Optimized Load Identification Using Total Harmonic Distortion and Artificial Bee Colony Algorithm

Electrical energy plays a crucial role in modern innovation and is essential in today's world. Saving energy is a key priority, making efficient energy management in buildings are vital for reducing electricity usage. To achieve this, it's important to provide proprietors with clear information about the energy consumption of individual appliances. This requires breaking down the total energy consumption from a house's main circuits into separate devices. Identifying electrical loads helps to determine the type of appliance, its operating conditions, and its electricity usage. This article presents an improved method to identify devices within a network of interconnected household appliances. The suggested approach consists of two primary stages. First, instantaneous current signal is acquired from the simulated combined load. Using sample shifting technique (SST), load feature as total harmonic distortion (THD) of the combined load is calculated in Matlab environment. In second step, the individual loads connected to the power line are identified by the Artificial Bee Colony (ABC) optimization algorithm in Python environment. Various power metrices are determined to validate the identification accuracy. Simulations in Matlab environment are conducted to authenticate the proposed method.