Hybrid Wavelet Stacking Ensemble Model for Insulators Contamination Forecasting (original) (raw)
Related papers
Electrical Insulator Fault Forecasting Based on a Wavelet Neuro-Fuzzy System
Energies, 2020
The surface contamination of electrical insulators can increase the electrical conductivity of these components, which may lead to faults in the electrical power system. During inspections, ultrasound equipment is employed to detect defective insulators or those that may cause failures within a certain period. Assuming that the signal collected by the ultrasound device can be processed and used for both the detection of defective insulators and prediction of failures, this study starts by presenting an experimental procedure considering a contaminated insulator removed from the distribution line for data acquisition. Based on the obtained data set, an offline time series forecasting approach with an Adaptive Neuro-Fuzzy Inference System (ANFIS) was conducted. To improve the time series forecasting performance and to reduce the noise, Wavelet Packets Transform (WPT) was associated to the ANFIS model. Once the ANFIS model associated with WPT has distinct parameters to be adjusted, a c...
Applied Soft Computing, 2012
A hybrid model incorporating wavelet and radial basis function neural network is presented which is used to detect, identify and characterize the acoustic signals due to surface discharge activity and hence differentiate abnormal operating conditions from the normal ones. The tests were carried out on cleaned and polluted high voltage glass insulators by using surface tracking and erosion test procedure of international electrotechnical commission 60587. A laboratory experiment was conducted by preparing the prototypes of the discharges. This study suggests a feature extraction and classification algorithm for surface discharge classification, which when combined together reduced the dimensionality of the feature space to a manageable dimension, by "marrying" the wavelet to radial basis function neural network very high levels of classification are achieved. Wavelet signal treatment toolbox is used to recover the surface discharge acoustic signals by eliminating the noisy portion and to reduce the dimension of the feature input vector. A radial basis function neural network classifier was used to classify the surface discharge and assess the suitability of this feature vector in classification. This learning method is proved to be effective by applying the wavelet radial basis function neural network in the classification of surface discharge fault data set. The test results show that the proposed approach is efficient and reliable.
In the present work, tracking phenomenon in high density polyethylene material has been studied under AC voltage, with ammonium chloride as a contaminant. It is observed that the tracking time depends on both conductivity and flow rate of the contaminants. The surface conditions of the insulation structure such as normal conditions and surface discharges or tracking were characterized by using the leakage current measurement, utilizing the multi resolution signal decomposition technique. The process of identification of surface condition of the insulation structure was automated using Artificial Neural Network. #
Neural networks recognition of weak points in power systems, based on wavelet features
…, 2005
Early locating and identifying basic weak-points (sharp-edge corona, polluted-insulator "baby arcs" and loose contact arcing) in electrical power systems significantly decrease the imminent failure, outage time and supply interruption. We previously introduced a method for detecting the basic weakpoints based on sound/waveform patterns and frequency analysis of their ultrasonic emissions. However, nonstationary patterns of the basic weak-points' emitted signals and background noise frequently led to confusing discrimination. Therefore, this paper develops an effective pattern recognition scheme, employing wavelet feature extraction and Artificial Neural Network (ANN) classification, to identify the basic weak-points and two weakpoint combinations (polluted insulator stressed by a transmission line with a sharp-edge and multiple sharp-edges on the same line), based on their modulated ultrasonic emissions. Extensive testing proved that the proposed scheme achieved average recognition rate of 98% when tested using weak-points underneath 33-kV and 132-kV transmission lines with 2-second detected signals. Moreover, increasing the acquisition time (>30 seconds) and classifying the weakpoints based on majority voting over the ANN's responses of multiple (15) consecutive sections, consistently led to 100% successful recognition of the considered weak-points.
Contaminated insulators in polluted areas may lead to flashovers if they are not cleaned periodically. Flashover in most cases leads to lengthy service outages; hence it has a considerable impact on power system reliability. In this paper, a combined image processing artificial neural networks algorithm has been developed for the estimation of contamination level in high voltage insulators. Image processing has been used to extract needed features form images captured by digital cameras. The type of features which is considered is the “histogram based statistical feature” such as mean, variance, skewness, kurtosis, energy and normalized histogram error. On the other hand, using these features, a neural network has been successfully designed to correlate the insulator captured image and the contamination level. Testing of the developed algorithm showed a high successful rate in estimating the contamination levels of unseen insulators.
