  Faults in photovoltaic (PV) systems can seriously affect the efficiency,
energy yield as well as the security of the entire PV plant, if not detected
and corrected quickly. Therefore, fault diagnosis of PV arrays is indispensable
for improving the reliability, efficiency, productivity and safety of PV power
stations. Instead of conventional thresholding methods and artificial
intelligent (AI) machine learning approaches, an innovative Gaussian Mixture
Model (GMM) based fault detection approach is proposed in this work. This
approach combines the superiority of GMM in modeling stochastic power outputs
of PV modules and the flexibility and simplicity of Sandia PV Array Performance
Model (SPAM) to accurately detect under-performing modules. Firstly, GMM is
proposed to represent the probabilistic distribution functions (PDF) of
different PV modules' power outputs, and the parameter sets of which are
obtained by Expectation Maximization algorithm. Secondly, a simplified explicit
expression for output power of PV modules, which highlights the influences of
tilt and azimuth angles, is deduced based on the SPAM. Then, an orientation
independent vector C is developed to eliminate the probability distribution
differences of power outputs caused by varying azimuth angles and tilt angles.
Jensen-Shannon (JS) divergence, which captures the differences between
probability density of C of each PV module, are generated and used as fault
indicators. Simulation data acquired from the original SPAM are used to assess
the performance of the proposed approach. Results show that the proposed
approach successfully detects faults in PV systems. This work is especially
suitable for the PV modules that have different installation parameters such as
azimuth angles and tilt angles, and it does not require the use of irradiance
or temperature sensors.
