Mitigating subsynchronous resonance and damping power system oscillation in a series compensated wind park using a novel static synchronous series compensator control algorithm

This paper presents a novel damping control algorithm for static synchronous series compensator (SSSC) in a series compensated wind park for mitigating subsynchronous resonance (SSR) and for damping power system oscillations. The sample test system, adapted from the IEEE first benchmark model on SSR replacing the synchronous generator, is employed aggregating wind park based self‐excited induction generator. Consequently, it investigates the SSR phenomena and the damping power system oscillation while integrating large wind park based on SEIG. The potential occurrence and mitigation of the SSR caused by induction generator effects as well as torsional interactions, in a series compensated wind park, are investigated. The auxiliary subsynchronous damping control loops for the SSSC based on a novel design procedure of non‐linear optimization are developed. The performance of the controller is tested in steady state operation and in response to system contingencies, taking into account the impact of short circuit ratios (SCRs). The simulation results are presented to demonstrate the capability of the controllers for mitigating the SSR, damping the power system oscillation and enhancing the transient stability margin in response to different SCRs. Copyright © 2011 John Wiley & Sons, Ltd.     

A novel power system reconfiguration for a distribution system with minimum load balancing index using bacterial foraging optimization algorithm

In this paper, the objective of minimum load balancing index (LBI) for the 16-bus distribution system is achieved using bacterial foraging optimization algorithm (BFOA). The feeder reconfiguration problem is formulated as a non-linear optimization problem and the optimal solution is obtained using BFOA. With the proposed reconfiguration method, the radial structure of the distribution system is retained and the burden on the optimization technique is reduced. Test results are presented for the 16-bus sample network, the proposed reconfiguration method has effectively decreased the LBI, and the BFOA technique is efficient in searching for the optimal solution.     

Fractional‐order sliding mode control for hybrid drive wind power generation system with disturbances in the grid

The proposal of hybrid drive grid‐connected wind turbine based on speed regulating differential mechanism (SRDM) has been made in this paper to generate constant‐frequency power without fully‐ or partially‐rated frequency converters so as well improve electric power quality. However, disturbances in the power grid including sudden load fluctuation and sub‐synchronous resonance (SSR) can lead to the pulsating torque to act on the shaft section between SG and exciter at the main generator collector, such that the speed regulating accuracy of SRDM is seriously affected. As a result, this paper synthesizes a new‐type fractional‐order sliding mode controller (FOSMC) with a load torque observer (LTO) for the high‐accuracy speed control of permanent magnet synchronous motor (PMSM) in SRDM. Taking advantage of ridge regression algorithm, related parameters including rotational inertia and viscous friction coefficient of speed regulating system are calculated accurately. Finally, comparative experiments are carried out under four cases of mean of 5, 10, 13, and 21 m/s wind speeds to verify the satisfactory performances of designed FOSMC with LTO. Comparative experimental results show that FOSMC with LTO can effectively eliminate undesirable chattering effect. Additionally, under operating conditions of changing wind speeds, SSR, and sudden load fluctuation in power grid, the output speed of SRDM that corresponds directly to the frequency output of SG can be steadily and accurately regulated by using proposed control scheme. SRDM equipped with designed controller enables the power frequency to meet the National Standard of PR China perfectly.     

Simulating Feedback Linearization control of wind turbines using high‐order models

As wind turbines are becoming larger, wind turbine control must now encompass load control objectives as well as power and speed control to achieve a low cost of energy. Due to the inherent non‐linearities in a wind turbine system, the use of non‐linear model‐based controllers has the potential to increase control performance. A non‐linear feedback linearization controller with an Extended Kalman Filter is successfully used to control a FAST model of the controls advanced research turbine with active blade, tower and drive‐train dynamics in above rated wind conditions. The controller exhibits reductions in low speed shaft fatigue damage equivalent loads, power regulation and speed regulation when compared to a Gain Scheduled Proportional Integral controller, designed for speed regulation alone. The feedback linearization controller shows better rotor speed regulation than a Linear Quadratic Regulator (LQR) at close to rated wind speeds, but poorer rotor speed regulation at higher wind speeds. This is due to modeling inaccuracies and the addition of unmodeled dynamics during simulation. Similar performance between the feedback linearization controller and the LQR in reducing drive‐train fatigue damage and power regulation is observed. Improvements in control performance may be achieved through increasing the accuracy of the non‐linear model used for controller design. Copyright © 2009 John Wiley & Sons, Ltd.     

