Parameter adaptive optimization algorithm of intelligent power system based on internet of things technology

With the advancement of technology and the continuous expansion of the power system scale, optimizing system parameters is of great significance for maintaining the stability of the power system. However, traditional adaptive optimization algorithms for power system parameters still have problems such as slow iteration speed and inaccurate parameter optimization. In order to better promote the development of the power system, this article aims to use Internet of Things technology to optimize the adaptive algorithm of intelligent power system parameters, in order to better meet the needs of the current power system. In the article, the power system structure was first constructed based on the Internet of Things architecture. Then, the power system containing stabilizers was designed and dynamically simulated in the time domain, and the impact of the introduction of power system stabilizers on power system stability was analyzed; Afterwards, a multi parameter nonlinear model is used for parameter adaptive algorithm optimization. Finally, to verify the application effect of IoT technology in the adaptive optimization algorithm of intelligent power system parameters, this article compares it with traditional algorithms. The research results show that the adaptive algorithm in this article has a fitness value of 0.08 when the number of iterations is 20. Parameter optimization has been completed in approximately 20 iterations. The traditional algorithm has a fitness value of 0.092 when the number of iterations is 30. The results indicate that the adaptive optimization algorithm for intelligent power system parameters using IoT technology has a faster iteration speed and more accurate parameter optimization. This study highlights the important impact of Internet of Things technology on the iteration speed and accuracy of parameter adaptive optimization algorithms in intelligent power systems, providing more ideas for achieving stable design of intelligent power systems. © 2024