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Deep Learning has ignited great international attention in modern artificial intelligence techniques. The method has been widely applied in many power system applications and produced promising results. A few attempts have been made to classify fault on transmission lines using various deep learning methods. However, a type of deep learning called...
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... model is used as a simulation platform to simulate the transmission line using various types of fault conditions which is influenced by fault location, fault resistance and inception angle. Figure 2 shows the single ended transmission line model used in this study. The model consists of two three phase sources and two three phase section lines with a 400-kV voltage supply and 100 km distance between the two different sources. ...
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... The effects of power outages can be reduced by quickly and accurately identifying faults to enable service restoration. Several studies have addressed electrical fault location [1]- [4]. Electrical faults are dangerous for the proper operation of any electrical system because they disturb regular system operation and create instability hazards. ...
This study proposes an intelligent protection relay design that uses artificial neural networks to secure electrical parts in power infrastructure from different faults. Electrical transformer and transmission lines are protected using intelligent differential and distance relay, respectively. Faults are categorized, and their locations are pinpointed using three-phase current values and zero-current characteristics to differentiate between non-earth and ground faults. The optimal aspects of the artificial neural network were chosen for optimal results with the least possible error. Levenberg-Marquardt was established as the ideal training technique for the suggested system comprising the differential relay. Levenberg-Marquardt was the optimal training technique for the proposed framework consisting of the differential relay. Fault detection and categorization were performed using 20 and 50 hidden layers, and the corresponding error rates were 9.9873e-3 and 1.1953e-29. In the context of fault detection by the distance relay, the hidden layer neuron counts were 400, 250, and 300 for fault detection, categorization, and location; training error rates were 7.8761e-2, 1.2063e-6, and 1.1616e-26, respectively.
... Omar et. al. [2] have classified faults in the transmission lines using LSTM networks. Power transmission lines are constantly affected by natural conditions such as rain, wind, lightning, sun, and unnatural conditions such as fires. ...
Overheating of the power transmission lines can cause faults, leading to interruptions on the power distribution system. This temperature rise may cause more significant failures in the medium and long term. Symptoms of these faults occur early in the transmission line with small temperature increases or changes in current/voltage values. Current/voltage changes in very high-, high-and medium-voltage lines may not be detected in cases caused by uninterrupted failures. Electrical, insulation, and mechanical failures occurring in the power transmission line cause an increase in the temperature. Although the temperature change occurring on the line is generally neglected within the tolerance values, it may lead to significant failures and cause material and labour losses. In this study, the temperature of a power transmission line was measured by a line-inspection robot, transmitted to a remote control center, and time, location, and temperature changes were recorded along the line. The temperature profile of the line was created using these data and made it possible to predict any faults that may occur in the future. It helps to detect situations such as overload, environmental factors, and physical wear and take precautions. It is the first example in the literature where the temperature of the transmission line is measured and mapped instantly by a robot. In the experiment, an artificially heated part of the line is detected and shown in a temperature map.
... For example, convolutional neural organizations, which are a unique kind of feed-forward neural organizations with two measurement organizations, have demonstrated colossal exactness in groups pictures through nearby open fields, shared loads, pooling, from straightforward transcribed digit acknowledgment to more perplexing face acknowledgment. In the demonstrating of consecutive examples, like, phoneme acknowledgment programmed discourse acknowledgment, discourse combination, discourse translation, chatbot, and numerous others, RNN or the more specific sort of RNN and the long-short term memory (LSTM) networks [5], [6] have demonstrated to be superior to large numbers of the conventional methodologies. This paper presents a similar investigation of three strategies to take care of the issue of Amazigh grammatical feature (POS) labeling. ...
