Abstract
This paper presents a fast hybrid fault location method for active distribution networks with distributed generation (DG) and microgrids. The method uses the voltage and current data from the measurement points at the main substation, and the connection points of DG and microgrids. The data is used in a single feedforward artificial neural network (ANN) to estimate the distances to fault from all the measuring points. A k-nearest neighbors (KNN) classifier then interprets the ANN outputs and estimates a single fault location. Simulation results validate the accuracy of the fault location method under different fault conditions including fault types, fault points, and fault resistances. The performance is also validated for non-synchronized measurements and measurement errors.
Highlights
Following the occurrence of a short circuit fault in a distribution network, the restoration process may take from tens of minutes to hours to complete
The results indicate that the proposed single multi-layered perceptron (MLP) artificial neural network (ANN) is able to estimate the fault distance of all different fault types with acceptable accuracy
The small root mean-squared error (RMSE) values of less than 100 m for all the considered test scenarios shown in Fig. 9b, clearly indicate the good performance and acceptable generalization accuracy of the proposed method for different fault types
Summary
Following the occurrence of a short circuit fault in a distribution network, the restoration process may take from tens of minutes to hours to complete. It is assumed that meters are installed at DG terminals, the microgrid point of connections and the main substation to measure the three-phase voltage and current (synchronized or non-synchronized) These measurements are used as inputs to a single feedforward ANN to estimate the distance to the fault from all measurement points. At the stage of the proposed method, a k-nearest neighbor classifier is employed to interpret the ANN outputs and estimate the faulted line section and fault location. The fundamental frequency component of three-phase voltage and current from all sources (i.e. main substation, DGs and microgrids) are the only required measurements. The measurements are not synchronized, and the magnitudes of three-phase voltage, current and apparent impedances of all sources are employed as ANN input features (i.e. 3 × 3 × n inputs). At the final stage of the proposed method, the use of a KNN classifier is proposed to interpret the
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