Many existing resources on PSO are not really that great or useful, in my experience.
Yilu Liu, Kevin Tomsovic Abstract There is a growing interest in investigating the high order contingency events that may result in large blackouts, which have been a great concern for power grid secure operation. The actual number of high order contingency is too huge for operators and planner to apply a brute-force enumerative analysis.
This thesis presents a heuristic searching method based on particle swarm optimization PSO and tabu search to select severe high order contingencies. The original PSO algorithm gives an intelligent strategy to search the feasible solution space, but tends to find the best solution only.
The proposed method combines the original PSO with tabu search such that a number of top candidates will be identified. This fits the need of high order contingency screening, which can be eventually the input to many other more complicate security analyses.
Reordering of branches of test system based on severity of N-1 contingencies is applied as a pre-processing to increase the convergence properties and efficiency of the algorithm.
With this reordering approach, many critical high order contingencies are located in a small area in the whole searching space. Therefore, the proposed algorithm tends to concentrate in searching this area such that the number of critical branch combinations searched will increase.
Therefore, the speedup ratio is found to increase significantly. The proposed algorithm is tested for N-2 and N-3 contingencies using two test systems modified from the IEEE bus and bus systems.
Variation of inertia weight, learning factors, and number of particles is tested and the range of values more suitable for this specific algorithm is suggested.
Although illustrated and tested with N-2 and N-3 contingency analysis, the proposed algorithm can be extended to even higher order contingencies but visualization will be difficult because of the increase in the problem dimensions corresponding to the order of contingencies.
For best results, right-click and select "save asTo determine the best parameter values of PRS, we employ an improved time variant Particle Swarm Optimization (PSO) algorithm with the objective of maximizing the annual net profit generated by PRS.
Due to the large number of component rules and swarm size, the optimization time is significant. Convergence Analysis for Particle Swarm Optimization Particle swarm optimization (PSO) is a very popular, randomized, nature- Convergence Analysis for Particle Swarm Optimization FAU Forschungen, Reihe B, Medizin, Naturwissenschaft, In this thesis, we study the convergence process in detail.
In order to mea-.
2 Particle Swarm Optimization Particle Swarm Optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a . using Particle Swarm Optimization and Tabu Search." I have examined the final electronic copy of this thesis for form and content and recommend that it be accepted in partial fulfillment of the requirements.
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Abstract Particle Swarm Optimization (PSO) is one of the most eﬁective optimization tools, which emerged in the last decade. Although, the original aim was to simulate the behavior of a group.