Evaluation of wavelet performance via an ANN-based electrical conductivity prediction model
The prediction of water quality parameters plays an important role in water resources and environmental systems. The use of electrical conductivity (EC) as a water quality indicator is one of the important parameters for estimating the amount of mineralization. This study describes the application of artificial neural network (ANN) and wavelet–neural network hybrid (WANN) models to predict the monthly EC of the Asi River at the Demirköprü gauging station, Turkey. In the proposed hybrid WANN model, the discrete wavelet transform (DWT) was linked to the ANN model for EC prediction using a feed-forward back propagation (FFBP) training algorithm. For this purpose, the original time series of monthly EC and discharge (Q) values were decomposed to several sub-time series by DWT, and these sub-time series were then presented to the ANN model as an input dataset to predict the monthly EC. Comparing the values predicted by the models indicated that the performance of the proposed WANN model was better than the conventional ANN model. The correlation of determination (R 2) were 0.949 and 0.381 for the WANN and ANN models, respectively. The results indicate that the peak EC values predicted by the WANN model are closer to the observed values, and this model simulates the hysteresis phenomena at an acceptable level as well.
Prediction Flashover Voltage on Polluted Porcelain Insulator Using ANN
Computers, Materials & Continua, 2021
This paper aims to assess the effect of dry band location of contaminated porcelain insulators under various flashover voltages due to humidity. Four locations of dry bands are proposed to be tested under different severity of contamination artificially produce using salt deposit density (SDD) sprayed on an insulator. Laboratory tests of polluted insulators under proposed scenarios have been conducted. The flashover voltage of clean insulators has been identified as a reference value to analyze the effect of contamination distribution and its severity. The dry band dimension has been taken into consideration in experimental tests. The flashover voltage has been predicted using an artificial neural network (ANN) technique based on the laboratory test data. The ANN approach is constructed with five input data (geometry the insulator and parameters of contamination) and flashover voltage as the output of the model. Results indicated that the pollution distribution based on the proposed scenario has a significant influence on the flashover voltage performances. Validation of the ANN model reveals that the relative error values between the experimental results and the prediction appeared to be within 5%. This indicates the significant efficiency of the ANN technique in predicting the flashover voltage insulator under test.
Individual component reliability can often be estimated from degradation signals. In this paper, we examine the utility of the wavelet transform in pre-processing degradation signals for on-line reliability estimation. Wavelet preprocessing facilitates examination of degradation signals in both the time-and frequency-domains, simultaneously. Neural networks are used for forecasting the degradation signals (or a transformation thereof) and estimating the likelihood that these signals would exceed a pre-determined critical plane representative of unit failure in the immediate future. This leads to an on-line estimate for individual unit reliability. The proposed method is applied for analyzing degradation signals collected from a vertical CNC drilling machine using drill-bits. The degradation signals, force and torque, were collected as the drillbits were destructively tested.
A New Hybrid Wavelet-Neural Network Approach for Forecasting Electricity
Energy Studies Review, 2020
This study investigates the performance of a novel neural network technique in the problem of price forecasting. To improve the prediction accuracy using each model’s unique features, this research proposes a hybrid approach that combines the -factor GARMA process, empirical wavelet transform and the local linear wavelet neural network (LLWNN) methods, to form the GARMA-WLLWNN process. In order to verify the validity of the model and the algorithm, the performance of the proposed model is evaluated using data from Polish electricity markets, and it is compared with the dual generalized long memory -factor GARMA-G-GARCH model and the individual WLLWNN. The empirical results demonstrated the proposed hybrid model can achieve a better predicting performance and prove that is the most suitable electricity market forecasting technique.
2013
Partial discharge (PD) pattern recognition has been applied for identifying the types of insulation defects in high voltage (HV) equipment. It can provide an effective means for condition assessment of the insulation system of HV equipment. This paper proposes a novel Bayesian neural network (BNN) and discrete wavelet transform (DWT) hybrid algorithm for PD pattern recognition. Laboratory experiments on a number of PD models have been conducted for evaluating the performance of the proposed algorithm. Index Terms-Bayesian neural network (BNN), discrete wavelet transform (DWT), Partial Discharge (PD), and pattern recognition. I.