Name
Title | ||
|---|---|---|
Automatic Generation Control By Hybrid Invasive Weed Optimization And Pattern Search Tuned 2-Dof Pid Controller |
Abstract | ||
|---|---|---|
A hybrid invasive weed optimization and pattern search (hIWO-PS) technique is proposed in this paper to design 2 degree of freedom proportionalintegral- derivative (2-DOF-PID) controllers for automatic generation control (AGC) of interconnected power systems. Firstly, the proposed approach is tested in an interconnected two-area thermal power system and the advantage of the proposed approach has been established by comparing the results with recently published methods like conventional Ziegler Nichols (ZN), differential evolution (DE), bacteria foraging optimization algorithm (BFOA), genetic algorithm (GA), particle swarm optimization (PSO), hybrid BFOA-PSO, hybrid PSO-PS and non-dominated shorting GA-II (NSGA-II) based controllers for the identical interconnected power system. Further, sensitivity investigation is executed to demonstrate the robustness of the proposed approach by changing the parameters of the system, operating loading conditions, locations as well as size of the disturbance. Additionally, the methodology is applied to a three area hydro thermal interconnected system with appropriate generation rate constraints (GRC). The superiority of the presented methodology is demonstrated by presenting comparative results of adaptive neuro fuzzy inference system (ANFIS), hybrid hBFOA-PSO as well as hybrid hPSO-PS based controllers for the identical system. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.15837/ijccc.2017.4.2751 | INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL |
Keywords | Field | DocType |
Automatic generation control, interconnected power system, governor, dead - band non linearity, 2 degree of freedom PID controller, invasive weed optimization, pattern search | Particle swarm optimization,PID controller,Control theory,Computer science,Electric power system,Differential evolution,Adaptive neuro fuzzy inference system,Genetic algorithm,Automatic Generation Control,Pattern search | Journal |
Volume | Issue | ISSN |
12 | 4 | 1841-9836 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Neelamegam Manoharan | 1 | 0 | 0.34 |
| Subhransu Sekhar Dash | 2 | 8 | 3.93 |
| Kurup Sathy Rajesh | 3 | 0 | 0.34 |
| Sidhartha Panda | 4 | 211 | 24.23 |