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The main purpose of this chapter is the introduction, explanation, and categorization of wind-farm-control algorithms and present their place in a detailed wind farm simulation framework. Specifically, this section will continue to give an introduction to the current practice in wind-farm operation, and the objectives and turbine control methods presented in the literature for coordinated wind farm control.
The growing share of Variable Renewable Energy sources (VRES) in power systems presents challenges for regula- tors, grid operators and energy producers. The VRES’ operation has limited flexibility in their operations, as they are highly dependent on ambient environments. To address these challenges, decision-makers must consider multiple objectives, among these are revenue, power system services and mechanical load on wind turbines. Coordinated operation of power plants and different wind farm control strategies are examples of measures that can benefit these objectives. This study proposes a Multi-Objective Linear Programming (MOLP) model to simulate the optimum operation of wind and hydropower plants that share limited transmission capacity. Further, wind farm control dynamics are included to obtain realistic output power and accumulated damage. From this, a case study based on a relevant location in Norway is presented to analyze the improved effect of wind- hydro coordination and wind farm control in achieving the objectives of accumulated wind turbine damage and total revenue of the hybrid power system. In addition, the study considers the potential advantages of adding a variable-speed pump to the hydropower plant. The results demonstrate that by considering multiple objectives in the optimization, one may achieve better overall performance of the objectives. By utilizing the flexibility of hydro storage, the decision maker may adjust to obtain the most desired outcome. Moreover, the added flexibility of utilizing a pump for hydro storage shows great improvements for the combined revenue of the power plants and reduced curtailment of the wind farm. However, less impact is observed from using a variable speed pump compared to a fixed speed pump.
This paper proposes a coordinated control of doubly fed induction generator (DFIG)-based wind farms' (WFs) post-fault active power and synchronous generators' (SGs) tripping with the aim of improving transient stability of both the first swing and multi swings. To achieve it, the impact mechanism of the WFs' post-fault active power and SGs' tripping on the stability margin of each swing is first presented by using extended equal area criterion (EEAC). Based on this, a principle of the coordinated control is put forward. The WFs' control period is designed as six stages, and the value of post-fault active power in each stage is suggested to improve the stability of the first five swings and maintain the post-fault steady state. To decrease the tripping amount of the SGs, the SGs are tripped only when WFs' control effect is not sufficient to avoid the instability. Then, by utilizing an “online pre-decision and real-time matching”scheme, an engineering application method for the control principle is proposed, where the control procedures and detailed parameters calculation are demonstrated. The numerical simulations show that the coordinated control has a better damping effect and a less control cost (less amount of tripped SGs) than the traditional DFIG control or traditional SG tripping.
Wind turbines in a wind farm operate individually to maximize their own performance regardless of the impact of aerodynamic interactions on neighboring turbines. Wind farm controls can be used to increase power production or reduce overall structural loads by properly coordinating turbines. One wind farm control strategy that is addressed in literature is known as wake steering, wherein upstream turbines operate in yaw misaligned conditions to redirect their wakes away from downstream turbines. The National Renewable Energy Laboratory (NREL) in Golden, CO conducted a demonstration of wake steering on a single utility-scale turbine. In this campaign, the turbine was operated at various yaw misalignment setpoints while a lidar mounted on the nacelle scanned five downstream distances. The lidar measurements were combined with turbine data, as well as measurements of the inflow made by a highly instrumented meteorological mast upstream. The full-scale measurements are used to validate controls-oriented tools, including wind turbine wake models, used for wind farm controls and optimization. This paper presents a quantitative comparison of the lidar data and controls-oriented wake models under different atmospheric conditions and turbine operation. The results show good agreement between the lidar data and the models under these different conditions.
Quantifying the impacts on turbine loads during wind farm control is an important consideration in assessing power production benefits. Wake steering controls aim to improve total wind farm performance by coordinating the control actions of individual turbines, wherein an upstream turbine is intentionally yawed at an offset angle from the measured wind direction. Consequently, this redirects its wake for improved power production and potentially reduces fatigue loads of the downstream turbines. This paper studies the lifetime fatigue loads associated with wake steering by using utility-scale wind turbine experimental data to conduct an analysis on a pair of wind turbines. This study was part of a large field experiment in which a group of five GE 1.5-MW SLE CWE turbines were selected as targets for conducting wake steering research. A standard loads instrumentation package and data acquisition system were installed on two turbines within the cluster to measure turbine fundamental loads. The time-series databases were used to calculate loads statistics as well as short-term and lifetime damage equivalent loads. Fatigue calculations followed the guidance in Annex H of the International Electrotechnical Commission standard 61400-1, Edition 4. Lifetime fatigue calculation results are presented in this analysis; three methods of assessing lifetime fatigue were used to determine percent difference for blade root moments, main shaft moments, main shaft torque, and tower top torque. For all three fatigue treatments, some components’ lifetime fatigue increases for the controlled turbine; however, the downwind turbine experienced a reduction in lifetime fatigue and combined effect for the turbine pair results in a reduction of fatigue when wake steering controls are applied.
