Explore the vast range of titles available.

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.

Transmission lines are vital components of electrical grids, ensuring the efficient transfer of electricity from power plants to consumers over extensive geographical areas. These lines are constructed with careful consideration of factors such as conductor materials, insulation levels, current ratings, and voltage ratings to maintain reliable and safe electricity delivery. However, various types of faults can occur in transmission lines, posing significant challenges, often leading to outages, equipment damage, and reduced system reliability. Accurate and fast fault classification is therefore a pressing requirement in modern smart grids, where proactive maintenance and resilience are critical. This research addresses the critical need for an efficient electric fault classification model. A comprehensive investigation is conducted, employing a variety of machine learning (ML) algorithms, including Decision Tree (DT), Random Forests (RF), Naive Bayes (NB), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and, AdaBoost, for fault classification. Additionally, fundamental ensemble techniques such as Hard-Voting, Soft-Voting, Stacking, and Blending are incorporated with five hybrid ML models (each constructed by combining various ML algorithms) to enhance fault classification performance and the reliability of transmission lines. Also, this research proposes a hybrid ML model, specifically (RF + DT + Stacking) , to classify transmission line data. The main contribution of this work is an application-oriented evaluation of classical and ensemble machine learning models for electrical fault classification, with an emphasis on benchmarking performance, model interpretability, and computational efficiency. This study demonstrates that a carefully configured hybrid ensemble (RF + DT + Stacking) can provide a practical and lightweight alternative to deep learning-based methods in grid fault monitoring scenarios. The dataset used encompasses various attributes affecting line performance, making accurate classification critical for proactive issue detection, optimized maintenance scheduling, and uninterrupted energy supply. Our hybrid model achieves high-performance metrics, including an accuracy of 93.64%, precision of 93.65%, recall of 93.64%, and F1 score of 93.64%, underscoring its effectiveness in enhancing decision-making processes and operational efficiency within electrical transmission networks.

Partial shading and electrical faults impose severe threats to engendered photovoltaic power. Hence, this article presents three innovatory photovoltaic array structures to alleviate mismatch power losses amid partial shading conditions (PSC), and cataclysmic electrical fault conditions. The performance characteristics of these innovative structures are investigated and compared with four classical topologies namely Total Cross Tied (TCT), Series Parallel-TCT, Bridge Link-TCT and Honey Comb- TCT for ten diverse atmospheric conditions like standard atmospheric conditions, partial shading scenarios, Line-Line faults and module faults. Assessment of these topologies has been addressed with regard to eight performance indices namely, Global maxima, Reduction of global maxima, Mismatch Power loss, Relative power loss, Performance Ratio, Fill Factor, Thermal Equivalent voltage, and total number of local peaks. A 4 × 4 PV array comprising of sixteen Vikram Solar modules of 340 W each, is considered for the analysis in MATLAB/Simulink. Moreover, the best suited configuration for each shading spectrum is diagnosed. From the analysis it is evident that, TCT surpasses for various spectrums. All the three proposed novel array structures have been substantiated to be the best performers for five shading patterns involving PSCs and module faults, thereby elucidating the effectiveness of the envisioned structures with reduced tie lines compared to the benchmark TCT, for several considered shading scenarios.

PurposeThis study presents the diagnosis of combined or multiple mechanical and electrical faults in an electromechanical system based on support vector machine (SVM).MethodsTime and continuous wavelet transform (CWT)-based features have been extracted from vibration and current signals acquired from ten combined fault conditions from an experimental test rig. Then, SVM algorithm based methodology has been developed for diagnosis the fault conditions.ResultsResults show that the combination of vibration and current signal improves the performance of the diagnosis especially at high-load condition in comparison to vibration or current signal alone. The diagnosis is found to be effective with both time domain and CWT features; however, it is slightly better with the CWT features in comparison to time domain-based features.ConclusionThe performance of the diagnosis is found to be better at higher load with both time domain and CWT features. Moreover, the present methodology does not perform well with ramp-up speed conditions.

The electric power transmission line is an essential medium for ensuring a continuous power supply.Faults occur more frequently when the number of transmission lines grows to keep up with rising demand. A variety of things might cause an electrical fault on transmission lines, which are designed to travel long distances

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.