Collection And Analysis Of The Electricity Information Based On HPLC

With the development of HPLC technology and the advancement of low voltage meter reading technology,the electricity information collection and analysis has become one of the effective means to improve the business management in the power industry.However,in the process of electricity information acquisition,the imperfect acquisition system will lead to many problems.The obtained electricity consumption data cannot be fully analyzed and studied,so the power industry cannot be provided more valuable information to help it understanding the users’electricity usage rules and characteristics.Above problems are studied in this thesis based on the actual needs.Firstly,the key modules and the business processes of the electricity information acquisition system are improved and designed according to the actual requirements and implementation principles.The collection for kinds of data is added in the ammeter file information module,and the real-time acquisition module is designed and realized.Through the above improvement and design,each meter can be more conveniently monitored and the meter information collection is more efficient in the station area.All above these improvement not only need to add other components,but also need to change the electricity meter in the powerline communication networks,so it saves cost.Then,in order to improve the analysability of the electricity consumption data,and to process the data well,the collected electricity information is preprocessed.The missing data are complemented so as to make the later analysis more comprehensive.The consistency of the data is checked,the threshold value is set to filter out the wrong data and the data not what it looks like,so that the cleaned data can meet the needs of subsequent analysis.The daily electricity consumption and power were analyzed according to the pre-processed data,and the basic electricity consumption law was obtained.Finally,in order to make a deeper feature classification analysis for the users’ electricity information,the data dimensionality is reduced by the principal component analysis algorithm,the data is classified by k-means clustering algorithm.GSA algorithm is introduced to determine k value of K-means clustering algorithm.The basic GSA algorithm is improved by setting the threshold value to obtain a better number of clusters,so as to play the advantages of K-means clustering algorithm well.The maximum and minimum distance algorithm is introduced to improve the generation of initial clustering centers,so as to improve the sensitivity of clustering centers existing in K-means clustering algorithm.The power demand and behavior of users are analyzed according to the data clustering and classification results,so the power industry can understand the power consumption of users and better distribute power,to provide the most appropriate personalized service to different users and improve the level of fine management of the power industry.