KEYWORDS: Data modeling, Power grids, Analytical research, Statistical modeling, Power consumption, Modeling, Failure analysis, 3D modeling, Process modeling, Power supplies
Electric power failure is the most common problem in power supply, and it is also one of the biggest problems for customers. From the perspective of power management, blackouts can be divided into fault blackouts and planned blackouts. There are three types of reasons for failure and power failure. One is overload of public transformer and special transformer, iron core grounding and lightning breakdown. The other is drop fuse failure and lack of equivalence for special transformer users. The last is power failure for residential users, line disconnection after meter, short circuit, leakage switch failure, etc. For planned outage, it is mainly caused by planned maintenance and repair according to the operation cycle of power line equipment. Among them, planned power outages are controllable, and the power management unit notifies the power customers in advance. However, the failure of power outages usually causes certain damage to the power customers, so it has certain harmfulness. In view of the above problems, this paper uses digital twinning technology to analyze the causes of failure and outage, and uses digital technology to carry out failure and outage prediction to reduce the losses caused by failure and outage.
KEYWORDS: Data modeling, Power grids, Instrument modeling, Visual process modeling, 3D modeling, Statistical modeling, Design and modelling, Analytical research, Transformers, Power supplies
Currently, there have been many studies on digital twin models and analysis algorithms for power application scenarios. These studies mainly focus on building digital twin models for static power equipment and existing historical data. With the positive development of China's economic situation, the number of electricity customers continues to grow, and the scale of corresponding power supply facilities and equipment is also expanding. Accordingly, digital twin models built for static power grid equipment and facilities and power grid structures have to be updated to meet new model application requirements. To address this issue, this paper proposes an intelligent adaptive digital twin model reconstruction method for distribution networks, which reconstructs and optimizes existing digital twin models and reduces the cost of model construction. Reconstruction of the digital twin model for distribution network equipment requires first extracting the basic data of the distribution network equipment, including equipment nameplates, geographic information, and point cloud scanning data. Then, by using the equipment as the identifier, multi-source heterogeneous data across topics are fused. Finally, based on probabilistic and statistical methods, the data structure within the model is reconstructed and optimized for grid business data requests.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.