Grey Wolf Optimization Technique Research Articles

Application of optimal fuzzy and PI hybrid controllers for permanent magnet synchronous generator–based wind energy conversion systems

Permanent magnet synchronous generator (PMSG)–based wind turbines (WTs) are preferred over doubly fed induction generators (DFIGs) due to some appealing features. The control techniques used in this paper are fuzzy-based pitch angle controller, optimal torque-controlled maximum power point tracking, and optimal fuzzy and proportional integral (PI) hybrid controller under MATLAB-Simulink software. A grey wolf optimization technique is used to optimize the input scaling factor of the fuzzy logic controller (FLC) and gains of the PI controller. The results indicate that optimal FLC-PI performs better in DC-link voltage control than an optimal PI controller in terms of peak overshoot (0.82%), undershoot (2.51%), and settling time (0.18 s). However, the peak overshoot (1.87%), undershoot (10.6%), and settling time (0.22 s) are recorded for DC-link voltage response while utilizing the optimal PI controller. The generator’s efficiency reaches up to 97.35% for the optimal FLC-PI controller while 96.01% is obtained with the optimal PI controller at rated wind speed.

Bagging ensemble of artificial neural networks with weighted averaging and grey wolf optimization for solar photovoltaic power generation prediction

ABSTRACT Solar power generation is highly uncertain and intermittent due to its strong dependence on climate and meteorological factors. In this study, we have proposed a bagging ensemble of artificial neural network (BagANN) model with weighted averaging optimized using grey wolf optimization (GWO) and investigate the impact of climatic and meteorological factors on the output power prediction of the solar power plant. Initially, we have preprocessed the data by applying feature engineering, normalization techniques, and appropriate features are selected for power prediction. The hyperparameters of the BagANN model are tuned and optimized using the GWO technique to improve the models predictive accuracy. Experiments are conducted to evaluate the generalization performance of the developed model using five-fold and 10-fold cross-validation techniques. The performance results are assessed using RMSE, coefficient of determination ( R 2 ), adjusted ( R 2 ), and the MAPE. Results show that the BagANN with weighted averaging has a higher level of precision with R 2 of 98.5% predictive accuracy, has less prediction errors with 0.1263 MSE and 0.0267 MAPE than the other developed models. From the result, it is evident that the BagANN model has significant improvements compared to other models available in the literature in terms of robustness of the prediction and predictive accuracy.

Controller Design Using Grey Wolf Optimization Technique for Reduced Order System

Controller Design Using Grey Wolf Optimization Technique for Reduced Order System

High-performance front end PFC controller design for light electric vehicle charger application

Power factor correction (PFC) boost converters operating in CCM (continuous conduction mode) typically utilize average current mode (ACM) control alongside a LPF (low-pass filter) to reduce the impact of double line frequency ripple on the current loop. However, the LPF limit the voltage loop bandwidth in ACM-regulated converters, resulting in sluggish dynamic response. Additionally, zero-crossing distortion (ZCD) often occurs in the current control loop due to inaccuracies in tracking the reference current at the zero crossing point of the waveform. To address these challenges, this paper proposes a feed-forward control strategy that utilizes supply voltage and output current, effectively eliminating the need for an LPF and enhancing transient response. The voltage loop is tuned using the conventional Z-N method, while the Grey Wolf Optimization (GWO) technique is employed to optimally tune the gain parameters of the current controller (KPi and KIi). This approach effectively reduces reference tracking errors and mitigates ZCD, offering a balance between simplicity and performance. The proposed method is simple, offering fast transient and steady-state response, low THD, near-unity PF, and tight voltage regulation under fluctuating conditions. The effectiveness of this approach is validated through MATLAB/Simulink simulations, and hardware verification is conducted using a 500 W laboratory prototype controlled by a dSPACE 1104 digital controller.

Thermoeconomic modeling of new energy system based on biogas upgrading to multigenerational purpose

