Fault diagnosis of photovoltaic array with multi-module fusion under hyperparameter optimization

Abstract

Photovoltaic (PV) arrays’ random and intermittent output characteristics impact power system safety. To improve the performance of the PV array fault diagnosis model, a novel online fault monitoring technique is introduced. (1) Fault diagnostic model construction: Significant differences in PV arrays’ I-V and P-V curves under various fault conditions led to constructing a 3D channel feature map based on I, V, and P features. (2) Multi-source information fusion network (MSIFN): this multi-module fusion model includes a time–frequency domain fusion module (TDFM), a multi-feature shuffle expansion convolution module (MSECM), a parameter-free parallel hybrid attention enhancement module, and a multi-scale mixed pooling fusion classification module (MMPCM). (3) Multi-strategy fusion whale optimization algorithm (MSFWOA): addressing the original WOA’s deficiencies, we designed time control, parameter modification, and greedy control strategies based on lens imaging to optimize MSIFN’s hyper-parameters. Experimental results show that the MSFWOA-MSIFN model excels in PV array fault diagnosis ($P_{\mathrm{accuracy}}$=$P_{\mathrm{precision}}$=$P_{\mathrm{recall}}$ = 99.92 %). In three types of noise experiments with 15 dB, 25 dB, and 30 dB, the average performance index remained above 99 %. In practical experiments, the average performance indices were$P_{\mathrm{accuracy}}$ = 97.53 %, $P_{\mathrm{precision}}$ = 97.32 %, and$P_{\mathrm{recall}}$ = 97.41 %, further demonstrating its excellent diagnostic performance. This model effectively diagnoses various faults in PV arrays, providing scientific and theoretical support for PV system operations.