Artificial Neural Network based Fault Detection System for 11 kV Transmission Line

Pandey, Aditya; Gadekar, Paritosh; Khadse, Chetan B.

doi:10.1109/icaect49130.2021.9392433

Cited by 12 publications

(8 citation statements)

References 9 publications

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“…In recent years, many studies and research have focused on the artificial intelligent technique such as ANN , fuzzy logic in Determine the locations of faults and their accuracy in power T.L, in addition to distinguishing between the types of faults in these networks. Aditya, Pandey et al [7] proposes a fault recognition system based on ANN for an 11 kV transmission line ,the system aims to detect and monitor different types of faults, including line to line, double line to ground, line to ground, triple line to ground faults and triple line, a MATLAB simulink model is developed to create fault conditions and generate data set for training the neural network ,the neural network is trained using current datasets under normal and fault conditions, and a target matrix is prepared based on the time duration of the fault in the current signal.…”

Section: Literature Reviewmentioning

confidence: 99%

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Detection and identification of fault in transmission lines based on ANN

Abdullah,

Ibrahim,

Ibrahim

2024

Diagnostyka

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Along with power transmission lines' efficiency, another crucial factor in electrical power transmission networks is reliability, which guarantees power transmission stability. One of the crucial and essential tasks for maintaining the continuity and stability of power transmission in transmission networks Capacity without any significant failures is identifying errors and malfunctions in power transmission lines as soon as possible. The goal of this article is to develop and apply ANN technology to overcome the obstacles faced by the electrical power transmission network. In order for the ANN to learn useful patterns and features from raw current measurements, pre-processing and feature extraction techniques are used during the training process. Real-time applications can benefit from the ANN's architecture, which is optimized for high accuracy, quick response times, and scalability. To validate the performance of the ANN-based fault detection system, extensive simulations are conducted using data from different transmission line scenarios, including various fault types that short-circuit. The results demonstrate the capability of the ANN model to accurately detect and classify faults, as well as disconnect the power grid after detect any fault. The results showed the accuracy and high speed of the proposed method using a neural network compared to traditional methods.

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Section: Literature Reviewmentioning

confidence: 99%

“…For these reasons, finding and detecting the faults are crucial and essential for customers as well as electrical networks. Electrical network protection stages play a critical role in anticipating and promptly resolving network failures [3].…”

Section: Introductionmentioning

confidence: 99%

Detection and identification of fault in transmission lines based on ANN

Abdullah,

Ibrahim,

Ibrahim

2024

Diagnostyka

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“…This means that comparable inputs produce diverse outputs. The error was found by starting from the last step at each point and iteration and sending the error backwards [32,33]. The optimal weight value (𝑤 𝑖𝑗 ) of the node is computed using the algorithm.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Fault Classification in a DG Connected Power System using Artificial Neural Network

Bhuyan

Panigrahi

Pati

2022

IJEEI

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Distributed generation is playing a major role in the power system to meet the growing load demand. Integration of Distributed Generator (DG) to grid leads to various issues of protection and control of power system structure. From the different fault issues that occur in a distributed generator integrated power system, classification of fault remains as one of the most vital issues even after years of in-depth research. Because researchers are attempting to detect and diagnose these faults as soon as possible in order to avoid financial losses, this work aims to investigate the sort of fault that happened in the hybrid system. This paper proposed artificial neural network-based approaches for fault disturbances in a microgrid made up of wind turbine generators, fuel cells and diesel generator. The voltage signal is retrieved at the point of common coupling (PCC). The extracted data are used for training and testing purposes. Artificial neural network technique is utilized for the classification of faults in the simulated model. Furthermore, performance indices (PIs) such as standard deviation and skewness are calculated for reduction of data size and better accuracy. Both the fault and parameters are varied to check the usefulness of the proposed method. Finally, the results are discussed and compared with different DG penetration.

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“…Moreover, due to increased computational power, many researchers have focused on deep learning techniques in various areas such as image analytics, video analytics, sequential modeling, and data analysis using data-driven models [5]. In various fields, artificial neural networks (ANNs) are also used for detection wherein the data used for analytics must be preprocessed in order to get faithful results [6,7]. Raw data contains missing information and noise which may hamper the end results of any applied techniques.…”

Section: Introductionmentioning

confidence: 99%

Novel MIA-LSTM Deep Learning Hybrid Model with Data Preprocessing for Forecasting of PM2.5

Narkhede

Hiwale²,

Tidke³

et al. 2023

Algorithms

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Day by day pollution in cities is increasing due to urbanization. One of the biggest challenges posed by the rapid migration of inhabitants into cities is increased air pollution. Sustainable Development Goal 11 indicates that 99 percent of the world’s urban population breathes polluted air. In such a trend of urbanization, predicting the concentrations of pollutants in advance is very important. Predictions of pollutants would help city administrations to take timely measures for ensuring Sustainable Development Goal 11. In data engineering, imputation and the removal of outliers are very important steps prior to forecasting the concentration of air pollutants. For pollution and meteorological data, missing values and outliers are critical problems that need to be addressed. This paper proposes a novel method called multiple iterative imputation using autoencoder-based long short-term memory (MIA-LSTM) which uses iterative imputation using an extra tree regressor as an estimator for the missing values in multivariate data followed by an LSTM autoencoder for the detection and removal of outliers present in the dataset. The preprocessed data were given to a multivariate LSTM for forecasting PM2.5 concentration. This paper also presents the effect of removing outliers and missing values from the dataset as well as the effect of imputing missing values in the process of forecasting the concentrations of air pollutants. The proposed method provides better results for forecasting with a root mean square error (RMSE) value of 9.8883. The obtained results were compared with the traditional gated recurrent unit (GRU), 1D convolutional neural network (CNN), and long short-term memory (LSTM) approaches for a dataset of the Aotizhonhxin area of Beijing in China. Similar results were observed for another two locations in China and one location in India. The results obtained show that imputation and outlier/anomaly removal improve the accuracy of air pollution forecasting.

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Artificial Neural Network based Fault Detection System for 11 kV Transmission Line

Cited by 12 publications

References 9 publications

Detection and identification of fault in transmission lines based on ANN

Detection and identification of fault in transmission lines based on ANN

Fault Classification in a DG Connected Power System using Artificial Neural Network

Novel MIA-LSTM Deep Learning Hybrid Model with Data Preprocessing for Forecasting of PM2.5

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