The following article outlines an integrated approach toward the valorization of biogas for energy, chilled water, hot water, freshwater, and liquid carbon dioxide production. The suggested system includes a biogas upgrading unit with a biomethane-fueled burner, a Kalian cycle, an LNG regasification process, and a multi-effect desalination unit that can provide CO2, electricity, cooling, and heating. The new ideas in the proposed system include a cryogenic biogas upgrading process that can be used in many industrial settings, the production of liquid carbon dioxide through biogas upgrading, and the use of biomethane in the integrated combustion process. Furthermore, the grey wolf optimization technique is applied in various multi-objective optimization contexts to achieve optimal operating conditions and preferred performance outcomes. In this simulation, the generation of electricity reaches a value of 755 kW. Attached to it is a cooling load of about 5102 kW, with a heating load of 4725 kW, freshwater capacity about 1.33 kg/s, production rate of liquid CO2 at 0.58 kg/s, and natural gas at 3.1 kg/s. Thermodynamic results reveal that the proposed multigeneration performance scheme providently enhances energy and exergy efficiencies up to 59.82 % and 5.74 %, respectively, compared to the condition of a single product. The performed exergy analysis shows that the overall irreversibility of the suggested configuration is equal to 18,047 kW while about 42.6 % of this value is contributed from the heat exchanger E-104. Moreover, the CO2 emission intensity of the process can be kept as 0.35 kg/kWh. Cryogenic separation can obtain about 88 % of CO2 in biogas and turn it into its liquid phase. The performed economic assessment has resulted in the following total cost rate as well as cost per unit exergy: 321.38 $/h and 83.06 $/GJ, respectively. Under optimal conditions, an exergy efficiency of 13.40 % and a net power output of 2034.39 kW are attained.

Robust variable step size least mean fourth with quotient form-based control scheme for DVR operation

ABSTRACT A robust variable step size least mean fourth with a quotient form control scheme (RVSSLMFQ) is implemented for addressing power quality concerns and delivering clean voltage to sensitive loads through a dynamic voltage restorer (DVR). This method estimates the fundamental components from distorted grid voltages, featuring an automated process with adaptive time-varying step sizes and utilizing a quotient structure to reduce mean squared error (MSE) effectively. The suggested control scheme overcomes the stability and performance issues of the least mean fourth (LMF) algorithm by improving convergence and adapting well to varying step sizes. Furthermore, RVSSLMFQ achieves remarkable results with reduced settling time and fewer peak overshoots compared to the least mean square (LMS) and LMF control algorithms. For tuning the load voltage and DC-link voltage proportional-integral (PI) controllers, the improved grey wolf optimization technique (I-GWO) is applied. The results are evaluated through MATLAB simulations.

Multi-objective-trust aware improved grey wolf optimization technique for uncovering adversarial attacks in WSNs

Multi-objective-trust aware improved grey wolf optimization technique for uncovering adversarial attacks in WSNs

English

English

Data-Aided Maximum Likelihood Joint Angle and Delay Estimator Over Orthogonal Frequency Division Multiplex Single-Input Multiple-Output Channels Based on New Gray Wolf Optimization Embedding Importance Sampling.

In this paper, we propose a new data-aided (DA) joint angle and delay (JADE) maximum likelihood (ML) estimator. The latter consists of a substantially modified and, hence, significantly improved gray wolf optimization (GWO) technique by fully integrating and embedding within it the powerful importance sampling (IS) concept. This new approach, referred to hereafter as GWOEIS (for "GWO embedding IS"), guarantees global optimality, and offers higher resolution capabilities over orthogonal frequency division multiplex (OFDM) (i.e., multi-carrier and multi-path) single-input multiple-output (SIMO) channels. The traditional GWO randomly initializes the wolfs' positions (angles and delays) and, hence, requires larger packs and longer hunting (iterations) to catch the prey, i.e., find the correct angles of arrival (AoAs) and time delays (TDs), thereby affecting its search efficiency, whereas GWOEIS ensures faster convergence by providing reliable initial estimates based on a simplified importance function. More importantly, and beyond simple initialization of GWO with IS (coined as IS-GWO hereafter), we modify and dynamically update the conventional simple expression for the convergence factor of the GWO algorithm that entirely drives its hunting and tracking mechanisms by accounting for new cumulative distribution functions (CDFs) derived from the IS technique. Simulations unequivocally confirm these significant benefits in terms of increased accuracy and speed Moreover, GWOEIS reaches the Cramér-Rao lower bound (CRLB), even at low SNR levels.

Techno-economic analysis and optimization of a binary geothermal combined district heat and power plant for Puga Valley, India

This article investigates the energy, exergy, and economic performance of a Puga Valley-specific binary geothermal combined district heat and power plant, taking into account geotechnical data and related cost functions. The power plant uses an organic Rankine cycle (ORC) with R123 as the working fluid, utilizing waste heat for district heating to enhance efficiency. Variations in operating and design parameters such as geothermal fluid flow rate, temperature, and ORC temperature of the evaporator and condenser are found to have significant effects on the overall performance of the system and cost rate. A multi-objective grey wolf optimization technique based on artificial neural networks has been used to determine the ideal values of the above-mentioned parameters. The optimization study revealed a Pareto optimal curve with overall exergy efficiency and total cost rate as objective functions from which the optimal point was identified using the technique for order preference by similarity to the ideal solution (TOPSIS) method. While operating the proposed CHP plant at TOPSIS optimal point, the plant delivers a net electrical output of 1.08 MW alongside 4.19 MW of thermal energy, resulting in a total cost rate of 33.84 USD/h, with energy and exergy efficiencies peaking at 32.26 % and 36.8 %, respectively. The exergy analysis at the optimal point also revealed a notable reduction of 550 kW in the total exergy destruction rate compared with the base-case scenario. Furthermore, the design requirements of a 5 MW net power output CHP plant for effective recovery of the Puga Valley geothermal resource are discussed.

