Abstract: With the development of the more and all electrical aircraft, onboard electrical equipment testing and load management has become more and more important. However, it is better to have fewer sensors set for onboard diagnosis. The Non-Intrusive Load Monitoring (NILM) method does not need to enter the internal load, and can accurately identify the load data by only detecting the bus load parameters. The steady-state current harmonic parameters are selected as the load signature, and the actual current waveform of the electrical equipment on the AC main bus of a certain type of aircraft is collected. The 1 st-19th harmonic contents are extracted to establish the feature database. The load of typical aircraft electrical equipment is identified by using the Generalized Regression Neural Network (GRNN) algorithm. The number of the samples and value of spread parameter are set appropriately to improve identification accuracy. Experimental results show that the GRNN algorithm is more accurate than the BP neural network algorithm and SVM algorithm in identification of electrical equipment on the bus, and is more applicable for management and monitoring of the aircraft electrical system due to better calculation speed. Application of the non-intrusive load monitoring method for analysis of the aircraft electrical power system can provide effective reference for management and fault diagnosis and prediction of aircraft electrical equipment.

Key words: aircraft electrical equipment, non-intrusive load monitoring, GRNN, steady-state current harmonic, load recognition