Enhancing fault detection function in wind farm‐integrated power network using Teaching Learning‐Based Optimization technique. (11th December 2020)

• Record Type: Journal Article
• Authors: Prasad, Ch. Durga; Biswal, Monalisa; Ray, Papia
• Is Part Of: International transactions on electrical energy systems. Volume 31:Number 10(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-11
• Subjects: fault -- mean error estimation -- particle swarm optimization (PSO) -- teaching learning‐based optimization (TLBO) -- transmission network -- wind farm; Electric power -- Periodicals; Electric power systems -- Periodicals; Electrical engineering -- Periodicals; 621.3
• DOI: 10.1002/2050-7038.12735
• Languages: English
• ISSNs: 2050-7038

Abstract

Summary: Fault detection units (FDUs) used for power system are based on the time‐domain, frequency‐domain, or both time‐ and frequency‐domain signal information. These days, the demand for renewable sources is increasing and in such a condition the complete review of the detection algorithm is essential. The penetration of large‐scale wind farms in the transmission system introduces a dynamic change in the time‐domain signal. Under such a condition, phasor estimation will be challenging. Again, any threshold value selected for the normal condition of the power system will not be valid for wind‐integrated systems. To mitigate this issue, a new approach is proposed in this work through which the threshold value can be set optimally for any operating condition of the power network. Teaching learning‐based optimization (TLBO) algorithm is employed to introduce the optimal threshold selection concept in FDU. First, a simple current sampled mean error concept‐based FDU is implemented, and later TLBO is applied for setting an optimal threshold for better decision making under various conditions of the wind‐integrated transmission system. The method is tested for different critical fault conditions as well as variations in source and wind parameters. A comparative analysis is also performed to highlight the superiority of the proposed method. Abstract : Large wind farm integration. Signal variations. Threshold selection issue. Optimal threshold. Enhanced fault detection during wind intermittency.