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Here, a data mining–driven scheme based on discrete wavelet transform (DWT) is proposed for high impedance fault (HIF) detection in active distribution networks. Correlation between the phase current signal and the related details of the current wavelet transform is presented as a new index for HIF detection. The proposed HIF detection method is implemented in two subsequent stages. In the first stage, the most important features for HIF detection are extracted using support vector machine (SVM) and decision tree (DT). The parameters of SVM are optimised using the genetic algorithm (GA) over the input scenarios. In second stage, SVM is utilised to classify the input data. The efficiency of the utilised SVM-based classifier is compared with a probabilistic neural network (PNN). A comprehensive list of scenarios including load switching, inrush current, solid short-circuit faults, HIF faults in the presence of harmonic loads is generated. The performance of the proposed algorithm is investigated for two active distribution networks including IEEE 13-Bus and IEEE 34-Bus systems.

This study investigates the impacts of stealthy false data injection (FDI) attacks that corrupt the state estimation operation of power distribution systems (PDS). In particular, the authors analyse FDI attacks that target the integrity of distribution systems optimal power flow (DSOPF) in order to maximise the system operator losses. The branch current state estimation method is implemented to accurately model the PDS, and convex relaxations are applied to the DSOPF model. The effects of the FDI attacks are analysed on the IEEE 34-bus unbalanced radial distribution system, with distributed energy resources (DERs) along the feeder. A 24 h DSPOF is performed, and the results depict the changes in the voltage profile and the additional power injection from the DERs, which consequently lead to the increase of the DSOPF cost.