Classification of Contaminated Insulators Using k-Nearest Neighbors Based on Computer Vision

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Wavelet LSTM for Fault Forecasting in Electrical Power Grids

An electric power distribution utility is responsible for providing energy to consumers in a continuous and stable way. Failures in the electrical power system reduce the reliability indexes of the grid, directly harming its performance. For this reason, there is a need for failure prediction to reestablish power in the shortest possible time. Considering an evaluation of the number of failures over time, this paper proposes performing failure prediction during the first year of the pandemic in Brazil (2020) to verify the feasibility of using time series forecasting models for fault prediction. The long short-term memory (LSTM) model will be evaluated to obtain a forecast result that an electric power utility can use to organize maintenance teams. The wavelet transform has shown itself to be promising in improving the predictive ability of LSTM, making the wavelet LSTM model suitable for the study at hand. The assessments show that the proposed approach has better results regarding the error in prediction and has robustness when statistical analysis is performed.N/

Group Method of Data Handling Using Christiano–Fitzgerald Random Walk Filter for Insulator Fault Prediction

Disruptive failures threaten the reliability of electric supply in power branches, often indicated by the rise of leakage current in distribution insulators. This paper presents a novel, hybrid method for fault prediction based on the time series of the leakage current of contaminated insulators. In a controlled high-voltage laboratory simulation, 15 kV-class insulators from an electrical power distribution network were exposed to increasing contamination in a salt chamber. The leakage current was recorded over 28 h of effective exposure, culminating in a flashover in all considered insulators. This flashover event served as the prediction mark that this paper proposes to evaluate. The proposed method applies the Christiano–Fitzgerald random walk (CFRW) filter for trend decomposition and the group data-handling (GMDH) method for time series prediction. The CFRW filter, with its versatility, proved to be more effective than the seasonal decomposition using moving averages in reducing non-linearities. The CFRW-GMDH method, with a root-mean-squared error of 3.44×10−12, outperformed both the standard GMDH and long short-term memory models in fault prediction. This superior performance suggested that the CFRW-GMDH method is a promising tool for predicting faults in power grid insulators based on leakage current data. This approach can provide power utilities with a reliable tool for monitoring insulator health and predicting failures, thereby enhancing the reliability of the power supply

Fault Prediction Based on Leakage Current in Contaminated Insulators Using Enhanced Time Series Forecasting Models

To improve the monitoring of the electrical power grid, it is necessary to evaluate the influence of contamination in relation to leakage current and its progression to a disruptive discharge. In this paper, insulators were tested in a saline chamber to simulate the increase of salt contamination on their surface. From the time series forecasting of the leakage current, it is possible to evaluate the development of the fault before a flashover occurs. In this paper, for a complete evaluation, the long short-term memory (LSTM), group method of data handling (GMDH), adaptive neuro-fuzzy inference system (ANFIS), bootstrap aggregation (bagging), sequential learning (boosting), random subspace, and stacked generalization (stacking) ensemble learning models are analyzed. From the results of the best structure of the models, the hyperparameters are evaluated and the wavelet transform is used to obtain an enhanced model. The contribution of this paper is related to the improvement of well-established models using the wavelet transform, thus obtaining hybrid models that can be used for several applications. The results showed that using the wavelet transform leads to an improvement in all the used models, especially the wavelet ANFIS model, which had a mean RMSE of 1.58 × 10−3, being the model that had the best result. Furthermore, the results for the standard deviation were 2.18 × 10−19, showing that the model is stable and robust for the application under study. Future work can be performed using other components of the distribution power grid susceptible to contamination because they are installed outdoors.N/

Semi-ProtoPNet Deep Neural Network for the Classification of Defective Power Grid Distribution Structures

Power distribution grids are typically installed outdoors and are exposed to environmental conditions. When contamination accumulates in the structures of the network, there may be shutdowns caused by electrical arcs. To improve the reliability of the network, visual inspections of the electrical power system can be carried out; these inspections can be automated using computer vision techniques based on deep neural networks. Based on this need, this paper proposes the Semi-ProtoPNet deep learning model to classify defective structures in the power distribution networks. The Semi-ProtoPNet deep neural network does not perform convex optimization of its last dense layer to maintain the impact of the negative reasoning process on image classification. The negative reasoning process rejects the incorrect classes of an input image; for this reason, it is possible to carry out an analysis with a low number of images that have different backgrounds, which is one of the challenges of this type of analysis. Semi-ProtoPNet achieves an accuracy of 97.22%, being superior to VGG-13, VGG-16, VGG-19, ResNet-34, ResNet-50, ResNet-152, DenseNet-121, DenseNet-161, DenseNet-201, and also models of the same class such as ProtoPNet, NP-ProtoPNet, Gen-ProtoPNet, and Ps-ProtoPNet

