Browsing by Subject "Particle swarm optimization"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Applying Different Wide-Area Response-Based Controls to Different Contingencies in Power Systems
    (IEEE Xplore, 2021) Iranmanesh, Shahrzad; Rovnyak, Steven M.; Electrical and Computer Engineering, School of Engineering and Technology
    Electrical disturbances in the power system can threaten stability. One-shot control is an effective method for stabilizing some events. In this paper, predetermined amounts of loads are increased or decreased around the network. Determining the amounts of loads, and the location for shedding is crucial. This paper is completed in two different sections. First, finding the effective control combinations, and second, finding an algorithm for applying different control combinations to different contingencies in real time. The particle swarm optimization (PSO) algorithm is used to find the effective control combinations. Next, decision trees (DT) are trained to assess the benefits of applying each of the three most effective control combinations found by PSO method. The DT outputs are combined into an algorithm for selecting the best control in real time. Finally, the algorithm is evaluated using a test set of contingencies. The results reveal a 46% improvement in comparison to previous studies.
  • Thumbnail Image
    Item
    Particle swarm optimization applied to real-time asset allocation
    (2015-05) Reynolds, Joshua; Christopher, Lauren; Eberhart, Russell; Salama, Paul; King, Brian
    Particle Swam Optimization (PSO) is especially useful for rapid optimization of problems involving multiple objectives and constraints in dynamic environments. It regularly and substantially outperforms other algorithms in benchmark tests. This paper describes research leading to the application of PSO to the autonomous asset management problem in electronic warfare. The PSO speed provides fast optimization of frequency allocations for receivers and jammers in highly complex and dynamic environments. The key contribution is the simultaneous optimization of the frequency allocations, signal priority, signal strength, and the spatial locations of the assets. The fitness function takes into account the assets' locations in 2 dimensions, maximizing their spatial distribution while maintaining allocations based on signal priority and power. The fast speed of the optimization enables rapid responses to changing conditions in these complex signal environments, which can have real-time battlefield impact. Results optimizing receiver frequencies and locations in 2 dimensions have been successful. Current run-times are between 450ms (3 receivers, 30 transmitters) and 1100ms (7 receivers, 50 transmitters) on a single-threaded x86 based PC. Run-times can be substantially decreased by an order of magnitude when smaller swarm populations and smart swarm termination methods are used, however a trade off exists between run-time and repeatability of solutions. The results of the research on the PSO parameters and fitness function for this problem are demonstrated.