Sample entropy-based fault detection for photovoltaic arrays

Abstract

Despite the growing deployment of photovoltaic (PV) systems, they are still facing challenges in developing the proper control and protection schemes. One of the main protection challenges of PV arrays is their low fault currents under lowirradiance, low-mismatch, and high-impedance faults. In addition to these conditions, the operation of the maximum power point tracking algorithm may lead to the faults within the PV array remain undetected, resulting in potential fire hazards and power loss. This study presents a fault detection scheme based on monitoring the output power of the PV array. Using the sample entropy-based complexity, the irregularity of the time series of the normalised fault-imposed component of PV power is quantified as the fault detection criterion. The proposed protection scheme is capable of distinguishing the line-to-line, line-toground, and open-circuit faults from the weather disturbances and partial shadings. Also, it does not require the training dataset and the prior information about the PV array and is effective for both grid-connected and islanded PV systems. Extensive timedomain simulation results demonstrate high accuracy of the proposed fault detection scheme.