## 1 Introduction  
With the aggravation of the global energy crisis and regional environmental pollution, Chinese photovoltaic power generation still faces key problems of sustainable development [1], of which maintaining solar power station safety and maintaining stable operations are important issues. At present, research on solar power station fault monitoring is mainly focused on the photovoltaic strings, modules, and inverters, but rarely on the photovoltaic arrays. The diagnosis of the photovoltaic arrays is an important issue, because the performance of photovoltaic modules affects the output characteristics of photovoltaic arrays directly, thus further affecting the stability of the photovoltaic generation system, so the fault monitoring of the solar power station can diagnose the photovoltaic arrays, locate the faulty photovoltaic modules in a certain area first, and then further precisely position the faulty photovoltaic modules. This diagnostic method can greatly reduce the number of sensors, thus reducing costs while ensuring that the solar power station operates safely and stably.  
In recent years, many fault detection and diagnosis methods of photovoltaic systems were proposed. The algorithm of the artificial neural network was presented to diagnose the fault [2; 3; 4; 5]. In [6], the identification of the fault type is carried out by analyzing and comparing the amount of error deviations of both simulated and measured current and voltage with respect to a set of error thresholds that are evaluated. In [7], a simple method to detect and diagnose short circuits and open circuit faults in photovoltaic systems based on the evaluation of three coefficients is presented. By analyzing the light and dark current-voltage (I-V) characteristics of the photovoltaic module, a fault identification method was used to distinguish the faulty photovoltaic modules [8]. The above-mentioned methods are for the photovoltaic modules, of which each photovoltaic module needs to be monitored, leading to high costs. In [9; 10; 11; 12; 13], the fault diagnosis methods of the photovoltaic arrays were introduced. The fault detection is based on the comparison between the measured and model prediction results of the power production [9]. [PERSON], [PERSON], et al., analyze the terminal characteristics of faulty photovoltaic arrays and reduce the number of sensors by optimizing its locations [10]. In [11], the authors diagnose the fault by detecting the change of PV internal resistance using the signals that are available in Extremum-Seeking Control (ESC)-based Maximum Power Point Tracking (MPPT). The methods in these works can only identify whether there is a fault or not, but the fault type is unknown. In other research [12], a method is proposed to detect faults and partial shading under all of the irradiation conditions using the measured values of array voltage, array current, and irradiance, but the diagnosis of fault type is not comprehensive enough.  
In order to diagnose whether there are faulty photovoltaic modules in a photovoltaic array or not and further judge its fault type, this paper presents a fault diagnosis method that is based on Least Squares Support Vector Machine (LSSVM) in the Bayesian framework. The algorithm of LSSVM in the Bayesian framework has been used in the field of fault diagnosis, in domains such as aeronautics, power transformation, biology, and so on [13; 14; 15; 16]; in this work, we introduce the algorithm into the field of photovoltaic fault diagnosis.