Statistical sensorless short-circuit fault detection algorithm for photovoltaic arrays

• Published in: Journal of renewable and sustainable energy Vol. 11; no. 5
• Main Authors: Maleki, Amir, Sadeghkhani, Iman, Fani, Bahador
• Format: Journal Article
• Language: English
• Published: Melville American Institute of Physics 01.09.2019
• Subjects: Algorithms; Computer simulation; Diodes; Energy dissipation; Fault detection; Irradiance; Kurtosis; Maximum power tracking; Model testing; Parameter uncertainty; Photovoltaic cells; Sensor arrays; Shading; Short circuits; Solar cells; Tracking systems
• ISSN: 1941-7012; 1941-7012
• DOI: 10.1063/1.5119055

One of the main challenges for the conventional protection system of a photovoltaic (PV) array is the occurrence of light fault conditions including a low location mismatch fault, a fault with a high fault path resistance, and a fault under low solar irradiance because the fault current increment is not enough for triggering current-based protective devices. The operation of the maximum power point tracking system and utilizing blocking diodes may also result in a light fault condition. An undetected fault condition causes a potential fire hazard and energy loss. This paper proposes a waveshape based statistical fault detection algorithm for light fault detection. The proposed algorithm quantifies the waveshape tailedness of superimposed PV array power by the kurtosis function. The proposed algorithm is able to discriminate the light fault condition from severe partial shading and is also effective for open-circuit faults. In addition to no need for additional sensors, it does not require a training dataset and prior information about the PV array. The merits of the proposed algorithm are corroborated through several case studies on a simulation model of a test PV array considering the parameter uncertainty and the presence of noise in the signals.