Distributed dynamic matrix frequency control method of interconnected power system considering wind power

Abstract

The invention discloses a distributed dynamic matrix frequency control method of interconnected power systems considering wind power, comprising the following steps: establishing a distributed discrete time state space model of the interconnected power systems considering the wind power based on a mechanism analysis modeling method and a discretization method, designing respective area control errors and quadratic control performance indexes weighted by a fan angular velocity output prediction deviation vector and a control increment prediction vector as rolling optimized objective functions, considering respective constraint conditions of a system output prediction deviation, a control prediction signal and an increment prediction signal, adopting a distributed constraint dynamic matrix control method based on a self-adaptive colony optimization strategy to realize load frequency coordinated optimization and control of the interconnected power systems in respective areas. Compared with the prior art, time domain response of load frequency of the interconnected power systems considering the wind power according to the invention has the advantages of faster regulation time, smaller dynamic oscillation amplitude, smaller steady-state error and higher robust performance.

Description

technical field [0001] The invention relates to an intelligent control technology in the field of complex interconnected power systems including distributed power sources such as wind power generation, in particular to a distributed dynamic matrix frequency control method for an interconnected power system considering wind power. Background technique [0002] With the access of a large number of distributed power sources such as photovoltaic power generation and wind power generation, the traditional power system is becoming larger and more complex in terms of scale, structure and operation mode. How to achieve stable and optimal operation of such a complex interconnected power system has become a serious challenge. Traditional centralized control methods are becoming increasingly difficult to meet this challenge. For complex interconnected power systems connected to distributed power sources with high penetration rates, how to develop efficient distributed load frequency c...

AI Technical Summary

Problems solved by technology

However, these existing technologies are mainly aimed at the traditional power system structure, and cannot be directly applied to the multi-region interconnected power system structure after new energy sources such as high-power wind farms and photovoltaic power stations are connected; in addition, the existing technologies are difficult to deal with multi-region The nonlinear, uncertain and complex constraints of the interconnected power system make it difficult to fully meet the stable operation requirements and dynamic response requirements under complex working conditions and uncertain factors of the system

[0004] Predictive control achieves optimal control of complex industrial control systems through basic modules such as predictive models, feedback corrections, and rolling optimization. Compared with traditional PID control, it has the ability to directly deal with coupled multivariable systems and handle various disturbances and uncertainties online. Complex process control systems such as petroleum, chemical industry, metallurgy, biopharmaceuticals, and food processing have been successfully applied; although the application of predictive control in fast process systems such as power electronic converters and power systems has broad prospects, it is currently in the At the initial stage, especially in the application of load frequency control in complex multi-regional interconnected power systems with high penetration rate distributed power access, there are few reports

At present, only a few scholars have used predictive control technology to research and explore the complex multi-regional interconnected power system including distributed new energy power systems, but the existing centralized predictive control technology still has serious shortcomings in terms of online calculation and fault tolerance. ; and only a few distributed predictive control technologies rely heavily on design experience, and there are obvious deficiencies in the adaptability of rolling optimization strategies and the ability to deal with complex constraints. Stable operation requirements and dynamic response requirements

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