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This study introduces a novel approach to power system fault diagnosis by synchronised phasor measurements. Conventionally, faults are diagnosed through the status of protective relays and circuit breakers which are activated following a fault. However, the hidden failures of the protection system has itself often been among the main suspects of partial or widespread blackouts. This study proposes an alternative fault diagnosis approach independent of the function of the protection system. An analytical method is introduced for power system fault diagnosis using dispersed synchronised measurements and bus impedance matrix (Zbus). Fault inception is first detected by local phasor measurement units (PMUs). Fault diagnosis is then carried out in a hierarchical manner so that first the faulted zone of the system is diagnosed, next the faulted line in the faulted zone is diagnosed and finally the fault point along the diagnosed line is located by gradient descent. The proposed method is applied to the WSCC 9-bus, where fault incidents on all of the transmission lines are examined. Moreover, the proposed method is successfully applied to the IEEE 118-bus test system consisting of 28 PMUs, which demonstrates successful fault diagnosis and location for a large-scale power system despite the limited coverage of PMUs.

This study presents a new support vector machine (SVM)-based identification method which effectively discriminates between various types of fault and power swing conditions. Different power swing cases, fault cases and fault during power swing cases have been generated with varying fault and system parameters using PSCAD/EMTDC software package. The performance of the developed algorithm has been tested over 3510 testing dataset. Using three phase current samples for half cycle duration of each simulation case of post-fault/power swing conditions, an overall classification accuracy of 98.71% is achieved. The proposed scheme is capable to detect faults during power swing condition accurately as the current waveform/signatures for both the cases are entirely different. On the other hand, the conventional scheme do not provide effective discrimination in the said situation as they compare magnitude/phase of the current signals (require pre/post-processing of signals). In addition, conventional scheme gives poor accuracy during unknown system/unseen dataset whereas the proposed scheme provides promising accuracy (97.61%) in the said situation.

Dynamic voltage restorer (DVR) is one of the custom power devices for compensating power quality indices which is used. The main function of DVR that is discussed in many studies is to compensate voltage sag at times when faults occur. For the first time, a new control structure is presented for considering the voltage sag as the main objective and voltage total harmonic distortion (THD) as the second objective of DVR controller. In this strategy, a new and powerful optimisation algorithm (known as chaotic accelerated particle swarm optimisation (CAPSO)) which is an improved version of particle swarm optimisation algorithm is used for determining the coefficients of the proportional–integral controller of DVR. These coefficients are determined in a way that voltage sag is considered as the main objective of optimisation algorithm and voltage THD is considered as its second objective. By fuzzifying the objectives, an appropriate objective function is proposed for the optimisation process. Results obtained from simulation and a comparison made between these results and those of other controllers show that the proposed strategy outperforms other strategies.

This letter addresses the challenges of complex fault classification in a high‐penetration new energy power grid, proposing a novel intelligent fault classifier based on a rule‐embedded parallel deep convolutional neural network (DCNN). Traditional fault classification methods are hindered from recognizing the complex faults since they rely solely on the data in terms of a single time‐frame. The classic Artificial Intelligence (AI) fault classifiers, mainly based on raw fault signals, also have a relatively limited ability to recognize complex faults. To address this, this study proposes a novel fault classification method by combining the model‐driven method with the data‐driven method. By designing the feature vector of parallel DCNN with characterized indexes, the proposed method inherits the ability of existing model‐driven methods to locate fault types. By employing the panoramic data and an advanced CNN, the hidden features of the transition fault, developing fault are revealed. Simulation results and quantitative analyses validated the superior performance of the proposed method in complex target scenarios, attaining 97.22% accuracy in complex fault classification. This letter proposes a novel intelligent fault classifier based on a rule‐embedded parallel DCNN for a high‐penetration new energy power grid. Combined with the coherent knowledge of existing phase‐selection criteria, the feature vector of parallel DCNN is designed based on sequence current phase‐difference and superimposed phase‐phase current difference to form a rule‐embedded intelligent model.

The effectiveness of deep learning‐based methods for insulator defect detection has been proven. However, in practical applications of power transmission lines, the complex and variable backgrounds in insulator images, coupled with the difficulty in labeling insulator defects, pose challenges to improving the robustness of such methods. Existing studies often utilize generative adversarial networks or forcefully combine foreground and background to augment training samples, but they overlook the rich semantic information in complex scenes, leading to distorted generated adversarial samples. To address this challenge, an innovative multi‐semantic contrast enhancement method that significantly enhances the robustness of defect detection by deeply integrating high‐level semantic knowledge and low‐level signal priors is proposed. Moreover, through adversarial training using generated samples with diverse semantics and real samples, the robustness of the method is further improved. Experimental results demonstrate that this method surpasses state‐of‐the‐art models, achieving significant performance on three independent cross‐scene datasets. Here, an innovative multi‐semantic contrast enhancement method (MSCE) that aims to radically improve the robustness of insulator defect detection is proposed. MSCE transforms enhanced insulator defect detection into an organic combination of a multi‐semantic contrast learning task and an adversarial training task. By deeply incorporating the interrelationship between high‐level semantic knowledge and grassroots signaling prior, MSCE significantly enhances the robustness of the defect detection method at the semantic level. At the same time, MSCE utilizes generated samples enriched with diverse semantics for adversarial training with real samples, thus achieving robustness enhancement at the sample level as well.

