Application of the Fuzzy Computational Intelligence in Power Quality Data Management

Hoda Farag

Abstract


In Electrical Power Distribution System the sustained availability and quality of electric power are the main challenge they need to satisfy so overcoming the power quality (PQ) degradation became an asset. This Paper addresses the perfect load management using the computational techniques by analyzing the data of the system taking into account the density of the  feeding nodes and its distribution  also the classification of major Power quality degradations such as power factor and harmonics in the System and The methodology will be illustrated, simulated and evaluated using the fuzzy technique clustering the data and on an Artificial Neural Network (ANN) to achieve the optimum utilization of the energy loads and perfect load management and optimization. Simulation results demonstrate the effectiveness of the proposed algorithm in reducing the power and energy losses, improving the quality of the electric power system.


Keywords


Power Quality Degradaion; fuzzy technique; Artificial Neural Network

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References


Rafael E. Bourguet and Panos J. Antsaklis,”Artificial Neural Networks In Electric Power Industry”, Technical Report of the ISIS Group at the University of Notre Dame, ISIS-94-007 April, 1994

Min Wang, Piotr ochenkowski , Alexander Mamishev, “Classification of power quality disturbances using time-frequency ambiguity plane and neural networks”, 2001 IEEE .

D. Saxena, K.S. Verma, and S.N. Singh, “Power quality event classification: an overview and key issues”,2010 .

Mahmoud El-Gammal, Amr Abou-Ghazala and Tarek El-Shennawy,” Detection, localization, and classification of power quality disturbances using discrete wavelet transform technique”,Alexandria Engineering Journal 2003.

Mohamed E.Salem Abozaed,”Detection and classification of power quality disturbances using S-Transform and wavelet algorithm”,International Journal; of Electrical , Computer,Energetic, Electronic and communication Engineering Vol7, No:6, 2013.

S.Suja,Jovitha Jerome, “Power signal disturbance classification using wavelet based neural network”,Serbian journal of electric engineering, Vol. 4, No. 1,June 2007,71-83.

Pearson,“Fuzzy Logic Fundamentals”, Chapter3 , 26 March 2001.

S.Rajasekaran, G.A.vijayalakshami, “Neural networks, fuzzy logic and genetic algorithm: synthesis and applications , july 2011.‏

Zahir Javed paracha ,Design and development of intelligent computational technique for power quality data monitoring and management”, Victoria university Australia, 2011.

Janusz Mindykowski, “Fundamentals of electric power assessment”, Proceedings ,XVII IMEKO world congress, Uune 22-27 2003, Dubrovnik, Coroatia.

Zadeh, L.A. (1965): Fuzzy sets, Information and Control 8(3):338–353.




DOI: http://dx.doi.org/10.21622/resd.2017.03.1.166

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