scholarly journals Automatic Generation Control of Multi-Area Solar-Thermal Power System Using Fruit-Fly Optimization Algorithm

2018 ◽  
Vol 7 (4.5) ◽  
pp. 56
Author(s):  
B. V.S.Acharyulu ◽  
P. K.Hota ◽  
Banaja Mohanty
Keyword(s):  
Optimization Algorithm ◽  
Pid Controller ◽  
Thermal Power ◽  
Fruit Fly ◽  
Automatic Generation ◽  
Automatic Generation Control ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Solar Thermal Power ◽  
Generation Control

In this paper, fruit-fly optimization algorithm (FOA) is applied to automatic generation control (AGC) of multi-area power systems. In the proposed three-area system, reheat thermal systems are considered in all areas incorporating solar thermal power plant (STPP) in one of the areas. The optimum gain of proportional-integral-derivative (PID) controller is optimized applying FOA technique. The strength of FOA is established by comparing the results with well-established Grey Wolf optimizer (GWO) technique for the same interconnected power system. The performances of the system with FOA technique are found to be better than GWO algorithm for both with and without incorporating STPP in area-1. Further, from the sensitivity analysis, it is evident that the PID controller gains obtained by FOA technique under normal conditions are found to be better even for large changes in slip and system load conditions.  

scholarly journals Automatic Generation Control using Genetic Algorithm Based PID Controller

2020 ◽  
Vol 9 (5) ◽  
pp. 1047-1053
Keyword(s):  
Genetic Algorithm ◽  
Pid Controller ◽  
Thermal Power ◽  
Optimal Solution ◽  
Automatic Generation ◽  
Automatic Generation Control ◽  
Parameter Uncertainties ◽  
Survival Of The Fittest ◽  
Generation Control ◽  
Plant Parameter

In this paper Automatic Generation Control (AGC) of a single-area thermal power plant without reheat turbine is introduced using a Proportional Integral Derivative (PID) controller. The gains of the controller are optimized using Genetic Algorithm (GA). The problem of tuning the PID controller is formulated as optimization problem with constraints on proportional, derivative and integral gains. The proposed algorithm uses Darwin’s law of natural selection and survival of the fittest to reach the optimal solution. The simulation results confirm the system’s ability to retain frequency while handling sudden load disturbances. The second part of the investigation includes robustness testing of the system against plant parameter variations. The results are verified and the system performance is found to be robust against parameter uncertainties

A new combined braking approach of magnetorheological fluids brake based on fuzzy controller and improved PID controller

2021 ◽  
pp. 095440622110171
Author(s):  
Dezheng Hua ◽  
Xinhua Liu ◽  
Munish Kumar Gupta ◽  
Pawel Fracz ◽  
Zhixiong Li
Keyword(s):  
Optimization Algorithm ◽  
Pid Controller ◽  
Fuzzy Controller ◽  
Fruit Fly ◽  
Magnetorheological Fluids ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Braking System ◽  
System A ◽  
Working Performance

With the aim to improve working performance of magnetorheological fluids (MRF) brake, a combined braking approach is proposed in this paper. A data acquisition system of MRF brake is designed and transfer function is identified for the device. Furthermore, based on the excitation current-extrusion pressure combined braking system, a fuzzy controller is designed to distribute the target torque. Moreover, through integration of fruit fly optimization algorithm (FOA) and particle swarm optimization algorithm (PSOA), an improved proportional-integral-differential (PID) controller is presented to conduct braking mechanism. Finally, the simulations and experiments are carried out. The results show that the combined braking approach with fuzzy controller and improved PID controller has characteristics of quick response and no overshoot, and superior to conventional PID and FOA PID controller.

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