Artificial intelligence based high voltage cable bonding to prevent cable termination faults

Abstract

Cable termination fault (CTF) is a major problem for high voltage cable lines (HVCL). Increasing of the sheath voltage (SV), zero sequence current (ZC) and current harmonic distortion (THDI) on metallic sheath (MS) of HVC are major factors for CTF. MS is grounded according to IEEE 575-1988 standard to reduce SV. However, these methods are not sufficient to prevent CTF based on ZC and THDI. The aims of this paper are minimization of SV, ZC and THDI to prevent CTF based on ZC and THDI. Thus, LSSB method is developed as a new bonding method. Also, LSSB parameters should be optimized to make the most economical and practical bonding. GA, DEA, PSO and iPSO are used optimization methods for optimization of LSSB. SV and THDI should be known for optimization of LSSB, so the forecasting methods (FM) are used as fitness function of optimization methods in LSSB optimization. The regression and hybrid artificial neural network methods are compared to determine the most suitable FM. When the optimized LSSB is used for bonding of long HVCL, SV reduces approximately 90%, ZC reduces approximately 93%, and THDI reduces approximately 70%. Thus CTF risk is minimized by using the optimized LSSB in HVCL.