WAMS-based genetic algorithm-improved power grid reactive capacity optimization configuration method

Abstract

The invention relates to a genetic algorithm-improved power grid reactive capacity optimization configuration method based on real-time information of a Wide Area Measurement System (WAMS) and an Energy Management System (EMS) of a power grid. The method includes: step S1. obtaining power grid measurement information from the power grid WAMS in real time; step S2. obtaining from the EMS the power grid structure, parameters and running section information in the EMS of a current time section, and forming an admittance matrix of a whole network containing all load node equivalent admittance; step S3. performing hybrid coding on a control variable of power grid reactive optimization; step S4. establishing a fitness function; step S5. executing a selection operator; step S6. executing a cross and mutation operator; and step S7. determining the rate of convergence and convergence termination criteria. The method in the invention makes improvements on the basis of a traditional genetic algorithm, makes improvements in multiple aspects to improve defects of the traditional genetic algorithm in reactive optimization, improves the rate of convergence and quality of a solution, and prevents the problem of occurrence of a local optimal solution.

Description

technical field [0001] The invention relates to a configuration method in the field of grid operation state evaluation and early warning of a grid intelligent dispatching support system, in particular to a grid reactive capacity optimal configuration method based on a WAMS improved genetic algorithm. Background technique [0002] With the rapid development of society, economy and power industry, the power grid has gradually developed into a large-scale, long-distance, UHV AC-DC interconnection and an increasing proportion of new energy access, which increases the uncertainty of power grid operation. The receiving-end power grid is mainly centered on the load-concentrated area, and is connected to the long-distance generalized originating power supply through the surrounding tie-lines, so as to achieve the balance between supply and demand of electric energy. Due to the mismatch between the distribution of energy and load center areas, as well as the constraints of the enviro...

AI Technical Summary

Problems solved by technology

However, the traditional genetic algorithm still has some shortcomings in the reactive power optimization process of the power system, such as slow convergence speed, premature phenomenon and the formation of local optimal solutions, etc.

Images