FOMCON Toolbox-Based Direct Approximation of Fractional Order Systems Using Gaze Cues Learning-Based Grey Wolf Optimizer

This study discusses a new method for the fractional-order system reduction. It offers an adaptable framework for approximating various fractional-order systems (FOSs), including commensurate and non-commensurate. The fractional-order modeling and control (FOMCON) toolbox in MATLAB and the gaze cues learning-based grey wolf optimizer (GGWO) technique form the basis of the recommended method. The fundamental advantage of the offered method is that it does not need intermediate steps, a mathematical substitution, or an operator-based approximation for the order reduction of a commensurate and non-commensurate FOS. The cost function is set up so that the sum of the integral squared differences in step responses and the root mean squared differences in Bode magnitude plots between the original FOS and the reduced models is as tiny as possible. Two case studies support the suggested method. The simulation results show that the reduced approximations constructed using the methodology under consideration have step and Bode responses more in line with the actual FOS. The effectiveness of the advocated strategy is further shown by contrasting several performance metrics with some of the contemporary approaches disseminated in academic journals.

Economic/sustainability optimization/analysis of an environmentally friendly trigeneration biomass gasification system using advanced machine learning

The present research presents an innovative thermal integration model for a biomass-based power plant to generate power, coolant, and liquefied hydrogen. This integrated arrangement comprises biomass gasification, gas turbine cycle, organic flash-bi-evaporator refrigeration cycle, multi-effect desalination, solid oxide electrolyzer, and Claude cycle. The selection of an eco-friendly fluid for the electricity-cooling production cycle is conduced relying on a comparative analysis. The subsequent examination evaluates the economic-sustainability performance criteria through sensitivity and contour analyses. The comparative assessment identifies R1234ze(Z) as the most suitable working fluid. Four objective functions are scrutinized, and two optimization scenarios are devised based on a machine learning approach using artificial neural networks and a multi-objective gray wolf optimization technique. In Scenario A, the focused objectives are the total destructed exergy and the cost of producing liquefied hydrogen. Meanwhile, Scenario B integrates the sustainability index and the overall system cost rate as primary criteria. Scenario A exhibits more favorable operational conditions and outcomes than scenario B, showing the objectives mentioned as 9955 kW and 3.25 $/kg, respectively. In this scenario, the sustainability index, the overall investment cost rate, and the net present value are obtained to be 1.74, 236 $/h, and 205 M$, correspondingly.

Optimization of off-grid hybrid renewable energy systems for cost-effective and reliable power supply in Gaita Selassie Ethiopia

This paper explores scenarios for powering rural areas in Gaita Selassie with renewable energy plants, aiming to reduce system costs by optimizing component numbers to meet energy demands. Various scenarios, such as combining solar photovoltaic (PV) with pumped hydro-energy storage (PHES), utilizing wind energy with PHES, and integrating a hybrid system of PV, wind, and PHES, have been evaluated based on diverse criteria, encompassing financial aspects and reliability. To achieve the results, meta-heuristics such as the Multiobjective Gray wolf optimization algorithm (MOGWO) and Multiobjective Grasshopper optimization algorithm (MOGOA) were applied using MATLAB software. Moreover, optimal component sizing has been investigated utilizing real-time assessment data and meteorological data from Gaita Sillasie, Ethiopia. Metaheuristic optimization techniques were employed to pinpoint the most favorable loss of power supply probability (LPSP) with the least cost of energy (COE) and total life cycle cost (TLCC) for the hybrid system, all while meeting operational requirements in various scenarios. The Multi-Objective Grey Wolf Optimization (MOGWO) technique outperformed the Multi-Objective Grasshopper Optimization Algorithm (MOGOA) in optimizing the problem, as suggested by the results. Furthermore, based on MOGWO findings, the hybrid solar PV-Wind-PHES system demonstrated the lowest COE (0.126€/kWh) and TLCC (€6,897,300), along with optimal satisfaction of the village's energy demand and LPSP value. In the PV-Wind-PHSS scenario, the TLCC and COE are 38%, 18%, 2%, and 1.5% lower than those for the Wind-PHS and PV-PHSS scenarios at LPSP 0%, according to MOGWO results. Overall, this research contributes valuable insights into the design and implementation of sustainable energy solutions for remote communities, paving the way for enhanced energy access and environmental sustainability.

