Wind Farm

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Jinkyoo Park - One of the best experts on this subject based on the ideXlab platform.

  • a data driven cooperative Wind Farm control to maximize the total power production
    Applied Energy, 2016
    Co-Authors: Jinkyoo Park, Kincho H Law
    Abstract:

    This study investigates the feasibility of using a data-driven optimization approach to determine the coordinated control actions of Wind turbines that maximize the total Wind Farm power production. Conventionally, for a given Wind condition, an individual Wind turbine maximizes its own power production without taking into consideration the conditions of other Wind turbines. Under this greedy control strategy, the wake formed by the upstream Wind turbine, resulting in reduced Wind speed and increased turbulence intensity inside the wake, would affect and lower the power productions of the downstream Wind turbines. To increase the overall Wind Farm power production, cooperative Wind turbine control approaches have been proposed to coordinate the control actions that mitigate the wake interference among the Wind turbines and would thus increase the total Wind Farm power production. This study explores the use of a data-driven approach to identify the optimum coordinated control actions of the Wind turbines using limited amount of data. Specifically, we study the feasibility of the Bayesian Ascent (BA) algorithm, a probabilistic optimization algorithm based on non-parametric Gaussian Process regression technique, for the Wind Farm power maximization problem. The BA algorithm is employed to maximize an analytical Wind Farm power function that is constructed based on Wind Farm configurations and Wind conditions. The results show that the BA algorithm can achieve a monotonic increase in the total Wind Farm power production using a small number of function evaluations and has the potentials to be used for real-time Wind Farm control.

  • cooperative Wind turbine control for maximizing Wind Farm power using sequential convex programming
    Energy Conversion and Management, 2015
    Co-Authors: Jinkyoo Park
    Abstract:

    Abstract This paper describes the use of a cooperative Wind Farm control approach to improve the power production of a Wind Farm. The power production by a downstream Wind turbine can decrease significantly due to reduced Wind speed caused by the upstream Wind turbines, thereby lowering the overall Wind Farm power production efficiency. In spite of the interactions among the Wind turbines, the conventional (greedy) Wind turbine control strategy tries to maximize the power of each individual Wind turbine by controlling its yaw angle, its blade pitch angle and its generator torque. To maximize the overall Wind Farm power production while taking the wake interference into account, this study employs a cooperative control strategy. We first derive the Wind Farm power as a differentiable function of the control actions for the Wind turbines in a Wind Farm. The Wind Farm power function is then maximized using sequential convex programming (SCP) to determine the optimum coordinated control actions for the Wind turbines. Using an example Wind Farm site and available Wind data, we show how the cooperative control strategy improves the power production of the Wind Farm.

  • layout optimization for maximizing Wind Farm power production using sequential convex programming
    Applied Energy, 2015
    Co-Authors: Jinkyoo Park
    Abstract:

    This paper describes an efficient method for optimizing the placement of Wind turbines to maximize the expected Wind Farm power. In a Wind Farm, the energy production of the downstream Wind turbines decreases due to reduced Wind speed and increased level of turbulence caused by the wakes formed by the upstream Wind turbines. As a result, the wake interference among Wind turbines lower the overall power efficiency of the Wind Farm. To improve the overall efficiency of a Wind Farm, researchers have studied the Wind Farm layout optimization problem to find the placement locations of Wind turbines that maximize the expected Wind Farm power. Most studies on Wind Farm layout optimization employ heuristic search-based optimization algorithms. In spite of their simplicity, optimization algorithms based on heuristic search are computationally expensive and have limitation in optimizing the locations of a large number of Wind turbines since the computational time for the search tends to increase exponentially with increasing number of Wind turbines. This study employs a mathematical optimization scheme to efficiently and effectively optimize the locations of a large number of Wind turbines with respect to maximizing the Wind Farm power production. To formulate the mathematical optimization problem, we derive a continuous wake model and express the expected Wind Farm power as a continuous and smooth function in terms of the locations of the Wind turbines. The constructed Wind Farm power function is then maximized using sequential convex programming (SCP) for the nonlinear mathematical problem. We show how SCP can be used to evaluate the efficiency of an existing Wind Farm and to optimize a Wind Farm layout consisting of 80 Wind turbines.

