Electrical Faults Research Articles

An advanced integral sliding mode controller design for residual current compensation inverters in compensated power networks to mitigate powerline bushfire hazards

AbstractThis work deals with designing an advanced integral sliding mode controller (AISMC) for residual current compensation (RCC) inverters connected with arc suppression coils to compensate for power distribution networks where the main idea is to alleviate hazardous circumstances caused by electric faults on powerlines. The key advancement in the proposed AISMC over traditional sliding mode controllers is the utilization of an improved exponential reaching law which ensures the faster convergence of the desired control objective that is the fault current compensation in this particular application. An improved exponential reaching law (IERL) used in this work is a combination of the exponential and constant‐proportional reaching laws (while existing approaches use constant reaching laws) which assists to minimize the current injection error through the RCC inverter in the steady‐state. The integral action in conjunction with the exponential function in the sliding surface, based on which the proposed controller is designed, helps to eliminate the chattering effects in a quickest way that can be evidenced from the settling time and percentage overshoot. The feasibility of the proposed AISMC is theoretically assessed by analyzing the stability using the Lyapunov stability theory. Simulation and processor‐in‐loop results further justify the theoretical foundation by confirming the desired current injection and compensating voltage and current due to the fault. Finally, results are compared with a TIMSC for demonstrating the superiority of the AISMC.

An effective approach for electric motor fault diagnosis using deep learning

An effective approach for electric motor fault diagnosis using deep learning

Advanced testbed to assess disturbances in electrical grids with DERs using relays/meters with varying sampling frequencies

Distributed energy resources (DERs) are vital to achieve clean energy goals, but in some cases, their rapid deployment has led to negative effects on their reliability because of possible voltages and/or current disturbances that need to be analyzed in detail. This study created an advanced testbed with a real-time simulator and synchronized time and trigger event systems was created to compare event disturbances from intelligent electronic devices (IEDs) with different sampling frequencies and locations in an electrical grid containing DERs. This testbed is capable of capturing the recorded events of multiple IEDs and the real-time simulator to assess the effect of the disturbances and IEDs’ behaviors, including phase voltages, phase current waveforms, and total harmonic distortion (THD) analysis. In this study, a three-line-to-ground electrical fault was simulated, the disturbance event was detected, and recorded events (from 4 to 133 samples per cycle) were collected from meters and relays located at different sites. A harmonic peak algorithm, which was based on measuring the phase voltage and phase current THD peaks at the first one-cycle of the disturbance events, was created to assess and detect the fault states and locations. Several important findings resulted from performing this study, such as selecting IEDs with high sampling frequencies, installing all IEDs with similar sampling frequencies, assessing the software limitations for computing THD, studying the effect of the IEDs’ locations (downstream or upstream) with respect to the electrical fault site and the distance from the disturbance event point, evaluating the percentage errors of the phase voltage THD peaks at the first one-cycle of disturbance events, and installing synchronized time source and trigger event signal systems for recording the IEDs’ events.

A Deep Learning-based Fault Detection and Classification in Smart Electrical Power Transmission System

Progressively, the energy demands and responsibilities to control the demands have expanded dramatically. Subsequently, various solutions have been introduced, including producing high-capacity electrical generating power plants, and applying the grid concept to synchronize the electrical power plants in geographically scattered grids. Electrical Power Transmission Networks (EPTN) are made of many complex, dynamic, and interrelated components. The transmission lines are essential components of the EPTN, and their fundamental duty is to transport electricity from the source area to the distribution network. These components, among others, are continually prone to electrical disturbance or failure. Hence, the EPTN required fault detection and activation of protective mechanisms in the shortest time possible to preserve stability. This research focuses on using a deep learning approach for early fault detection to improve the stability of the EPTN. Early fault detection swiftly identifies and isolates faults, preventing cascading failures and enabling rapid corrective actions. This ensures the resilience and reliability of the grid, optimizing its operation even in the face of disruptions. The design of the deep learning approach comprises a long-term and short-term memory (LSTM) model. The LSTM model is trained on an electrical fault detection dataset that contains three-phase currents and voltages at one end serving as inputs and fault detection as outputs. The proposed LSTM model has attained an accuracy of 99.65 percent with an error rate of just 1.17 percent and outperforms neural network (NN) and convolutional neural network (CNN) models.

