105 research outputs found

    Evaluation of Flashover Voltage Levels of Contaminated Hydrophobic Polymer Insulators Using Regression Trees, Neural Networks, and Adaptive Neuro-Fuzzy

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    Polluted insulators at high voltages has acquired considerable importance with the rise of voltage transmission lines. The contamination may lead to flashover voltage. As a result, flashover voltage could lead to service outage and affects negatively the reliability of the power system. This paper presents a dynamic model of ac 50Hz flashover voltages of polluted hydrophobic polymer insulators. The models are constructed using the regression tree method, artificial neural network (ANN), and adaptive neuro-fuzzy (ANFIS). For this purpose, more than 2000 different experimental testing conditions were used to generate a training set. The study of the ac flashover voltages depends on silicone rubber (SiR) percentage content in ethylene propylene diene monomer (EPDM) rubber. Besides, water conductivity (μS/cm), number of droplets on the surface, and volume of water droplet (ml) are considered. The regression tree model is obtained and the performance of the proposed system with other intelligence methods is compa ed. It can be concluded that the performance of the least squares regression tree model outperforms the other intelligence methods, which gives the proposed model better generalization ability

    INVESTIGATION OF LEAKAGE CURRENT OF INSULATOR USING ARTIFICIAL NEURAL NETWORK

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    In order to improve the reliability of power transmission lines, one of the key issues is to reduce the hazard of contamination flashovers. At the present time, the most efficient way is to clean (or replace) the heavily polluted insulators. This study laboratory based tests were carried out on the model under ac voltage at different pollution levels. A new model based on artificial neural network has been developed to predict flashover from the analysis of leakage current. The input variable to the artificial neural network are mean (Imean), maximum (Imax), and standard deviation (Is) of leakage current extracted along with the input voltage (V) and relative humidity (RH). The target obtained was used to evaluate the performance of the neural network model. The comparison of the simulated and actual (measured) results demonstrates that the ESDD prediction model from the stage characteristics

    Salt Contamination Calculation in Insulators During Monsoon Using Artificial Neural Network

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    Control framework consistent quality depends mostly on the natural and climate conditions which cause flashover on contaminated protectors. Such flashover prompts framework blackouts. Close to the beach front zones the salt pollution can be quickly based on the surface of the shields and frame a directing layer by retaining wet from the fog. This layer at times prompts flashover. Investigation of defilement of separator under marine contamination is the point of this examination, and the impacts of different meteorological elements on the disease seriousness have been researched altogether. In the present paper, an endeavour has been made to gauge the contamination severity under different climate conditions amid the stormy season utilising Artificial Neural Organize. The anomaly determination issue has been considered in this work to take out few exceedingly scattered exploratory information. The connection between ESDD with temperature T, stickiness H, weight P, precipitation R and wind speed WV has been created utilising ANN as a function estimator

    Sensoring Leakage Current to Predict Pollution Levels to Improve Transmission Line Model via ANN

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    Pollution insulator is a serious threat to the safety operations of electric power systems. Leakage current detection is widely employed in transmission line insulators to assess pollution levels. This paper presents the prediction of pollution levels on insulators based on simulated leakage current and voltage in a transmission tower.The simulation parameters are based on improved transmission line model with leakage current resistance insertion between buses. Artificial neural network (ANN) is employed to predict the level of pollution with different locations of simulated leakage current and voltage between two buses. With a sufficient number of training, the test results showed a significant potential for pollution level prediction with more than 95% Correct Classification Rate (CCR) and output of the ANN showed high agreement with Simulink results

    Use of wavelet transform to analyze leakage current of silicone rubber insulators under polluted conditions

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

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    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

    Prediction of polluted insulator based on leakage current resistance insertion performance of short and medium transmission line model

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    The main objective of the transmission lines is to deliver power from the generator to the customers, with less losses and without any interruptions. however, pollution sources are increasing around the world, which are affecting one of the most important components of a power line, namely, the high voltage outdoor insulators. the accumulation of pollution on the surface of the insulator can affect its physical properties and create leakage current resistance. under suitable conditions, this resistance will lead to leakage current on the surface of the insulator. in previous studies, leakage current measurement on the insulator surface was ignored because it is negligible. however, increasing pollution levels and the large number of transmission line insulators should take into account the effect of leakage current resistance in the transmission line model. in this thesis, an improved model is introduced to examine the effect of leakage current resistance on the parameters of the transmission line, the amount of additional active power losses, voltage drop and increased real power generation in power networks for both short and medium transmission lines. three levels of leakage resistance (high, medium, and low) that represent the three levels of pollution are incorporated into the transmission line model through a series of delta to star and star to delta conversion using a two-port network concept. then, by inserting the leakage current resistance, a simulation model was used to measure leakage current and voltage of the leakage current resistance. a simulation sensor is used to predict the level of pollution on the insulator and the location of highly polluted insulators using artificial neural network. this study was able to determine the changes in each parameter and the effects of these changes on the active power losses and voltage drop in three different systems. the application of the improved model have shown an increase in detection of power losses by 25.63% in high pollution conditions at the insulators in all short and medium transmission lines. thus, to compensate for these high losses, the system needs to increase real power generation by 0.61% when compared with during normal conditions. the prediction results by the simulation model for the 5- bus system clearly demonstrated that the overall correct classification rates for the predicted pollution levels were very high at 97.67% and 98.03%, for both short and medium models, respectively. meanwhile, the correct classification rate for the predicted locations of highly polluted insulators is 100% for both short and medium models. the results obtained in this study offer accurate information for polluted transmission line insulators, which could be used for maintenance and calculation of power loss for polluted insulators, in order to keep the power system in a reliable state

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

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    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/
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