This paper presents an effective and intelligent methodology for the recognition of several underlying causes responsible for power quality (PQ) disturbances. The disturbance signals at 40 dB signal to noise ratio (SNR) from the various locations of the power distribution system have been analyzed with computationally efficient stockwell transform (ST). Besides residual voltage and the duration, characteristic like automatic segmented phase angle jump (PAJ) has been extracted and proposed for linking the PQ disturbance with the respective underlying cause. PQ disturbances have been simulated in a MATLAB environment as per IEEE-1159 standard with the five causes namely symmetrical fault, unsymmetrical fault, induction motor starting, capacitor bank energizing and transformer energizing. A block scheme has been devised and presented here for the effective recognition of the underlying cause in a power distribution network so that the corrective measures can be proposed for the trouble-free operation of the equipment. The performance of the proposed method has also been evaluated with the real time PQ data to judge the effectiveness of the algorithm in the real environment.

The increasing trends toward the accurate identification of power quality disturbances (PQD) via power quality (PQ) monitoring require an appropriate digital signal processing (DSP) technique and a robust classifier. To this end, Stockwell transform (ST), one of the most efficient feature extraction DSP tools, and its several variants play an utmost role in PQ assessment framework. Its time-varying spectral characteristics generally extract the local instantaneous frequency spectrum from the global temporary behavior of PQD signal. However, the Standard ST suffers from thepoor time-frequency resolution because of its frequency-dependent Gaussian window (GW). While the analysis of the statistically time-varying signals requires a suitable balance between time and frequency resolution. To this end, this paper provides a comprehensive literature review on several modified versions of Standard ST for the first time to reduce the computational complexity of the algorithm as well as maximize the energy concentration of the time-frequency plane. A comparative analysis of all the modified STs has been presented in tabular form to provide the key characteristics of each technique. Additionally, a case study has been presented to substantiate the highest accuracy of the proposed algorithm over the other ST variants. Apart from the PQD classification, miscellaneous applications of Standard ST and its modified variants have been indicated. This review paper may provide a valuable resource to the researchers for further improvement of the time-frequency resolution of ST not only in classifying PQD but also for its other wide applications.

This paper shows the health monitoring and assessment of a three‐phase induction motor in abnormal conditions using a machine learning algorithm. The convolutional neural network (CNN) and recurrent neural network (RNN) algorithms are the prominent methods used in machine learning algorithms, and the combined method is known as the CRNN method. The abnormal conditions of a three phase‐induction motor are represented by three‐phase faults, line‐to‐ground faults, etc. The pattern of fault current is traced, and key features are extracted by the CRNN algorithm. The performance parameters like THD (%), accuracy, and reliability of abnormal conditions are measured with the CRNN algorithm. The assessment of abnormal conditions is being realized at the terminals of a three‐phase induction motor. A fuzzy logic controller (FLC) is also used to assess such abnormalities. It is observed that performance parameters are found to be better with the CRNN method in comparison to CNN, RNN, ANN, and other methods. Such a realization makes the system more compatible with abnormality recognition.

The article presents the design and development of invasive weed optimization (IWO) for the sensor less speed control of doubly fed induction generator (DFIG) under balanced and unbalanced conditions. A healthy condition represents the balanced condition while unbalancing condition is characterized by unhealthy conditions like three-phase fault, single line to ground fault, the line-to-line fault, and double line to ground fault. The DFIG is driven by wind and integrated with the grid. The advantages associated with IWO technique are a simple mathematical approach and less data computation. Normally, DFIG consists of two back-to-back converters namely grid side converter (GSC) and rotor side converter (RSC). The GSC is an uncontrolled converter while the RSC is a controlled converter. The existing methods have poor performance parameters like settling time, peak overshoot for balanced conditions, and poor power quality parameters like total harmonic distortion (THD) for unbalanced conditions. An IWO technique has been applied to overcome such limitations. The effectiveness of the sensor less speed control is also analyzed with other techniques like Adaptive Neuro-Fuzzy Interference System (AN-FIS) & artificial neural network (ANN). The design of ANN is based on the feed-forward method using back propagation delay and the design of ANFIS is based on adaptive control and state space control strategy. It is observed that performance parameters like peak overshoot and settling time for the sensor less speed of DFIG are found to be more profound with IWO in comparison to ANFIS, ANN, and other existing techniques for balanced conditions. Similarly in the unbalanced condition, faulty current approaches are quite closer to their healthy state with the IWO method in comparison to other methods. In addition to this, minimum distortion (%THD) for the grid current under unbalanced conditions is also attained with IWO in comparison to ANFIS, ANN, and other existing techniques. Such application of IWO makes the system highly efficient and robust.

The proposed methodology aims at the identification of multiple and multi-stage power quality (PQ) disturbances along with their underlying causes. To this end, a computationally efficient S-Transform with decision tree (DT) based online PQ monitoring algorithm has been presented in this paper. This online algorithm provides a sample-based analysis of 40-dB noisy multiple and multi-stage PQ disturbance signals simulated in MATLAB environment with the different combinations of underlying causes as per the IEEE-1159 standard. The features like residual voltage, instantaneous phase-angle jump (IPAJ), and number of zero crossings (NZC) have been extracted from the transformed contours, viz., maximum voltage amplitude versus time (MVAVT) and phase-angle versus time (PAVT). Henceforth, the accurate recognition of most common underlying causes like three-phase fault, induction motor starting, and capacitor bank energizing has been achieved. For the first time, an online PQ monitoring algorithm is effectively assessing the type and underlying cause of a multiple or multi-stage disturbance simultaneously. The proposed methodology has also been experimentally validated with real-time PQ disturbance signals acquired in the laboratory.