Case Study Of Wind Farm Research Articles

Integrating Framing Approaches as a Tool for Managing Complex Transitioning to Renewable Energy (TRE) Projects: The Yatir Wind Farm Case Study

Transitioning to renewable energy is an urgent global goal. Wind energy is a promising renewable source with contentious obstacles. Using the Yatir Wind Farm project in Israel as a case study, we explore the potential of framing in identifying and mitigating obstacles in a wind farm project. The cognitive and strategic frames employed by stakeholders were elicited through 18 semi-structured interviews and more than 100 documents. This analysis highlights three conflicting issues: potential or perceived harm to neighboring residents, protecting birds and bats, and adapting to changing regulations throughout the process. Regarding residents’ opposition to feared harm from the project, initial cautious curiosity was followed by distrust and deep disappointment due to a lack of transparency and a sense of abandonment facing perceived existential threats to their health and homes. This led residents to conduct legal battles, which resulted in their claims being rejected. It also led to the breakdown of relationships among neighbors opposing and promoting the wind farms. In the case of bird and bat protection, stakeholders initially framed concerns emotionally until the dialogue shifted to scientific discourse, resulting in the successful refinement of mutually agreed upon regulatory guidelines. The structural appeal mechanisms effectively addressed evolving regulations, overcoming the lack of mutual understanding and resulting in the adoption of the majority of the new regulatory requirements. The analysis underscores the importance of understanding stakeholders’ frames for effectively working through the complex and transdisciplinary nature of sustainability transitions and achieving successful outcomes. It also reveals the need for formal mechanisms to validate stakeholder needs and integrate them into decision-making processes. Recommendations include early and meaningful public involvement, process improvement for stakeholder engagement, and enhanced transparency in decision-making processes.

Assessment of Five Wind-Farm Parameterizations in the Weather Research and Forecasting Model: A Case Study of Wind Farms in the North Sea

Abstract To simulate the large-scale impacts of wind farms, wind turbines are parameterized within mesoscale models in which grid sizes are typically much larger than turbine scales. Five wind-farm parameterizations were implemented in the Weather Research and Forecasting (WRF) Model v4.3.3 to simulate multiple operational wind farms in the North Sea, which were verified against a satellite image, airborne measurements, and the FINO-1 meteorological mast data on 14 October 2017. The parameterization by Volker et al. underestimated the turbulence and wind speed deficit compared to measurements and to the parameterization of Fitch et al., which is the default in WRF. The Abkar and Porté-Agel parameterization gave close predictions of wind speed to that of Fitch et al. with a lower magnitude of predicted turbulence, although the parameterization was sensitive to a tunable constant. The parameterization by Pan and Archer resulted in turbine-induced thrust and turbulence that were slightly less than that of Fitch et al., but resulted in a substantial drop in power generation due to the magnification of wind speed differences in the power calculation. The parameterization by Redfern et al. was not substantially different from Fitch et al. in the absence of conditions such as strong wind veer. The simulations indicated the need for a turbine-induced turbulence source within a wind-farm parameterization for improved prediction of near-surface wind speed, near-surface temperature, and turbulence. The induced turbulence was responsible for enhancing turbulent momentum flux near the surface, causing a local speed-up of near-surface wind speed inside a wind farm. Our findings highlighted that wakes from large offshore wind farms could extend 100 km downwind, reducing downwind power production as in the case of the 400-MW Bard Offshore 1 wind farm whose power output was reduced by the wakes of the 402-MW Veja Mate wind farm for this case study. Significance Statement Because wind farms are smaller than the common grid spacing of numerical weather prediction models, the impacts of wind farms on the weather have to be indirectly incorporated through parameterizations. Several approaches to parameterization are available and the most appropriate scheme is not always clear. The absence of a turbulence source in a parameterization leads to substantial inaccuracies in predicting near-surface wind speed and turbulence over a wind farm. The impact of large clusters of offshore wind turbines in the wind field can exceed 100 km downwind, resulting in a substantial loss of power for downwind turbines. The prediction of this power loss can be sensitive to the chosen parameterization, contributing to uncertainty in wind-farm economic planning.

