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This article offers an islanding detection approach based on dual-tree complex discrete wavelet transform (DTCDWT) and extreme gradient boosting method (XGBoost). Initially, the DTCDWT is used to extract the feature vector from collected negative sequence voltage and current signals at the respective distributed energy resource ends. Afterward, the nonlinear synthetic minority oversampling technique (SMOTE)-based XGBoost is used to classify the islanding (I) and nonislanding (NI) events with the following characteristics: high accuracy, good sensitivity, and faster speed. Here, the SMOTE is used as an offline prefeature processing approach to reduce the unbalancing/uncertainty of “I” and “NI” classes and helps the intelligent classifier to impart a fair classification result. This scheme is found to be effective for the detection of I and NI conditions, satisfying the IEEE islanding detection standard. Additionally, the obtained simulation results are compared with other existing works to show its usefulness. [ABSTRACT FROM AUTHOR]

A typical 2 × 25 kV traction power supply system consists of both primary and secondary equipment housed in control rooms, and the traction overhead wiring (OHW) that runs between the substations. The substation secondary equipment consists of the protection and control intelligent electronic devices (IEDs) known as relays. Each section of the OHW between the substations is protected by a set of two relays at both ends of a line for impedance protection and their backup relays. At substations facing the sections at the end of the line, where there is no other substation on the other end, usually the overcurrent and earth fault protection is used. At the substations, there are disturbance fault recorders installed or relays equipped with a capability to record and store disturbance fault records. An analysis of these fault records makes a significant contribution to the efficient running of a traction system such as the Gautrain rapid rail system. A combination of the analysis of the faults that occur in the OHW as recorded and stored in the relays as well as the performance of the IEDs and the protection settings using the on-site tests are a subject of this article. [ABSTRACT FROM AUTHOR]

The fundamental voltage, current, and phase angle are required for a wide variety of power system applications. An algorithm that is capable of calculating or estimating these quantities in real time, in the presence of distorted waveforms, finds application in diverse areas of power systems. Techniques to detect voltage sag include the root mean square (rms), Fourier transform, and peak voltage detection methods. The problem with these methods is that they use a windowing technique and can therefore be too slow when applied to detect voltage sags for mitigation since they use historical data. Recent work in the field of signal processing has led to an algorithm that can extract a single non-stationary sinusoidal signal out of a given multi-component input signal. The algorithm is capable of estimating the amplitude, phase and frequency. In this paper, the algorithm is compared to existing methods of sag detection. [ABSTRACT FROM AUTHOR]