Reactive power optimization method of electrical power system

Abstract

The invention discloses a reactive power optimization method of an electrical power system. The bacterial foraging algorithm and the particle swarm optimization algorithm are combined to be applied to the reactive power optimization of the electrical power system. The reactive power optimization method of the electrical power system comprises the following steps: adopting a Newton-Raphson load flow calculation procedure to provide values of each state variable and a network loss value for optimal calculation; setting a reactive power optimization model; initializing a bacterial colony; invoking load flow iterative program appraisal to record an adaptation degree and an optimal value of bacteria; carrying out chemotaxis operation; remaining a good population and breeding the population; carrying out migration operation, and the bacteria die or are born again in a certain probability; and updating the bacterial colony, and outputting a reactive power optimization result after the rated number of iterations is reached. The reactive power optimization method of the electrical power system introduces the colony in an optimal solution, avoids blind and random problems, can exceed a locally optimal solution, combines an existing bacteria foraging optimization algorithm with the particle swarm optimization algorithm, lowers transmission losses by controlling an engine, reactive power output of reactive compensation equipment and tapping points of an adjustable transformer, has the advantages of being rapid in convergence, efficient and stable, and is suitable for resolving the problem of reactive power optimization in the electrical power system.

Description

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AI Technical Summary

Benefits of technology

This patented technology improves upon existing methods used for optimizing reactions within an electric circuit design. It allows for more flexibility than traditional approaches like linear or quadratic programming (LQP) that require complex calculations. Additionally it provides new ways to combine different types of particles called Swarnes Intelligence algorithms (SIs). These techniques help optimize electrical energy consumption while reducing harmful emissions from fossil fuel combustion sources. Overall this innovation enhances the efficiency and effectiveness of renewable energies such as solar cells.

Problems solved by technology

Technological Problem addressed in this patents relating to power supply networks involves finding the best possible solution without causing any negative effects due to changes caused by external factors like weather patterns or environmental concerns during planning phases. However, current techniques have limitations with regards to achieving efficient power transfer while addressing challenges related to changing environments.

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