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The utilization of monitoring sensors in machinery has led to the mainstream adoption of fault detection and diagnosis in time series data across various industrial applications. Deep learning techniques, specifically in constructing fault diagnosis models by extracting insights from historical equipment fault data, are receiving widespread attention as crucial tools in ensuring the safety and reliability of motor systems. In this study, a CNN-LSTM-based deep learning model is proposed for the detection of electric motor faults. Three distinct sets of accelerometer sensor data are provided as input to the model, enabling a comprehensive evaluation of its performance across various sensor configurations. The model demonstrated a remarkable capacity for generalization, achieving impressive accuracy rates of 99.96% for Accelerometer-1, 98.88% for Accelerometer-2, and 99.37% for Accelerometer-3. This underscores the robustness and adaptability of the proposed CNN-LSTM model in effectively detecting electric motor faults regardless of the specific accelerometer sensor employed.

An arc flash is caused by electrical faults or mechanical non-contact such as insulation failure, short circuit, partial disconnection, and poor contact, emits high energy and strong light, so it can be detected by optical methods with relatively simple structures. The optical detection methods using the wavelength of light may malfunction depending on the surrounding environment, the sunlight and lighting. In addition, with such methods, it is difficult to measure the detection distance to the point where the arc flash occurs, measure the detection range of the detection sensor, and monitor how amounts of harmful from the arc flash. This paper aims to examine the techniques for quantitatively detecting arc flash caused by electrical faults. To this end, a system for detecting the time when an arc flash occurs and the techniques for calibrating this system were analyzed, respectively. A field test was also conducted to detect the arc flash caused by mechanical non-contact between the pantograph of electric railway vehicles and the catenary. In the field test, 9 number of arc flashes were detected using the percentage (%) technique for quantitative detection. The technique of detecting the time when an arc flash occurs is a new quantitative evaluation method, which is expected to be widely used to prevent negligent accidents in the electrical field as well as being able to determine in real time whether there is a harmful arc.

The requirements for the increased penetration of renewable energy sources in electrical power systems have led to a dominance of power electronic interfaces. As a result, short-circuit currents have been reduced by the thermal limitations of power electronics, leading to problems associated with the sensitivity, selectivity, and reliability of protective relays. Although many solutions can be found in the literature, these depend on communications and are not reliable in all grid topologies or under different types of electrical fault. Hence, in this paper, the analysis of ground fault currents and voltages using a wavelet transform in combination with a new algorithm not only detects such ground faults but also allows them to be cleared quickly and selectively in scenarios with low fault current contribution due to a full penetration converter-interface-based generation. To verify and validate the proposed protection system, different ground faults are simulated using an arc ground fault model in a grid scheme based on the IEEE nine-bus standard test system, with only grid-forming power converters as generation sources. The test system is modelled in the MATLAB/Simulink environment. Therefore, the protection relays that verify all the steps established in the new algorithm can detect and clear any ground defect. Simulations are also presented involving different fault locations to demonstrate the effectiveness of the proposed ground fault protection method.

This paper presents a fault detection method in three-phase induction motors using Wavelet Packet Transform (WPT). The proposed algorithm takes a frame of samples from the three-phase supply current of an induction motor. The three phase current samples are then combined to generate a single current signal by computing the Root Mean Square (RMS) value of the three phase current samples at each time stamp. The resulting current samples are then divided into windows of 64 samples. Each resulting window of samples is then processed separately. The proposed algorithm uses two methods to create window samples, which are called non-overlapping window samples and moving/overlapping window samples. Non-overlapping window samples are created by simply dividing the current samples into windows of 64 samples, while the moving window samples are generated by taking the first 64 current samples, and then the consequent moving window samples are generated by moving the window across the current samples by one sample each time. The new window of samples consists of the last 63 samples of the previous window and one new sample. The overlapping method reduces the fault detection time to a single sample accuracy. However, it is computationally more expensive than the non-overlapping method and requires more computer memory. The resulting window samples are separately processed as follows: The proposed algorithm performs two level WPT on each resulting window samples, dividing its coefficients into its four wavelet subbands. Information in wavelet high frequency subbands is then used for fault detection and activating the trip signal to disconnect the motor from the power supply. The proposed algorithm was first implemented in the MATLAB platform, and the Entropy power Energy (EE) of the high frequency WPT subbands’ coefficients was used to determine the condition of the motor. If the induction motor is faulty, the algorithm proceeds to identify the type of the fault. An empirical setup of the proposed system was then implemented, and the proposed algorithm condition was tested under real, where different faults were practically induced to the induction motor. Experimental results confirmed the effectiveness of the proposed technique. To generalize the proposed method, the experiment was repeated on different types of induction motors with different working ages and with different power ratings. Experimental results show that the capability of the proposed method is independent of the types of motors used and their ages.

