Classification of power quality disturbances using wavelet transform and K-nearest neighbor classifier

This paper compares classification techniques to identify several power quality disturbances in a frame of smart metering design for smart grids with high penetration of PV systems. These techniques are: Linear Discriminant Analysis (LDA), Nearest Neighbor Method (kNN), Learning Vector Quantization (LVQ) and Support Vector Machine (SVM). For this purpose, fourteen power-quality features based in higher-order statistics are used to assist classification. Special attention is paid to the spectral kurtosis, whose nature enables measurement options related to the impulsiveness of the power quality events. The best technique of those compared is selected according to correlation and mistake rates. Results clearly reveal the potential capability of the methodology in classifying the single disturbances. Concretely, the SVM classifier obtained an average correlation rate of 99%. Hence, concluding that it is a robust classification method.978-1-4799-7800-7/15/$31.00 ©2015 IEEE

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“…The decision rule is then to choose the class that appears most frequently among the k-neighbors [14], [15].…”

Section: B Nearest Neighbor Methodsmentioning

confidence: 99%

Smart grids power quality analysis based in classification techniques and higher-order statistics: Proposal for photovoltaic systems

Palomares-Salas

Rosa

Agüera-Pérez

et al. 2015

2015 IEEE International Conference on Industrial Technology (ICIT)

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“…In this method, accuracy value has been taken as the objective function to optimise the hyper-parameters. However, the theoretical background of all the classification techniques has been given in [16, [22][23][24][25][26]. The detail classification performance of the proposed method using SVM classifier is presented in Table 4.…”

Section: Figure 10mentioning

confidence: 99%

Deep learning–based surface contamination severity prediction of metal oxide surge arrester in power system

Das

Dalai

Chatterjee

2021

IET Science Measure & Tech

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This paper presents an advanced technique based on cross-Stockwell transform (XST) and sparse autoencoder to predict the surface contamination severity of metal oxide surge arrester (MOSA) employing leakage current signal. Generally, MOSAs in power system network are exposed to different environmental conditions where its condition may degrade due to accumulation of pollutants, which may cause premature failure of it. Hence, system reliability can get affected. Therefore, monitoring the surface condition of MOSA is very important. In this proposed technique, MOSA leakage current signals of different surface contamination severity have been cross-correlated with leakage current recorded at the clean surface in joint time-frequency plane through XST, which is an extended version of ST. Thereafter, sparse autoencoder, a deep learning framework, has been applied to extract potential deep feature from leakage current-converted XST matrices. The extracted deep features have been classified through different classifiers. It has been observed that the proposed technique yields satisfactory accuracy regarding the estimation of surface contamination severity of MOSA. Therefore, the proposed method can be implemented to monitor the surface condition of MOSA, and it may be applied for topologically similar problems.

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“…The idea to use the Pareto fronts as a tool to select data in the forecasting process originated from the fact that similar, well-known and described in many articles [25][26][27] machine learning algorithm, k nearest neighbors (kNN), has been successfully applied to that task. This algorithm has been used and described in the literature both as a classification algorithm [28,29] and as a forecasting model. This algorithm is a powerful forecasting tool due to the combination of its simplicity and accuracy.…”

Section: The Idea Behind the Pareto Fronts Usagementioning

confidence: 99%

Usage of the Pareto Fronts as a Tool to Select Data in the Forecasting Process—A Short-Term Electric Energy Demand Forecasting Case

Sabat

Baczyński

2021

Energies

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Transmission, distribution, and micro-grid system operators are struggling with the increasing number of renewables and the changing nature of energy demand. This necessitates the use of prognostic methods based on ever shorter time series. This study depicted an attempt to develop an appropriate method by introducing a novel forecasting model based on the idea to use the Pareto fronts as a tool to select data in the forecasting process. The proposed model was implemented to forecast short-term electric energy demand in Poland using historical hourly demand values from Polish TSO. The study rather intended on implementing the range of different approaches—scenarios of Pareto fronts usage than on a complex evaluation of the obtained results. However, performance of proposed models was compared with a few benchmark forecasting models, including naïve approach, SARIMAX, kNN, and regression. For two scenarios, it has outperformed all other models by minimum 7.7%.

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Classification of power quality disturbances using wavelet transform and K-nearest neighbor classifier

Cited by 25 publications

References 9 publications

Smart grids power quality analysis based in classification techniques and higher-order statistics: Proposal for photovoltaic systems

Smart grids power quality analysis based in classification techniques and higher-order statistics: Proposal for photovoltaic systems

Deep learning–based surface contamination severity prediction of metal oxide surge arrester in power system

Usage of the Pareto Fronts as a Tool to Select Data in the Forecasting Process—A Short-Term Electric Energy Demand Forecasting Case

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