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The Power System Short-term Load Forecasting Model Based on HHT

Author: BaiZuoLi
Tutor: LiuZhiGang
School: Southwest Jiaotong University
Course: Rail transportation electrification and automation
Keywords: Power system Short - term load forecasting Hilbert-Huang Transform Modal aliasing Artificial Neural Networks Particle Swarm Optimization
CLC: TM715
Type: Master's thesis
Year: 2010
Downloads: 128
Quote: 1
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Abstract


Power system short-term load forecasting power system to optimize the operation of the foundation , have a significant impact for the security of the power system operation , reliability and economy . Therefore, the search for effective load forecasting method to improve prediction accuracy has important practical significance . So far , researchers have proposed many effective prediction method , but there are still a lot of room for improvement . Based on application the HHT (Nilbert-Huang transform ) to load into the decomposition of the more popular neural network , support vector machines , particle swarm optimization and other methods of power system short-term load forecasting , mainly for the following work : through power system short-term load data preprocessing , the Wright guidelines to eliminate abnormal values ??and wavelet denoising . Differential operator in the process of application HHT load data decomposition and cumulative sum method to improve the modal aliasing , in order to better achieve the separation of each band . High to low single- component signal IMF through a series of frequency decomposition , reconstruct the low-frequency signal . Then select the appropriate frequency characteristics of each IMF forecast model . Finally, the predicted results of each component are added to obtain a final prediction value . For frequency random component IMF1 , because of its volatility , consider the factors of temperature and holidays , and its combination forecasting using neural network and particle swarm optimization (PSO) , the simulation results show that the scheme to achieve better predict . Finally, actual load data to 2006 for a power system modeling and prediction sample set , and the relative error for the performance indicators have been tested . The results show that the method to achieve a higher prediction accuracy .

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CLC: > Industrial Technology > Electrotechnical > Transmission and distribution engineering, power network and power system > Theory and Analysis > Power system planning
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