A deep learning approach for electric motor fault diagnosis based on modified InceptionV3

Electric motors are essential equipment widely employed in various sectors. However, factors such as prolonged operation, environmental conditions, and inadequate maintenance make electric motors prone to various failures. In this study, we propose a thermography-based motor fault detection method based on InceptionV3 model. To enhance the detection accuracy, we apply Contrast Limited Adaptive Histogram Equalization (CLAHE) to the input images. Furthermore, we improved the performance of the InceptionV3 by integrating a Squeeze-and-Excitation (SE) channel attention mechanism. The proposed model was tested using a dataset containing 369 thermal images of an electric motor with 11 types of faults. Image augmentation was employed to increase the data size and the evaluation was conducted using fivefold cross validation. Experimental results indicate that the proposed model can achieve accuracy, precision, recall, and F1 score of 98.82%, 98.93%, 98.82%, and 98.87%, respectively. Additionally, by freezing the fully connected layers of the InceptionV3 model for feature extraction and training a Support Vector Machines (SVM) to perform classification, it is able to achieve 100% detection rate across all four evaluation metrics. This research contributes to the field of industrial motor fault diagnosis. By incorporating deep learning techniques based on InceptionV3 and SE channel attention mechanism with a traditional classifier, the proposed method can accurately classify different motor faults.

Fault Detection and Normal Operating Condition in Power Transformers via Pattern Recognition Artificial Neural Network

Aging, degradation, or damage to internal insulation materials often contribute to transformer failures. Furthermore, combustible gases can be produced when these insulation materials experience thermal or electrical stresses. This paper presents an artificial neural network for pattern recognition (PRN) to classify the operating conditions of power transformers (normal, thermal faults, and electrical faults) depending on the combustible gases present in them. Two network configurations were presented, one with five and the other with ten neurons in the hidden layer. The main advantage of applying this model through artificial neural networks is its ability to capture the nonlinear characteristics of the samples under study, thus avoiding the need for iterative procedures. The effectiveness and applicability of the proposed methodology were evaluated on 815 real data samples. Based on the results, the PRN performed well in both training and validation (for samples that were not part of the training), with a mean squared error (MSE) close to expected (0.001). The network was able to classify the samples with a 98% accuracy rate of the 815 samples presented and with 100% accuracy in validation, showing that the methodology developed is capable of acting as a tool for diagnosing the operability of power transformers.

Electric vehicle motor fault diagnosis using improved wavelet packet decomposition and particle swarm optimization algorithm

This study addresses the issue of diagnosing faults in electric vehicle motors and presents a method utilizing Improved Wavelet Packet Decomposition (IWPD) combined with particle swarm optimization (PSO). Initially, the analysis focuses on common demagnetization faults, inter turn short circuit faults, and eccentricity faults of permanent magnet synchronous motors. The proposed approach involves the application of IWPD for extracting signal feature vectors, incorporating the energy spectrum scale, and extracting the feature vectors of the signal using the energy spectrum scale. Subsequently, a binary particle swarm optimization algorithm is employed to formulate strategies for updating particle velocity and position. Further optimization of the binary particle swarm algorithm using chaos theory and the simulated annealing algorithm results in the development of a motor fault diagnosis model based on the enhanced particle swarm optimization algorithm. The results demonstrate that the chaotic simulated annealing algorithm achieves the highest accuracy and recall rates, at 0.96 and 0.92, respectively. The model exhibits the highest fault accuracy rates on both the test and training sets, exceeding 98.2%, with a minimal loss function of 0.0035. Following extraction of fault signal feature vectors, the optimal fitness reaches 97.4%. In summary, the model constructed in this study demonstrates effective application in detecting faults in electric vehicle motors, holding significant implications for the advancement of the electric vehicle industry.

Design And Development Of Automated Electrical Circuit Fault Protector With Alarm System

In response to the critical need for fire and accident prevention in homes and establishments, this research presents an automated electrical circuit fault protector with an alarm system. The device automatically interrupts current flow upon detecting short circuits and overloads, safeguarding against potential hazards. Objectives include assessing sensitivity to faults, evaluating wireless remote-control performance, examining sound clarity, and ensuring voltage stability post-fault events. Equipped with a resettable system, the device allows manual or remote control. Using a developmental research method, data collection employed researcher-made observation and evaluation sheets. Thirty expert evaluators assessed the device. Micro testing revealed that the device was sensitive to faults and automatically triggered the alarm. With an overload rating of 1,500-3,000 watts, the device shuts off power automatically after the overload occurs and back to normal with a stable voltage once the problem was fixed, though this device was primarily designed only for micro-testing this device was customizable to user needs. Its alarm system boasts an audible range of up to 20 meters the same is through with remote control operation. Overall, the device effectively detects faults, incorporates an audible alarm, and supports remote operation, serving as a crucial safety measure against electrical mishaps and very acceptable in terms of design, composition, safety, and operating performance.

Mechanical and electrical faults detection in induction motor across multiple sensors with CNN-LSTM deep learning model

Mechanical and electrical faults detection in induction motor across multiple sensors with CNN-LSTM deep learning model

Isolating electrical and cryogen flow for safe operation and design validation experimentation of HTS power cables for electric transportation

The need for protecting the cryogenic infrastructure during a fault in high-temperature superconducting (HTS) power distribution systems of electric transport platforms is highlighted. A dual grounding scheme with an isolator between the electrical and cryogen flow paths of HTS power cables is needed. Finite element analysis of the electric field distribution of the vacuum jacketed isolator showed electric field intensity at the triple points. The electric field intensity at the triple points is reasonable if high vacuum quality is maintained. There is a need for further investigations for design validation with experimentation of practical designs for effective isolation and protection of cryogenic infrastructure from damage during an electrical fault in a section of the power distribution network of electrical transport platforms, such as an electric aircraft or ship.

A deep residual neural network model for synchronous motor fault diagnostics

Synchronous motors play a significant role in a wide range of industrial applications. Their reliable operation is paramount. Any faults in synchronous motors can lead to costly downtime, decreased productivity, and potential safety hazards. By accurately diagnosing and classifying faults, we can proactively address issues before they escalate, ensuring the smooth operation of synchronous motors and minimizing the risk of equipment failure. The accurate diagnosis and fault detection in synchronous motors pose a significant challenge in their operation and maintenance. In the existing models, the feature data at various depths are not thoroughly extracted to maximize their feature extraction capability. Additionally, they employ a single support vector machine to make the final decision on the output. The single support vector machine may not consistently produce more accurate outcomes. Therefore, this paper proposes a novel fault diagnosis model based on a deep residual neural network and multiple support vector machines to diagnose mechanical and electrical faults of synchronous motors. The proposed model improves upon existing fault diagnosis models in two key aspects. Firstly, by employing a deep neural network, the model is able to effectively process and extract fault features from the motor fault dataset, capturing more nuanced information that may be missed by existing models. Secondly, the use of multiple support vector machines enhances the decision-making capability of the model, allowing for more accurate fault diagnosis. By combining these two aspects, the proposed model achieves superior diagnostic performance compared to single support vector machine-based models. Our proposed model has been rigorously evaluated using mechanical and electrical fault datasets, and the results of experimental tests clearly demonstrate its superior diagnostic performance when compared to existing fault diagnosis models. The synergy of deep neural networks and multiple support vector machines not only improves fault detection accuracy but also enhances the robustness and generalizability of the model, making it a valuable tool for real-world industrial applications.

Electrical Ground Fault Monitoring System Utilizing Arduino and Android Application

Abstract: The increasing residential fire incidence caused by electrical ground faults worldwide challenges present and future researchers to develop a system capable of mitigating its occurrence. The absence of periodic equipment maintenance and monitoring facilities leads to potential electrical hazards to humans and properties. However, technological advances have made online electrical ground fault monitoring equipment possible. Users can now remotely gain information regarding the operational condition of electrical equipment in their homes. This paper presents the developed Electrical Ground Fault Monitoring System using Arduino with Android Application (EGFMSAAA), which can detect and transmit electrical ground fault parameters and safety precautions. This study developed a ground fault detector and a mobile application for the monitoring system.