  • Wind Farm power maximization based on a cooperative static game approach
    Proceedings of SPIE, 2013
    Co-Authors: Jinkyoo Park, Soonduck Kwon, Kincho H Law
    Abstract:

    The objective of this study is to improve the cost-effectiveness and production efficiency of Wind Farms using cooperative control. The key factors in determining the power production and the loading for a Wind turbine are the nacelle yaw and blade pitch angles. However, the nacelle and blade angles may adjust the wake direction and intensity in a way that may adversely affect the performance of other Wind turbines in the Wind Farm. Conventional Wind-turbine control methods maximize the power production of a single turbine, but can lower the overall Wind-Farm power efficiency due to wake interference. This paper introduces a cooperative game concept to derive the power production of individual Wind turbine so that the total Wind-Farm power efficiency is optimized. Based on a wake interaction model relating the yaw offset angles and the induction factors of Wind turbines to the Wind speeds experienced by the Wind turbines, an optimization problem is formulated with the objective of maximizing the sum of the power production of a Wind Farm. A steepest descent algorithm is applied to find the optimal combination of yaw offset angles and the induction factors that increases the total Wind Farm power production. Numerical simulations show that the cooperative control strategy can increase the power productions in a Wind Farm.

Frede Blaabjerg - One of the best experts on this subject based on the ideXlab platform.

  • centralised power control of Wind Farm with doubly fed induction generators
    Renewable Energy, 2006
    Co-Authors: Poul Ejnar Sorensen, Florin Iov, Frede Blaabjerg
    Abstract:

    At the moment, the control ability of Wind Farms is a prime research concern for the grid integration of large Wind Farms, due to their required active role in the power system. This paper describes the on-going work of a research project, whose overall objective is to analyse and assess the possibilities for control of different Wind Farm concepts. The scope of this paper is the control of a Wind Farm made up exclusively of doubly fed induction generators. The paper addresses the design and implementation issues of such a controller and focuses on the ability of the Wind Farm control strategy to regulate the Wind Farm power production to the reference power ordered by the system operators. The presented Wind Farm control has a hierarchical structure with both a central control level and a local control level. The central Wind Farm control level controls the power production of the whole Farm by sending out reference power signals to each individual Wind turbine, while the local Wind turbine control level ensures that the reference power signal send by the central control level is reached. The performance of the control strategy is assessed and discussed by means of simulations illustrated both at the Wind Farm level and at each individual Wind turbine level.

  • dynamic modelling of Wind Farm grid interaction
    Wind Engineering, 2002
    Co-Authors: Anca Daniela Hansen, Poul Ejnar Sorensen, Frede Blaabjerg, John Becho
    Abstract:

    This paper describes a dynamic model of a Wind Farm and its nearest utility grid. It is intended to use this model in studies addressing the dynamic interaction between a Wind Farm and a power system, both during normal operation of the Wind Farm and during transient grid fault events. The model comprises the substation where the Wind Farm is connected, the internal power collection system of the Wind Farm, the electrical, mechanical and aerodynamic models for the Wind turbines, and a Wind model. The integrated model is built to enable the assessment of power quality and control strategies. It is implemented in the commercial dedicated power system simulation tool DIgSILENT.

Matthias Wolf - One of the best experts on this subject based on the ideXlab platform.

  • engineering negative cycle canceling for Wind Farm cabling
    European Symposium on Algorithms, 2019
    Co-Authors: Sascha Gritzbach, Torsten Ueckerdt, Dorothea Wagner, Franziska Wegner, Matthias Wolf
    Abstract:

    In a Wind Farm turbines convert Wind energy into electrical energy. The generation of each turbine is transmitted, possibly via other turbines, to a substation that is connected to the power grid. On every possible interconnection there can be at most one of various different cable types. Each cable type comes with a cost per unit length and with a capacity. Designing a cost-minimal cable layout for a Wind Farm to feed all turbine production into the power grid is called the Wind Farm Cabling Problem (WCP). We consider a formulation of WCP as a flow problem on a graph where the cost of a flow on an edge is modeled by a step function originating from the cable types. Recently, we presented a proof-of-concept for a negative cycle canceling-based algorithm for WCP [Sascha Gritzbach et al., 2018]. We extend key steps of that heuristic and build a theoretical foundation that explains how this heuristic tackles the problems arising from the special structure of WCP. A thorough experimental evaluation identifies the best setup of the algorithm and compares it to existing methods from the literature such as Mixed-integer Linear Programming (MILP) and Simulated Annealing (SA). The heuristic runs in a range of half a millisecond to under two minutes on instances with up to 500 turbines. It provides solutions of similar quality compared to both competitors with running times of one hour and one day. When comparing the solution quality after a running time of two seconds, our algorithm outperforms the MILP- and SA-approaches, which allows it to be applied in interactive Wind Farm planning.

