Maximum Wind Power Research Articles

Turbine performance and wind energy potential using probability distribution in Africa

The energy requirement in the world is increasing day by day. The non-renewable energy production is one of the reasons for climate change. The present study explores the selection of possible locations to establish a wind power plant in Ethiopia. The possible locations from Ethiopia that were considered in this study are Weraelu, Enewari and Mekdela. The wind speed characterization, wind direction and wind power densities were analyzed at Waraelu, Enewari and Mekdela. The results of this study reveals that Enewari is the best suitable place to establish the wind power plants among Waraelu, Enewari and Mekdela. In Enewari site the maximum wind speed at 10m height is 6.11 m/s. 75% of wind direction at Enewari site is from North to North West and North to North East. Weibull distribution analysis was incorporated to find average wind speed and probability density among five years of wind speed data. The average wind speed and probability density over five years of Enewari site is 5 m/s and 18.5%. The wind power densities were determined by Weibull method and the maximum wind power obtained from Enewari site is 495.33 w/m^2.

Reinforcement learning-based particle swarm optimization for wind farm layout problems

Optimizing wind farm layouts is critical to maximizing wind power generation. The wake effect significantly impacts turbines located downwind, making farm layout a key determinant of power generation efficiency. Traditional algorithms often overlook the value of leveraging historical information, which can lead to entrapment in local optima. Our survey reveals that previous studies on wind farm layout optimization (WFLO) have not adequately integrated the historical data of particle swarm optimization (PSO) with reinforcement learning’s empirical pool, resulting in the loss of valuable information. Here, we present a novel approach that enhances algorithm development and exploration by utilizing historical data and integrating proximal policy optimization from reinforcement learning with an experience pool. This method markedly outperforms the conventional genetic PSO in terms of performance. Extensive numerical experiments across wind farms of various sizes and four distinct wind scenarios demonstrate the superior efficacy of our reinforcement learning-based particle swarm optimization (RPSO) algorithm compared to 12 state-of-the-art methods. Under four wind scenarios, the average power conversion efficiencies of RPSO for the three turbine scales reach 98.68%, 98.14%, and 97.33%, respectively, underscoring the high competitiveness of the proposed RPSO for WFLO in diverse wind conditions.

Perturbation estimation based single-loop decoupling control strategy for DC-based DFIG to augment MPPT and FRT capabilities

This paper proposes a single-loop decoupling controller with high-gain perturbation observer (HP-FLC) for double-VSC DFIG which can be connected to DC grid to extract maximum wind power and improve fault ride-through (FRT) capability. Lumped perturbation terms are defined in the HP-FLC to include all unknown, time-varying dynamic and external perturbations, which are actively estimated by high-gain perturbation observer. Then, a more time-efficient single-loop feedback decoupling controller is designed to compensate the perturbation estimation. This control strategy combines the advantages of classical feedback controller and perturbation observer, which is separated from the accurate system model and easy to implement. Simulation results show that the proposed control strategy can achieve better wind power extraction and stronger FRT capability compared with double-loop feedback linearization controller (DL-FLC) and single-loop feedback linearization controller (SL-FLC), and the transient performance is better than that of the sliding-mode feedback controller (SMC-FLC). Finally, an experimental platform is built to confirm the practical implementation of the proposed approach.

Complementary operation based sizing and scheduling strategy for hybrid hydro-PV-wind generation systems connected to long-distance transmission lines

Wind and solar power are expected to play important roles in many countries to achieve carbon neutrality; however, their inherent instabilities pose significant threats to existing power grids. Because hydropower has been recognized as a viable compensatory resource for solar and wind energy uncertainties, many studies have sought to determine optimal scheduling strategies for hydro-PV, hydro-wind, and hydro-PV-wind systems. However, few studies have simultaneously considered the sizing and scheduling of large-scale hydro-PV-wind hybrid systems connected to long-distance transmission lines. Therefore, this paper develops a mathematical metric to measure the wind and solar output complementarity and incorporates it into a multi-objective sizing and scheduling model for a hybrid hydro-PV-wind system, in which resource complementarity, profit generation, system reliability, and power curtailment trade-offs are concurrently considered. The ɛ-constraint method is employed and decision makers’ attitude parameters are introduced to transform the proposed model into its equivalent single objective form. A case study in China reveals that the maximum wind and solar power output complementarity rate can be at least 0.19 for the studied hybrid hydro-PV-wind system. The incorporation of wind enables the hybrid hydro-PV-wind system to provide 4.11% more load than a hydro-PV system. In addition, if 2% of power curtailment is permitted, 1.53% more load can be served and 14.9% higher profits can be earned. To better explore the application of hybrid systems, management recommendations are provided for system operators, the power industry, and management administrations.