Explainable Artificial Intelligence Approach for Diagnosing Faults in an Induction Furnace

For over a century, induction furnaces have been used in the core of foundries for metal melting and heating. They provide high melting/heating rates with optimal efficiency. The occurrence of faults not only imposes safety risks but also reduces productivity due to unscheduled shutdowns. The problem of diagnosing faults in induction furnaces has not yet been studied, and this work is the first to propose a data-driven framework for diagnosing faults in this application. This paper presents a deep neural network framework for diagnosing electrical faults by measuring real-time electrical parameters at the supply side. Experimental and sensory measurements are collected from multiple energy analyzer devices installed in the foundry. Next, a semi-supervised learning approach, known as the local outlier factor, has been used to discriminate normal and faulty samples from each other and label the data samples. Then, a deep neural network is trained with the collected labeled samples. The performance of the developed model is compared with several state-of-the-art techniques in terms of various performance metrics. The results demonstrate the superior performance of the selected deep neural network model over other classifiers, with an average F-measure of 0.9187. Due to the black box nature of the constructed neural network, the model predictions are interpreted by Shapley additive explanations and local interpretable model-agnostic explanations. The interpretability analysis reveals that classified faults are closely linked to variations in odd voltage/current harmonics of order 3, 11, 13, and 17, highlighting the critical impact of these parameters on the model’s prediction.

A multilayer integrative approach for diagnosis, classification and severity detection of electrical faults in photovoltaic arrays

Early detection of faults in photovoltaic (PV) systems is crucial for improving their lifespan and reliability. Conventional protection devices may not be able to detect electrical faults under critical conditions. This usually occurs due to (i) the current-limiting nature and non-linear output characteristics of PV arrays, (ii) the changing environmental conditions (such as irradiance and temperature variations), and (iii) the influence of the maximum power point tracker (MPPT), particularly in the presence of critical fault impedance values or mismatch levels. Therefore, modern data-driven methods are required for fault detection and classification in PV arrays. However, careful investigation in previous studies reveals the existing gaps and limitations, such as poor accuracy, and incomprehensive models which have not considered various electrical faults, low mismatch levels, and critical fault impedance values. To this end, in this study, a comprehensive multilayer model is proposed which consists of six layers for diagnosis, classification and severity identification of electrical faults in PV arrays. Each layer adopts a weighted ensemble learning (WEL) algorithm consisting of three classifiers namely Support Vector Machine (SVM), Naïve Bayes (NB) and Logistic Regression (LR). Besides, a genetic algorithm (GA) is employed in each layer to find the optimal weights for each classifier (i.e., SVM, NB and LR). The initial dataset is acquired through an investigation into the PV array current–voltage (I-V) characteristic curve and extraction of several features under various faulty and normal conditions. To reduce the dataset dimensionality thus computationally simplifying the training process, the sequential floating forward selection (SFFS) algorithm is utilized in each layer as a powerful feature selection technique. The results show a highly accurate performance in fault diagnosis, classification, and severity assessment, with an average simulation and experimental accuracies of 98.9% and 98.37%, respectively.

Electrical line fault prediction using a novel grey wolf optimization algorithm based on multilayer perceptron

AbstractGrey wolf optimization algorithm (GWO) has achieved great results in the optimization of neural network parameters. However, it has some problems such as insufficient precision, poor robustness, weak searching ability and easy to fall into local optimal solution. Therefore, a grey wolf optimization algorithm combining Levy flight and nonlinear inertia weights (LGWO) is proposed in this paper. The combination of Levy flight and nonlinear inertia weight is to improve the search efficiency and solve the problem that the search ability is weak and it is easy to fall into the local optimal solution. In summary, LGWO solves the problems of insufficient precision, poor robustness, weak searching ability and easy to fall into local optimal. This paper uses Congress on Evolutionary Computation benchmark function and combines algorithms with neural network for power line fault classification prediction to verify the effectiveness of each strategy improvement in LGWO and its comparison with other excellent algorithms (sine cosine algorithm, tree seed algorithm, wind driven optimization, and gravitational search algorithm). In the combination of neural networks and optimization algorithms, the accuracy of LGWO has been improved compared to the basic GWO, and LGWO has achieved the best performance in multiple algorithm comparisons.

