Regression technique for prediction of salt contamination severity on high voltage insulators

Almad, A.S.; Ahmad, H.; Salam, Md. Abdus; Sabri, Ahmad

doi:10.1109/ceidp.2000.885266

Cited by 9 publications

(12 citation statements)

References 12 publications

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“…References [4][5][6][7]9] show there is a close relationship between insulators ESDD and meteorological factors. Table 1 shows the comparisons between WNN combination forecasting model and LCF, MLR, BP and LSSVM.…”

Section: Resultsmentioning

confidence: 99%

A New Multi-method Combination Forecasting Model for ESDD Predicting

Shuai

Gong

2010

2010 Asia-Pacific Power and Energy Engineering Conference

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Equal Salt Deposit Density (ESDD) is a main factor to classify contamination severity and draw pollution distribution map. The precise ESDD forecasting plays an important role in the safety, economy and reliability of power system. To cope with the problems existing in the ESDD predicting by multivariate linear regression (MLR), back propagation (BP) neural network and least squares support vector machines (LSSVM), a nonlinear combination forecasting model based on wavelet neural network (WNN) for ESDD is proposed. The model is a WNN with three layers, whose input layer has three neurons and output layer has one neuron, namely, regarding the ESDD forecasting results of MLR, BP and LSSVM as the inputs of the model and the observed value as the output. In the interest of better reflection of the influence of each single forecasting model on ESDD and increase of the accuracy of ESDD prediction, Morlet wavelet is used to construct WNN, error backpropagation algorithm is adopted to train the network and genetic algorithm is used to determine the initials of the parameters. Simulation results show that the accuracy of the proposed combination ESDD forecasting model is higher than that of any single model and that of traditional linear combination forecasting (LCF) model. The model provides a new feasible way to increase the accuracy of pollution distribution map of power network.

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Section: Resultsmentioning

confidence: 99%

A New Multi-method Combination Forecasting Model for ESDD Predicting

Shuai

Gong

2010

2010 Asia-Pacific Power and Energy Engineering Conference

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“…The finding suggests that the surface area of insulator plays an important contributing factor for the elevation of ESDD. A relationship between the ESDD and other parameters involving in wind, speed, rainfall and surface conductivity have been developed using dimensional analysis to determine the degree of pollution level [4]. In addition, a mathematical model expressed in terms of the creepage distance of the insulator and SDD has been developed by using dimensional analysis [5] to determine the pollution level of insulator.…”

Section: Introductionmentioning

confidence: 99%

Measurement of pollution level of 66 kV transmission line insulators

Salam

Ang

Ong

et al. 2013

2013 Annual Report Conference on Electrical Insulation and Dielectric Phenomena

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The presence of unwanted constituent on insulator surface of transmission tower plays an important ignition property for the likelihood of occurrence of flashover. In this paper, the pollution level of two aged insulators namely the longrod silicon rubber and the cap-and-pin glass insulators are measured. The results from the measurement shows that the Equivalent Salt Deposit Density (ESDD) of the cap-and-pin glass insulator, particularly near the pin is found to be 0.169 mg/cm 2 while the composition over the cap surface is 0.136 mg/cm 2 . For the case of longrod silicon rubber insulator, the ESDD for both the pin and the cap are found to be 0.063 mg/cm 2 and 0.016 mg/cm 2 respectively. The outcomes from the ESDD measurements suggest that the cap-and-pin glass insulator string is severely contaminated with salt spray as compared to the longrod silicon rubber. Furthermore, a mathematical model of ESDD expressed in terms of salinity and temperature for both insulators is derived from multivariable regression method. The coefficients for both the cap-and-pin glass and longrod silicon rubber insulator are found to be 0.9984 and 0.9867 respectively.

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“…Recent years, researchers overseas and domestic have carried out many investigations on the ESDD prediction theories and methods, including traditional least squares, neural networks, et al [5][6][7][8][9]. References [5][6] adopted multiple linear regression technique based on least squares to forecast the ESDD on insulators influenced by multiple factors.…”

Section: Introductionmentioning

confidence: 99%

“…References [5][6] adopted multiple linear regression technique based on least squares to forecast the ESDD on insulators influenced by multiple factors. But the way belongs to a large-sample statistics which can easily deal with linear function, but difficultly nonlinear one.…”

Section: Introductionmentioning

confidence: 99%

“…Many researches [5][6][7][8][9] show there is a close relationship between ESDD on insulators and meteorological factors. In order to reflect the influences of meteorological factors on ESDD and improve the precision of ESDD prediction, the paper adopts the meteorological data (Wind Velocity, Air Pressure, Rainfall, Temperature and Relative Humidity) and ESDD data supplied by Optical Sensor System for the ESDD Monitoring of Transmission Equipment which was patented in Project Supported by Ministry of Science and Technology of China (NCSIE-2006-JKZX-174) 2005 to develop a pair of multi-input single-output, and uses WNN to establish an intelligent insulators ESDD prediction model.…”

Section: Introductionmentioning

confidence: 99%

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Insulators ESDD Predicting Based on Wavelet Neural Network

Shuai

2009

2009 International Workshop on Intelligent Systems and Applications

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Wavelet neural network (WNN) is a feed-forward neural network which is based on wavelet transform. The network overcomes the intrinsic shortcomings of artificial neural network, namely, slow learning speed, difficulty to determine rationally the network structure and existence of partial minimum points. Hence, WNN has more freedom degree and better adaptability than traditional multi-layer feed-forward neural network. In the interest of better reflection of the influence of meteorological factors on insulators equal salt density (ESDD) and increase of the accuracy of ESDD prediction ,the paper uses Morlet wavelet to construct WNN , adopts error backpropagation algorithm to train the network and applies the ESDD data and meteorological data of Qingshan District ,Wuhan, which were measured from April to June in 2005, and the same times in 2006 respectively, to model and forecast ESDD. The predicted results are very close to the measured ones which show the WNN model can effectively improve the speed and accuracy of the forecasting. Therefore, the model presented provides a doable thought for the computerization of pollution area map of power network. Keywords-wavelet neural network(WNN) wavelet transform equal salt deposit density (ESDD) insulators978-1-4244-3894-5/09/$25.00 ©2009 IEEE

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Regression technique for prediction of salt contamination severity on high voltage insulators

Cited by 9 publications

References 12 publications

A New Multi-method Combination Forecasting Model for ESDD Predicting

A New Multi-method Combination Forecasting Model for ESDD Predicting

Measurement of pollution level of 66 kV transmission line insulators

Insulators ESDD Predicting Based on Wavelet Neural Network

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