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Feature Extraction-Based Hellinger Distance Algorithm for Nonintrusive Aging Load Identification in Residential Buildings

Though steady-state power features such as real power (P), reactive power (Q), and total voltage/current harmonic distortions (VTHD/ITHD) may contain sufficient information, adopting them directly for nonintrusive aging load monitoring (NIALM) identification process requires higher computational burden and larger memory. To effectively reduce the number of needed steady-state power features without degrading the performance of NIALM, a Hellinger distance (HD) algorithm is proposed and presented in this paper. To minimize the training time and improve recognition accuracy, a particle swarm optimization (PSO) is adopted in this paper to optimize parameters of training algorithm in artificial neural networks (ANNs). The proposed methods are used to analyze and identify the load characteristics of aging loads in residential buildings. The results show that the accuracy can be improved by using the proposed method.