KEYWORDS: Data modeling, Particles, Power consumption, Data acquisition, Particle swarm optimization, Telecommunications, Education and training, Classification systems, Instrument modeling, Support vector machines
With the massive use of mobile IoT technology and shared thinking innovative electricity consumption mode, shared electricity consumption smart devices have come into being, and the identification of abnormal electrical energy metering data in the process of shared electricity consumption collection has become an urgent research problem. In this paper, by studying the method of identifying abnormal electrical energy metering data in shared electricity information collection system, we select the improved particle swarm algorithm to optimize the parameters of support vector machine kernel function, construct the power quality disturbance model, and implement the classification of abnormal electrical energy metering data collected by electricity information system; use LOF algorithm to calculate the abnormality factor, and use the disturbance model determined by fly away abnormal intelligent analysis method to judge whether the displayed value of electrical energy meter The LOF algorithm is used to calculate the abnormality factor, and the disturbance model determined by the fly-away abnormality analysis method is used to determine whether the displayed value of the energy meter is abnormal or not. The experimental results show that the method of this paper is more accurate in classifying abnormal data and can better prevent the occurrence of error judgment, which can effectively improve the quality and efficiency of abnormal judgment of electric energy metering data and better realize the accurate measurement of shared electricity.
This paper discusses the multi-objective optimization problem of how demand side resources respond flexibly to power grid dispatching based on market environment. Demand side resources dominated by large industrial users have great scheduling potential and response enthusiasm, especially flexible loads dominated by energy storage, adjustable loads and interruptible loads. Therefore, based on the analysis of the characteristics of flexible load participating in market aggregation, aiming at the minimum power abandonment of new energy and the lowest power consumption cost of users, comprehensively considering the power constraints of unit output and electrical equipment, the particle swarm optimization algorithm is used to verify and solve the example. Finally, the optimization goal of peak shaving and valley filling is realized to a certain extent, but there is still room for improvement.
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