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has been cited by the following article:

• TITLE: Predicting of Power Quality Steady State Index Based on Chaotic Theory Using Least Squares Support Vector Machine

  AUTHORS: Aiqiang Pan, Jian Zhou, Peng Zhang, Shunfu Lin, Jikai Tang

  KEYWORDS: Chaotic Theory, Least Squares Support Vector Machine (LSSVM), Power Quality Steady State Index, Phase Space Reconstruction, Particle Swarm Optimization

  JOURNAL NAME: Energy and Power Engineering, Vol.9 No.4B, April 6, 2017

  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.