Fault-Tolerant Controller Applied to a Wind System Using a Doubly Fed Induction Generator

Wind systems are sustainable and economical options for producing electrical energy. These systems efficiently manage the power flow by maximizing wind power and consuming reactive power from the grid. In addition, wind systems must maintain operation despite utility grid electrical failure; hence, their control system must not collapse. This study proposes a fault-tolerant converter controller to ensure the efficient operation of wind system converters. The central concept behind this is that when there is an imbalance in the utility grid voltage due to a fault nearby or far away, positive and negative sequence voltages are created in the time domain. Then, two parallel controllers operate to allow the wind system to continue operating despite the failure. One controller utilizes positive sequence voltages as inputs to regulate the generator’s electromagnetic torque. This helps in maximizing the amount of wind energy. The second controller uses negative sequence voltages as inputs, which helps to cancel out the produced torque in the opposite direction, thereby preventing generator overload. Finally, the controllers proposed in this article are validated through simulations, and the results are presented.

A novel robust nonlinear optimal second-order sliding mode control scheme for power optimization of wind energy conversion systems

In this article, we propose a novel robust nonlinear optimal second-order sliding mode controller using the homotopy perturbation method (RNOSOSMC-HPM) to maximize wind power capture and minimize the mechanical stress on the drive train. To design the nonlinear optimal controller, the homotopy perturbation method (HPM) is applied to compute the approximate solution of the partial differential Hamilton-Jacobi-Bellman (HJB) equation. Next, the nonlinear optimal controller is combined with a second-order sliding mode controller to create robustness and eliminate chattering. The RNOSOSMC-HPM controller can provide safe wind turbine operation under uncertainties and create a good trade-off between maximizing the wind power captured and attenuating the mechanical loads by minimizing the control input. To investigate the effectiveness of the presented the RNOSOSMC-HPM controller, we compare the results of the proposed method with some existing control schemes in two different scenarios. The results indicate that the RNOSOSMC-HPM controller furnishes desired responses.

Grid‐side flexibility assessment based on security distance of dispatchable region for AC/DC hybrid system using approximate second‐order cone relaxation

AbstractThe rapid growth of offshore wind power has resulted in a mismatch between generation and demand because of its variability. To quantify the maximum wind power penetration of the transmission network, the dispatchable region is defined as the largest region in the uncertainty space. The security distance is defined as the minimum distance from the operating point to the boundaries of the dispatchable region. System operators can use security distance as a metric to assess the flexibility of the power system. This paper proposes a method to construct the dispatchable region for the AC/DC hybrid system with VSC‐HVDC by outer convex relaxation firstly. The second‐order cone relaxation is employed to reformulate non‐convex and non‐linear power flow equations. Next, a polyhedral approximation is adopted to obtain the convex hull of the dispatchable region. Subsequently, an efficient algorithm known as adaptive constraint generation (Ad‐CG) is introduced to calculate the boundaries of the dispatchable region. Furthermore, solving the Chebyshev centre problem determines the minimal security distance. The modified IEEE 5‐bus and 39‐bus system is used for validating effectiveness of the proposed method and evaluating the impact of the converter reactive power compensation capacity and generation dispatch on the dispatchable region.

High order sliding mode observer-based fault-tolerant control scheme for DFIG in wind energy applications with model predictive controller

This research presents a novel fault-tolerant predictive power control method for a Doubly-fed induction generator (DFIG) used in wind turbine control systems. Due to the proposed control mechanism, the system can continue to function effectively despite open-circuit or short-circuit faults in the insulated-gate bipolar transistors (IGBTs) of the MPC controller. Depending on the type of problem and its location, the tolerant IGBT overcomes power oscillations and limits the power converter's potential switching states. By monitoring the optimal generator speed, wind turbine control systems strive to maximize power output. For wind turbines operating in the partial-load area, a fault-tolerant model predictive control strategy is recommended in order to achieve control goals despite disturbances, uncertainties, sensor, and actuator difficulties. A high order sliding mode observer (HOSMO) is used to evaluate both the actual states and sensor-faults at the same time. A high order sliding mode (HOSM) control strategy based on the MPC controller is used to regulate the speed of wind turbines in order to harness the wind's maximum power.

