The line loss rate of power supply enterprises is an important economic and technical index, which directly reflects the lean level of marketing professional management of power supply enterprises. At present, the confusion of transformer-user archives in transformer area is still widespread, which is a key issue in line loss management in power supply enterprises. As the downlink communication channel, low-voltage power line carrier can realize cross-station reading, which brings great difficulties to the identification of transformer-user relation. Because the data of calculating the line loss rate of the transformer area depends on the electrical information acquisition system, the running state of the electrical information acquisition equipment is also one of the important factors affecting the line loss rate of the transformer area. This paper will focus on the fault diagnosis technology of electrical information acquisition equipment, and use it to carry out the identification of transformer-user transformers.
The information collection system of State Grid Corporation has accessed a large number of smart energy meter, water meter, gas meter and heat meter data, implemented the “multi-integration” collection and construction to improve the public service level of the society, and actively respond to the call for government smart city construction. However, many operating equipment and complex operating environments often generate various equipment failure problems. It is difficult to identify faults and has low efficiency in troubleshooting. The intelligent diagnosis of the collection equipment fault is based on various information such as operation and maintenance, environment, fault analysis and acquisition. It uses big data analysis and artificial intelligence technology to conduct Intelligent Diagnosis Model of Acquisition Fault research, so as to solve the accurate judgment of the failure of the collection device, qualitative analysis of equipment fault and on-site, workflow troubleshooting guidance issues. Using the intelligent diagnosis of faults to solve the problem of “multi-integration” collection and maintenance, it can effectively improve the efficiency of fault handling, better promote the application of “multi-integration” and promote the construction of smart cities.
Equipment maintainability support is the most important influencing factor which affects weapon battle ability. Equipment maintainability support capability assessment result can be used as steering for equipment maintenance blue print configuration. Based on analysis of equipment maintenance support, maintenance scenario establish process is discussed in paper. Then neural network-based equipment maintainability support assessment model is established. BP dynamic neural network is applied in constructing model and the amelioration weight reset arithmetic is imported in equipment maintainability support assessment model. Finally, the steps of equipment maintenance support assessment are described. According to assessment arithmetic, equipment maintenance center computes assessment result. Equipment development department and army can optimize maintainability support scheme based on assessment result and maintain equipment.
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