Islanding Detection and Power Quality Diagnosis of Wind Power Integrated Microgrid with Reduced Feature Trained Novel Optimized Random Decision Forest

Distributed generations (DGs) have been increasingly addressing the ongoing power deficit in the electricity market. However, a significant concern in DG-integrated microgrids is the detection of accidental islanding. To tackle this issue, this article proposes a cost-friendly, novel data-driven passive islanding detection scheme named EEMD-HOBRC, combining noise-assisted ensemble empirical mode decomposition (EEMD) and a hybrid optimization-based random forest classifier (HOBRFC). The detection scheme employs a diverse set of features extracted from both raw and EEMD decomposed signals. Essential features are selected using the binary grey wolf optimizer (BGWO) to reduce computational burden. To further improve classification accuracy, the parameters of the random forest classifier are optimized through a hybrid particle swarm and reformed grey wolf optimization (PSRGWO) technique with Cohen’s kappa index as the cost function. The proposed technique is rigorously validated in two different multi-DG environments, encompassing islanding and various nonislanding events. The results demonstrate the effectiveness of the approach in terms of enhanced accuracy, detection time, and performance under both noisy and noise-free conditions. The accuracy of detection under ideal and high noise scenarios is found to be 99.88% and 99.2%, respectively, with maximum detection time of 34.27 ms. Comparative analysis with other algorithms also supports the superiority of the proposed technique. Finally, the method is successfully applied to shrink the nondetection zone (NDZ) with minimal power mismatch, further enhancing its utility in practical applications.

A Novel Hybrid MPPT Controller for PEMFC Fed High Step-Up Single Switch DC-DC Converter

At present, there are different types of Renewable Energy Resources (RESs) available in nature which are wind, tidal, fuel cell, and solar. The wind, tidal, and solar power systems give discontinuous power supply which is not suitable for the present automotive systems. Here, the Proton Exchange Membrane Fuel Stack (PEMFS) is used for supplying the power to the electrical vehicle systems. The features of fuel stack networks are very quick static response, plus low atmospheric pollution. Also, this type of power supply system consists of high flexibility and more reliability. However, the fuel stack drawback is a nonlinear power supply nature. As a result, the functioning point of the fuel stack varies from one position to another position on the V-I curve of the fuel stack. Here, the first objective of the work is the development of the Grey Wolf Optimization Technique (GWOT) involving a Fuzzy Logic Controller (FLC) for finding the Maximum Power Point (MPP) of the fuel stack. This hybrid GWOT-FLC controller stabilizes the source power under various operating temperature conditions of the fuel stack. However, the fuel stack supplies very little output voltage which is improved by introducing the Single Switch Universal Supply Voltage Boost Converter (SSUSVBC) in the second objective. The features of this proposed DC-DC converter are fewer voltage distortions of the fuel stack output voltage, high voltage conversion ratio, and low-level voltage stress on switches. The fuel stack integrated SSUSVBC is analyzed by selecting the MATLAB/Simulink window. Also, the proposed DC-DC converter is tested by utilizing the programmable DC source.

A novel hybrid method based MPP tracking design using boost converter for solar power systems

The convergence time is one of the key parameters for evaluating the energy conversion efficiency of PV power systems in the experimental process of tracking the maximum power point (MPP) in real-time. When the PV system is partially shaded, for example, MPP tracking easily slips into the local MPP and takes a long time. To overcome this drawback, a new solution for a stand-alone PV power system has been proposed that combines the incremental conductance (In-Cond) algorithm and the improved grey wolf optimization (GWO) approach. The improved GWO technique changed the methodology is used to find the global power area in this proposed method, and it is integrated with the In-Cond algorithm to fast obtain the global MPP. To demonstrate the efficacy of the suggested strategy, MATLAB/ Simulation and experiment results of the PV system are provided. The proposed hybrid method has a quick response time of 0.18 to 0.42 seconds for good transient oscillation and global power tracking, whereas the classic GWO and particle swarm optimization (PSO) methods take 0.82 to 2.1 seconds and 0.68 to 2.2 seconds, respectively. The global MPP was obtained not only with uniform irradiance intensity, but also with partial shade.

