scholarly journals Optimal Voltage and Frequency Control of an Islanded Microgrid using Grasshopper Optimization Algorithm

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3191 ◽  
Author(s):  
Touqeer Ahmed Jumani ◽  
Mohd Wazir Mustafa ◽  
Madihah Md Rasid ◽  
Nayyar Hussain Mirjat ◽  
Zohaib Hussain Leghari ◽  
...  
Keyword(s):  
Power Quality ◽  
Optimization Algorithm ◽  
Pi Controller ◽  
Parameter Selection ◽  
Current Control ◽  
Control Architecture ◽  
Frequency Regulation ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization

Due to the lack of inertia and uncertainty in the selection of optimal Proportional Integral (PI) controller gains, the voltage and frequency variations are higher in the islanded mode of the operation of a Microgrid (MG) compared to the grid-connected mode. This study, as such, develops an optimal control strategy for the voltage and frequency regulation of Photovoltaic (PV) based MG systems operating in islanding mode using Grasshopper Optimization Algorithm (GOA). The intelligence of the GOA is utilized to optimize the PI controller parameters. This ensures an enhanced dynamic response and power quality of the studied MG system during Distributed Generators (DG) insertion and load change conditions. A droop control is also employed within the control architecture, alongside the voltage and current control loops, as a power-sharing controller. In order to validate the performance of the proposed control architecture, its effectiveness in regulating MG voltage, frequency, and power quality is compared with the precedent Artificial Intelligence (AI) based control architectures for the same control objectives. The effectiveness of the proposed GOA based parameter selection method is also validated by analyzing its performance with respect to the improved transient response and power quality of the studied MG system in comparison with that of the Particle Swarm Optimization (PSO) and Whales Optimization Algorithm (WOA) based parameter selection methods. The simulation results establish that the GOA provides a faster and better solution than PSO and WOA which resulted in a minimum voltage and frequency overshoot with minimum output current and Total Harmonic Distortion (THD).

scholarly journals Optimal Power Flow Controller for Grid-Connected Microgrids using Grasshopper Optimization Algorithm

Electronics ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 111 ◽  
Author(s):  
Touqeer Jumani ◽  
Mohd Mustafa ◽  
Madihah Rasid ◽  
Nayyar Mirjat ◽  
Mazhar Baloch ◽  
...  
Keyword(s):  
Dynamic Response ◽  
Power Quality ◽  
Optimization Algorithm ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Harmonic Distortion ◽  
Optimal Power ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization ◽  
Power Flow Controller

Despite the vast benefits of integrating renewable energy sources (RES) with the utility grid, they pose stability and power quality problems when interconnected with the existing power system. This is due to the production of high voltages and current overshoots/undershoots during their injection or disconnection into/from the power system. In addition, the high harmonic distortion in the output voltage and current waveforms may also be observed due to the excessive inverter switching frequencies used for controlling distributed generator’s (DG) power output. Hence, the development of a robust and intelligent controller for the grid-connected microgrid (MG) is the need of the hour. As such, this paper aims to develop a robust and intelligent optimal power flow controller using a grasshopper optimization algorithm (GOA) to optimize the dynamic response and power quality of the grid-connected MG while sharing the desired amount of power with the grid. To validate the effectiveness of proposed GOA-based controller, its performance in achieving the desired power sharing ratio with optimal dynamic response and power quality is compared with that of its precedent particle swarm optimization (PSO)-based controller under MG injection and abrupt load change conditions. The proposed controller provides tremendous system’s dynamic response with minimum current harmonic distortion even at higher DG penetration levels.

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.

scholarly journals Speed controller design for three-phase induction motor based on dynamic adjustment grasshopper optimization algorithm

2021 ◽  
Vol 11 (2) ◽  
pp. 1143
Author(s):  
Ammar Falah Algamluoli ◽  
Nizar Hadi Abbas
Keyword(s):  
Induction Motor ◽  
Optimization Algorithm ◽  
Applied Load ◽  
Pi Controller ◽  
Dynamic Adjustment ◽  
Load Torque ◽  
Speed Controller ◽  
Three Phase ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization

Three-phase induction motor (TIM) is widely used in industrial application like paper mills, water treatment and sewage plants in the urban area. In these applications, the speed of TIM is very important that should be not varying with applied load torque. In this study, direct on line (DOL) motor starting without controller is modelled to evaluate the motor response when connected directly to main supply. Conventional PI controller for stator direct current and stator quadrature current of induction motor are designed as an inner loop controller as well as a second conventional PI controller is designed in the outer loop for controlling the TIM speed. Proposed combined PI-lead (CPIL) controllers for inner and outer loops are designed to improve the overall performance of the TIM as compared with the conventional controller. In this paper, dynamic adjustment grasshopper optimization algorithm (DAGOA) is proposed for tuning the proposed controller of the system. Numerical results based on well-selected test function demonstrate that DAGOA has a better performance in terms of speed of convergence, solution accuracy and reliability than SGOA. The study results revealed that the currents and speed of TIM system using CPIL-DAGOA are faster than system using conventional PI and CPIL controllers tuned by SGOA. Moreover, the speed controller of TIM system with CPIL controlling scheme based on DAGOA reached the steady state faster than others when applied load torque.

