Abstract In order to achieve the multi-objective requirements of high quality, high efficiency and low cost of the automatic verification system of electric energy metering instruments intermediate checks, a mathematical model was established to minimize the comprehensive verification cost and maximize verification efficiency. To solve the model, an improved fast non-dominated multi-objective optimization algorithm (INSGA-Ⅱ) was proposed, which improved the searching ability of the population by designing a multi-point crossover mutation strategy, and using perturbation population to improve the diversity of the population and prevent the population from falling into local optimum. The effectiveness and feasibility of the model and algorithm were verified by example simulation and algorithm comparison. The results showed that INSGA-Ⅱ algorithm can effectively obtain the non-dominated solution of the problem, which is of guiding significance to improve the verification quality, verification efficiency and reduce the verification cost. Finally, the optimal solution of the model was applied to the intermediate checks of the automatic verification system of low-voltage current transformer in Hubei Electric Power Metering Center of State Grid, and the control chart was drawn to monitor the operation status of the verification system online, which ensured that the measurement process was always in the state of statistical control.
LI Liang-bo,XIE Jin-fang,GENG Rui, et al. Research on Intermediate Checks Scheme of Automatic Verification System of Electric Energy Metering Instruments Based on INSGA-Ⅱ[J]. Acta Metrologica Sinica, 2023, 44(8): 1248-1255.
Zhuang L, Fu Z B, Li L, et al. Research on intelligent monitoring of the measurement property in electric energy meter automatic verification ［J］. Instrument Standardization & Metrology, 2016, (6): 38-40, 46.
Wang J, Zhang Q. Optimization of calibration period for automatic test system based on innovation weibull mode ［J］. Journal of Electronic Measurement and Instrument, 2011, 25(2): 159-163.
［11］
Morello R, Claudio, De Capua, et al. An ISO/IEC/IEEE 21451 compliant algorithm for detecting sensor faults ［J］. IEEE Sensors Journal, 2015, 15(5): 2541-2548.
Wang R, Yang F, Yuan J, et al. A life prediction method of the smart meter based on wei-bull distribution and maximum likelihood estimation［J］. Acta Metrologica Sinica, 2019, 40(6A): 125-129.
Zhu L F, Xue Y, Han Z J. Caliberation interval optimization of multi characteristics measurement system［J］. Electronic Engineer, 2005, 31(8): 15-17, 63.
［15］
Deb K, Agarwal S, Pratap A, et al. A fast and elitist multiobjective genetic algorithm: NSGA-Ⅱ［J］. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 0-197.
Liu B, Liu Z R, Zhao Z B, et al. Research on multi-population multi-objective marticle swarm optimization algorithm based on velocity communication［J］. Acta Metrologica Sinica, 2020, 41(8): 1002-1011.
Du S W, Peng C Y, Xu S M, et al. Online Verification Method of Meter Calibration Equipment Based on Hierarchical Bayes Model ［J］. Automation of Electric Power Systems, 2018, 42(18): 177-181.
Yue R H, Yang X M, Xu Z Y, Sun J J. The prediction of calibration interval for test equipment based on particle Swarm-Optimized support vector machines ［J］. Aerospace Control, 2013, 31(2): 84-88.
［16］
Long J, Zheng Z, Gao x, et al. A hybrid multi-objective evolutionary algorithm based on NSGA-Ⅱ for practical scheduling with release times in steel plants ［J］. Journal of the Operational Research Society, 2016, 67(9): 1184-1199.
［19］
Bhesdadiya R H, Trivedi I N, Jangir P, et al. An NSGA-ⅡI algorithm for solving multi-objective economic/environmental dispatch problem ［J］. Cogent Engineering, 2016, 3(1): 126-149.
ZHAO Z W, Liu Y, Xiong Z J, et al. Many-objective evolutionary algorithm based on multi-subpopulation and density estimation for load distribution of cold rolling［J］. Acta Metrologica Sinica, 2022, 43(1): 65-71.
［22］
Ren Y, Zhang C, Zhao F, et al. An asynchronous parallel disassembly planning based on genetic algorithm ［J］. European Journal of Operational Research, 2018, 269(2): 647-660.
Fan Q C, Zheng L, Jiang R H. One way of accuracy and reliable method for intermediate checks ［J］. Acta Metrologica Sinica, 2015, 36 (6): 662-665.
Yao G, Zhao J J, Sun J J. Research on the optimization of calibration cycle for measuring device of weapon system ［J］. Process Automatic Instrumentation, 2014, 35(5): 28-31.
［14］
张月义. 质量损失函数与测量系统研究［D］. 南京: 南京理工大学, 2010.
［21］
JJF 1033-2016计量标准考核规范［S］. 2016.
Fan Q C, Li Y, Jiang W J, et al. A kind of intermediate check method for electrical energy measurement standards ［J］. Electrical Measurement ＆ Instrumentation, 2016, 53 (1): 75-78, 83.
Wang R B, Pan D X. Research on adjustment method of calibration intervals of measuring instruments ［J］. Journal of Astronautic Metrology and Measurement, 2020, 40(3): 94-100.
Wang H P, Qi M R, Chen M K. Reserach on mixed production optimization of prefabricated companentflow shop based NSGA-Ⅲ ［J］. Journal of Industrial Engineering and Engineering Management, 2022, 36(1): 240-251.
Guo S H, Li C, Zhang, J W. Multi-objective optimization of parameters of series compensation device for distribution network based on NSGA II algorithm ［J］. Power Capacitor & Reactive Power Compensation, 2020, 41(1): 30-37.
2023, Vol. 44 
