This paper presents a non-intrusive method for identifying the load state of a distribution network. The method focuses on continuously varying loads. By considering the load onoff state switching points and the continuous features at on state, a deep convolutional method considering non-local spatiotemporal features is proposed. The addition of an attention component to the convolutional network enhances the non-local feature extraction capability of the convolutional network. Ultimately, the effectiveness of the method is demonstrated in an experimental setting. In addition, this paper demonstrates that the proposed method can effectively integrate switching point features as well as persistent features through neural network visualization techniques.
A Cyber‐Physical System (CPS) is a spatiotemporal multi‐dimensional and heterogeneous hybrid autonomous system composed of deep integration of information resources and physical systems. With the development of digitisation and digitalisation, a large number of data acquisition equipment, computing equipment, and electrical equipment are interconnected between the power grid and the information communication network. The power grid has thus been restructured as a mature and highly complex CPS. In order to promote the development of power grid CPS technologies and provide a reference for relevant researchers in the field, the origin and concept of CPS and features in power grid CPS are introduced firstly. Then the key technologies of power grid CPS simulation are discussed and further analysed from three perspectives, including modelling theory, simulation methods, and system‐level simulation. On this basis, the application of CPS simulation technology in future power grids has been prospected.
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