Study of Back Propagation Neural Network for Watt-hours Measuring Method

• Main Authors: Chun-Liang Lu, 呂俊良
• Other Authors: Yong-Lin Kuo
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/2grw32

碩士 === 國立臺灣科技大學 === 自動化及控制研究所 === 107 === Abstract In recent years, due to the rapid development of big data, various analytical methods and artificial intelligence technology, how to apply this related technology in the field of electro-energy measurement accuracy and verify its feasibility, is a new technology application in the field of electric energy testing. In artificial intelligence technology, the Artificial Neural Network(ANN) is widely and effectively applied in various fields of predictive analysis and verification because of its ability to process the collection of input and output data in parallel and the advantages of self-learning. This research is to approach the method of accuracy measurement with different Watt-hour meters, and the research of accuracy error difference via Back Propagation Neural Network(BPNN), in order to verify the feasibility of applying Artificial Neural Network technology in watt-hour measurement method. The measurement method of the accuracy error of the watt-hour meter is tested according to the measure method of the International Verification Standard (IEC62053-11, IEC62053-22), and the parameters of the test influence include the accuracy, watt value, phase angle, constant etc. After normalization of the experimental data, using the Artificial Neural Network Model Tool to predict the accuracy error value of the Watt-hour meters under test method of the polyphase balanced load. The experimental results show that the Mean Square Error (MSE) is 1.1915 x 10-4, the Root Mean Square Error (RMSE) is 0.011 and the R2 is 0.96 those which are the optimized model experimental results using the Back Propagation Neural Network. As explored of the difference verification of this study that it is feasible and high accuracy in Back Propagation Neural Network combined with the Watt-hour meter accuracy error. Keywords：Back Propagation Neural Network；Measurement Validation；Watt-hour meter