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 quality of power in modern‐day power system is polluted with increased penetration of converter‐based distributed generations such as wind farm, solar PV system. In such scenarios detection of islanding and power quality disturbances as well as the removal of these from the system is quite crucial for equipment and maintenance personnel safety. Here, a down‐sampling empirical mode decomposition (DEMD) and optimized random forest (RF) machine learning approach are hybridized to detect islanding conditions with reduced non‐detection zone (NDZ) and classify non‐islanding power quality events in a highly wind energy penetrated distribution generation system. DEMD has a special ability to filter out the fundamental signal from the polluted signal and random forest is quite an unbiased non‐linear machine learning approach. Moreover, an improved grey wolf optimization technique is proposed to optimize the parameter of RF. The proposed technique is simulated in MATLAB/Simulink with IEEE 13‐Bus test grid. The efficacy of the proposed method is evaluated through comparative analysis with existing machine learning techniques under normal and noisy environments as well as validated in narrow NDZ with lesser detection time.

Solar power being stochastic and asynchronous in nature, its integration with conventional power generation has so many challenges in damping of low‐frequency oscillations. This study presents an investigation of low‐frequency oscillation and damping of stochastic solar power integrated power system by a unified power flow controller (UPFC)‐based dual optimal controller, which gains are optimised by a novel hybrid particle swarm optimisation and improved grey wolf optimiser. The dual controller simultaneously controls the modulation index of series and phase angle of shunt converters of UPFC, which co‐operate with each other, optimising its efficacy and thereby implementing the advantages of both static synchronous series compensator and static synchronous compensator. A detailed Eigen value analysis has been performed with time domain simulations to study the damped oscillatory response of variable and random solar penetration with a power system along with the interaction of solar power with variable synchronous power generations. For a multi‐machine system, a multi‐input single output dual controller is proposed. A maximum sensor and time delay have been considered to design the controller providing a realistic approach. It is observed that random and heavy solar penetration has a more detrimental effect on system oscillations, which can be damped heavily with the proposed controller, in contrast to particle swarm optimisation, differential evolution, grey wolf optimiser, differential evolution particle swarm optimisation optimised lead‐lag and dual controllers.

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.

In this work a UPFC based novel optimal fuzzy PID controller is proposed whose gains are optimized by PSO-GWO algorithm considering integral of time weighted absolute error. The proposed controller has been employed to damp low frequency oscillations in power system considering wide range of operating conditions like change in input mechanical power, line loading, change in line reactance with single and multi machine power system. The fuzzy optimized PID controller has been compared with normal optimized PID controller with same optimization techniques to justify its effectiveness. Also the supremacy of PSO-GWO technique has been compared with PSO and GWO techniques by optimizing the gains of fuzzy PID controller. The detailed eigen value analysis and system response prove that proposed fuzzy PID controller is much superior than PID controller and PSO-GWO optimized controller is also superior as compared to PSO and GWO optimized fuzzy PID controllers to damp low frequency oscillations in power system and there by enhancing small signal stability of power system.

A novel islanding detection technique by hybridising empirical mode decomposition (EMD) and multi‐scale mathematical morphology (MMM) is proposed to detect the islanding condition in a distributed generation system to ensure personnel and equipment safety. The proposed method first uses EMD to efficiently separate the collected raw signal into the number of intrinsic mode functions (IMFs) with different frequency scales and the signal is reconstructed considering important IMFs which carry transient features for further analysis using their correlation coefficients. MMM is used for determining a ratio index named as MMMRI, the threshold value of the proposed MMMRI decides islanding and other power quality (PQ) disturbances. The proposed hybrid method name coined as EMD‐MMMRI. The main motivation behind hybridising two signal processing techniques is to reduce detection time and improve accuracy. The efficacy of the method is demonstrated for different PQ disturbances and islanding events simulated on a grid‐connected, heavily wind energy penetrated distributed generation system using MATLAB/Simulink environment. The test bench validation of the proposed technique is obtained through TMS 320C6713 Starter Kit (DSK) in digital signal processor platform. The efficacy of proposed work is demonstrated with large number of simulation studies.