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Predicting of Power Quality Steady State Index Based on Chaotic Theory Using Least Squares Support Vector Machine
Electric Power Research Institute State Grid Shanghai Municipal Electric Power Company, Shanghai, China
Electric Power Research Institute State Grid Shanghai Municipal Electric Power Company, Shanghai, China
Electric Power Research Institute State Grid Shanghai Municipal Electric Power Company, Shanghai, China
College of Electrical Engineering, Shanghai University of Electric Power, Shanghai, China
College of Electrical Engineering, Shanghai University of Electric Power, Shanghai, China
- 1 Electric Power Research Institute State Grid Shanghai Municipal Electric Power Company, Shanghai, China
- 2 Electric Power Research Institute State Grid Shanghai Municipal Electric Power Company, Shanghai, China
- 3 Electric Power Research Institute State Grid Shanghai Municipal Electric Power Company, Shanghai, China
- 4 College of Electrical Engineering, Shanghai University of Electric Power, Shanghai, China
- 5 College of Electrical Engineering, Shanghai University of Electric Power, Shanghai, China
Energy and Power Engineering·Volume 09 (2017)·Pages 713–724·Published 6 April 2017·DOI10.4236/epe.2017.94B077
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Abstract
An effective power quality prediction for regional power grid can provide valuable references and contribute to the discovering and solving of power quality problems. So a predicting model for power quality steady state index based on chaotic theory and least squares support vector machine (LSSVM) is proposed in this paper. At first, the phase space reconstruction of original power quality data is performed to form a new data space containing the attractor. The new data space is used as training samples for the LSSVM. Then in order to predict power quality steady state index accurately, the particle swarm algorithm is adopted to optimize parameters of the LSSVM model. According to the simulation results based on power quality data measured in a certain distribution network, the model applies to several indexes with higher forecasting accuracy and strong practicability.
KeywordsChaotic TheoryLeast Squares Support Vector Machine (LSSVM)Power Quality Steady State IndexPhase Space ReconstructionParticle Swarm Optimization
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