scholarly journals Combined fuzzy PID regulator for frequency regulation of smart grid and conventional power systems

2021 ◽  
Vol 24 (1) ◽  
pp. 12
Author(s):  
Smrutiranjan Nayak ◽  
Sanjeeb Kumar Kar ◽  
Subhransu Sekhar Dash
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Optimization Algorithm ◽  
Pid Controller ◽  
Thermal Power ◽  
Pi Controller ◽  
Proportional Integral Derivative ◽  
Swarm Optimization ◽  
Proportional Integral ◽  
Thermal Power System

In continually increasing area and structure of modern power system having burden demand uncertainties, the use of knowledgeable and vigorous frequency power strategy is essential for the satisfactory functioning of the Power system. A combined fuzzy proportional-integral-derivative (CFPID) controller is suggested for frequency supervision of the power system. To optimize the controller parameters, a review of sine and cosine work adjusted improved whale optimization algorithm (SCiWOA) has been utilized. The next practical application of power-system frequency control is performed by designing a CFPID controller using the proposed SCiWOA technique for a smart grid system having inexhaustible sources like sun oriented, wind, photovoltaic and capacity gadgets like a battery, flywheel just as module electric vehicles. The first advantages of the SCiWOA tuned CFPID controller over hybrid-particle-swarm-optimization and pattern-search (hPSO-PS) adjusted fuzzy proportional-integral (FPI) controller, hybrid bacterial foraging optimization algorithm-particle swarm optimization (hBFOA-PSO) adjusted proportional-integral (PI) controller, genetic algorithm (GA) tuned proportional and integral (PI) controller, BFOA adjusted PI controller, jaya algoritm (JA) tuned PID with derivative filter (PIDN) controller and teaching learning based optimization (TLBO) tuned proportional-integral-derivative (PID) controller are demonstrated for the two-area non-reheat thermal power system. The second advantages of the SCiWOA tuned CFPID controller over artificial-bee-colony (ABC) tuned PID controller, SOSA tuned PID controller and Firefly algorithm (FA) tuned PID controller are demonstrated for two-area reheat thermal power system. It is seen that SCiWOA based CFPID controller is more effective in controlling the recurrence comparative with PID regulator.

scholarly journals Combined Economic and Emission Dispatch Problem of Wind-Thermal Power System Using Gravitational Particle Swarm Optimization Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-19 ◽  
Author(s):  
Shanhe Jiang ◽  
Chaolong Zhang ◽  
Wenjin Wu ◽  
Shijun Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Particle Swarm Optimization Algorithm ◽  
Thermal Power ◽  
Particle Swarm ◽  
Compromise Solution ◽  
Fuel Cost ◽  
Swarm Optimization ◽  
Thermal Power System ◽  
Emission Dispatch

In this paper, a novel hybrid optimization approach, namely, gravitational particle swarm optimization algorithm (GPSOA), is introduced based on particle swarm optimization (PSO) and gravitational search algorithm (GSA) to solve combined economic and emission dispatch (CEED) problem considering wind power availability for the wind-thermal power system. The proposed algorithm shows an interesting hybrid strategy and perfectly integrates the collective behaviors of PSO with the Newtonian gravitation laws of GSA. GPSOA updates particle’s velocity caused by the dependent random cooperation of GSA gravitational acceleration and PSO velocity. To describe the stochastic characteristics of wind speed and output power, Weibull-based probability density function (PDF) is utilized. The CEED model employed consists of the fuel cost objective and emission-level target produced by conventional thermal generators and the operational cost generated by wind turbines. The effectiveness of the suggested GPSOA is tested on the conventional thermal generator system and the modified wind-thermal power system. Results of GPSOA-based CEED problems by means of the optimal fuel cost, emission value, and best compromise solution are compared with the original PSO, GSA, and other state-of-the-art optimization approaches to reveal that the introduced GPSOA exhibits competitive performance improvements in finding lower fuel cost and emission cost and best compromise solution.

A novel cascaded fuzzy PD-PI controller for load frequency study of solar-thermal/wind generator-based interconnected power system using grasshopper optimization algorithm

2020 ◽  
pp. 002072092093036
Author(s):  
Debasis Tripathy ◽  
NB Dev Choudhury ◽  
BK Sahu
Keyword(s):  
Power System ◽  
Optimization Algorithm ◽  
Pi Controller ◽  
Solar Thermal ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Load Frequency ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization ◽  
Fuzzy Pd

This work analyses the load-frequency responses of a multi-unit based two-area power system by proposing a novel cascaded fuzzy Proportional Derivative-Proportional Integral (PD-PI) controller tuned with a recently proposed grasshopper optimization algorithm. Performance of the power system comprising of conventional sources like hydro and thermal generating units is evaluated by cascaded fuzzy PD-PI controller optimised by grasshopper optimization algorithm. The potential of grasshopper optimization algorithm is validated by comparing with other algorithms. Further, load-frequency response is studied by penetrating solar-thermal and wind power generating units into the recommended system. The power system integrated with renewable sources puts forth a great stability challenge in the wake of high load perturbation. Hence, a robust secondary controller named cascaded fuzzy PD-PI controller is designed by endorsing a profound grasshopper optimization algorithm technique, to tackle this stability challenge. The credibility of the cascaded fuzzy PD-PI controller with/without nonlinearities presented in the system is validated by comparing the results obtained from proportional–integral–derivative and fuzzy-proportional–integral–derivative controllers. Besides this, the performance of the system under highly perturbed step load variation confers the robustness of the proposed method.

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