Proposed Extension of the U.S.–Caribbean Super Grid to South America for Resilience during Hurricanes

Climate change mitigation, adaptation to intensifying hurricanes, and decarbonization challenges in developing countries emphasize the urgent need for resilient high-voltage grids to facilitate the expansion of renewables. This research explores the technical feasibility of extending the U.S.–Caribbean Super Grid to include the Virgin Islands, Guadeloupe, Martinique, Trinidad and Tobago, Guyana, Suriname, French Guyana, and the northeastern part of Brazil in South America. This proposed extension aims to capitalize on the recent introduction of a new generation of wind turbines certified for operation under strong hurricane forces. The research utilizes modeling and simulation techniques to evaluate the performance of the proposed extension. A method for modeling and estimating spatiotemporal wind power profiles is applied, and the results demonstrate a reduction in maximum wind power variability within the U.S.–Caribbean Super Grid. Depending on the hurricane trajectory, the variability is reduced from 56.6% to less than 43.2%. This reduction takes effect by distributing peak surplus wind power alongside the proposed U.S.–Caribbean–South America Super Grid (UCASG). The research concludes by acknowledging the merits and limitations of the study and discussing potential directions for future research in this field.

Low carbon scheduling method of electric power system considering energy-intensive load regulation of electrofused magnesium and wind powerfluctuation stabilization

The significant expansion of wind power has presented challenging obstacles to power system operation. The inherent stochasticity and variability of wind power have emerged as critical issues that impede the advancement of wind energy, affecting its integration and volatility control. Moreover, with the momentum of carbon peaking and carbon neutrality objectives, reducing CO2 emissions has become urgent. This study proposes a low-carbon dispatching methodology for power systems to address these challenges. It considers regulating energy-intensive loads of electrically fused magnesium and mitigating wind power fluctuations. The approach involves integrating electrically Fused magnesium loads with tight regulation characteristics on the demand side alongside thermal power plants to optimize the power grid and dispatch collectively. And battery energy storage devices are introduced into various wind power stations on the supply side to smooth out wind power fluctuations. The objective function aims to minimize the sum of the average variance of power fluctuations at all wind power busbars while adhering to constraints of maximum wind power consumption and minimum carbon emissions from thermal power plants. The problem is solved using genetic algorithms. Ultimately, we show how effective our proposed low-carbon dispatching method for power systems is, which deals with regulating energy-intensive loads of electrically fused magnesium and reducing wind power fluctuations. We demonstrate this through a case study of wind curtailment, CO2 emissions, wind power fluctuations, and other related parameters under different conditions. The approach can enhance renewable energy consumption, reduce carbon emissions, and mitigate the power fluctuations of renewable energy.

Coordinated Planning of Power Systems under Uncertain Characteristics Based on the Multilinear Monte Carlo Method

The randomness of the power supply side and the load side of comprehensive energy systems is increasingly prominent. It is very difficult to meet demand through traditional planning methods. To solve this problem, this paper explores the coordinated planning of a power system under uncertain characteristics using the multilinear Monte Carlo method. The uncertain characteristic model and probability density function of the system’s power supply side and load side are established. Taking the optimal operating cost and the maximum wind power consumption as the system planning objectives, a system coordination planning scheme is established, and it is solved by multilinear Monte Carlo simulation. The superiority of this method is verified by taking the modified IEEE 39-bus test system as an example. This method can provide a reference for system planning.

Multi-objective Optimal Scheduling of Power System Based on Non-dominated Sorting Genetic Algorithm

With the rapid development of green energy, wind power has been applied more and more. In this paper, a multi-objective optimization model of a power system was established based on a non-dominant sorting genetic algorithm to solve the problem of system instability in wind power output, taking maximum wind power consumption and minimum power purchase cost as the objective function, and simulation verification is conducted. The results show that using this algorithm for model solving can get the corresponding Pareto optimal set of extreme solutions, verify the effectiveness of the algorithm, and achieve wind power consumption maximization, electricity purchase cost minimization, and better economic benefits.