A Pragmatic Framework for Data-Driven Decision-Making Process in the Energy Sector: Insights from a Wind Farm Case Study

In an era of big data, organizations increasingly aim to adopt data-driven decision-making processes to enhance their performance. This paper investigates the data-driven decision-making process by developing a framework tailored for application in the energy sector. The proposed framework integrates interdisciplinary approaches to comprehensively address the “data, information, knowledge” triad, applying it to both operational and maintenance decision-making. Designed to be managerially focused rather than technically oriented, the framework aims to engage all employees, including those without technical backgrounds, enabling them to effectively contribute to the decision-making process from their respective roles. To demonstrate the practical application of the proposed framework, this paper presents a case study of an energy organization managing a wind farm project, which implemented the framework to improve its decision-making process. The case study examines how the organization identified its objectives and information needs, formulated key performance questions for each stakeholder, explicitly defined and measured the key performance indicators, employed data collection and organization methods, managed the progression from data to information to knowledge, and transformed the acquired knowledge into informed decisions. By adopting this pragmatic framework, energy organizations are anticipated to solve problems, predict trends, and discover new opportunities, thereby enhancing their efficiency and predictability.

Environmental life cycle assessment of a novel offshore wind energy design project: A United States based case study

Renewable energy resources, particularly offshore wind energy, and their role in combating global climate change have gained significant interest in recent years. Considering the potential life cycle impacts of such systems is essential to support effective policy and decision making. This study used life cycle assessment to compare the environmental sustainability of an offshore wind farm case study employing a novel foundation design to one employing a conventional foundation design. Literature has not yet examined the life cycle environmental sustainability of large-scale United States based facilities using a detailed data inventory from a real case study. The life cycle environmental single score of the novel design was 18% lower than that of the conventional design, highlighting that alternative foundation materials like concrete can help reduce the overall life cycle environmental impact of a large scale offshore wind farm. In general, the proposed novel design is favorable from a life cycle perspective compared to the conventional design, particularly in the impact categories of ecotoxicity, eutrophication, carcinogenics, noncarcinogenics, and respiratory effects. The energy payback time was found to be less than one year for both designs. Additional benefits of the novel design concept include lower costs, easier installation, domestic job opportunities, decreased reliance on foreign supply chains, ability to expand the design to deeper and disaster prone waters globally, and increased support for current political goals. Scenario sensitivity analyses showed that the environmental impact of wind energy systems may decrease with domestic sourcing of foundation materials and foundation reuse after decommissioning.

Investigating the impact of a dynamic thermal rating on wind farm integration

AbstractWith an increased focus on renewable power generation, dynamic thermal ratings of various power equipment are being investigated to connect the new intermittent generation to the grid without needing to replace existing infrastructure. Previous research is mainly focused on a dynamic thermal rating for overhead lines. End‐of‐line equipment are also restricted by a thermal limit. This paper combines the thermal model for overhead lines with the transformer thermal model, to investigate the impact of environmental conditions on both. A wind farm case study is used to determine the potential increase in capacity using a combined thermal model. The impact of a higher thermal limit on the loss of life of the transformer is compared to the amount of curtailed wind and the potential revenue from the added wind farm. This analysis serves to provide a method to analyse the risks of using a higher transformer thermal limit, compared to the benefits of increased wind penetration and additional revenue.

A Straightforward Approach to Site-Wide Assessment of Wind Turbine Tower Lifetime Extension Potential

This contribution presents a novel methodology to evaluate the lifetime extension potential of wind turbines—taking towers as the key component that preserves onshore turbines’ structural integrity—as a consequence of the difference between design and site-specific loads. Specifically, attention is drawn to the site-specific wind direction distribution, which provides an additional source of lifetime extension potential. For this purpose, variants of closed-form solutions (based on the tower section’s normal stress) are developed to enable fatigue damage accumulation due to fore-aft and side-to-side bending moments at any point on the tower circumference without the need for further information on tower section geometry or material properties. Based on the degree of data availability, different scenarios are defined to estimate lifetime extension potential from the accurate tower’s normal stress and approximations using resultant bending moment, fore-aft bending moment, and finally, wind rose data only. The methodology is applied to a wind farm case study using the actual SCADA data with a partially validated turbine’s aeroelastic model to obtain operational loads. The results indicate that this quick and fairly accurate approach can be used as an initial stage in identifying wind turbines across large farms, which have the largest lifetime extension potential.

FALCON- FArm Level CONtrol for wind turbines using multi-agent deep reinforcement learning

Turbines in a wind farm dynamically influence each other through wakes. Therefore trade-offs exist between energy output of upstream turbines and the health of downstream turbines. Using both model-based predictive control (MPC) and machine learning techniques, existing works have explored the energy-fatigue trade-off either in a single turbine or only with few turbines due to issues of scalability and complexity. To address this gap, this paper proposes a multi-agent deep reinforcement learning-based coordinated control for wind farms, called FALCON. FALCON addresses the multi-objective optimization problem of maximizing energy while minimizing fatigue damage by jointly controlling pitch and yaw of all turbines. FALCON achieves scale by using multiple reinforcement learning agents; capturing the global state-space efficiently using an auto-encoder; and pruning the action-space using domain knowledge. FALCON is evaluated through a real-world wind-farm case study with 21 turbines; and performs better than the default baseline PID controller and a learning-based distributed control.

