Power quality events recognition using enhanced empirical mode decomposition and optimized extreme learning machine

In this paper, a novel approach based on Empirical Mode Decomposition (EMD) and an Extreme Learning Machine (ELM) for the detection and classification of Power Quality Events (PQEs) is proposed. The EMD technique is used for computing the prominent features required to characterize the PQE signals. A down-sampled Kriging Interpolation (KI) based EMD is suggested to enhance the performance of the EMD operation in terms of accuracy and speed. The ELM is applied for the classification of Power Quality Disturbances (PQDs), considering all the derived features through the KI-EMD approach. Symbiotic Organism Search (SOS) optimization technique is applied to enhance the performance and robustness of ELM by optimally computing the values of the system parameters. The performance of the proposed approach is justified with test cases under diverse noise conditions. Comparative results and analysis are provided to show an improvement of 2-5% in terms of accuracy, speed, and robustness compared to other conventional methods. Experimental results validate the efficacy of the proposed approach under realtime conditions.

Automated summary

This paper proposes a novel approach based on EMD and ELM for the detection and classification of PQEs. The EMD technique is used to compute the prominent features of PQE signals, and a down-sampled KI-EMD approach is suggested to enhance the performance. The ELM is applied for the classification of PQDs, considering all the derived features through the KI-EMD approach. The experimental results demonstrate a 2-5% improvement in accuracy, speed, and robustness compared to other conventional methods.