Pan, Aiqiang and Zhou, Jian and Zhang, Peng and Lin, Shunfu and Tang, Jikai (2017) Predicting of Power Quality Steady State Index Based on Chaotic Theory Using Least Squares Support Vector Machine. Energy and Power Engineering, 09 (04). pp. 713-724. ISSN 1949-243X
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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.
Item Type: | Article |
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Subjects: | Librbary Digital > Engineering |
Depositing User: | Unnamed user with email support@librbarydigit.com |
Date Deposited: | 16 May 2023 07:29 |
Last Modified: | 16 Sep 2024 10:35 |
URI: | http://info.openarchivelibrary.com/id/eprint/677 |