Scholarlink

With the advancement in renewable energy technology, the grid integration of large wind power plants is increasing day by day. The source impedance of large wind power plants is variable and during faulty conditions, it creates the problem in accurate fault detection and classification. This paper discusses an artificial neural network (ANN)-based classifier utilizing discrete wavelet transform (DWT) of voltage signals for fault classification and detection under large wind power plant integration. The data for DWT-ANN classifier is collected using PSCAD platform and simulations are performed on a modified IEEE-14 bus test system. In this article, first the effect of large wind power plant on the fault detection is presented by simulation on PSCAD through distance relay characteristic. Further, the wide area monitoring system (WAMS) is utilized to collect the voltage signals of nearby buses as an input of DWT-ANN classifier. The information from voltage signals is extracted using detailed coefficient of DWT. These detailed coefficients are further utilized to determine the important features of the signal. Finally, these features are utilized as input of ANN for fault detection and classification.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

DWT-ANN-Based Fault Detection and Classification Approach for Transmission Lines Connected to Large Wind Farm

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Nilesh Kumar Rajalwal,
  • Debomita Ghosh

摘要

With the advancement in renewable energy technology, the grid integration of large wind power plants is increasing day by day. The source impedance of large wind power plants is variable and during faulty conditions, it creates the problem in accurate fault detection and classification. This paper discusses an artificial neural network (ANN)-based classifier utilizing discrete wavelet transform (DWT) of voltage signals for fault classification and detection under large wind power plant integration. The data for DWT-ANN classifier is collected using PSCAD platform and simulations are performed on a modified IEEE-14 bus test system. In this article, first the effect of large wind power plant on the fault detection is presented by simulation on PSCAD through distance relay characteristic. Further, the wide area monitoring system (WAMS) is utilized to collect the voltage signals of nearby buses as an input of DWT-ANN classifier. The information from voltage signals is extracted using detailed coefficient of DWT. These detailed coefficients are further utilized to determine the important features of the signal. Finally, these features are utilized as input of ANN for fault detection and classification.