With the rapid development of digital technologies such as intelligent control and information communication, digitization has gradually become an inevitable trend to promote technology innovation and industrial upgrading for the power grid. As the basis of digital technology, digital sensing technology will gradually penetrate all aspects of the power system for comprehensive perception and high operational reliability. First, this article introduces the application status and technical requirements of power intelligent sensor technology in various aspects of the power system, including the generation, transmission, transformation, distribution, and consumption of electricity. Next, domestic and foreign scholars' latest research achievements in power sensing technology are described from three major aspects, that is, optical fiber sensing technology, MEMS sensing technology, and sensor self‐powered technology. Finally, future challenges for the electric power intelligent sensors are discussed and an outlook of future widespread application in the clean, safe, and efficient digital power grid is provided.

As wind power generation technology rapidly advances, the threat of wind turbine failures to the secure and stable operation of the power grid is gaining increasing attention. Real-time monitoring of operation status and predicting potential failures in wind turbines are indispensable requirements for the safe integration of wind power. In this paper, a model based on the least squares support vector machine (LSSVM), whose parameters are optimized by the Big Bang–Big Crunch algorithm, is constructed to improve the monitoring of wind turbine operation status and fault prediction accuracy. The research methodology consists of several key stages. Firstly, the initial wind turbine sensing data are preprocessed, utilizing factor analysis to reduce dimensionality and obtain the main influencing factors of wind turbine operation. Then, an improved failure prediction model for wind turbines, based on the least squares support vector machine, is developed using the preprocessed data. The model parameters are optimized by utilizing the Big Bang–Big Crunch optimization algorithm to enhance the prediction accuracy of wind turbine failures. Finally, the feasibility and accuracy of the proposed method are validated through a case study conducted on a regional power grid with wind farm integration.