Multi-target normal behaviour models for wind farm condition monitoring

The trend towards larger wind turbines and remote locations of wind farms fuels the demand for automated condition monitoring strategies that can reduce the operating cost and avoid unplanned downtime. Normal behaviour modelling has been introduced to detect anomalous deviations from normal operation based on the turbine’s SCADA data. A growing number of machine learning models of the normal behaviour of turbine subsystems are being developed by wind farm managers to this end. However, these models need to be kept track of, be maintained and require frequent updates. This research explores multi-target models as a new approach to capturing a wind turbine’s normal behaviour. We present an overview of multi-target regression methods, motivate their application and benefits in SCADA-based wind turbine condition monitoring, and assess their performance in a wind farm case study. We find that multi-target models are advantageous in comparison to single-target modelling in that they can reduce the cost and effort of practical condition monitoring without compromising on the accuracy. We also outline some areas of future research.

Does wind speed effects performance and cost of energy? A case study of wind farm

PurposeThe rapid rising of renewable energy sources particularly wind energy cannot be ignored. The numerical increase in wind energy farms throughout the world is the best example. The purpose of this paper is to assess the basic question of whether wind characteristics affect the performance and cost of energy. The importance of this question cannot be ruled out while comparing renewable energy to a conventional form of energy more specifically especially for the developing country where the cost of energy is very high.Design/methodology/approachThe research design of this paper is consists of an assessment of local wind characteristics of the wind farm site using Weibull k and c parameters. The performance model is used to assess the performance of the wind turbine (WT) corresponding to local wind characteristics. The wind correlation with WT in terms of changing wind speed has been assessed to quantify the effects of wind speed on the WT behavior and failure of WT components. Similarly, the power curve of WT is assessed and compared with the International Electrotechnical Commission standards 61400-12-2. The WT power coefficient and tip speed ratio corresponding to wind speed is also investigated. The energy volume and cost of energy lost model is used to determine the cost and volume loss of energy/kWh of the wind farm.FindingsThe findings of practical wind farms showed that the wind conditions of the site are showing a strong tendency that can be determined from the results of Weibull k and c parameters. The k and c parameters are observed to be 3.44 and 9.16 m/s, respectively, for a period of a year. The standard deviation is observed to be 2.56 for a period of a year. WT shows the efficient behavior can be obtained from the power coefficient and tip speed of WT at different wind speeds. Also, wind farm observation showed that to be some increasing wind speed cause of based WT component failures. The results of energy volume and cost/kWh assessment showed that the major portion of energy volume and cost of energy is lost owing to network, voltage dip and frequency surge, electrical and mechanical components failures.Originality/valueGenerally, it can be concluded that the WTs are now able to cope with variable wind speeds. However, the results of this paper are showing that WT performance and availability decreased due to increased wind speeds. It can also be a reason to decreased volume and increase the cost of energy/kWh.

Multi-Objective Predictive Control Optimization with Varying Term Objectives: A Wind Farm Case Study

This paper introduces the incentive of an optimization strategy taking into account short-term and long-term cost objectives. The rationale underlying the methodology presented in this work is that the choice of the cost objectives and their time based interval affect the overall efficiency/cost balance of wide area control systems in general. The problem of cost effective optimization of system output is taken into account in a multi-objective predictive control formulation and applied on a windmill park case study. A strategy is proposed to enable selection of optimality criteria as a function of context conditions of system operating conditions. Long-term economic objectives are included and realistic simulations of a windmill park are performed. The results indicate the global optimal criterium is no longer feasible when long-term economic objectives are introduced. Instead, local sub-optimal solutions are likely to enable long-term energy efficiency in terms of balanced production of energy and costs for distribution and maintenance of a windmill park.

A Model Predictive Wind Farm Controller with Linear Parameter-Varying Models

In this paper, we present an implementation of a model predictive controller (MPC) for wind farm power tracking problem. The controller is evaluated in the high-fidelity PAral-lelized Large-eddy simulation Model (PALM). By taking measurements from PALM, we show that the closed-loop MPC can provide power reference tracking while reducing force variations on a farm level by solving a constrained optimization problem at each time step. A six turbine wind farm case study is presented in which the controller operates with yawed turbines that increases the potential power that can be harvested with the wind farm, and we show that it is possible to track a reference power signal that temporarily exceeds the power harvested when operating under the so-called greedy control settings.