An Integrated Scheme for Forecasting and Controlling Ramps in Offshore Wind Farms Considering Wind Power Uncertainties during Extreme Storms

Global warming-induced extreme tropical storms disrupt the operation of offshore wind farms, causing wind power ramp events and threatening the safety of the interconnected onshore grid. In order to attenuate the impact of these ramps, this paper proposes an integrated strategy for forecasting and controlling ramps in offshore wind farms. First, the characteristics of wind power ramps during tropical storms are studied, and a general ramp control framework is established. Second, a wind power ramp prediction scheme is designed based on a minimal gated memory network (MGMN). Third, by taking into account the wind power ramp prediction results and wind power uncertainties, a chance-constraint programming-based optimal ramp control scheme is developed to simultaneously maximize wind power absorption and minimize ramp control costs. Finally, we use real-world offshore wind farm data to validate the effectiveness of the proposed strategy.

Doubly fed induction generator, with crow-bar system, under micro-interruptions fault

The work presented in this paper focuses on studying the application of doubly fed induction generators in wind energy production under micro-interruption fault. In this context, we set out to improve the performance of a wind turbine either from an energetic efficiency, or from the arrangement with behavioral of grid disturbances. To reach the maximum wind power extraction, wind turbine has to reduce their disconnection. For this reason grid operators impose, by theirs grid connection requirements, to wind turbine producer to support some grid disturbance. This paper deals with the behavior of wind turbine equipped with a Doubly Fed Induction Generator (DFIG) under micro-interruption. A scheme tolerant microinterruption is proposed. A control strategy of the Unified Power Flow Control (UPFC) using PI controller is presented. And finally fuzzy logic controller is illustrated and compared to PI controller.

Development and assessment of integrated hydrogen and renewable energy systems for a sustainable city

Advances in renewable energies, hydrogen, electrification, and improvements in efficiency are projected to be the main avenues for carbon emission reductions. Therefore, a combination of renewable energy sources integrated with hydrogen production is needed to meet the climate targets. The novel system combinations studied in this paper promote renewable energies, hydrogen production, and electrification to reduce and/or eliminate the dependency on fossil fuels for sustainability. The communities chosen for this study were in Canada [A], South Africa [B], the Netherlands [C] and Denmark [D]. The proposed systems consisted of wind plants (including offshore), solar photovoltaics, and biomass-based integrated systems for power generation, heat recovery, fresh water, bioethanol and hydrogen production. The hydrogen production is considered using a proton exchange membrane (PEM) electrolyzer and excess electricity generated from renewables. Sustainable fuel sources for transportation proposed in this system are hydrogen, electrical charging stations and bioethanol. All locations had good potential for renewable power generation and hydrogen production. The maximum capacities for the wind and solar farms in the selected locations are identified as 187.5 MW and 32.7 MW respectively. The actual power generated from the plants varies based on the climate conditions of different locations. At the maximum wind and solar power generated (without accounting for community electrical demand), the maximum annual potential capacities obtained for hydrogen generation are 307 tons for location [A], 232 tons for location [B], 399 tons for location [C] and 487 tons for location [D].

A Comparative Experimental Investigation of MPPT Controls for Variable Speed Wind Turbines

This work presents an experimental comparative investigation between Maximum power point tracking control methods used in variable speed wind turbines. In order to enhance the efficiency of the wind turbine system, the maximum power point tracking control has been applied for extracting and exploiting the maximum available wind power. Furthermore, two maximum power point tracking controls have been analyzed, developed, and investigated in real-time using Dspace. The first was optimal torque control without speed control, whereas the second was with speed control. The maximum power point tracking control performance comparison has been performed in a real-time experimental validation to illustrate the advantages of these control on the real wind energy system. The results have been achieved and discussed, where the power efficiency improvements appeared in the transit time and in the steady-state as well. In addition, the proposed optimal torque control for maximum power point tracking with speed control decreased the response time and oscillations, while it increased the power to an interval of 12,5% to 75% compared to that of strategy without speed control in the steady-state and transit state, respectively.