Advances and Technologies in High Voltage Power Systems Operation, Control, Protection and Security

The electrical demands in several countries around the world are increasing due to the huge energy requirements of prosperous economies and the human activities of modern life. In order to economically transfer electrical powers from the generation side to the demand side, these powers need to be transferred at high-voltage levels through suitable transmission systems and power substations. To this end, high-voltage transmission systems and power substations are in demand. Actually, they are at the heart of interconnected power systems, in which any faults might lead to unsuitable consequences, abnormal operation situations, security issues, and even power cuts and blackouts. In order to cope with the ever-increasing operation and control complexity and security in interconnected high-voltage power systems, new architectures, concepts, algorithms, and procedures are essential. This book aims to encourage researchers to address the technical issues and research gaps in high-voltage transmission systems and power substations in modern energy systems

Modelling of a protective scheme for AC 330 kV transmission line in Nigeria

Transmission lines play a vital role in the reliable and efficient delivery of electrical power over long distances, and these lines are affected by faults that occur due to lightning strikes, equipment failures, human, animal or vegetation interference, environmental factors, ageing equipment, voltage sag or grid faults adverse effects on the line. Therefore, protecting these transmission lines becomes crucial with the increasing demand for electricity and the need to ensure grid stability. The modelling process involves the development of a comprehensive protection scheme utilising modern technologies and advanced algorithms. The protection scheme encompasses various elements, including fault detection, fault classification, fault location, and fault clearance. It incorporates intelligent devices, such as protective relays and communication systems, to enable rapid and accurate fault identification and isolation. First, a 330 kV, 500 km three-phase Delta transmission line is modelled using MATLAB/SIMULINK. A section of the Delta network in Delta State Nigeria was used since the entire Nigeria 330 kV network is large. Faulty current and voltage data were generated for training using the CatBoost, 93340 data sizes comprising fault data from three-phase current and voltage extracted from the Delta transmission line model in Nigeria were designed, and twelve fault conditions were used. The CatBoost classifier was employed to classify the faults after different machine language algorithm was used to train the same data with other parameters. The trainer achieved the best accuracy of 99.54%, with an error of 0.46%, at 748 iterations out of 1000 compared to GBoost, XBoost and other classification techniques. Second, the Artificial Neural Network technique was used to train this data, and an accuracy of 100% was attained for fault detection and about 99.5% for fault localisation at different distances with 0.0017 microseconds of detection and an average error of 0% to 0.5%. This model performs better than Support Vector Machine and Principal Component Analysis with a higher fault detection time. The effect of noise signal on the ANN model was studied, and the discrete wavelet technique was used to de-noise the signal for better performance and to enhance the model’s accuracy during transient. Third, the wavelet transforms as a data extraction model to detect the threshold value of current and voltage and the coordination time for the backup relay to trip if the primary relay does not operate or clear the fault on time. The difference between the proposed model and the model without the threshold value was analysed. The simulated result shows that the trip time of the two relays demonstrates a fast and precise trip time of 60% to 99.87% compared to other techniques used without the threshold values. The proposed model can eliminate the trial-and-error in programming the instantaneous overcurrent relay setting for optimal performance. Fourth, the PSO-PID controller algorithm was used to moderate the load frequency of the transmission network. Due to the instability between the generation and distribution, there is always a switch in the stability of the transmission or load frequency; therefore, the PSO-PID algorithm was used to stabilise the Delta power station as a pilot survey from the Nigerian transmission network. Also, a hybrid system with five types of generation and two load centres was used in this model. It has been shown that the proposed control algorithm is effective and improves system performance significantly. As a result, the suggested PSO-PID controller is recommended for producing high-quality, dependable electricity. Moreover, the PSO-PID algorithm produces 0.00 seconds settling time and 0.0005757 ITAE. It’s essential to carefully consider potential drawbacks like complexity and computational overhead, sensitivity to algorithm parameters, potential parameter convergence and limited interpretability and assess their impact on the specific LFC application before implementing a PSO-PID controller in a power system. When implemented with the model in this research, the Delta transmission line network will reduce the excessive fault that occurs in the transmission line and improve the energy efficiency of the entire network when replicated with the Nigerian network. Generally, for the effective design and implementation of the protection scheme of the 330 kV transmission line, the fault must be detected and classified, and the exact location of the fault must be ascertained before the relay protection and load frequency control will be applied for effective fault management and control system

Industrial Applications: New Solutions for the New Era

This book reprints articles from the Special Issue "Industrial Applications: New Solutions for the New Age" published online in the open-access journal Machines (ISSN 2075-1702). This book consists of twelve published articles. This special edition belongs to the "Mechatronic and Intelligent Machines" section

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed