The reliable operation of power transmission systems is essential for maintaining the stability and efficiency of the electrical grid. Rapid and accurate detection of faults in transmission lines is crucial for minimizing downtime and preventing cascading failures. This research presents a novel approach to fault detection and classification in transmission lines employing 2D Convolutional Neural Networks (2D-CNN).The proposed methodology leverages the inherent spatial characteristics of fault signals, converting them as 2D scalogram images for input to the CNN model. By converting fault signals into scalogram representations, the network can capture both temporal and frequency domain features, enabling a more comprehensive analysis of fault patterns. The 2D-CNN architecture is designed to automatically learn hierarchical features, allowing for effective discrimination between different fault types. To evaluate the performance of the proposed approach, extensive simulations and experiments were conducted using MATLAB/SIMULINK modeled transmission line data. The results demonstrate the superior fault detection accuracy and classification capabilities of the 2D-CNN model. The performance of the proposed model is evaluated using 10-fold cross-validation, and its effectiveness is assessed by comparing it with current state-of-the-art techniques. Proposed 2D-CNN model has evidenced an accuracy of 99.9074 with ideal dataset for 12- class fault classification and performing consistently in presence of noise, having an accuracy of 99.629 %,99.72 % and 99.814 % in 20.30 and 40 dB noises respectively. The proposed model also verified in high resistance fault condition. The model exhibits robustness to noise and is capable of generalizing well to various fault scenarios. The proposed methodology offers a scalable and efficient solution for transmission line fault analysis, paving the way for the integration of advanced machine learning techniques into the operation and maintenance of power transmission infrastructure.

The trend of integrating different distributed generation sources into the existing grid have increased the probability of power quality disturbances to a threatening level. Eventually, detection, protection and mitigation of these disturbances are even more challenging. In this regard, the article presents an intelligent power quality disturbance classification scheme using a 2D convolutional neural network designed from a systematic and structural standpoint. A total of 8 singular, 5 complex power quality events are simulated and voltage data collection is made from a test system designed in MATLAB Simulink environment. The three-phase voltage data is converted to a single signal arrangement through a newly proposed Unique Clark’s Transformed Sequence. In addition to that, the scheme completely eliminates the worry of a signal processing stage by proposing a novel scaled matrix image created out of 2-cycle data collected at 6.4kHz sampling frequency that acts as input to the CNN architecture designed in the PYTHON environment. Further, the novelty extended to design a pseudo-real-time setup where the MATLAB environment continuously runs the test system, producing scaled matrix images. These images are saved to a shared directory, enabling a PYTHON loop for prompt event classification through the trained CNN model. The model performance is found to be 100% under ideal conditions. It has also tested under three different noise conditions of 40dB, 30dB and 20dB and obtained an overall accuracy of 98.86% with singular events. Further, the method is also verified for complex and unsymmetrical dataset and found to be equally effective. Additionally, the validation is likewise made with a trivial set of real-time simulated data using OPAL-RT 4510 setup. Finally, the proposed PQ detection scheme is compared with recently published work to express its superiority over other similar studies in terms of classification accuracy.

The modern era power system is constantly undergoing constructive changes and implementations both in source and load side. Certainly, the distributed generators, unconventional/nonlinear loads, charging stations etc are mostly integrated through power electronics interfaces. As a result, frequent power quality disturbances appear in the system that is to be mitigated at the earliest. Since detection is the prerequisite for mitigation, therefore the article presents a novel intelligent power quality detection scheme to detect and classify the PQ Events. At first, the energy feature of the 5 band limited modes are calculated from variational mode decomposed voltage signals. Then the mode energy features are utilized to train a novel Hybrid Arithmetic Whale Optimized light gradient boosting machine classifier. A total of 15 different PQ events have been investigated and exceptional classification results have obtained with optimum computational complexity, both under noiseless and noisy conditions. Moreover, the accuracy of the proposed PQ classification schemes found to be towering against other related pre-published works. Finally, the ability of the proposed detection scheme is validated in real time though OPAL-RT 4510 and grid simulator hardware in loop setup.

