Research On Parameter Identification Method Of Synchronous Generator Model Based On Improved Fruit Fly Optimization Algorithm

With the development and application of new technologies,World’s power system currently contains tens of thousands of power generation,transformation,transmission,and distribution equipment.In order to monitor and control the system’s operational status in real-time,ensure the controllability and stability of the system operation,it is necessary to establish an accurate and complete system model and conduct model simulation.Only by obtaining accurate simulation parameters can we grasp system operation information.In the event of accidents that threaten the safe and stable operation of the system,emergency measures should be taken in a timely manner to avoid causing large-scale power outages.Synchronous generators are essential components of the power system,and establishing accurate mathematical modeling of synchronous motors is crucial for analyzing power grid conditions.Due to the inability to take into account the actual working conditions of the system,conventional parameter recognition methods are somewhat limited in new application scenarios.At this time,online parameter recognition based on system measurement data has become an important research method in the field of parameter recognition.Based on this,this article proposes research on parameter identification methods for synchronous generator models,and the following research contents have been completed around this research goal:(1)Study the dynamic equivalent model and its identifiability of synchronous generators.Through the linearization analysis of the nonlinear model,it is concluded that the electrical parameters of the synchronous generator can be identified,solving the problem of electrical parameter identifiability.If mechanical parameters need to be identified,further parameter conditions need to be added.(2)Study the parameter identification of synchronous generator models.Integrating EMD decomposition with Prony algorithm for identifying model parameters of synchronous generators.The EMD decomposition function can be used to deal with high-order harmonics and noise problems in synchronous generators,and the output results can be filtered out using EMD decomposition to remove high-frequency components.A parameter identification method combining Tabu search algorithm and Drosophila algorithm(TS-FOA)is proposed.The TS-FOA algorithm integrates two algorithms: First,introduce FOA algorithm to ensure global search capability in the early stage and achieve fast iteration of initial search.Second,introducing the concept of TS to optimize traditional FOA algorithms,further reducing iteration time.And it can avoid the problem of falling into local optimal solutions in the later stage of iteration.This can significantly improve the optimization efficiency of parameter identification in synchronous generator models.(3)Then we study the model and parameter accuracy evaluation based on PMU data.Thesis proposes a method for evaluating the accuracy of a model using a comprehensive similarity index based on multi scene disturbance data.Thesis calculates the comprehensive similarity index of multiple single disturbance fault data,and Discriminative model.Then I made further parameter corrections to the units with larger errors.The simulation case demonstrates the effectiveness of this method.