I. Business Background

With the reshaping of the power landscape by the new round of power system reform, the new era strategic goal of "building a world-class energy Internet enterprise with outstanding competitiveness" proposed by State Grid, in the strategic system of "two substitutions, one return, and one improvement", the power grid framework is the network hub for energy transmission and conversion utilization, and is at the central link of the energy revolution. Among them, the distribution network framework is closely related to the user's electricity consumption experience. Therefore, achieving the integration of distribution network energy flow and integrating multi-source network frameworks such as marketing and production can promote efficient coordination between operation and distribution, and enhance the management level of core businesses such as distribution network planning and design, dispatching, operation and maintenance, and marketing.

Ii. Problems and Challenges

1. The data of various business systems are isolated and not unified from each other, resulting in errors in the network structure files themselves and the relationships between files within a single system, as well as errors in the consistency and relationships of network structure files across systems. The demand for multi-source heterogeneous data fusion is urgent.

2. It is difficult to share and integrate data across business, making it hard to effectively enhance the value of data. Moreover, the original network structure has problems such as poor scalability, slow response to modifications, and high maintenance costs. It is expected to build a unified grid topology standard that is enterprise-oriented and freely scalable.

3. The data collected from the measurement type of electricity consumption have abnormal problems such as missing readings, incorrect ratios, and sudden changes in electricity quantity, which have a significant impact on the transmission quality of energy flow in the power grid. It is necessary to implement a solution for abnormal verification and analysis of electrical energy data based on big data in accordance with the standard grid structure.

4. As time goes by, the results of the integration of operation and distribution have cross-professional and cross-system business boundaries, unclear data accountability, and a lack of synchronous updates and effective positioning of grid structure differences. There is an urgent need to achieve an efficient and intelligent automatic perception system for grid structure differences and accurately locate the difference points.

Iv. Core Functions

V. Product Features

Vi. Application Fields

Vii. Specific Cases

The verification project of abnormal line loss data of a certain provincial power company of State Grid during the same period

A provincial power company is currently carrying out line loss control work. Whether the line loss calculation is accurate or not, a large part of the reason is whether the basic equipment file information and the correlation information between equipment are recorded accurately. Since the data are respectively sourced from multiple systems such as the calibration system, the production system, and the marketing system, data integration has become the greatest challenge. Meilin Data has built the grid topology structures of the production system and the marketing system respectively through business modeling, and instantiated the grid topology through knowledge graphs to construct the grid topology map. And through complex relationship computing and deep learning, a fusion model of the network topology of the production system and the marketing system was constructed, efficiently achieving data interconnection between the systems.