Harmonic emission level estimation method based on an improved bald eagle search optimized extreme learning machine

A harmonic emission level estimation method based on an improved bald eagle search (IBES) optimized extreme learning machine (ELM) is proposed to address the problem that it is difficult to measure that level directly. First, the Tent chaotic mapping and the Cauchy variant operator are introduced into the traditional bald eagle search algorithm, and the input weights and thresholds of the ELM model are optimized using the IBES algorithm. Second, the harmonic voltage and current at the point of common coupling (PCC) are input and substituted into the IBES-ELM model to estimate the customer-side and system-side harmonic emission levels. Finally, simulations and engineering examples are analyzed and the estimation results are compared with those of other algorithms. The results show that the estimation accuracy of the proposed IBES-ELM method is better than that of long short-term memory (LSTM), convolution neural network (CNN), the back propagation neural network (BP) and CNN-LSTM algorithm models. This verifies the effectiveness and stability of the method. © 2024 Power System Protection and Control Press. All rights reserved.