Computational fluid dynamic and response surface methodology coupling: A new method for optimization of the duct to be used in ducted wind turbines

Wind energy technology, particularly power generation by wind turbines, has received substantial attention due to resource depletion and global warming concerns. These concerns highlight the importance of conducting studies to enhance their efficiency by increasing their power output. The goal of this work was to combine the RSM (Response Surface Methodology (RSM) with CFD (Computational Fluid Dynamics) to discover the optimal design parameters and conditions for ducted wind turbines. To that purpose, twenty-seven runs were chosen using Central Composite Design (CCD) in the design phase. Duct simulation was performed by employing different dimensional parameters and feeding them into a third-order polynomial that fitted to an eight-order function. The analyzed runs discussed the maximum available wind velocity and power at the throat area of the various designed ducts. The wind-enhanced power and speed were studied under different design parameters, and their effects were discussed. The optimum design conditions to capture maximum power were 0.16 m, 2, and 1.5 for design parameters of the duct's throat diameter, contraction ratio, and length-to-throat diameter ratio, respectively. A good selection of design parameters can increase the outpour power up to six times as a general result. By modeling CFD simulations using the RSM method, it is possible to minimize the time and cost of calculation to find the optimized range for the design parameters of the ducts.

MPPT 를 위한 PMSG 풍력터빈시스템에 대한 분수-차수 슬라이딩 모드 제어기 및 분수-차수 확장상태 관측기 설계

This paper designs a fractional-order sliding mode control (FSMC) of a permanent magnet synchronous generator (PMSG)-based wind energy conversion system, where attempts to extract the maximum wind power point tracking (MPPT). The combination of generator nonlinearities and parameter uncertainties may be issued to obtain the desired maximum wind power. A robust FSMC and fractional-order extended state observer are considered to address the given challenges and yield the enhanced control performance of the PMSG based wind turbine system by the MPPT technique. Simulations are carried out to examine the proposed control system for two cases with the variable step and randomly varied wind speed conditions. The simulation results verified the effectiveness and superiority of the FSMC system compared with the conventional control methods.

Optimal low voltage ride through of wind turbine doubly fed induction generator based on bonobo optimization algorithm

The large-scale wind energy conversion system (WECS) based on a doubly fed induction generator (DFIG) has gained popularity in recent years because of its various economic and technical merits. The fast integration of WECS with existing power grids has caused negative influence on the stability and reliability of power systems. Grid voltage sags produce a high overcurrent in the DFIG rotor circuit. Such these challenges emphasise the necessity of the low voltage ride through (LVRT) capability of a DFIG for ensuring power grid stability during voltage dips. To deal with these issues simultaneously, this paper aims to obtain the optimal values of injected rotor phase voltage for DFIG and wind turbine pitch angles for all operating wind speeds in order to achieve LVRT capability. Bonobo optimizer (BO) is a new optimization algorithm that is applied to crop the optimum values of injected rotor phase voltage for DFIG and wind turbine pitch angles. These optimal values provide the maximum possible DFIG mechanical power to guarantee rotor and stator currents do not exceed the rated values and also deliver the maximum reactive power for supporting grid voltage during faults. The ideal power curve of a 2.4 MW wind turbine has been estimated to get the allowable maximum wind power for all wind speeds. To validate the results accuracy, the BO results are compared to two other optimization algorithms: particle swarm optimizer and driving training optimizer. Adaptive neuro fuzzy inference system is employed as an adaptive controller for the prediction of the values of rotor voltage and wind turbine pitch angle for any stator voltage dip and any wind speed.

Probabilistic wind power expansion planning of bundled wind-thermal generation system with retrofitted coal-fired plants using load transfer optimization

The bundled wind-thermal generation system (BWTGS) is an attractive option to integrate wind energy to actively participate in power systems. With the greatly enhanced penetration of wind energy in BWTGS, wind curtailment against sharp wind power fluctuations is inevitable. Thus, our study is stimulated by whether and to what extent we can maximize wind power penetration with proper provision of dispatchable loads against wind curtailment, and this is exactly the problem we aim to solve in this paper. Here, we initially focus on the flexible characteristics of retrofitted coal-fired units in BWTGS and the load transfer optimization (LTO) for high voltage distribution networks (HVDNs). Then, an appropriate probabilistic wind power capacity expansion planning method for BWTGS with retrofitted coal-fired units using LTO can be reformulated as a mixed-integer second-order cone programming problem. Moreover, since load and wind power data have inherited uncertainty, we extend this proposed model to be a probabilistic expanding model. This contributes to better decision-making for the short-term wind capacity expansion under quantified uncertainty. Numerical results of the practical HVDNs demonstrate the effectiveness of the proposed method.