PLC Based Transformer Protection from Over Voltage and Under Voltage

A PLC (Programmable Logic Controller) based transformer protection system is an electronic system designed to protect industrial transformers from various electrical faults and abnormalities. The system utilizes sensors to detect electrical parameters such as voltage, current, and temperature and uses a PLC to process the data and control the transformer's operation. The protection system can be programmed to detect faults such as overload, overvoltage, undervoltage, phase loss, phase reversal, and over-temperature conditions. Once a fault is detected, the PLC will automatically shut down the transformer to prevent further damage. The system provides reliable and accurate protection to the transformer, reducing the risk of costly downtime and repairs. Transformer protection from over and under voltages is a critical aspect of any transformer control system. Overvoltage can damage the transformer's insulation, bearings, and winding, while undervoltage can cause the transformer to stall or overheat. To protect the transformer from these conditions, various protection devices can be employed, including overload relays, circuit breakers, and voltage monitors. To protect against overvoltage, a voltage monitor can be installed, which will detect any voltage spikes or surges and trigger an alarm or shut down the transformer if the voltage exceeds a predetermined threshold. Similarly, an undervoltage relay can be used to protect the transformer from low voltage conditions. The undervoltage relay will monitor the voltage levels and shut down the transformer if the voltage drops below a certain level.. Key Words:-PLC (Programmable Logic Controller), SMPS, Voltage Controlled Relay, LED indicators, Transformer and Dimmer.

Application of neural networks in diagnosing engine faults based on vibration signals

This paper investigates and applies artificial intelligence (AI) to improve the monitoring and diagnosis process of electrical engine faults based on vibration signals. The research aims to build a model to collect sample data from engines and utilize three different AI networks in this study, including YOLO (You Only Look Once), Resnet (Residual neural network), and SVM (Support Vector Machine). By applying these models to independently identify faults using the common input signal of vibration, particularly focusing on bearing-related faults in engine systems, the paper concentrates on exploring various faults. The experimental results presented in the paper demonstrate the accuracy of using these networks in diagnosing engine faults and provide important insights into the accuracy and practical applicability of AI networks in the field of industrial equipment maintenance and management.

An improved electric equipment fault prediction based on recursive multi-parameter prediction algorithm

This paper proposes a method of beforehand prediction of electric equipment faults based on chain-linked recurrent neural network algorithm, which takes the operating parameters of power equipment and other relevant environmental factors as inputs, and takes the fault characteristics as output judgment marks, and constructs a machine learning training model to realize the prediction of power equipment faults. The neural network algorithm adopted in this paper adopts a tree structure. Each sub-node can transfer information with its multiple superior nodes, so that the correlation between the data of the front and back nodes can be obtained, which meets the needs of the equipment fault prediction model. Considering that the occurrence of power transformer faults is sudden and greatly affected by changes in the surrounding environment, the input of prediction algorithms should consider more environmental factors. This method takes the historical data of various parameters including meteorological phenomena, geography data, and temperature of adjacent equipment and facilities as the training sample set, improves the learning model, gives the trend curve of each index, and gives a prompt at its threshold to ensure the prediction accuracy and give the index prediction.

CAR PROBLEM DETECTOR DEVICE UNIT

This research paper examines the development and implementation of a problem detector device unit for automobiles. The device utilizes advanced sensors and algorithms to diagnose and detect various car issues in real-time. Through rigorous testing and validation, the device demonstrates high accuracy, reliability, and efficiency in identifying problems such as engine malfunctions, electrical faults, and fluid leaks. The study explores the potential applications and benefits of the device in enhancing vehicle maintenance, safety, and overall performance. Furthermore, it discusses future research directions aimed at further improving the technology and its integration into automotive systems.

Network-based System of Mechanical Vibration Fault Diagnosis

Nowadays researchers are investing in electrical machines fault diagnosis area. The users and manufacturers are strong for containing diagnostic features for reliability and scalability improvement. The regular monitoring enables machine faults early detection and hence helpful for automation by providing process control. The fault detection performance and machine-learning algorithms classification are highly dependent on features involved. The aim of this paper is to solve the network mechanical vibration problem and for that a research on mechanical vibration fault diagnosis is proposed. The first is to use the vibration signal receiving device to record the vibration signal of the target device. In the process of receiving the signal, the measurement point is related to the accuracy of the received signal, so it is necessary to prepare the measurement point. Secondly, the principle of fault detection based on vibration detection is introduced. The main purpose of this method is to identify the fault characteristics, simulate the fault with MATLAB, and obtain the error time-frequency diagram behavior. The feature vector dimension obtained by the idling confirmation example is the same as that of the rotor, which is 14, including 8 relative wavelet packet intensity entropy feature indexes and higher values, minimum value, peak-to-peak value, mean value, mean square error and variance. Finally, the deficiencies of the detected vibration faults are identified and similar improvements are proposed. Improvements only reduce signal vibration, disrupting feature isolation and identifying patterns.The observed percentage accuracy for classification of faults through proposed approach is 98.2%.