Aiming at the problems of the northern goshawk optimization algorithm (NGO), such as low convergence accuracy and easy to fall into local optimum, an improved northern goshawk optimization algorithm (INGO) is proposed and applied to the fault diagnosis of photovoltaic array. Firstly, circle mapping, adaptive weight factor and Levy flight strategy are used to improve the INGO. Combined with Gaussian detection mechanism and hybrid kernel extreme learning machine ( HKELM), the INGO-HKELM fault diagnosis model is built. Secondly, the INGO algorithm is compared with the NGO, the particle swarm optimization algorithm ( PSO), and the whale optimization algorithm (WOA) on the test functions, which shows that it has advantages in optimization ability and stability. Then, the operating characteristics of photovoltaic arrays under different operating states are analyzed, and a 5-D fault feature vector is proposed as the input of data. Finally, the four algorithms are used to optimize the kernel parameters of HKELM and achieve fault classification. The results show that the proposed method can accurately detect abnormal states of photovoltaic modules, and the accuracy of INGO-HKELM model reaches 93. 74%, which verifies the effectiveness and feasibility of the proposed algorithm.