Measurement of time varying harmonics has assumed great importance for power quality estimation in micro and smart grid environments. It is well known that the power signals undergo dynamic transitions during power quality and fault type disturbances. Also accurate estimation of amplitude, phase and frequency of a sinusoid in the presence of harmonics/inter harmonics and noise plays an important role in wide variety of power system applications, like protection, power quality control and state monitoring. One of the widely used approach like the Fourier linear combiners become ineffective to track the sudden changes in the power harmonic amplitude and phase angles in the presence noise and distortions. This paper, therefore, presents a new adaptive robust Kalman filter (REKF) for estimating the fundamental and harmonic phasors and frequency in a distributed generation systems suitable for the detection of islanding, nonislanding and switching events. The REKF is based on linear H-infinity technique to avoid the error divergence due to linearization and Jacobian formulation in the usual extended Kalman filter (EKF). Further the noise covariance matrices of the robust Kalman filter are tuned iteratively to produce faster convergence and speed of accuracy in tracking time varying harmonic phasors.

Distributed generations (DGs) have been increasingly addressing the ongoing power deficit in the electricity market. However, a significant concern in DG‐integrated microgrids is the detection of accidental islanding. To tackle this issue, this article proposes a cost‐friendly, novel data‐driven passive islanding detection scheme named EEMD‐HOBRC, combining noise‐assisted ensemble empirical mode decomposition (EEMD) and a hybrid optimization‐based random forest classifier (HOBRFC). The detection scheme employs a diverse set of features extracted from both raw and EEMD decomposed signals. Essential features are selected using the binary grey wolf optimizer (BGWO) to reduce computational burden. To further improve classification accuracy, the parameters of the random forest classifier are optimized through a hybrid particle swarm and reformed grey wolf optimization (PSRGWO) technique with Cohen’s kappa index as the cost function. The proposed technique is rigorously validated in two different multi‐DG environments, encompassing islanding and various nonislanding events. The results demonstrate the effectiveness of the approach in terms of enhanced accuracy, detection time, and performance under both noisy and noise‐free conditions. The accuracy of detection under ideal and high noise scenarios is found to be 99.88% and 99.2%, respectively, with maximum detection time of 34.27 ms. Comparative analysis with other algorithms also supports the superiority of the proposed technique. Finally, the method is successfully applied to shrink the nondetection zone (NDZ) with minimal power mismatch, further enhancing its utility in practical applications.

This study focuses on detecting Power Quality Disturbance Events (PQDE) in microgrids integrated with a Solar Energy Conversion System (SECS). The research proposes a novel signal reduction technique called Clark Transformed Modal, which reduces three-phase voltage to a single unit signal, optimizing memory utilization and reducing computational load during feature extraction. A total of 16 features are extracted from the proposed modal signal by performing multi-resolution analysis through Maximum Overlap Discrete Wavelet Transform (MODWT). Various disturbances, including sag, swell, transients, notches, and flicker, are intentionally simulated in a PV-grid tied MATLAB/Simulink model to obtain a dataset of 10800 samples. Further, the dataset is randomly divided into training-testing subsets to verify the classification ability of a novel ensemble classifier called subspace weighted k-nearest Neighbor (SWKNN). In addition to that the optimum mother wavelet (dmay) is identified to even further boost the classifier performance. The results demonstrate the superior classification capabilities of the proposed MODWT-SWKNN classifier in terms of various performance metrics like precision, recall and F1-score. It also outperformed when compared with several competitive PQ classification models based on PV-integrated systems both under ideal and noisy conditions. Additionally, the disturbance detection system is validated in an OPAL-RT real-time environment to demonstrate its efficiency in terms of detection time. The accuracy of detection is found to be 99.74% in ideal case and fall back to no more than 3% regulation i.e., 97.28% even in dense noise of 20dB with as low as 8 WKNN subspaces. Further, average detection time with 500 trails is found to be 0.0285 seconds. The efficacy of the proposed PQ detection algorithm is also tested in a large PV integrated IEEE 13-bus system.