To enhance the accuracy of photovoltaic (PV) array fault diagnosis, this study proposes a novel method that utilizes an improved golden jackal optimization (IGJO) algorithm to optimize a deep hybrid kernel extreme learning machine (DHKELM) for PV array fault diagnosis. Initially, a range of PV array faults are simulated using the MATLAB/Simulink platform. Based on a comprehensive analysis of fault characteristics, a 12-dimensional feature set is proposed for fault diagnosis. Subsequently, the golden jackal algorithm is improved by introducing lens imaging reverse learning strategy, cosine and sine algorithm strategy, and adaptive T-distribution perturbation strategy to enhance its convergence speed and global optimization capability. Additionally, IGJO is compared with other optimization algorithms using test functions. Furthermore, radial basis kernel functions and polynomial kernel functions are incorporated into the extreme learning machine and combined with an autoencoder to form the DHKELM model. Finally, IGJO is employed to optimize the initial parameters of the DHKELM model, resulting in the establishment of the IGJO-DHKELM PV array fault diagnosis model. Analysis of the results indicates that the proposed 12-dimensional feature set provides higher diagnostic accuracy compared to traditional 4-dimensional and 5-dimensional feature sets. Moreover, the IGJO-DHKELM-based fault diagnosis method demonstrates superior diagnostic accuracy compared to other fault diagnosis models.