Wind farm control, in which turbine controllers are coordinated to improve farmwide performance, is an active field of research. One form of wind farm control is wake steering, in which a turbine is yawed to the inflow to redirect its wake away from downstream turbines. Wake steering has been studied in depth in simulations as well as in wind tunnels and scaled test facilities. This work performs a field test of wake steering on a full-scale turbine. In the campaign, the yaw controller of the turbine has been set to track different yaw misalignment set points while a nacelle-mounted lidar scans the wake at several ranges downwind. The lidar measurements are combined with turbine data, as well as measurements of the inflow made by a highly instrumented meteorological mast. In conclusion, these measurements are then compared to the predictions of a wind farm control-oriented model of wakes.
This paper introduces a novel approach for estimating the wind field over an entire wind farm using a mobile sensor to collect limited amounts of data. The proposed method estimates the boundary conditions of a simplified turbine wake model by computing the model sensitivity matrix and using a recursive least-squares algorithm to recover the model parameters from the wind field measurements. To address the fact that it is not practical to take measurements across the entire wind farm, the proposed method classifies each area on the map based on its sensitivity to parameter variations. This classification is then used to generate a suitable path for a mobile sensor, which is charged with collecting data for the recursive least-squares algorithm. The proposed framework can successfully estimate the model boundary conditions using just the measurements collected along the path of the mobile sensor. This preliminary result paves the way for using real-time wind field estimates for the coordinated control of all the turbines within a wind farm.
As wind generation grows, its influence on power system performance will becoming increasingly noticeable. Wind generation differs from traditional forms of generation in numerous ways though, motivating the need to reconsider the usual approaches to power system assessment and performance enhancement. The project has investigated the impact of wind generation on transient stability and voltage control, identifying and addressing issues at three distinct levels of the power system: 1) at the device level, the physical characteristics of wind turbine generators (WTGs) are quite unlike those of synchronous machines, 2) at the wind-farm level, the provision of reactive support is achieved through coordination of numerous dissimilar devices, rather than straightforward generator control, and 3) from a systems perspective, the location of wind-farms on the sub-transmission network, coupled with the variability inherent in their power output, can cause complex voltage control issues. The project has sought to develop a thorough understanding of the dynamic behaviour of type-3 WTGs, and in particular the WECC generic model. The behaviour of such models is governed by interactions between the continuous dynamics of state variables and discrete events associated with limits. It was shown that these interactions can be quite complex, and may lead to switching deadlock that prevents continuation of the trajectory. Switching hysteresis was proposed for eliminating deadlock situations. Various type-3 WTG models include control blocks that duplicate integrators. It was shown that this leads to non-uniqueness in the conditions governing steady-state, and may result in pre- and post-disturbance equilibria not coinciding. It also gives rise to a zero eigenvalue in the linearized WTG model. In order to eliminate the anomalous behaviour revealed through this investigation, WECC has now released a new generic model for type-3 WTGs.
Wind farm control is an active and growing field of research in which the control actions of individual turbines in a farm are coordinated, accounting for inter-turbine aerodynamic interaction, to improve the overall performance of the wind farm and to reduce costs. The primary objectives of wind farm control include increasing power production, reducing turbine loads, and providing electricity grid support services. Additional objectives include improving reliability or reducing external impacts to the environment and communities. In 2019, a European research project (FarmConners) was started with the main goal of providing an overview of the state-of-the-art in wind farm control, identifying consensus of research findings, data sets, and best practices, providing a summary of the main research challenges, and establishing a roadmap on how to address these challenges. Complementary to the FarmConners project, an IEA Wind Topical Expert Meeting (TEM) and two rounds of surveys among experts were performed. From these events we can clearly identify an interest in more public validation campaigns. Additionally, a deeper understanding of the mechanical loads and the uncertainties concerning the effectiveness of wind farm control are considered two major research gaps.
Wind turbines in a wind farm typically operate individually to maximize their own performance regardless of the impact of aerodynamic interactions on neighboring turbines. Properly coordinating turbines, by operating some turbines suboptimally, within a wind farm has the potential to improve overall wind farm performance. Computing the optimal control strategy under varying atmospheric conditions can be computationally intense for large wind farms. As wind power farms increase in size and related models become more complex, computationally efficient algorithms are needed to perform real-time optimization and control. This study proposes a distributed optimization framework and computationally efficient wake steering wind farm control strategy that uses the yaw angle of a turbine to alter the behavior of a turbine wake and minimize turbine interactions. This computational efficiency allows the strategy to be feasible for real-time control.
In this work, we focus on the integration of wind farms in a hydro dominated power system, and analyze the challenges and solutions associated with this integration. Due to sudden increase in wind power, the system frequency can increase, causing excessive reduction in the run-of-river (ROR) system’s power. This can result in damage to the physical structure due to water level violations and cause issues for downstream customers. To prevent excessive and prolonged ROR power deviations, and to prevent tie-line deviations, we are proposing a wind-hydro centralized coordination scheme, which is not existing currently. The controller showed promising results by reducing the system frequency extrema by 0.8Hz.
