Tengfei Li1, Wenhui Zhang1, Ke Mi1, Qingming Lin1, Shuangwei Zhao2,*, Jiayi Song2
Energy Engineering, Vol.121, No.7, pp. 1991-2007, 2024, DOI:10.32604/ee.2024.049460
Abstract Aiming at the problem of energy storage unit failure in the spring operating mechanism of low voltage circuit breakers (LVCBs). A fault diagnosis algorithm based on an improved Sparrow Search Algorithm (ISSA) optimized Backpropagation Neural Network (BPNN) is proposed to improve the operational safety of LVCB. Taking the 1.5kV/4000A/75kA LVCB as an example. According to the current operating characteristics of the energy storage motor, fault characteristics are extracted based on Empirical Wavelet Transform (EWT). Traditional BPNN has problems such as difficulty adjusting network weights and thresholds, being sensitive to initial weights, and quickly falling into More >
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