: A review” Renewable and Sustainable Energy Reviews 13 (2009) pp 2134–2141.

. Rajesh K.S. Prof. S. Lyla, Prof. Bisharathu Beevi “Economic Load Dispatch based on Genetic Algorithm”

. K. Nithiyananthan, V. Ramachandran” Enhanced Genetic Algorithm Based Model for Power System”Vol I June 2011.

. R. Vijay “Intelligent Bacterial Foraging Optimization Technique to Economic Load Dispatch Problem” International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-2, May 2012.

. P. S. Bhowmik, D. V. Rajan, S. P. Bose “Load Flow Analysis: An Overview” World Academy of Science, Engineering and Technology, 2012.

. Salomon, Torres, Martins, “Load Flow Computation via Particle Swarm Optimization” ISEE Electrical Systems and Energy Institute.

. Satyendra Singh, K.S. Verma” Optimal Power Flow using Genetic Algorithm andParticle Swarm Optimization” Vol. 2 Issue 1, Jan.2012, pp. 046-049.

. Djillani BEN ATTOUS, Yacine LABBI” Particle Swarm Optimization based Optimal Power Flow for Units with Non-Smooth Fuel Cost Functions” El-Oued University Center, Algeria. pp 377-381.

.Anish Ahmed, Nitin Singh, Tarun Varshney “A New Approach for solving economic load dispatch problem”, MIT International journal of Electrical and instrumentation Engineering, Vol 1 Aug 2011 pp 93-98.

. Dong Hwa Kim , Ajith Abraham ,, Jae Hoon Cho” A hybrid genetic algorithm and bacterial foraging approach For global optimization“Publisher Elsevier 2007 pp 3918-3937.

Debirupa Hore was born in Guwahati Assam India on April 19th 1983.She received her B.E Degree in Electrical Engg. from Assam Engg.College Guwahati in 2006 and M.Tech in Energy and Power Systems in 2010 from NIT Silchar. She worked in GIMT Guwahati for 5 years as Assistant Professor.

Currently she is working in KJ Educational Institutes, KJCOEMR ,Pune (Maharashtra).Her research areas of interest includes Power Systems, AI Techniques, Power Electronics and Drives.

Netra Lokhande received her B.E Degree in Electrical and Electronics Engg. from Karnatak University in 1997 and M.E in Power Systems in 2004 from Government College of Engg.Pune(Pune University). She is currently pursuing Ph.D in Image Processing. She is having around 15 years of Teaching Experience as Assistant Professor. Currently she is working in KJ Educational Institutes, KJCOEMR, Pune(Maharashtra).as Head of Department. Her research areas of interest includes Power Systems, AI Techniques, Power Electronics and Drives, Image Processing.


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