Enhancing Fault Detection Function In Wind Farm-Integrated Power Network Using Teaching Learning-Based Optimization Technique

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.