Prediction method of leakage current of insulators on the transmission line based on BP neural network

Gao, Song; Jia, Yuxi; Bi, Xiaotian; Cao, Bin; Xu, Long; Yang, Daiming; Wang, Liming

doi:10.1109/cieec.2018.8745839

Cited by 7 publications

(9 citation statements)

References 5 publications

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“…From a literature survey, it is evident that insulator contamination is related to weather parameters such as temperature, relative humidity, pressure, wind speed, and ultraviolet [13][14][15][16]. Since the leakage current on the surface of the insulator is affected by the material, surface contamination, and surrounding environment, as well as its nonlinear characteristics, artificial intelligence algorithms such as machine learning technology have been applied to analyze the leakage current or estimate contamination on the surface of insulators in some related studies [10,11,13,[17][18][19][20][21][22]. For instance, artificial neural networks (ANNs) have been used to build a leakage current model [18,19], support vector machines (SVMs) to evaluate contamination degree [20,21], and random forests to predict equivalent salt deposit density (ESDD) based on parameters such as pollution and weather [22].…”

Section: Leakage Current Rangementioning

confidence: 99%

Real-Time Salt Contamination Monitoring System and Method for Transmission Line Insulator Based on Artificial Intelligence

Lin,

Kuo

2024

Applied Sciences

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Insulators on overhead power lines have long been exposed to the outdoors and are susceptible to pollution and salt contamination. Due to factors such as wind and gravity, pollution in the atmosphere gradually deposits on the surface of the insulator. In humid and windy conditions, conductive pollutants begin to dissolve in the water on the surface of the insulator, increasing the leakage current and affecting insulation performance. This study mainly uses a data acquisition system to measure the leakage current of the insulator and weather parameters (including temperature, relative humidity, pressure, wind speed, and ultraviolet) around the insulator. Artificial intelligence is then applied to establish a prediction model for leakage current based on weather parameters. The established model accurately predicts insulator leakage current through weather parameters. In order to observe the real-time status of the insulator, this study establishes a monitoring platform that integrates the predicted leakage current with weather parameters. It allows users or maintenance personnel to connect to the server through the network to observe the predicted results and weather parameters. The results can establish a real-time salt contamination monitoring system for insulators on transmission lines, enabling operation and maintenance personnel to understand the actual insulation situation of the insulator in real-time. This can not only prevent power outages due to salt contamination or pollution but also reduce the workload for maintenance personnel. Moreover, the maintenance strategy is upgraded from time-base maintenance to condition-base maintenance, significantly improving the efficiency of operation and maintenance for power lines.

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Section: Leakage Current Rangementioning

confidence: 99%

Real-Time Salt Contamination Monitoring System and Method for Transmission Line Insulator Based on Artificial Intelligence

Lin,

Kuo

2024

Applied Sciences

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“…This area is 3.95km from the coast and 1,47km from the river, which is seriously affected by strong wind and salt fog pollution. These severe environmental conditions polluted an outdoor insulator heavily and caused the leakage/discharge current on insulators [11], [24], [25]. Therefore, in this project, the weather parameters, which are the temperature, humidity, dew point, rainfall, wind speed, wind direction, air pressure, and solar illuminance, are collected every hour at the Liuqing area.…”

Section: A Collecting the Weather Parametersmentioning

confidence: 99%

“…Zhicheng et al proved the strong relationship and correlation between leakage current and weather parameters, such as altitude and humidity [19]. Gao et al utilized quarterly the backpropagation neural network to predict the leakage current of the insulator [11]. Pinotti and Meyer developed a mathematic model based on non-linear regression methodology to predict the leakage current of 25kV insulators [13].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Leakage Current Classification of 15kV and 25kV Distribution Insulators Based on Bidirectional Long Short-Term Memory Networks With Deep Learning Machine