Wind Power Resource Assessment in Complex Terrain: Villonaco Case-study Using Computational Fluid Dynamics Analysis

Abstract This paper describes a complex terrain wind farm case study in the Ecuadorian Andes. The Windfarm Villonaco is located in southern Ecuador, 640km of Quito, 2700 m a.s.l. with 16.5 MW power output. The objective is to compare actual power production from an existing wind farm with power production predicted by Meteodyn WT, which is a CFD tool based on a nonlinear flow model. The results of this work show that the calculation of the annual energy production of the Villonaco Wind Farm using Meteodyn WT is equal to 69.0 GWh / year, with a capacity factor of 53%.

A novel forecasting model based on a hybrid processing strategy and an optimized local linear fuzzy neural network to make wind power forecasting: A case study of wind farms in China

As a crucial issue in the wind power industry, it is a tough and challenging task to predict the wind power accurately because of its nonlinearity, non-stationary and chaos. In this paper, we propose a novel hybrid model, which combines an integrated processing strategy and an optimized local linear fuzzy neural network, to forecast the wind power. First, discrete wavelet transform and singular spectrum analysis are used to filter out the noises and extract the trends from original wind power series, respectively. Then, the novel no-negative-constraint-combination theory together with the CS algorithm are adopted to integrate these two subseries obtained from the first step to retain strengths of each method. Based on the phase space reconstruction model, we could determine the most proper structure of the input sets and the output sets. Next, the local linear fuzzy neural network, with the initial rule consequent parameters optimized by the seeker optimization algorithm, is utilized to make wind power forecasts for a selected number of forward time steps. The numerical results from two experiments demonstrate that the proposed hybrid model is an effective approach to predict wind power, and the accuracy of prediction is highly improved compared with conventional forecasting models.

Impact of static synchronous compensator on the stability of a wind farm: Case study of wind farm in Tunisia

The static synchronous compensator is one of the FACTS (Flexible Alternating Current Transmission System) device capable of maintaining the stability of wind turbines during a sudden default. Among these faults, the voltage drops at the connection bus wind turbines. For this fault case, the static synchronous compensator intervenes by injection of the reactive power to compensate the voltage drop. In this article, as application case, we study the wind farm of Bizerte (north of Tunisia). This farm is composed of fixed speed aero-generators using squirrel cage induction generators. Our study begins with modeling the wind system. Next, we describe the technical requirements for connection of a wind energy system to the grid and outfit at the voltage dips (low-voltage ride through) according to STEG (Tunisian Company of Electricity and Gas). We also present the structure of static synchronous compensator. Finally, we present the simulation results of the wind farm under low-voltage ride through with and without static synchronous compensator.

Economic and Technical Analysis of Distributed Generation Connection: A Wind Farm Case Study

This study has been completed for the proposed construction of a wind farm to supply an industrial factory in the north east of the England. The study assessed the potential benefits of changing the operation philosophy of distribution network and embedded generation dealing with factory owner aspects, which are to reduce the electricity bills, reduce the interruption in the network, gain revenue by selling the electricity for the supply company and gain green certificate renewable obligation. The following study presents a comprehensive review of the critical factors and considerations analyzed for installing an embedded generation (Wind Farm) at the factory site. Furthermore, the feasibility study in this report includes factory site description and wind data, design study and wind farm sizing, and economic study.

After-sales maintenance service strategies optimization. An offshore wind farm case study

Abstract: After-sale maintenance services for machine manufacturers can be a very profitable source of incomes. Although historically machine manufacturers have mainly been focused on improving their production processes, in the last years many machine manufacturers have also included after-sale service processes into their business strategies. When defining after-sale services, one of the main difficulties is to identify, firstly, which maintenance requirements has each asset, secondly, which maintenance strategy (i.e. corrective-preventive-predictive mix) should be followed in each particular case, and thirdly which resources are needed to schedule and perform the maintenance tasks. With the purpose of supporting this decision making process concerning asset management strategies, in the proposed research it is developed an agent-based simulation tool that allows to identify the correct maintenance strategy. The experiments performed for this particular study allows identifying critical service parameters and resources vs. results (i.e. availability and benefits) trade off optimization for an offshore wind farm case study.

