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: (i) selecting the best combination of statistical measures in the sense of classification rate (CR); (ii) combining the k-NN classifier with the voting rule method in order to search for the best number of voting vectors; and (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.
Highlights
Balancing production and electricity consumption is a daily principal concern of electricity suppliers
We propose to use statistical harmonics vectors of electric current and to evaluate these descriptors on k-nearest neighbors (k-NN) classifiers combined with the voting rules method
Our first key ideas are the use of statistical features of harmonics and the application of the k-NN classifier combined with the voting rule method
Summary
Balancing production and electricity consumption is a daily principal concern of electricity suppliers. The implementation of feature extraction methods from the current measurements for electrical appliance identification was widely discussed in the literature [1,2,3,4]. Many models and mathematical methods can be developed for the identification system, such as HMM [15], support vector machines (SVMs) [16], artificial neural networks (ANNs) [17], and k-NN [18] These models require information extracted from the current signals that are considered as relevant descriptors of the appliance. We have applied a filter selection algorithm with the joint mutual information (JMI) criterion, which uses MI estimation as a relevance measure of the features. We selected the JMI strategy because of its good trade-off in terms of accuracy, stability, and flexibility with small data samples [23]
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