A Non-intrusive Load Monitoring System Using an Embedded System for Applications to Unbalanced Residential Distribution Systems

AbstractA Non-intrusive load monitoring (NILM) system is an energy demand monitoring and load identification system that only uses voltage and current sensors that are installed at the power service entrance of an electric system. The system is better than traditional intrusive monitoring systems because it is able to reduce the cost of sensors and installations. In this study, a real single-phase three-wire unbalanced 220V/110V distribution system model of a residential building is designed and implemented, and some non-intrusive techniques are executed in the Intel Atom Embedded System and a LabView program. To enhance the performance, the paper proposes using Particle Swarm Optimization (PSO) algorithm to optimize the parameters of a Back-propagation Artificial Neural Network (BP-ANN) for training steady-state power signatures such as real and reactive power (PQ). In this paper, the NILM system can identify some major appliances correctly in an unbalanced 220V/110V distribution system of a residential building. The real test identification accuracy can reach 100%