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Substation Voltage & Reactive Power Control Based on Load Forecasting Using Fuzzy Clustering Analysis and RBF Neural Network

Author: ZhouChongQuan
Tutor: LiLinChuan
School: Tianjin University
Course: Proceedings of the
Keywords: Fuzzy Clustering Analysis RBF Neural Network Short - term load forecasting Transformer substation Reactive Power Control
CLC: TP18
Type: Master's thesis
Year: 2007
Downloads: 242
Quote: 2
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Abstract


Ensure that the voltage quality and reduce network loss is the main purpose of the safe and economic operation of the power grid . Substation voltage and reactive power control through the load the regulating transformer tap switching control and reactive power compensation device , to achieve a reasonable distribution of the reactive and help to improve voltage quality and reduce the power loss . Reactive Power Control Equipment Figure principle , nine districts likely to cause frequent adjustment of transformer taps and capacitor switching frequently , affect the service life of transformers and capacitors . In order to reduce the number of transformer tap action , this paper presents an analysis based on fuzzy clustering RBF neural network load forecasting substation voltage and reactive power control strategy , forecasting the next day substation active and reactive load voltage and high voltage side of the transformer , according to the transformer connectors action times of day restrictions , considering the active and reactive power and voltage combinations segments substation voltage and reactive power control . Performance control strategy is good or bad depends on the accuracy of the load forecasting . Comprehensive consideration of the impact load date type , temperature , relative humidity , weather conditions and other factors , the use of fuzzy clustering analysis of the historical data classification , select the forecast day the same type of day as the sample data set , then using RBF neural networks to predict . The forecasting method to overcome the lack of the same type of day is selected based only on working days , rest days , while improving the quality of the RBF neural network training samples , prediction accuracy has been significantly improved . Taking into account the lack of voltage and reactive power control is only active or reactive segments , transformer tap the day of action times limit considering active , reactive power and voltage combinations segmented control . For two-winding transformer and three-winding transformer were discussed . Calculated through Tianjin substation simulation , to verify the effectiveness of the proposed method .

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