Virtual synchronous generator virtual inertia and virtual damping coefficient adaptive control method

Abstract

The invention provides a virtual synchronous generator virtual inertia and virtual damping coefficient adaptive control method, which relates to the technical field of smart grid and intelligent algorithms. Firstly, the inverter based on the virtual synchronous generator is modeled, and the correlation between the output frequency of the inverter and the virtual inertia J and the virtual damping coefficient D is obtained. Then the fitness function of the adaptive control method of virtual inertia J and virtual damping coefficient D based on the improved particle swarm optimization algorithm isdetermined. Finally, the improved particle swarm optimization algorithm is applied to the active power. In the frequency control part, the real-time adaptive control of virtual inertia J and virtualdamping coefficient D is realized in order to minimize the frequency deviation and stabilize the system. The invention provides a virtual synchronous generator virtual inertia and virtual damping coefficient real-time adaptive control method, which fully utilizes the characteristics of the virtual inertia and introduces the virtual damping coefficient, so that the inverter is more stable and the frequency offset is smaller.

Description

technical field [0001] The invention relates to the technical field of smart grids and smart algorithms, in particular to an adaptive control method for virtual inertia and virtual damping coefficient of a virtual synchronous generator. Background technique [0002] The traditional power grid has always been the main power supply channel and has developed rapidly with the continuous increase in power demand. However, the centralized large power grid also has many disadvantages: high cost, difficult operation, and difficult to meet the increasingly high safety and reliability requirements of users. It is easier and faster to develop distributed power than to improve security by retrofitting the grid. Distributed power generation has many advantages: most of them use secondary energy, less pollution, and high energy utilization; flexible installation locations can save power transmission and distribution resources and operating costs, and reduce line losses; they can reduce t...

AI Technical Summary

Problems solved by technology

However, the increase in the penetration rate of distributed generation also has many problems: difficult control, poor compatibility with large power grids, and large frequency fluctuations during load switching

At present, it has been widely used in function optimization, neural network training, fuzzy system control and other multi-objective optimization applications. There is no method to apply particle swarm optimization to virtual synchronous generator control.

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