Photovoltaic (PV) arrays often operate in complex and harsh environments, making them susceptible to various types and degrees of faults. To enhance the accuracy of fault diagnosis in such challenging conditions, this study proposes a novel fault diagnosis model based on feature extraction and an improved pelican optimization algorithm (IPOA) optimized support vector machine (SVM). Firstly, 15 typical fault states are simulated on the MATLAB/Simulink platform, from which a 12-dimensional fault feature vector is constructed. Kernel principal component analysis (KPCA) is then applied for feature fusion and extraction to improve feature representation capabilities. Secondly, to address the limitations of traditional pelican optimization algorithms in balancing global search and local exploitation, enhancements are introduced, including the Tent chaotic map, inertia weight, nonlinear convergence factors, and an adaptive t-distribution mutation strategy, all of which significantly improve the algorithm's optimization performance. Finally, the IPOA is used to optimize the penalty factor C and kernel parameter γ of the SVM model, establishing the IPOA-SVM PV array fault diagnosis model, which is then validated through both simulation and experimental tests. The results show that, compared to the traditional 6-dimensional feature set, the proposed 12-dimensional feature set achieves higher diagnostic accuracy. The improved model demonstrates fault diagnosis classification accuracies of 98.55% and 97.93% for simulation and experimental data, respectively, significantly outperforming other comparison models and demonstrating higher accuracy in PV array fault diagnosis.