  • engineering negative cycle canceling for Wind Farm cabling
    arXiv: Data Structures and Algorithms, 2019
    Co-Authors: Sascha Gritzbach, Torsten Ueckerdt, Dorothea Wagner, Franziska Wegner, Matthias Wolf
    Abstract:

    In a Wind Farm turbines convert Wind energy into electrical energy. The generation of each turbine is transmitted, possibly via other turbines, to a substation that is connected to the power grid. On every possible interconnection there can be at most one of various different cable types. Each type comes with a cost per unit length and with a capacity. Designing a cost-minimal cable layout for a Wind Farm to feed all turbine production into the power grid is called the Wind Farm Cabling Problem (WCP). We consider a formulation of WCP as a flow problem on a graph where the cost of a flow on an edge is modeled by a step function originating from the cable types. Recently, we presented a proof-of-concept for a negative cycle canceling-based algorithm for WCP [14]. We extend key steps of that heuristic and build a theoretical foundation that explains how this heuristic tackles the problems arising from the special structure of WCP. A thorough experimental evaluation identifies the best setup of the algorithm and compares it to existing methods from the literature such as Mixed-integer Linear Programming (MILP) and Simulated Annealing (SA). The heuristic runs in a range of half a millisecond to approximately one and a half minutes on instances with up to 500 turbines. It provides solutions of similar quality compared to both competitors with running times of one hour and one day. When comparing the solution quality after a running time of two seconds, our algorithm outperforms the MILP- and SA-approaches, which allows it to be applied in interactive Wind Farm planning.

Alex Q. Huang - One of the best experts on this subject based on the ideXlab platform.

  • Optimal control of battery energy storage for Wind Farm dispatching
    IEEE Transactions on Energy Conversion, 2010
    Co-Authors: Sercan Teleke, Mesut. E. Baran, Subhashish Bhattacharya, Alex Q. Huang
    Abstract:

    Integrating a battery energy storage system (BESS) with a large Wind Farm can make a Wind Farm more dispatchable. This paper focuses on development of a control strategy for optimal use of the BESS for this purpose. The paper considers an open-loop optimal control scheme to incorporate the operating constraints of the BESS, such as state of charge limits, charge/discharge current limits, and lifetime. The goal of the control is to have the BESS to provide as much smoothing as possible, so that the Wind Farm can be dispatched on an hourly basis based on the forecasted Wind conditions. The effectiveness of this control strategy has been tested by using an actual Wind Farm data. Furthermore, a real-time implementation strategy using model predictive control is also proposed. Finally, it is shown that the control strategy is very important in improving the BESS performance for this application.

  • statcom with energy storage for smoothing intermittent Wind Farm power
    Power and Energy Society General Meeting, 2008
    Co-Authors: Mesut. E. Baran, Sercan Teleke, Loren Anderson, Subhashish Bhattacharya, Alex Q. Huang, Stanley Atcitty
    Abstract:

    In this paper, STATCOM with battery energy storage system (BESS) is proposed for smoothing intermittent power output of a large Wind Farm. The main advantage of the system is that it can inject both real power (by charging/discharging the BESS) and the reactive power to the system. Simulations based on an actual Wind Farm data indicate that indeed with the proposed control strategy the power output of the Wind Farm can be smoothed to facilitate 1/2 h dispatching of the Wind Farm. Also, the reactive power compensation provided by the STATCOM helps to smooth out the fast voltage variations at the interconnecting bus.

Loren Anderson - One of the best experts on this subject based on the ideXlab platform.

  • Control strategies for battery energy storage for Wind Farm dispatching
    IEEE Transactions on Energy Conversion, 2009
    Co-Authors: Loren Anderson
    Abstract:

    Integrating a battery energy storage system (BESS) with a large Wind Farm can smooth out the intermittent power from the Wind Farm. This paper focuses on development of a control strategy for optimal use of the BESS for this purpose. The paper considers a conventional feedback-based control scheme with revisions to incorporate the operating constraints of the BESS, such as state of charge limits, charge/discharge rate, and lifetime. The goal of the control is to have the BESS provide as much smoothing as possible so that the Wind Farm can be dispatched on an hourly basis based on the forecasted Wind conditions. The effectiveness of this control strategy has been tested by using an actual Wind Farm data. Finally, it is shown that the control strategy is very important in determining the proper BESS size needed for this application.

  • statcom with energy storage for smoothing intermittent Wind Farm power
    Power and Energy Society General Meeting, 2008
    Co-Authors: Mesut. E. Baran, Sercan Teleke, Loren Anderson, Subhashish Bhattacharya, Alex Q. Huang, Stanley Atcitty
    Abstract:

    In this paper, STATCOM with battery energy storage system (BESS) is proposed for smoothing intermittent power output of a large Wind Farm. The main advantage of the system is that it can inject both real power (by charging/discharging the BESS) and the reactive power to the system. Simulations based on an actual Wind Farm data indicate that indeed with the proposed control strategy the power output of the Wind Farm can be smoothed to facilitate 1/2 h dispatching of the Wind Farm. Also, the reactive power compensation provided by the STATCOM helps to smooth out the fast voltage variations at the interconnecting bus.