Situation Analysis

According to the "double carbon" goal, China will achieve non-fossil energy consumption accounting for about 20% of primary energy consumption by 2025 , and the construction of new energy stations is growing rapidly. The traditional new energy station management method is no longer able to meet the needs of the increasingly rapidly developing new energy power generation industry. Artificial intelligence and big data analysis technology are used to build performance analysis models, build intelligent monitoring, performance analysis and intelligent early warning systems to assist Manual daily management of new energy stations is an effective way to solve the centralized remote monitoring of massive new energy equipment and improve power generation efficiency.

solution

Deploy a new energy intelligent auxiliary monitoring system in the centralized control center. Based on AI and 13 performance analysis big data models, it can realize intelligent early warning and power generation efficiency analysis functions, complete stable operation monitoring of large-scale wind power and photovoltaic measurement points, and achieve equipment standardization. , data "diversion, classification, layering" settings and data governance functions.

Through the in-depth application of production data, equipment monitoring capabilities are improved to ensure safe and stable operation of equipment, thereby improving the efficiency of wind and solar resource utilization, achieving economical operation of each power station, and better ensuring safe and stable operation. Improve the efficiency of operation and maintenance. Through unified data query, trend analysis, mathematical statistics, correlation analysis, fault analysis and other functions, it is convenient to comprehensively analyze the status of equipment and facilities, improve the efficiency of analysis, help operation and maintenance personnel detect equipment problems early, and reduce equipment costs. Failure rate, increased operational reliability, shortened equipment maintenance time, and saved maintenance costs.

Model driven

Data Fusion

Intelligent disk monitoring

Intelligent early warning

Performance analysis