Optimal reactive power planning using oppositional grey wolf optimization by considering bus vulnerability analysis

This paper proposes a co-ordinated reactive power control strategy analysis in Line Side Converter (LSC) of Doubly Fed induction generator (DFIG) and in the Automatic voltage regulator (AVR) of synchronous generator (SG) in isolated Dish Stirling solar Thermal(DSTS) – Biomass hybrid power system. This co-ordinated control strategy greatly maintains the autonomous hybrid power system voltage deviation by adjusting the reactive power imbalance caused due to the impulsive reactive power nature of DFIG coupled with the DSTS and load. Gain values of Integral Order Proportional Integral (IO-PI) controller in LSC and AVR are optimized to maintain the system voltage stability. Modern and simple Grey wolf optimization algorithm has been used to extract the superior PI gain values to maintain system stability with less reaction time. The proposed co-ordinated control strategy has been tested with various load disturbance and with dynamic input power feed from DSTS to validate the system robustness. Also, the optimized gain value based voltage deviation values are validated in real time with dSPACE.

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Fuzzy grey wolf optimization for controlled low-voltage ride-through conditions in grid-connected wind turbine with doubly fed induction generator

SIMULATION ◽

10.1177/0037549718777607 ◽

2018 ◽

Vol 95 (4) ◽

pp. 327-338 ◽

Cited By ~ 2

Author(s):

Charan Jeet Madan ◽

Naresh Kumar

Keyword(s):

Control System ◽

Wind Turbine ◽

Nuclear Power ◽

Power Plants ◽

Reactive Power ◽

Low Voltage ◽

Grey Wolf ◽

Grey Wolf Optimization ◽

Low Voltage Ride Through ◽

Conventional Methods

With its enormous environmental and monetary benefits, the wind turbine has become an acceptable alternative to the generation of electricity by fossil fuel or nuclear power plants. Research remains focused on improving the performance of wind turbines with maximum flexibility and gains. The main objective of the paper is to simulate a low-voltage ride-through (LVRT) control system that is convenient for the development of a controller that should have the ability to rectify fault signals. This paper proposes a novel method called grey wolf optimization with fuzzified error (GWFE) model to simulate the optimized control system. Further, it compares the GWFE-based LVRT system with the standard LVRT system, systems with minimum and maximum gain, and conventional methods like genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), ant bee colony (ABC), and grey wolf optimization (GWO) algorithms. Accordingly, it analyses the simulation results regarding qualitative analysis like active power, [Formula: see text] comparison, gain, pitch degree, reactive power, rotor current, stator current, and [Formula: see text] and [Formula: see text] measurements; and quantitative analysis like RMSE computation of [Formula: see text] with varying speed. Hence, the proposed GWFE algorithm is beneficial for simulating the LVRT system compared to other conventional methods.

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Power Quality Improvement in a Grid Coupled Solar PV System using Grey Wolf Optimization

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.j1154.0881019 ◽

2019 ◽

Vol 8 (10) ◽

pp. 3200-3204

Keyword(s):

Quality Improvement ◽

Power Quality ◽

Reactive Power ◽

Optimization Technique ◽

Solar Irradiation ◽

Solar Pv ◽

Grey Wolf ◽

Grey Wolf Optimization ◽

Power Quality Improvement ◽

Utility Grid

Since the PV penetration in the utility grid is increasing rapidly, there is a need of a control strategy for the purpose of energy optimization and for providing clean and green electric power to the utility grid. In this paper, a dynamic technique is proposed employing a Fractional Order PI controller tuned using Grey Wolf Optimization Technique. The strategy provides the independent control active as well as reactive power being injected into the grid. A complete investigation on performance analysis and THD levels at different solar irradiation value were conducted on MATLAB/SIMULINK software. The efficacy of the work is validated by comparing the results obtained by using Grey Wolf Optimization with permissible IEEE standards and the observations proves the power quality improvement by reducing the THD i.e. Total Harmonic Distortion levels.

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Reactive Power Control of Isolated Wind-diesel Hybrid Power System Using Grey Wolf Optimization Technique

Procedia Computer Science ◽

10.1016/j.procs.2016.07.389 ◽

2016 ◽

Vol 92 ◽

pp. 345-354 ◽

Cited By ~ 6

Author(s):

Sasmita Behera ◽

Chinmay Prakash Sahoo ◽

Bidyadhar Subudhi ◽

Bibhuti Bhusan Pati

Keyword(s):

Power System ◽

Power Control ◽

Reactive Power ◽

Optimization Technique ◽

Grey Wolf ◽

Grey Wolf Optimization ◽

Reactive Power Control ◽

Hybrid Power System ◽

Hybrid Power

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Evaluation of Grey Wolf Optimization Algorithm on Rigid and Flexible Receptor Docking

10.26434/chemrxiv.11733393 ◽

2020 ◽

Author(s):

Kin Meng Wong ◽

Shirley Siu

Keyword(s):

Scoring Function ◽

Ligand Docking ◽

Receptor Protein ◽

Pose Prediction ◽

Grey Wolf ◽

Success Rates ◽

Autodock Vina ◽

Grey Wolf Optimization ◽

Structure Based Drug Design ◽

Docking Program

Protein-ligand docking programs are indispensable tools for predicting the binding pose of a ligand to the receptor protein in current structure-based drug design. In this paper, we evaluate the performance of grey wolf optimization (GWO) in protein-ligand docking. Two versions of the GWO docking program – the original GWO and the modified one with random walk – were implemented based on AutoDock Vina. Our rigid docking experiments show that the GWO programs have enhanced exploration capability leading to significant speedup in the search while maintaining comparable binding pose prediction accuracy to AutoDock Vina. For flexible receptor docking, the GWO methods are competitive in pose ranking but lower in success rates than AutoDockFR. Successful redocking of all the flexible cases to their holo structures reveals that inaccurate scoring function and lack of proper treatment of backbone are the major causes of docking failures.

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