A hybrid wind speed forecasting model based on phase space reconstruction theory and Markov model: A case study of wind farms in northwest China

Forecasting wind speed is a crucial issue in the wind power industry, and wind energy is gradually coming to be viewed as one of the most promising alternative energy sources because of its cleanliness and renewability for large-scale commercial production. With the aim of developing accurate tools for forecasting wind speed, this paper presents a novel hybrid intelligent forecasting model based on Least Square Support Vector Machine and the Markov model. Prior to conducting the wind speed forecasting, the original wind speed series is processed using the C–C method, which is a phase space reconstruction method, to automatically determine the input form. Subsequently, the LSSVM (least squares support vector machine) model, which is optimized using the PSOGSA (partical swarm optimization combined with gravitational search algorithm) algorithm, is employed to forecast wind speed. Additionally, the Markov model is developed to make error corrections with the state ranges determined using the FCM (fuzzy C-means) model. The proposed model takes the average forecasting values between the outputs generated by LSSVM and error corrected LSSVM model as the final forecasting results. The simulation results indicate that the proposed model can outperform the other models discussed in this paper with respect to forecasting accuracy and can be a suitable tool for forecasting wind speeds.

Differential Evolution Based IDWNN Controller for Fault Ride-Through of Grid-Connected Doubly Fed Induction Wind Generators.

The key objective of wind turbine development is to ensure that output power is continuously increased. It is authenticated that wind turbines (WTs) supply the necessary reactive power to the grid at the time of fault and after fault to aid the flowing grid voltage. At this juncture, this paper introduces a novel heuristic based controller module employing differential evolution and neural network architecture to improve the low-voltage ride-through rate of grid-connected wind turbines, which are connected along with doubly fed induction generators (DFIGs). The traditional crowbar-based systems were basically applied to secure the rotor-side converter during the occurrence of grid faults. This traditional controller is found not to satisfy the desired requirement, since DFIG during the connection of crowbar acts like a squirrel cage module and absorbs the reactive power from the grid. This limitation is taken care of in this paper by introducing heuristic controllers that remove the usage of crowbar and ensure that wind turbines supply necessary reactive power to the grid during faults. The controller is designed in this paper to enhance the DFIG converter during the grid fault and this controller takes care of the ride-through fault without employing any other hardware modules. The paper introduces a double wavelet neural network controller which is appropriately tuned employing differential evolution. To validate the proposed controller module, a case study of wind farm with 1.5 MW wind turbines connected to a 25 kV distribution system exporting power to a 120 kV grid through a 30 km 25 kV feeder is carried out by simulation.

Voltage sag problems in large‐scale clustering wind farms

Accompanied by the development of large‐scale wind energy and integration into grid in China, wind farm cascading trip‐off accidents resulting from voltage sag have occurred frequently in the past few years. Before proposing countermeasures, it is necessary to clarify the voltage sag problem of wind farms. First, three voltage sag risk indices are proposed. Second, the simplified equivalent circuit of double fed induction generator (DFIG) for short‐circuit calculation, stochastic model of wind speed and the fault model of buses/lines are presented in this paper. Third, assessment method of voltage sag in wind farm, implemented by Digsilent dynamic programming language (DPL), is proposed to analyze the severity and probability of voltage sag. Finally, a case study of wind farm connected to WSCC nine‐bus benchmark systems is analyzed in detail, and the validity of the proposed approach is confirmed. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Can the “Social Licence to Operate” Concept Enhance Engagement and Increase Acceptance of Renewable Energy? A Case Study of Wind Farms in Australia

Social licence to operate (SLO) is the ongoing acceptance or approval for a development that is granted by the local community and other stakeholders. From the current media and political attention on Australian wind farms, it appears that many specific wind farms, or indeed the industry as a whole, may not hold an SLO with affected stakeholders. This research was undertaken to examine whether the SLO might be a useful framework to enhance engagement and increase societal understanding of wind farms. Twenty-seven interviews across nine wind farms were conducted with stakeholders representing wind companies, local government authorities, local opposition, local support and turbine hosts. The interviews revealed a complexity of concerns that informed the stakeholders’ perspectives, including “game-changing” issues that may stand to significantly increase wind farm acceptance. The results are presented with practical steps towards the development of a preliminary working model of an SLO for Australian wind farms that addresses identified concerns. The emerging concept of SLO appears to provide a useful framework for wind farm developers to incorporate an improved model of consultation that engages local communities in ways that could enhance transparency and local support, and complement formal regulatory processes.