Design and performances improvement of an UWB antenna with DGS structure using a grey wolf optimization algorithm

In this article, we propose the design of a rectangular-shaped patch antenna suitable for ultra-wideband (UWB) applications and short and long–range Millimeter-Wave Communications. We begin with the design of a high-gain UWB rectangular patch antenna featuring a partial ground plane and operating within the 3.1–10.6 GHz bandwidth. Complementary Split Ring Resonators (CSRRs) are integrated on both sides of the structure to meet desired specifications. The resulting UWB antenna boasts an extended frequency bandwidth, covering 2.38–22.5 GHz (twice that of the original antenna), with a peak gain of 6.5 dBi and an 88% radiation efficiency. The grey wolf optimization technique (GWO) determines optimal structural dimensions. Validation of the antenna's performance is demonstrated through the strong agreement between measurement and simulation.

Third generation semiconductor device research: Optimizing CMOS and HEMT designs

The third-generation semiconductor device known as High Electron Mobility Transistors (HEMT) has found extensive applications in high-frequency and high-speed electronic systems. Its widespread usage in critical technologies such as radio telescopes, satellite broadcast receivers, and cellular base stations has established HEMT as a foundational technology underpinning our information and communication society. This paper provides an in-depth exploration of these semiconductor advancements. Firstly, the paper utilizes the CMOS inverter as a representative example to elucidate the fundamental structure of Complementary Metal-Oxide-Semiconductor (CMOS) technology. Additionally, it employs Gallium Arsenide (GaAs) HEMT as an illustrative instance to expound upon the architecture of HEMT devices. Furthermore, the paper delves into the optimization of CMOS technology, focusing on topics such as Multi-Threshold CMOS and the impact of the Width/Length (W/L) ratio. These discussions shed light on ways to enhance the performance of CMOS-based components. Additionally, the paper explores strategies to optimize HEMT devices, including the introduction of carbon doping and the application of the Grey-Wolf optimization technique. These approaches are critical in achieving higher efficiency and performance in HEMT-based applications.

Performance comparison of dynamically controlled particle swarm optimization and grey wolf optimizer for dynamic power reserve scheduling in multi areas problems

The primary goal of Multi-Area Economic Dispatch (MAED) is to effectively meet the dynamic power demands of multiple interconnected regions while optimizing the economical generation dispatch strategy. This paper introduces a comprehensive investigation into the application of two advanced optimization techniques, namely Dynamically Controlled Particle Swarm Optimization (DCPSO) and thereafter Grey Wolf Optimizer (GWO), and their variant adaptations to address the complex challenges posed by MAED issue. The performance and efficacy of these strategies are rigorously assessed in three distinct scenarios: (a) Single-Area Power System with Three Generation Units: This scenario represents a fundamental single-area power network comprising three generation units. (b) Two-Area System with Four Generating Units: In this case, a two-area system with four generating units is analyzed. (c) Four-Area System with Forty Generation Units and Six Tie Lines. In the most intricate scenario, a four-area interconnected power system is scrutinized, featuring a total of forty generation units. GWO technique produces the cost saving of $ 0.1 for single area, 2 area cost saving is $ 0.11 likewise for 4 area the cost saving is $ 474.10 over the PSO. The execution time taken by GWO is 12.7 sec. less compared to DPSO.

Online optimal tuning of fuzzy PID controller using grey wolf optimizer for quarter car semi-active suspension system

In order to reduce vibration and increase ride comfort, this article utilizes a system of quarter-car suspension integrated with a Fuzzy PID controller. To build and improve the Fuzzy PID controller for the semi-active suspension system used in quarter cars, using a novel meta-heuristic technique known as Grey Wolf Optimizer (GWO). Here the magnetorheological damper (MR) fluid with the Fuzzy PID controller was examined to optimize using the GWO algorithm. With the GWO technique and the integral of time absolute error (IAE) as a fitness function, the three gain parameters of the Fuzzy PID controller – Kp, Ki, and Kd– have been optimally set. The suggested approach has additional advantages for the optimization of functions with three variables, including simplicity in implementation, quick convergence traits, and superior computational capabilities. This work is significant, to the best of the author’s knowledge there is no optimization method using GWO to online tune a Fuzzy PID controller for a semi-active suspension system. The optimal output parameters of the controller can be updated online in real-time by GWO. The performance of the proposed controller was examined by assessing the root mean square (RMS) values and peak-to-peak (PTP) values of body displacement and body acceleration under various road profiles. To ensure that the intelligent controller was of the highest caliber, an online test rig was constructed. Results from simulations and online experiments demonstrated that the Fuzzy GWO PID controller significantly improved ride comfort under a variety of road conditions when compared to the Fuzzy PID controller and passive suspension system.