基于改进AHA-ELM的光伏并网断路器异常检测方法

Abnormal detection method of photovoltaic grid-connected circuit breaker based on improved AHA-ELM

摘要

针对传统光伏并网断路器检测方法对复杂非线性特征提取和分类能力不足的问题，提出一种基于改进人工蜂鸟算法(AHA)优化极限学习机(ELM)的伏并网断路器异常检测方法。首先，研究影响光伏并网断路器健康状态因素，分析断路器常见故障类型和故障特征，选取适当的特征信号作为能够反映断路器工作状态的高质量数据集；其次，引入动态飞行策略和混沌映射机制优化人工蜂鸟算法，利用改进后的人工蜂鸟算法对极限学习机的输入权重和隐藏层偏置进行优化，增强模型对高维复杂特征的学习能力；然后，采用改进的AHA-ELM算法快速获取断路器各种故障下的数据特征，训练得到光伏并网断路器异常检测模型，对断路器状态进行异常检测；最后，基于某实际含光伏并网的配电网进行仿真验证，结果表明所提方法的具有可行性和有效性。

Abstract

To address the limitations of traditional detection methods for PV grid-connected circuit breakers, which struggle to extract and classify complex nonlinear features, an anomaly detetion method based on an improred AHA and an optimized ELM is proposed. Firstly, the factors affecting the health status of PV grid-connected circuit breakers are studied, the common fault types and fault characteristics of circuit breakers are analyzed, and appropriate characteristic signals are selected as high-quality data sets that can reflect the working status of circuit breakers. Secondly, dynamic flight strategy and chaotic mapping mechanism are introduced to optimize the AHA, and the improved AHA is used to optimize the input weight and hidden layer bias of the ELM, so as to enhance the learning ability of the model for high-dimensional complex features. Then, the improved AHA-ELM algorithm is used to quickly obtain the data characteristics of circuit breakers under various faults, and the abnormal detection model of PV grid-connected circuit breakers is trained to detect the abnormal state of circuit breakers. Finally, based on a practical distribution network with photovoltaic grid connection, the simulation result show that the proposed method is feasible and effective.

关键词

光伏并网断路器 / 异常检测 / 极限学习机 / 人工蜂鸟算法 / 混沌映射

Key words

photovoltaic grid-connected circuit breaker / anomaly detection / extreme learning machine / artificial hummingbird algorithm / chaotic mapping

Author summay

沈兴杰(1992—), 男, 工程师, 硕士, 主要从事电气工程及其自动化研究。E-mail: [e-mail]

基金资助

国网天津市电力公司科技项目(城南-研发2024-01)