Electrical power systems are undergoing a radical change in structure, components, and operational paradigms, and are progressively approaching the new concept of smart grids (SGs). Future power distribution systems will be characterized by the simultaneous presence of various distributed resources, such as renewable energy systems (i.e., photovoltaic power plant and wind farms), storage systems, and controllable/non-controllable loads. Control and optimization architectures will enable network-wide coordination of these grid components in order to improve system efficiency and reliability and to limit greenhouse gas emissions. In this context, the energy flows will be bidirectional from large power plants to end users and vice versa; producers and consumers will continuously interact at different voltage levels to determine in advance the requests of loads and to adapt the production and demand for electricity flexibly and efficiently also taking into account the presence of storage systems.
Wind plant control strategies, including axial induction and wake steering control, aim to improve the performance of wind farms, including increasing energy production and decreasing turbine loads. This paper presents a linear model of wake characteristics for use with a distributed model predictive control method for the purpose of optimizing axial induction and yaw misalignment setpoints. In particular, we use an iterative, distributed control method with nearest neighbor communication to coordinate turbine control actions that account for wake interactions between turbines. Simulations of the model and controller are performed on a 2x3 array of turbines using a modified version of the FLOw Redirection and Induction in Steady-state (FLORIS) model to dynamically track the relevant wake parameters. Preliminary results show the controller's ability to follow an arbitrary wind farm power reference signal for the purpose of providing active power control (APC) ancillary services for power grid stability. This efficient distributed control strategy can enable real-time wind farm optimization and control, even for very large scale farms.
Wind turbines in a wind farm operate individually to maximize their own power regardless of the impact of aerodynamic interactions on neighboring turbines. There is the potential to increase power and reduce overall structural loads by properly coordinating turbines. To perform control design and analysis, a model needs to be of low computational cost, but retains the necessary dynamics seen in high-fidelity models. The objective of this work is to obtain a reduced-order model that represents the full-order flow computed using a high-fidelity model. A variety of methods, including proper orthogonal decomposition and dynamic mode decomposition, can be used to extract the dominant flow structures and obtain a reduced-order model. In this paper, we combine proper orthogonal decomposition with a system identification technique to produce an input-output reduced-order model. This technique is used to construct a reduced-order model of the flow within a two-turbine array computed using a large-eddy simulation.
The Wind Hybrid Open Controller (WHOC) is a python-based tool for real-time plant-level wind farm control and wind-based hybrid plant control. WHOC is primarily run in simulation, although we intend that it could be used for physical plants in future. WHOC provides simple farm-level (and hybrid plant-level) controls such as wake steering control, spatial filtering/consensus, active power control, and coordinated control of hybrid power plant assets; and creates an entry point for the development of more advanced controllers.
This paper focuses on the challenges and solutions associated with the integration of wind farms in a hydro- dominated power system. Due to sudden increases in wind power, the system frequency can increase, causing excessive reduction in the run-of-river (ROR) system’s power. Considering their small pondage, this can result in damage to the physical structure due to water level violations which are also penalized by stakeholders. To prevent excessive ROR power deviations, ROR governor wear and tear, and to prevent tie-line deviations, a centralized, distributed, and hybrid scheme for coordinating the response from a cluster of ROR plants is examined. Both centralized and distributed control as well as their hybrids can reduce frequency peaks. However, the inclusion of decentralized cooperative controls leads to reduced governor actions from fast- acting plants while enhancing net response from the hydro unit cluster.
This study reviews and compares the performance of reactive power dispatch strategies for the loss minimization of Doubly Fed Induction Generator (DFIG)-based Wind Farms (WFs). Twelve possible combinations of three WF level reactive power dispatch strategies and four Wind Turbine (WT) level reactive power control strategies are investigated. All of the combined strategies are formulated based on the comprehensive loss models of WFs, including the loss models of DFIGs, converters, filters, transformers, and cables of the collection system. Optimization problems are solved by a Modified Particle Swarm Optimization (MPSO) algorithm. The effectiveness of these strategies is evaluated by simulations on a carefully designed WF under a series of cases with different wind speeds and reactive power requirements of the WF. The wind speed at each WT inside the WF is calculated using the Jensen wake model. The results show that the best reactive power dispatch strategy for loss minimization comes when the WF level strategy and WT level control are coordinated and the losses from each device in the WF are considered in the objective.
The Texas Tech University (TTU) research group is actively studying wind turbine wake development, as part of developing innovative wake control strategies to improve the performance of wind farms. The team has a set of eight ground lidars to perform field measurements at the Sandia National Laboratories SWiFT site. This document describes tests details including configurations, timeframe, hardware, and the required collaboration from the Sandia team. This test plan will facilitate the coordination between both TTU and the Sandia team in terms of site accessibility, staff training, and data sharing to meet the specific objectives of the tests.