Today, renewable energy sources have been gaining great attention because of the lack of greenhouse gas production to generate electricity. Meanwhile, the use of solar energy due to technical, economic, and environmental advantages is increasesd significantly worldwide. Photovoltaic energy is used for both public and agricultural uses, either as grid-independent power plants or grid-connected ones, with the fixed and mobile structures as the small and low-capacity units to supply the electricity needed for small/large loads/networks. One of the main challenges for the conventional protection system of a photovoltaic (PV) array is the occurrence of light fault conditions including low location mismatch fault, fault with high fault path resistance, and fault under low solar irradiance because their fault current increment is not enough for triggering the 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. The 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 kurtosis function. The proposed algorithm is able to discriminate the light fault condition from the 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 data set and the 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.