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This paper proposes a novel method for quantifying fault level in future grid scenarios with various penetrations of power electronics‐connected renewable energy sources. As it is known, the information regarding the fault level is critically important for designing protection schemes, different control loops, understanding voltage profile in the grid, etc. This method is focused on the steady‐state fault level calculation and it can be used to analyse future grid scenarios including uniform and non‐uniform penetration of power electronics‐based generation displacing all, or just specific conventional synchronous generation in the grid. Due to different possibilities for type, size, and location of power electronics‐based RES generation in future grid, it is required to analyse the unprecedented scale of scenarios. The proposed method for FLC enables us to assess the system fault level for large numbers of FG scenarios without a need for detailed system modelling and/or time‐domain simulations. The simulation results demonstrated the suitability of our proposed FLC method for various penetration levels of PE‐based RESs in the 2‐area and the IEEE 39‐bus test systems. The obtained results are compared with time‐domain simulations and the IEC 60909 standards performed in DIgSILENT PowerFactory, where the efficacy of the proposed methodology is demonstrated.

The fault level is used as a simple indicator for scanning the system strength in power systems. To an extent, this has proven its efficacy in classical power systems based on synchronous generation (SG). However, power electronics-based renewable energy sources (RESs), due to their controlled and limited fault current contribution, may affect the impedance, fault level, and system strength in a non-linear manner. Hence, this raises a question about the validity of using the fault level as a measure reflecting the system strength in future grids. This paper intends to shed light on the above question by examining the correlation between the fault level and the system strength in future grid scenarios. This is achieved in two steps: first, by employing the measure-based Thevenin impedance for fault level estimation in renewable-rich grids, and second, by comparing these estimated fault levels with those obtained from steady-state and dynamic simulations. While the results have demonstrated the suitability of using the fault level for system strength scanning in scenarios of low penetration of RESs, they revealed that such a tool might be misleading with very high RES penetrations. The findings have been verified using the adjusted IEEE nine-bus test system in DIgSILENT PowerFactory.

The increasing integration of Power Electronics (PE)-based renewable energy sources into the electric power system has significantly affected the traditional levels and characteristics of fault currents compared to the ones observed in power systems dominated by synchronous generating units. The secure operation of a renewable rich power system requires the proper estimation of fault currents with wide range of scenarios of the high share of renewables. Although the utilization of detailed and complex time-domain dynamic simulations allows for calculating the fault currents, the resulting modeling complexity and computational burden might not be adequate from the operational perspective. Thus, it is necessary to develop alternative quicker data-driven fault current estimation approaches to support the system operator. For this purpose, this paper utilizes an Artificial Neural Network (ANN)-based tool to estimate the characteristics of short circuit currents in power systems with high penetration of power electronics-based renewables. The short circuits against different penetration of renewables are produced offline using the DIgSILENT PowerFactory considering the control requirements for renewables (e.g., fault ride through requirement). The resulting dataset is utilized to train the ANN to provide the mapping between the penetration level and the characteristics of the short circuit currents. The application of the approach using the modified IEEE 9-bus test system demonstrates its effectiveness to estimate the components of short circuit currents (subtransient current, transient current, and peak current) with high accuracy based only on the penetration of power electronics-based renewables.INDEX TERMS Artificial neural networks, future power systems, photovoltaic systems, power electronics, short circuit currents.

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