The conventional O&M mode is post-operation and maintenance, which causes great power loss and labor cost consumption, and affects the overall income of the power station.
A perfect operation and management system of renewable energy stations has not been established, or the system has not been implemented, resulting in different management standards and failing to shape systematic operations.
Conventional O&M mode requires huge human resources, which brings higher O&M costs, affecting the overall income of the project.
Conventional O&M mode is subject to station monitoring system, but fails to realize data sharing and transparent O&M, resulting in the "isolated islands of information" and hindering business development.
Combined with wind and PV characteristics, the system establishes a multi-dimensional fault prediction model integrating SCADA data, EMS data and step-up substation data, which lowers the frequency of equipment failures and improves the revenue of power stations.
It is to further analyze the value of the data collected by the acquisition terminal, explore the hidden value and internal relationship beyond the data, and draw important summative conclusions, so as to guide the improvement of on-site operation and maintenance efficiency, the evaluation of operation and maintenance effect, and the optimization of production indicators.
Based on the operation and management experience of TBEA, a comprehensive production and operation management system is established and applied in the system to realize the standardized and transparent daily operations and maximize work efficiency.
Established a dynamic monitoring model integrating the three-level system of equipment, power station, and group, and combined with abnormal data characteristics to realize automatic push of fault information to maximize the efficiency of operation and maintenance;
The system is applied in various types of TBEA power stations, and the actual demand on site is deeply analyzed and realized, which ensures better feasibility and practicability.
The system is equipped with AI middleground and BI middleground for the rapid establishment and continuous optimization of big data analysis model.
There is an excellent algorithm team which, by virtue of cutting-edge technologies in AI and IoT such as image recognition, machine learning, big data and cloud computing, realizes digital management of the whole process.
Industry-advanced technical architecture, based on distributed architecture, improves platform performance and product stability by combining Hbase, Storm, Kafka and other resources.
It is an excellent case of distributed PV power. The collection data, calculation data and production data are used to establish the whole scenario index analysis, through which the company can intuitively view the operation status of each station.
A comprehensive digital solution is provided. Service resources are integrated through the mode of "one platform, and three levels of management and control", to explore the way of lean O&M, improve the organizational and operational efficiency of the renewable energy enterprise, and help the client to obtain higher investment returns.
The self-owned operation management system was established and implanted into the project to form standardized operations, improve work efficiency and reduce labor costs.
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