Natural language processing (NLP) is a part of artificial intelligence that dissects, comprehends, and changes common dialects with computers in composed and spoken settings. At that point in scripts. Grammatical features part-of-speech (POS) allow marking the word as per its statement. We find in the literature that POS is used in a few dialects, in particular: French and English. This paper investigates the attention-based long short-term memory (LSTM) networks and simple recurrent neural network (RNN) in Tifinagh POS tagging when it is compared to conditional random fields (CRF) and decision tree. The attractiveness of LSTM networks is their strength in modeling long-distance dependencies. The experiment results show that LSTM networks perform better than RNN, CRF and decision tree that has a near performance.
... The ML models are developed for predicting the failure of heart utilizing grey wolf optimization (GWO) for the feature selection [27]. The long short-term memory (LSTM) network is used for fault classification of the transmission line [28]. The pattern recognition method between normal and autistic affected children was developed using deep learning algorithm [29]. ...
... We use the dataset of Dow Jones Index (DJIA) and Qualcomm. The dataset is extracted from Yahoo finance website and spans for a period of 30 years (1992-01-02 to 2020- [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]. The data is split into train, test and COVID-19. ...
Trading strategies to maximize profits by tracking and responding to dynamic stock market variations is a complex task. This paper proposes to use a multilayer perceptron method (a part of artificial neural networks (ANNs)), that can be used to deploy deep reinforcement strategies to learn the process of predicting and analyzing the stock market products with the aim to maximize profit making. We trained a deep reinforcement agent using the four algorithms: proximal policy optimization (PPO), deep Q-learning (DQN), deep deterministic policy gradient (DDPG) method, and advantage actor critic (A2C). The proposed system, comprising these algorithms, is tested using real time stock data of two products: Dow Jones (DJIA-index), and Qualcomm (shares). The performance of the agent linked to the individual algorithms was evaluated, compared and analyzed using Sharpe ratio, Sortino ratio, Skew and Kurtosis, thus leading to the most effective algorithm being chosen. Based on the parameter values, the algorithm that maximizes profit making for the respective financial product was determined. We also extended the same approach to study and ascertain the predictive performance of the algorithms on trading under highly volatile scenario, such as the pandemic coronavirus disease 2019 (COVID-19).
Accurate fault detection and classification help to analyze fault causes and quickly restore faulty phases. Deep learning can automatically extract fault features and identify fault types from the original three-phase voltage and current signals. However, this still imposes challenges such as recognition accuracy and computational complexity. More importantly, high level fault features cannot be extracted in the one-dimensional time series. This paper presents a robust fault classification method based on SA-MobileNetV3 for transmission systems. Considering that the SE (Squeeze-and-Excitation) attention module cannot aggregate the spatial dimension information on the channel, SA (shuffle attention) module is introduced into MobileNetV3, which can effectively fuse the importance of pixels in different channels and in different locations at the same channel. Also, transforming the time series three-phase voltage and current signals into two-dimensional images based on CWT (continuous wavelet transform) makes the proposed method be similar to image recognition, which can mine high level fault features and classify the faults visually. To verify the effectiveness of the method, a 735kV transmission line model is built for data generation through Simulink. Various kinds of fault conditions and factors are considered to verify the adaptability and generalizability. Simulation results show that the method can quickly and accurately identify 11 types of faults, and the accuracy rate is as high as 99.90%. A comparison between the proposed method and other existing techniques shows the superiority of the proposed SA- MobileNetV3, and better anti-noise performance makes it more suitable for real fault signals taken on-site.
The application of spacer is more and more widely, which will greatly affect the safe operation of transmission line when it is damaged. In this paper, ANSYS finite element simulation software is used to simulate the centripetal force of FJZ-445-27-80 spacer in case of short circuit. The stress status and weak points of each component of the spacer in the case of short circuit fault are defined. The simulation results show that: under the condition of short circuit, the displacement of the end of spacer clamp and the middle of frame changes the most; The maximum stress appears in the root and middle part of the connection between the spacer clamp body and the frame. The simulation results can provide a reference for the optimal design of the spacer, and provide a theoretical basis for the popularization and application of the spacer in EHV transmission lines.
KeywordsTransmission lineSpacerClampFrameworkCentripetal force
