A Data-Driven Global Sensitivity Analysis of Output Power to Electrical Faults in Different SPV Array Topologies

Solar photovoltaic (SPV) arrays are subject to various electrical faults, such as line-to-line and line-to-ground. Quantifying the power injection during high impedance array faults and faults under low irradiance is challenging due to the maximum power point tracking control and the associated blocking and bypass diodes. Hence, global sensitivity analysis (GSA) of output power to random SPV array faults is imperative to develop efficient control, operation, and planning strategies for a renewable-integrated power system. Therefore, in this paper, a data-driven approach based on the polynomial chaos Kriging method is proposed for GSA. Four different state-of-the-art topologies of SPV array, namely, series-parallel, total-cross-tied, honey-comb, and bridge-linked, have been analyzed to find out the sensitivity of power to various electrical faults at different fault resistances. A sparse set of orthonormal polynomials approximate the global behavior, whereas analysis of variance kernel-based Kriging analyzes the local variability of the system output. This creates a hybrid metamodel that reflects the global relationship between the output power and random SPV array faults. With the developed metamodel, Sobol indices are calculated analytically to assess the sensitivity of outputs to the input variations, thus determining the severity of faults for array topology. The suggested methodology is less data intensive and is verified on a real-time hardware set-up of a grid-connected SPV system. Comparison results with the existing approaches substantiate the efficacy of the proposed method in terms of accuracy and scalability.