Research on Comprehensive Evaluation and Prediction of Power Quality in Low Voltage Area

Meng Ming, Zhu Liu, Yumin Liu, Wenjing Li, L. Gao, J. Xiao
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Abstract

The results of comprehensive evaluation of power quality are clearly time-series and nonlinear, making it feasible to study future trends based on historical data. In this paper, the comprehensive evaluation and prediction of power quality are linked, and the purpose is to predict the overall change trend of the power quality in a period of time in the future while evaluating it of the low voltage areas. In our scheme, we first use DS-AHP (Dempster-Shafer Analytic Hierarchy Process, DS-AHP)) to calculate the weight of the indicator to reduce the error caused by subjectivity. Secondly, the power quality is analyzed based on the radar chart, and the sample set is constructed with the comprehensive evaluation score obtained. Finally, we use PSO(Particle Swarm optimization, PSO) to optimize the parameters of SVM(Support Vector Machine, SVM), and establish an optimal prediction model that can reflect the change trend of power quality. Experiments show that the scheme for evaluating and predicting power quality proposed in this paper has good effectiveness and accuracy, and it can provide strong technical support for grasping the variation law of power quality in low voltage areas.
低压地区电能质量综合评价与预测研究
电能质量综合评价结果具有明显的时间序列和非线性特征,可以根据历史数据研究未来趋势。本文将电能质量综合评价与预测相结合,目的是在对低压区电能质量进行评价的同时,预测未来一段时间内电能质量的整体变化趋势。在我们的方案中,我们首先使用DS-AHP (Dempster-Shafer Analytic Hierarchy Process, DS-AHP)来计算指标的权重,以减少主观性带来的误差。其次,基于雷达图对电能质量进行分析,并利用得到的综合评价分数构建样本集;最后,利用粒子群算法(PSO)对支持向量机(SVM)的参数进行优化,建立能反映电能质量变化趋势的最优预测模型。实验表明,本文提出的电能质量评价与预测方案具有良好的有效性和准确性,可为掌握低压地区电能质量变化规律提供有力的技术支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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