Comparative Study Of Ant Colony Algorithm And Classical Method In Active Power Optimization Of Power Systems

A very important part of power system economic dispatch is the active power optimization.There are many methods for processing the active power optimization of the power system,including the classic method and ant colony intelligent algorithm.The classic method has strong reliability,fast calculation speed,and accuracy higher characteristics.The ant colony intelligent algorithm has strong anti-interference and adopts a positive feedback mechanism.This thesis improves and optimizes on the basis of these two basic algorithms,proposes improved algorithms and makes a comparative study.Firstly,the traditional storage method and improved storage method of node admittance matrix,the traditional CU triangular decomposition method and the improved CU triangular decomposition method are introduced to solve linear equations.The PQ polar coordinate decomposition method is proposed and improved by the polar Newton-Raphson method.Secondly,through the comparison of examples,it is found that the improved storage method and the symmetry and sparseness of the matrix can accelerate the speed of solving the linear equations of the power system,thereby reducing the time of power flow calculation.Then introduces and deduces four methods of solving the network loss micro-increasing rate in detail,and improves the method,and then proposes an improved classic method for active power optimization and uses examples to verify the feasibility of the improvement.Finally,the basic ant colony algorithm and three single-parameter modified ant colony algorithms are introduced in detail.Based on these methods,a multi-parameter improved ant colony algorithm is proposed and applied to active power optimization of power systems.The examples are compared with traditional classical methods.The feasibility of the improved ant colony algorithm in active power optimization of power systems is verified.