This paper presents experimental testing results which describe the response of modern residential loads and electric vehicle (EV) chargers to various voltage magnitude, frequency, and phase angle disturbances. The purpose of these tests is to replicate real life network conditions and assist Network Service Providers and the Australian Energy Market Operator in identifying and predicting potential power variation and system stability issues caused by load behaviour during power system transient phenomena. By examining the behaviour of typical loads connected to distribution networks, a deeper understanding of their response can be achieved, enabling the refinement of composite load models that are compatible with the Western Electricity Coordinating Council dynamic composite load model (CMPLDW) structure presently used for dynamic studies. The performance of a wide range of common appliances found in residential settings, such as refrigerators, microwave ovens, air conditioners, direct-on-line motor-based appliances, and EV chargers, has been evaluated. The results obtained from these tests offer valuable insights into the behaviour of different load types and illustrate differing performances from established model parameters, identifying the need to refine existing CMPLDW models. The results also support the reclassification of several appliances within the composite load model, motivate the introduction of a dedicated EV charger component, and empower network operators to improve the modelling of modern power network responses.

The use of multi-terminal high voltage direct current (HVDC) power transmission systems is being adopted in many new links between different generation and consumption areas due to their high efficiency. In these systems, cable energization must be performed at the rated voltage. Healthy energizations at the rated voltage result in large inrush currents, especially in long cables, primarily due to ground capacitance. State-of-the-art protection functions struggle to distinguish between transients caused by switching and those associated with ground faults, leading to potential unwanted tripping of the protection systems. To prevent this, tripping is usually blocked during the energization transient, which delays fault detection and clearing. This paper presents a novel method for prompt discrimination between healthy and faulty energizations. The proposed method outperforms conventional protection functions as this discrimination allows for earlier and more reliable tripping, thus avoiding extensive damage to the cable and the converter due to trip blocking. The method is based on the transient analysis of the current in the cable shields, therefore, another technical advantage is that high voltage-insulated measuring devices are not required. Two distinct tripping criteria are proposed: one attending to the change in current polarity, and the other to the change in current derivative sign. Extensive computer simulations and laboratory tests confirmed the correct operation in both cases.

Modern power systems incorporate ultra-high-voltage transmission lines with series compensation to enhance their reliability and stability. These configurations generate complex data patterns in electrical signals, which present challenges for accurate fault diagnosis. This complexity arises from various potential faults, such as high-impedance faults and inductive-capacitive interactions, compounded by data scarcity owing to privacy and cybersecurity constraints. This study employs a simulation-based analysis to facilitate fault detection, classification, and localization as unified Multi-Classification tasks. The methodology improves the time-frequency domain data through meta-feature engineering and utilizes consensus-based feature selection by combining gradient boosting, variance thresholding, and mutual information. Model learning is conducted using Bayesian optimization within a Stacking Ensemble, integrating Extra Trees, Light Gradient Boosting Machine, and Extreme Gradient Boosting with an optimized Random Forest meta-classifier. The evaluation considers the accuracy, computational time, inference speed, and memory usage under challenging conditions to address practical limitations. The results indicate that the Stacking Ensemble achieves the highest performance despite its greater computational demand. Extra Trees offers the fastest execution but shows decreased accuracy with increased classification complexity. The Light Gradient Boosting Machine and Extreme Gradient Boosting demonstrate optimized performance across tasks owing to enhanced data processing. These findings confirm the practical applicability of the proposed fault diagnosis framework to modern ultra-high-voltage power systems.

The rapid growth of grid integrated renewable energy (RE) sources resulted in development of the hybrid grids. Variable nature of RE generation resulted in problems related to the power quality (PQ), power system reliability, and adversely affects the protection relay operation. High penetration of RE to the utility grid is achieved using multi-tapped lines for integrating the wind and solar energy and also to supply loads. This created considerable challenges for power system protection. To overcome these challenges, an algorithm is introduced in this paper for providing protection to the hybrid grid with high RE penetration level. All types of fault were identified using a fault index (FI), which is based on both the voltage and current features. This FI is computed using element to element multiplication of current-based Wigner distribution index (WD-index) and voltage-based alienation index (ALN-index). Application of the algorithm is generalized by testing the algorithm for the recognition of faults during different scenarios such as fault at different locations on hybrid grid, different fault incident angles, fault impedances, sampling frequency, hybrid line consisting of overhead (OH) line and underground (UG) cable sections, and presence of noise. The algorithm is successfully tested for discriminating the switching events from the faulty events. Faults were classified using the number of faulty phases recognized using FI. A ground fault index (GFI) computed using the zero sequence current-based WD-index is also introduced for differentiating double phase and double phase to ground faults. The algorithm is validated using IEEE-13 nodes test network modelled as hybrid grid by integrating wind and solar energy plants. Performance of algorithm is effectively established by comparing with the discrete wavelet transform (DWT) and Stockwell transform based protection schemes.