A Classification Method of Current Impulse Waveforms for Non-instrusive Load Monitoring

Focusing on the current impulse waveforms generated when the fixed or variable frequency air conditioners, vacuum cleaner, microwave oven and other electrical appliances are turned on, the characteristic parameters such as impulse amplitude, rising time, dropping amplitude, and falling time are defined, and a multi-feature Bayesian classification model is established. Considering that the classification algorithm works on the smart meter hardware, the computing and storage resources are limited, as a result that the model scale needs to be reduced as much as possible, a meant parameters Bayesian classification method is proposed. The current impulse waveform samples of different appliances are divided into several groups, and the mean value of the characteristic values of the samples is used as the parameters of the classification model. Under the same model capacity, the generalization error of the model is reduced. The simulation results show that the accuracy of Bayesian classifier constructed with meant value parameters is better than that of ordinary Bayesian classifier. In the laboratory, the impulse classification test is carried out on a single-phase smart meter, and the test results verify the feasibility of the proposed method.