Abstract

High-quality power equipment is the basis for ensuring the safe operation of the power grid and improving the reliability of power supply. In actual operation, although some electrical equipment can continue to be used, abnormal operation or hidden dangers will affect the safety of people, equipment and power grid, reliable and economic operation of power grid and equipment, equipment output or life span, and power quality. Therefore, research on multi-dimensional analysis of the same batch of substation equipment, and identification of suspected family defects and frequent defects is of great significance for improving the company’s equipment management and defect management. The maturity and promotion of unstructured text data mining, graph computing technology, and semantic analysis have provided a wider dimensional space for the analysis of power equipment defect data. Aiming at the shortcomings of traditional defect data analysis, this article summarizes and analyzes the defects and hidden dangers found in the equipment, traces the distribution status of the same equipment through the physical “ID”, carries out multi-dimensional analysis of the same batch of equipment, and investigates the hidden dangers of equipment family defects. A big data analysis algorithm to build automatic identification models of suspected familial defects and frequent defects in main substation equipment such as transformers, disconnectors, circuit breakers, and use graph computing technology to quickly integrate analysis capabilities in multi-source heterogeneous data fusion with hidden relationship discovery capabilities, identify equipment defects with potential impact relationships. From historical data, discover which manufacturers or purchased batches of equipment are more likely to have the same defect, and which defect types have a higher frequency, improve the recognition model of family defects and frequent defects, and improve the accuracy and comprehensiveness of defect recognition.

The manufacturing industry consumes electricity and natural gas to provide the power and heat required for manufacturing. Additionally, large amounts of electric energy and heat energy are used, and the electricity cost, amount of environmental pollution, and equipment maintenance cost are high. Thus, optimizing the management of equipment with new energy is important to satisfy the load demand from the system. This paper formulates the scheduling problem of these multiple energy systems as a multi-objective linear regression model (MLRM), and an energy management system is designed focusing on the economy and on greenhouse gas emissions. Furthermore, a variety of optimization objectives and constraints are proposed to make the energy management scheme more practical. Then, grey theory is combined with the common MLRM to accurately represent the uncertainty in the system and to make the model better reflect the actual situation. This paper takes load fluctuation, total grid operation cost, and environmental pollution value as reference standards to measure the effect of the gray optimization algorithm. Lastly, the model is applied to optimize the energy supply plan and its performance is demonstrated using numerical examples. The verification results meet the optimized operating conditions of the multi-energy microgrid system.

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