Extraction and selection of statistical harmonics features for electrical appliances identification using k-NN classifier combined with voting rules method

Authors : Fateh GHAZALI, Abdenour HACINEGHARBI, Philippe RAVIER, Tayeb MOHAMADI

Pages : 2980-2997

View : 22 | Download : 12

Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :In this paper, we propose a novel framework for electrical appliances identification using statistical harmonic features of current signals and the use of the k-NN classifier combined with a voting rule strategy. Harmonic coefficients are computed over time using short-time Fourier series of the current signals. From these sequences of coefficients, the mean, standard deviation, skewness, and kurtosis are computed, which provide the statistical harmonic features. This framework has three novelties: insert ignore into journalissuearticles values(i); selecting the best combination of statistical measures in the sense of classification rate insert ignore into journalissuearticles values(CR);; insert ignore into journalissuearticles values(ii); combining the k-NN classifier with the voting rule method in order to search for the best number of voting vectors; and insert ignore into journalissuearticles values(iii); selecting relevant features for the task of appliances identification by using one of the relevant feature selection algorithms based on mutual information. Results evaluated on the Plaid dataset clearly show that the mean and standard deviation statistics combination gives the best CR of 92% with 500 features and gives the minimal computing time compared to the system based on HMM models. Moreover, combining the k-NN classifier with the voting rule using the above features increases the CR up to 95%. Using this combination, the results also show that an increase of the training dataset size further improves identification performance results in terms of precision, sensitivity, and F-score. A feature selection procedure based on joint mutual information strategy shows that using a selected subset of five features is sufficient, giving similar CR results to those obtained using the total number of features, whatever the training dataset size.
Keywords : Electrical appliance identification, harmonics analysis, short time Fourier series, statistical features extraction, k nearest neighbors, voting rules method, filters features selection, mutual information