scholarly journals Development of a Method to Measure the Quality of Working Life Using the Improved Metaheuristic Grasshopper Optimization Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Alireza Jafari Doudaran ◽  
Rouzbeh Ghousi ◽  
Ahmad Makui ◽  
Mostafa Jafari
Keyword(s):  
Human Resource ◽  
Optimization Algorithm ◽  
Fuzzy Inference ◽  
Working Life ◽  
Bees Algorithm ◽  
Quality Of Working Life ◽  
Inference System ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization

This paper provides a method to numerically measure the quality of working life based on the reduction of human resource risks. It is conducted through the improved metaheuristic grasshopper optimization algorithm in two phases. First, a go-to study is carried out to identify the relationship between quality of working life and human resource risks in the capital market and to obtain the factors from quality of working life which reduce the risks. Then, a method is presented for the numerical measurement of these factors using a fuzzy inference system based on an adaptive neural network and a new hybrid method called the improved grasshopper optimization algorithm. This algorithm consists of the grasshopper optimization algorithm and the bees algorithm. It is found that the newly proposed method performs better and provides more accurate results than the conventional one.

scholarly journals Voltage control of switched reluctance generator using grasshopper optimization algorithm

2020 ◽  
Vol 11 (1) ◽  
pp. 75
Author(s):  
Mohamed Bahy ◽  
Adel S. Nada ◽  
Sayed H. Elbanna ◽  
Mohamed A. M. Shanab
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Control Process ◽  
Voltage Control ◽  
Pi Controller ◽  
Switched Reluctance ◽  
Control Approach ◽  
Grasshopper Optimization Algorithm ◽  
Whale Optimization ◽  
Grasshopper Optimization

<p>This paper presents<strong> </strong>a terminal voltage control approach of a Switched Reluctance Generator (SRG) based wind turbine generating systems. The control process is employed using a closed loop stimulated by the error between the reference voltage and the generator output voltage due to load and wind speed variation. This error feeds the tuned Proportional Integral controller (PI).</p><p>Tuning of PI controller by conventional analysis methods is difficult by the existence of a significant non-linearity. A novel strategy method is presented here to determine optimum PI controller parameters of voltage control of SRG using Grasshopper Optimization Algorithm (GOA). This proposed approach is a simple and effective algorithm that is able to solve many optimization problems. The simplicity of algorithm provides high quality tuning of optimal PI controller parameters. The integral of time weighted squared error (ITSE) is used as the performance of the proposed GOA-PI controller. The effectiveness of the proposed strategy is tested with the three-phase 12/8 structure SRG. Outcomes indicate the supremacy of GOA over Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO) in terms of control performance measures.</p>

scholarly journals Grasshopper optimization algorithm optimized multistage controller for automatic generation control of a power system with FACTS devices

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Pratap Chabdra Nayak ◽  
Ramesh Chandra Prusty ◽  
Sidhartha Panda
Keyword(s):  
Power System ◽  
Real Time ◽  
Optimization Algorithm ◽  
Automatic Generation ◽  
Pi Controller ◽  
Automatic Generation Control ◽  
Facts Devices ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization ◽  
Generation Control

AbstractThis paper uses a Grasshopper Optimization Algorithm (GOA) optimized PDF plus (1 + PI) controller for Automatic generation control (AGC) of a power system with Flexible AC Transmission system (FACTS) devices. Three differently rated reheat turbine operated thermal units with appropriate generation rate constraint (GRC) are considered along with different FACTS devices. A new multistage controller design structure of a PDF plus (1 + PI) is introduced in the FACTS empowered power system for AGC while the controller gains are tuned by the GOA. The superiority of the proposed algorithm over the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms is demonstrated. The dynamic responses of GOA optimized PDF plus (1 + PI) are compared with PIDF, PID and PI controllers on the same system. It is demonstrated that GOA optimized PDF plus (1 + PI) controller provides optimum responses in terms of settling time and peak deviations compared to other controllers. In addition, a GOA-tuned PDF plus (1 + PI) controller with Interline Power Flow Controller (IPFC) exhibits optimal results compared to other FACTS devices. The sturdiness of the projected controller is validated using sensitivity analysis with numerous load patterns and a wide variation of parameterization. To further validate the real-time feasibility of the proposed method, experiments using OPAL-RT OP5700 RCP/HIL and FPGA based real-time simulations are carried out.

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