Partitioning approach for large wind farms: Active power control for optimizing power reserve

Siniscalchi-Minna, Sara; Ocampo‐Martínez, Carlos; Bianchi, Fernando D.; De-Prada-Gil, Mikel; DeSchutter, B.

doi:10.1109/cdc.2018.8619052

Cited by 4 publications

(5 citation statements)

References 14 publications

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“…Extending the previous results in [20], the present paper proposes a hierarchical non-centralized model predictive control (MPC) scheme relaying on a virtual partitioning of a large-scale wind farm. The main contributions are:…”

Section: Introductionmentioning

confidence: 85%

“…That is, wind turbines are organized in subsets according to the coupling level associated with the wake effect. Among the different approaches proposed for partitioning large-scale systems [26,8,14], here the partitioning approach proposed in [20] is considered and improved in order to provide a more robust partitioning algorithm.…”

Section: Wind Farm Partitioningmentioning

confidence: 99%

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A non-centralized predictive control strategy for wind farm active power control: A wake-based partitioning approach

et al. 2020

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Owing to wake effects, the power production of each turbine in a wind farm is highly coupled to the operating conditions of the other turbines. Wind farm control strategies must take into account these couplings and produce individual power commands for each turbine. In this case, centralized control approaches might be prone to failures due to the high computational burden and communication dependency. To overcome this problem, this paper proposes a noncentralized scheme based on splitting the wind farm into almost uncoupled sets of turbines by solving a mixed-integer partitioning problem. In each set of turbines, a model predictive control strategy seeks to optimize the distribution of the power set-points among turbines such that the impact of the power losses due to the wake effect is reduced. Then, a supervisory controller coordinates the generation of each group to satisfy the power demanded by the grid operator. The effectiveness of the proposed control scheme in terms of reduction of computational costs and power regulation is confirmed by simulations for a wind farm of 42 turbines.

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Section: Introductionmentioning

confidence: 85%

Section: Wind Farm Partitioningmentioning

confidence: 99%

A non-centralized predictive control strategy for wind farm active power control: A wake-based partitioning approach

et al. 2020

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“…The resulting control structure consists of a local controller at the turbine level, which follows a LQG control law, while a wind farm coordinator applies a single averaging operation to compensate for deviations in the power set-points at the wind farm level. The control approach is tested with [191,192] Receding horizon controller to follow power reference and reduce changes in thrust coefficient MPC to minimize tracking power, electrical power losses, variation in power reference and maximise available power P re f Jensen-Park model SimWindFarm Siniscalchi-Minna et al [203,204] Partitioning of wind farm to minimise wake effects; dispatch of power reference on partition; local MPC to follow power reference and maximise available power SimWindFarm and resulted in a 35% average reduction of fatigue damage in the wind turbine towers compared to the case where each turbine maintains its nominal power reference. In [209], a distributed approach to solve the Fatigue-load minimization optimal control problem is proposed.…”

Section: Linear Quadratic Regulatormentioning

confidence: 99%

Wind farm control ‐ Part I: A review on control system concepts and structures

Andersson

Anaya‐Lara

Tande

et al. 2021

IET Renewable Power Gen

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Wind farm control design is a recently new area of research that has rapidly become a key enabler for the development of large wind farm projects and their safe and efficient connection to the power grid. A comprehensive review of the intense research conducted in this area over the last 10 years is presented. Part I reviews control system concepts and structures and classifies them depending on their main objective (i.e. to maximise power production or to provide grid services. The work and key findings in each paper are discussed in detail with particular emphasis on the turbine side. Additionally, the review contributes to the existing reviews on the area by providing an elegant classification between model testing and control approaches. Areas where significant work is still needed are also discussed. In Part II, a thorough review on aerodynamic wind farm models for control design purposes is provided.

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“…Different control strategies have different coordination control results. Some scholars formulated the coordination control problem as multi-objective optimisation problems, which coordinate the power adjustment amount undertaken between WTs by multi-objective optimisation algorithm (MOOPA) [16][17][18][19][20][21][22]. The minimum target power tracking error, the minimum energy consumption cost and the minimum mechanical load of WTs are taken as the multi-objectives to optimise the output power of WTs, which is used to coordinate all WTs as a whole could optimally work at the desired operating states.…”

Section: Introductionmentioning

confidence: 99%

“…Authors took the pitch angle, rotation speed and wind speed of WTs as input, used fuzzy theory to evaluate the WTs' adjustment performance and adopted the ability weight distribution algorithm (AWDA) to coordinate all WTs [28]. The control strategy of [16][17][18][19][20][21][22][23][24][25][26][27][28] is shown in Fig. 1 (below the dotted line).…”

Section: Introductionmentioning

confidence: 99%

Active power dynamic interval control based on operation data mining for wind farms to improve regulation performance in AGC

Liu

Wang

Chen

et al. 2020

IET Generation Trans & Dist

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With the real‐time changes of wind speed and operating conditions, it is a challenge to fully tap the active power regulation ability and improve the control performance of automatic generation control (AGC) in a wind farm (WF). The essence of tapping the active power regulation ability is to realise the coordination and complementarity of each wind turbine's (WT's) dynamic adjustment performance (DAP). To address this, a novel data mining method is developed to derive the internal relations between WTs’ output power and pitch angle, impeller speed and pitch angle during the power adjustment process, and a unified mechanism model is established to describe DAP of WTs. Based on the discovered relationship between WTs’ DAP and its operating states, an active power distribution algorithm and a dynamic interval control method are proposed. Then, an active power dynamic interval control strategy that has been implemented using Java script in MyEclipse for WFs is further developed. The control strategy has been tested and applied in a 50 MW WF in northwest China. The preliminary results showed that the control strategy has improved the rapidity and accuracy of AGC in the WF.

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Partitioning approach for large wind farms: Active power control for optimizing power reserve

Cited by 4 publications

References 14 publications

A non-centralized predictive control strategy for wind farm active power control: A wake-based partitioning approach

A non-centralized predictive control strategy for wind farm active power control: A wake-based partitioning approach

Wind farm control ‐ Part I: A review on control system concepts and structures

Active power dynamic interval control based on operation data mining for wind farms to improve regulation performance in AGC

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