In this paper, a complementary and simplified scheme to diagnose electrical faults in a three-phase induction motor using the parity equations approach during steady state operation bases on the stator current reference frame is presented. The proposed scheme allows us to identify the motor phase affected due to faults related to the stator side, such as current sensors, voltage sensors, and resistance. The results obtained in this work complement a detection system that uses the DQ model of the three-phase induction motor and parity equations focused on the synchronous reference frame, which can detect stator-side faults but cannot locate the affected phase. In addition, considering practical and operational aspects, the residual detection set obtained is simplified to three simple algebraic equations that are easy to implement. The simulation results using the PSIM simulation software and the experimental test allow us to validate the proposed scheme.

This letter proposes a concept to discriminate an electrical fault from a cyber attack in the modern power system. A cyber attack factor is introduced which may mislead the bus voltage stability virtually at load buses. The proposed cyber attack models are validated by executing multiple cyber attacks at a time on Western system coordinating council (WSCC) 9 bus test power system by using Siemens PSS/E and MATLAB softwares. Further, the impact of electrical fault and cyber attack on the WSCC 9 bus test power systems voltage stability has been analysed to develop a discrimination algorithm in reference to chosen load index. Despite its simplicity, the proposed discrimination algorithm is robust, accurate and quite suitable to develop intelligent measures for mal-operations against cyber attacks in the smart electric grid.

Electric faults in photovoltaic (PV) systems cause negative economic and safety impacts, reducing their performance and causing unwanted electric connections that can be dangerous for the user. Line to line, ground and open circuit faults, are three of the main faults that happen in a photovoltaic array system. This work proposes a characterization of the equivalent circuits and the voltage-power (VP) curves at the output of multiple PV arrays under different topological configurations and fault conditions to evaluate the effects of these three main faults on the performance of a photovoltaic array system, taking into account the temperature and solar radiation influence. This work presents a validation of the characterization by measuring the output VP curves of a low-power photovoltaic array system under real outdoors conditions. This method can be useful in future works to develop low cost systems capable of detecting and classifying electric faults in photovoltaic array systems.

This brief investigates the multi‐point fault repair problem in active distribution networks, establishing resilience assessment metrics and constructing a fault repair mathematical model with road network coupling. An improved genetic algorithm is proposed, utilizing a comparison crossover operator and population information entropy. Compared to traditional algorithms, the comparison crossover operator preserves high‐quality genes, while population information entropy maintains diversity, preventing the algorithm from converging on local optima. Simulation analysis demonstrates that the proposed fault repair method can restore normal power supply to distribution network lines within a short period and offers significant advantages in fault repair tasks. This brief investigates the multi‐point fault repair problem in active distribution networks, establishing resilience assessment metrics and constructing a fault repair mathematical model with road network coupling. An improved genetic algorithm is proposed, utilizing a comparison crossover operator and population information entropy. Simulation analysis demonstrates that the proposed fault repair method can restore normal power supply to distribution network lines within a short period and offers significant advantages in fault repair tasks.

Condition monitoring of the insulating system within power transformers has a massive importance according to the electrical utilities. Dissolved gas analysis (DGA) is frequently used for this purpose. However, DGA lacks the necessary level of accuracy to identify all equipment faults, particularly in their initial stages of degradation. Also, it does not have the capability for real‐time monitoring and relies on manual sampling and laboratory testing, causing potential delays in fault identification. Additionally, the interpretation of DGA data necessitates specialised expertise, which may pose difficulties for smaller entities that have limited access to resources. Therefore, the contribution of this research is to use infrared spectroscopy measurements as a new effective technique substituting the DGA method for fault diagnosis in insulating oil. The inception faults that were considered in this study were the electrical fault (discharges of high energy) and the thermal fault (300°C < Temperature < 700°C). Regarding that, two test cells were crafted especially for serving the simulation processes inside the laboratory for both types of inception faults. Subsequently, six samples of pure paraffinic mineral oil were taken to be degraded in the laboratory. Following that, all of them besides another sample that were not subjected to any kind of faults were taken to be examined by Fourier transform infrared (FTIR) spectroscopy to obtain an overview of the oil's behaviour in each fault case. After that, the FTIR analysis was initially verified utilising the DGA method. Then, for further affirmation, the dielectric dissipation factor (DDF) for all samples was measured. In the final analysis, the verification tests provide experimental evidence about the outperformance of this new optical technique in detecting the transformer's inception faults in addition to proving its potential for being a superior alternative to the well‐known traditional diagnostic techniques.

This study introduces an innovative method for electrical fault detection and classification prediction, aiming to meet the need for rapid identification and accurate classification of common faults in power systems. We developed a two-stage prediction hybrid model integrating CatBoost and LightGBM, specifically designed to improve the prediction accuracy of electrical fault detection and classification. Through multiple experimental validations based on simulated circuit data, our model demonstrated superior performance compared to traditional models (LR, SVM, RF, XGBoost, LightGBM, CatBoost), particularly in terms of higher accuracy, precision, and F1-score across all categories. In addition, this study also presents a targeted feature engineering approach, effectively extracting key features from fault signals. The research findings provide reliable technical support for fault prevention and maintenance in power systems, which is of significant importance for ensuring the safe and stable operation of the system, and further reveal the tremendous potential of integrating advanced machine learning technologies to enhance the stability of the power grid.