肖斐;艾芊;

• 1:电力传输与功率变换控制教育部重点实验室(上海交通大学电气工程系)

摘要(Abstract)：

配网运行环境的日益复杂化使电能质量预测的难度不断增大。为解决区域配网的电能质量预测问题,挖掘电力扰动模式,提出一种基于多阶段离散隐马尔科夫模型(discrete hidden markov model,DHMM)的扰动事件预估方法。首先采用数据清洗和离散化技术对天气类型和扰动事件严重程度进行划分;然后通过信息熵相关性分析和时间序列合成的多阶段预处理,提取天气敏感型区域配网的电能质量扰动事件观测序列;最后利用DHMM对观测序列进行训练,实现电力扰动模式提取和扰动事件预估。依据实际城市电网电能质量数据,验证了本文方法的有效性和实用性。相比于传统方法,计及天气因素的DHMM具备更高的电力扰动事件预估准确率。

关键词(KeyWords)： 电能质量;电力扰动模式;离散隐马尔科夫模型;信息熵

Abstract:

Keywords:

基金项目(Foundation): 国家重点研发计划资助项目(2016YFB0901300);; 国家自然科学基金项目(51577115)~~

作者(Author): 肖斐;艾芊;

Email:

DOI: 10.13335/j.1000-3673.pst.2017.0414

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