A distributed consensus control under disturbances for wind farm power maximization

Gionfra, Nicolò; Sandou, Guillaume; Siguerdidjane, Houria; Faille, Damien; Loevenbruck, Philippe

doi:10.1109/cdc.2017.8263944

Cited by 6 publications

(9 citation statements)

References 20 publications

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“…In one such study, a dynamic average consensus estimator is used in Ebegbulem and Guay (2017) to estimate an overall cost function for turbines communicating via an undirected network where the goal is to maximize total wind farm power production. Similar power maximization approaches using consensus-based approaches for an undirected graph can be found in Wang et al (2017) and Gionfra et al (2017). Finally, the research of Baros and Ilic (2017) allows turbines to self-organize using torque control and storage to regulate total wind farm power output.…”

Section: Introductionmentioning

confidence: 90%

Wind direction estimation using SCADA data with consensus-based optimization

et al. 2019

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Abstract. Wind turbines in a wind farm typically operate individually to maximize their own performance and do not take into account information from nearby turbines. To enable cooperation to achieve farm-level objectives, turbines will need to use information from nearby turbines to optimize performance, ensure resiliency when other sensors fail, and adapt to changing local conditions. A key element of achieving a more efficient wind farm is to develop algorithms that ensure reliable, robust, real-time, and efficient operation of wind turbines in a wind farm using local sensor information that is already being collected, such as supervisory control and data acquisition (SCADA) data, local meteorological stations, and nearby radars/sodars/lidars. This article presents a framework for developing a cooperative wind farm that incorporates information from nearby turbines in real time to better align turbines in a wind farm. SCADA data from multiple turbines can be used to make better estimates of the local inflow conditions at each individual turbine. By incorporating measurements from multiple nearby turbines, a more reliable estimate of the wind direction can be obtained at an individual turbine. The consensus-based approach presented in this paper uses information from nearby turbines to estimate wind direction in an iterative way rather than aggregating all the data in a wind farm at once. Results indicate that this estimate of the wind direction can be used to improve the turbine's knowledge of the wind direction. This estimated wind direction signal has implications for potentially decreasing dynamic yaw misalignment, decreasing the amount of time a turbine spends yawing due to a more reliable input to the yaw controller, increasing resiliency to faulty wind-vane measurements, and increasing the potential for wind farm control strategies such as wake steering.

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Section: Introductionmentioning

confidence: 90%

Wind direction estimation using SCADA data with consensus-based optimization

et al. 2019

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“…Information is communicated across these edges to determine local atmospheric conditions-such as wind direction or wind speed-at a particular turbine. 25…”

Section: Wind Farm As a Networkmentioning

confidence: 99%

“…In this case, the objective function is convex and can be updated with a closed form solution (Boyd and Vandenberghe (2004)). In addition to the node objective, the edge objective incorporates information from nearby turbines to ensure a robust measurement of the wind direction at an individual 25 turbine. The edge objective can be written as: where w jk is a weight placed on the connection between turbines, x j is the estimated wind direction at turbine j, and x k is the estimated wind direction at turbine k. The edge objective, g jk (x j , x k ), minimizes the differences in estimated wind direction between neighboring turbines.…”

Section: Node and Edge Objective Functionsmentioning

confidence: 99%

“…In one such study, a dynamic average consensus estimator is used in Ebegbulem and Guay (2017) to estimate an overall cost function for turbines communicating via an undirected network 30 where the goal is to maximize total wind farm power production. Similar power maximization approaches using consensusbased approaches for an undirected graph can be found in Wang et al (2017); Gionfra et al (2017). Finally, the research of Baros and Ilic (2017) allows turbines to self-organize using torque control and storage to regulate total wind farm power output.…”

mentioning

confidence: 90%

“…Consensus-based algorithms are a specific class of optimization algorithms that have the potential to accommodate sensor 25 errors caused by failure, miscalibration, and noise by assuming that turbines experience wind inflow directions that share similar characteristics with that of their neighbors. Although consensus optimization is an active area of research for many applications, especially in multiagent systems, its use in wind farm applications is relatively new.…”

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confidence: 99%

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A Framework for Autonomous Wind Farms: Wind Direction Consensus

Annoni

Bay

Johnson

et al. 2018

Preprint

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Wind turbines in a wind farm typically operate individually to maximize their own performance and do not take into account information from nearby turbines. In an autonomous wind farm, enabling cooperation to achieve farm-level objectives, turbines will need to use information from nearby turbines to optimize performance, ensure resiliency when other sensors fail, and adapt to changing local conditions. A key element of achieving an autonomous wind farm is to develop algorithms that provide 5 necessary information to ensure reliable, robust, and efficient operation of wind turbines in a wind plant using local sensor information that is already being collected, such as supervisory control and data acquistion (SCADA) data, local meteorological stations, and nearby radars/sodars/lidars. This article presents a framework for implementing an autonomous wind farm that incorporates information from local sensors in real time to better align turbines in a wind farm. Oftentimes, measurements made at an individual turbine are noisy and unreliable. By incorporating measurements from multiple nearby turbines, a more 10 robust estimate of the wind direction can be obtained at an individual turbine. Results indicate that this estimate of the wind direction can be used to improve the turbine's knowledge of the wind direction and could decrease dynamic yaw misalignment, decrease the amount of time a turbine spends yawing due to a more robust input to the yaw controller, and increase resiliency to faulty wind-vane measurements.

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A Discrete-time PID-like Consensus Control: Application to the Wind Farm Distributed Control Problem

Gionfra

Sandou

Siguerdidjane

et al. 2019

Informatics in Control, Automation and Robotics

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The problem of discrete-time multi-agent systems governed by general MIMO dynamics is addressed. By employing a PID-like distributed protocol, we aim to solve two relevant consensus problems, namely the leaderless weighted consensus under disturbances and leaderfollower weighted consensus under time-varying reference state. Sufficient conditions for stability as well as a LMI approach to tune the controller gains are provided. The two consensus techniques are then applied to solve two issues concerning the wind farm (WF) power maximization problem under wake effect. Leaderless consensus aims at averaging out zero-mean wind disturbance effects on the optimal WF power sharing, while leader-follower control is employed to restore it in the case of power reference errors. Simulations are carried out on a small WF example, whose units are the NREL's CART wind turbines.

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A distributed consensus control under disturbances for wind farm power maximization

Cited by 6 publications

References 20 publications

Wind direction estimation using SCADA data with consensus-based optimization

Wind direction estimation using SCADA data with consensus-based optimization

A Framework for Autonomous Wind Farms: Wind Direction Consensus

A Discrete-time PID-like Consensus Control: Application to the Wind Farm Distributed Control Problem

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