An Event Detection Approach Based on Improved CUSUM Algorithm and Kalman Filter

Non-intrusive load monitoring (NILM) is an intelligent perception technology of electricity consumption information on the consumer side. Event detection, the fundamental and indispensable step of the NILM framework, has a direct impact on the accuracy of the results of load decomposition. This paper presents a novel NILM event detection approach based on Kalman filter and improved cumulative sum (CUSUM) algorithm. Firstly, this paper use Kalman filter to handle acquired data for sliding windows to make the power curve more smooth and suitable for next event detection step. An adaptive factor is introduced in the traditional CUSUM algorithm to improve the detection accuracy. The experiment results show that the proposed approach can not only effectively detect various input and cutoff events with different power level but also suits for the detection of the long transient events.