Power quality disturbance signal classification in microgrid based on kernel extreme learning machine

This paper presents a kernel extreme learning machine (KELM) integrated with the improved whale optimization algorithm (IWOA) to address the power quality disturbance (PQD) issue in microgrids. First, an adaptive variational mode decomposition method is employed to extract PQD signals in microgrids. Then, the IWOA is utilized to optimize the penalty factor and kernel function parameters for the KELM classifier model, thereby enhancing the performance of the classifier. Furthermore, the test results indicate that the proposed IWOA-KELM achieves high classification accuracy and rapid convergence for complex PQD signals. This paper presents a kernel extreme learning machine integrated with the improved whale optimization algorithm to address power quality issues in microgrids resulting from distributed power access. In this work, the adaptive variational mode decomposition method is employed to decompose the complex disturbance signals in microgrids. image