2022

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This paper presents an online monitoring system to classify the leakage/discharge current of the insulator in coastal sites using Bidirectional Long short-term memory (Bi-LSTM) models on a web-based service. The remote monitoring methodology uses environmental parameters to classify the peak level of leakage/discharge current of the 15kV and 25kV distribution insulators. The sequential weather data, the humidity, temperature, rainfall, dew point, solar illumination, wind speed, air pressure, and wind direction are automatically collected hourly in real-time and transferred to data servers. The hyperparameter optimization for the structure of Bi-LSTM is utilized through the grid search capability in a deep learning machine. The optimized design of Bi-LSTMs improves the performance and accuracy in predicting the leakage/discharge current classification for HDPE and SR of 15kV and 25kV insulators. Compared with persistent methodologies such as recurrent neural networks (RNN), long short-term memory (LSTM), and the gated recurrent unit (GRU), the Bi-LSTM has better performance and higher accuracy in predicting the leakage/discharge levels, which are utilized to evaluate the surface pollution of insulators. The optimized structure of the proposed Bi-LSTM model could achieve a maximum improvement of 49.529% error, 12.761% accuracy, 72.736% error, and 36.641% accuracy for training and validating data compared with other models. Moreover, web-based service is developed for maintenance staff to interact with all current and predicted status of insulators. This online leakage current classification has been installed in Mailiao Township in Taiwan and could establish a reasonable maintenance mechanism for 15kV and 25kV distribution insulators.INDEX TERMS Bidirectional long short-term memory, deep learning machine, gated recurrent unit, insulator leakage current classification, long short-term memory, online monitoring leakage current, recurrent neural network.

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“…e output delta for hidden layers is calculated in equation ( 9). e weight vectors are updated iteratively in equation (10) with the learning rate value. e BPNN could be used to predict the leakage current of the insulator when the training process is completed with different independent datasets.…”

Section: E Back Propagation Neuralmentioning

confidence: 99%

“…e insulator's condition could be classi ed by measuring the leakage current on the surface [1][2][3]. Many studies have been developed to predict the insulator leakage current by employing neural networks and meteorological data [4][5][6][7][8][9][10][11][12][13]. Zhicheng et al have proved the strong correlation between leakage current and other meteorological data, such as humidity and rainfall.…”

Section: Introductionmentioning

confidence: 99%

Insulator Leakage Current Prediction Using Hybrid of Particle Swarm Optimization and Gene Algorithm-Based Neural Network and Surface Spark Discharge Data

Thanh

Cho

2022

Computational Intelligence and Neuroscience

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This study proposes a new superior hybrid algorithm, which is the particle swarm optimization (PSO) and gene algorithm (GA)-based neural network to predict the leakage current of insulators. The developed algorithm was utilized for the online monitoring systems, which were completely installed on the 69 kV and 161 kV transmission towers in Taiwan. This hybrid algorithm utilizes the local meteorological data as input parameters combined with the extracted enhanced data: the percentage of spark discharge areas and the brightness change in the image of the discharge phenomenon. These data with a high correlation with the leakage current are utilized as input vectors to improve the accuracy and effectiveness of the developed hybrid model. The performance of the developed algorithm is compared with a traditional PSO-based neural network and backpropagation neural network (BPNN) to evaluate and analyze. The comparative simulation results prove the effectiveness of the combination of hybrid PSO-GA-based neural network and surface discharge data, which achieved a maximum improvement of 38.54% MSE, 10.62% MAPE, and 3.41% R square for 161 kV data and 39.28% MSE, 12.62% MAPE, and 1.61% R square for 69 kV data. Moreover, the data with enhanced inputs outperform the traditional data in most benchmark factors, improving the accuracy and effectiveness in defining the deteriorative insulators. The developed methodology with a noticeable improvement was utilized in the online monitoring system to reduce the operational and maintenance cost of transmission lines in Taiwan Power Company.

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Prediction method of leakage current of insulators on the transmission line based on BP neural network

Cited by 7 publications

References 5 publications

Real-Time Salt Contamination Monitoring System and Method for Transmission Line Insulator Based on Artificial Intelligence

Real-Time Salt Contamination Monitoring System and Method for Transmission Line Insulator Based on Artificial Intelligence

Real-Time Leakage Current Classification of 15kV and 25kV Distribution Insulators Based on Bidirectional Long Short-Term Memory Networks With Deep Learning Machine

Insulator Leakage Current Prediction Using Hybrid of Particle Swarm Optimization and Gene Algorithm-Based Neural Network and Surface Spark Discharge Data

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