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.
[1] ANUBHAV D. Power plant design and performance analysis of a manned all-electric helicopter[J]. Journal of Propulsion and Power,2014,39(2):490-494.
[2] PATRICK W W. An overview of the more electrical aircraft[J]. Procceeding IMechE Part G:J Aerospace Engineering,2012,21(3):90-95.
[3] CUTTS S J. A collaborative approach to the more electric aircraft[C]//2002 International Conference on Power Electronics, Machines and Drives. Piscataway:IEEE Press, 2002:23-28.
[4] LIU Y,GENG G C,GAO S,et al. Non-intrusive ener gy use monitoring for a group of electrical appliances[J]. IEEE Transactions on Smart Grid,2018,9(4):3801-3810.
[5] CHEN Y Q,YUAN Y Y,ZHANG H,et al. Compari son of inspiratory effort,workload and cycling synchronization between non-invasive proportional-assist ventilation and pressure-support ventilation using different models of respiratory mechanics[J].Medical Science,2019,25(2):9047-9058.
[6] JIANG H S,DONG S J,ZHANG H N,et al.Optimization on conventional and electric air-cycle refrigeration systems of aircraft:A short-cut method and analysis[J].Chinese Journal of Aeronautics,2020,33(7):1877-1888.
[7] 余贻鑫,刘博,栾文鹏.非侵入式居民电力负荷监测与分解技术[J].南方电网技术,2013,7(4):1-5. YU Y X,LIU B,LUAN W P.Nonintrusive residential load monitoring and decomposition technology[J].Southern Power System Technology,2013,7(4):1-5(in Chinese).
[8] 周明,宋旭帆,涂京,等.基于非侵入式负荷监测的居民用电行为分析[J].电网技术,2018,42(10):3268-3272. ZHOU M,SONG X F,TU J,et al. Residential electricity consumption behavior analysis based on non-intrusive load monitoring[J]. Power System Technology,2018,42(10):3268-3272(in Chinese).
[9] MARCHIORI A, HAKKARINEN D, HAN Q, et al. Circuit-level load monitoring for household energy management[J]. IEEE Pervas Comput, 2011, 10(1):40-48.
[10] XIAO Y J, XU W Y, JIA Z H, et al.NIPAD:A non-invasive power-based anomaly detection scheme for programmable logic controllers[J].Frontiers of Information Technology & Electronic Engineering, 2017, 18(4):519-524.
[11] SUN C F,WANG Y R.A multi-criteria fusion feature selection algorithm for fault diagnosis of helicopter planetary gear train[J].Chinese Journal of Aeronautics, 2020, 33(5):1549-1561
[12] 易怀军,刘宁.基于优化的非等间隔灰色理论和BP神经网络的身管磨损量预测[J].兵工学报,2016,37(12):2220-2225. YI H J,LIU N.Prediction of gun barrel wear based on improved non-equal interval grey model and BP neural network[J].Acta Armamentarii, 2016, 37(12):2220-2225(in Chinese).
[13] 翟艳辉.舰船电力系统的非侵入式电力负荷分解技术[J].舰船科学技术,2020,42(3A):94-98. ZHAI Y H. Research on non-intrusive power load decomposition technology of ship power system[J].Ship Science and Technology,2020,42(3A):94-98(in Chinese).
[14] 张安安,庄景泰.结合图半监督与广义回归神经网络的非侵入式海洋平台负荷监测[J].电力系统保护与控制,2020,48(7):84-88. ZHANG A A,ZHUANG J T. Non-intrusive offshore platform load monitoring based on graph-based semi-supervised learning and generalized regression neural networks[J].Power System Protection and Control,2020,48(7):84-88(in Chinese).
[15] EMADI K, EHSANI M. Aircraft power systems:Technology, state of the art, and future trends[J]. IEEE Aerospace and Electronic Systems Magazine, 2000,15(2):28-32.
[16] 蔡林,张玲, 杨善水.大型飞机供配电系统可靠性评估与分析[J].航空学报,2011,32(8):1488-1496. CAI L,ZHANG L,YANG S S. Reliability assessment and analysis of large aircraft power distribution systems[J]. Acta Aeronautica et Astronautica Sinica,2011,32(8):1488-1496(in Chinese).
[17] 王小川,史峰,郁磊.MATLAB神经网络43个案例分析[M]. 北京:北京航空航天大学出版社,2013. WANG X C,SHI F,YU L.MATLAB neural network 43 cases analysis[M]. Beijing:Beihang University Press,2013(in Chinese).
[18] LIANG J,NG S K K,KENDALL G,et al. Load signature study-Part I:Basic concept, structure, and methodology[J]. IEEE Transactions on Power Delivery, 2010,25(2):551-560.
[19] LIANG J,NG S,KENDALL G,et al.Load signature study-part Ⅱ:Disaggregation framework simulation and applications[J]. IEEE Transactions on Power Delivery,2010,25(2):561-569.
[20] HASSAN T, JAVED F, ARSHAD N. An empirical investigation of V-I trajectory based load signatures for non-intrusive load monitoring[J]. IEEE Transactions on Smart Grid, 2014, 5(2):870-878.
[21] 钟诗胜,雷达.卷积和离散过程神经网络及其在航空发动机排气温度预测中的应用[J].航空学报,2012,33(3):438-445. ZHONG S S,LEI D. Convolution sum discrete process neural network and its application in aeroengine exhausted gas temperature prediction[J].Acta Aeronautica et Astronautica Sinica,2012,33(3):438-445(in Chinese).
[22] CHEN S L,GAO Z H, ZHU X, et al.Unstable unsteady aerodynamic modeling based on least squares support vector machines with general excitation[J]. Chinese Journal of Aeronautics, 2020, 33(10):2499-2509.
CJA