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The detection, mitigation, and classification of power quality (PQ) disturbances have been issues of interest in the power system field. This paper proposes an approach to detect and classify various types of PQ disturbances based on the Stockwell transform (ST) and decision tree (DT) methods. At first, the ST is developed based on the moving, localizing, and scalable Gaussian window to detect five statistical features of PQ disturbances such as the high frequency of oscillatory transient, distinction between stationary and non-stationary, the voltage amplitude oscillation around an average value, the existence of harmonics in a disturbance signal, and the root mean square voltage at the internal period of sag, swell or interruption. Then, these features are classified into nine types, such as normal, sag, swell, interruption, harmonic, flicker, oscillatory transient, harmonic voltage sag, and harmonic voltage swell by using the DT algorithm that is based on a set of rules with the structure “if…then’’. This proposed study is simulated using MATLAB simulation. The IEEE 13-bus system, the recorded real data based on PQube, and the experiment based on the laboratory environment are applied to verify the effectiveness.

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.

Efficient recognition and localization of power quality disturbances (PQDs) are essential for ensuring resilient power distribution. This paper proposes a real-time PQD detection and localization framework for the solar-penetrated IEEE 13-bus system using recurrence plots and deep learning. The network is divided into three zones, with each zone monitored at a selected bus to ensure voltage observability. Three-phase voltage signals are analyzed using a 10-cycle moving window, updated every 250 microseconds, to enable high-resolution disturbance detection. PQD detection is initiated when the cosine similarity index (CSI), computed from the recurrence plot of the moving window of three-phase voltage samples, deviates from that of normal operation and falls below a predefined threshold. This triggers the identification of actual disturbances. Localization is performed using a zone-based detection algorithm that compares the CSI values across all three zones. Real-time signal analysis is conducted on a high-speed x86-based system, while classification is handled on a separate workstation using an EfficientNet model integrated with Squeeze-and-Excitation (SE) blocks. The proposed framework is validated through RTDS simulations and Hardware-in-the-Loop (HIL) testing, demonstrating high accuracy, precise localization, and robustness across various signal-to-noise ratio (SNR) levels.