Load case reduction for offshore wind turbine support structure fatigue assessment by importance sampling with two‐stage filtering

A complete fatigue assessment for operational conditions for offshore wind turbines involves simulating thousands of environmental states. For applications such as optimization, where this assessment needs to be repeated many times, that presents a significant computational problem. Here, we propose a novel way of reducing the number of simulated environmental states (load cases) while maintaining an acceptable accuracy. From one full fatigue analysis of a base design, the OC3 monopile (with the NREL 5MW turbine), the distribution of fatigue damage per load case can be used to estimate the lifetime fatigue damage of a range of modified designs. Using importance sampling and a specially adapted two‐stage filtering procedure, we obtain pseudo‐optimal sets of load cases from which the fatigue damage is estimated. This is applied to seven different designs that have been modified to emulate iterations of an optimization loop. For several of these designs, sampling less than 1% of all load cases can give damage estimates with median errors of less than 2%. Even for the most severe cases, using 3% of the environmental states yields a maximum error of 10%. While further refinement is possible, the method is considered viable for applications within design optimization and preliminary design.     

Optimal positioning of wind turbines on Gökçeada using multi‐objective genetic algorithm

This paper addresses the problem of optimal placement of wind turbines in a farm on Gokçeada Island located at the north‐east of Aegean Sea bearing full potential of wind energy generation. A multi‐objective genetic algorithm approach is employed to obtain optimal placement of wind turbines by maximizing the power production capacity while constraining the budget of installed turbines. Considering the speed and direction history, wind with constant intensity from a single direction is used during optimization. This study is based on wake deficit model mainly because of its simplicity, accuracy and fast calculation time. The individuals of the Pareto optimal solution set are evaluated with respect to various criteria, and the best configurations are presented. In addition to best placement layouts, results include objective function values, total power output, cost and number of turbines for each configuration. Copyright © 2009 John Wiley & Sons, Ltd.     

Identification in closed‐loop operation of models for collective pitch robust controller design

To achieve load reduction and power optimization, wind turbine controllers design requires the availability of reliable control‐oriented linear models. These are needed for model‐based controller design. Model identification of wind turbine while operating in closed loop is an appropriate solution that has recently shown its capabilities when linear time‐invariant controllers and complicated control structures are present. However, the collective pitch control loop, one of the most important wind turbine loops, uses non‐linear controllers. Typically, this non‐linear controller is a combination of a linear controller and a gain scheduling. This paper presents a new algorithm for identification in closed‐loop operation that allows the use of this kind of non‐linear controllers. The algorithm is applied for identification the collective pitch demand to generator speed of a wind turbine at various operating points. The obtained models are presented and discussed from a control point of view. The validity of these models is illustrated by their use for the design of a linear fix robust controller. The performance based on simulation data of this linear controller is similar to that obtained with simulations based on a linear controller with gain scheduling, but its design and implementation is much simpler. Copyright © 2012 John Wiley & Sons, Ltd.     

采用STATCOM抑制次同步谐振的理论与仿真研究

针对静止同步补偿器（STATCOM）在抑制次同步谐振（SSR）方面的理论与应用进行了研究。分析了STATCOM抑制SSR的基本工作原理;提出了STATCOM抑制SSR的控制策略,并设计了相应的控制器,以IEEE第一标准模型为例,采用特征值分析和时域仿真分析2种方法验证STATCOM对SSR的抑制效果。此外,利用PSCAD／EMTDC电磁暂态仿真软件,建立了锦界电厂串补送出系统的电磁暂态仿真平台,并进行了相应的仿真分析。仿真结果表明,STATCOM~有效抑制SSR的发生,为解决国内外交流串补输电工程中的SSR问题提供了参考,也为STATCOM装置的应用提供了依据。     

Performance enhancement of the artificial neural network–based reinforcement learning for wind turbine yaw control

The yaw angle control of a wind turbine allows maximization of the power absorbed from the wind and, thus, the increment of the system efficiency. Conventionally, classical control algorithms have been used for the yaw angle control of wind turbines. Nevertheless, in recent years, advanced control strategies have been designed and implemented for this purpose. These advanced control strategies are considered to offer improved features in comparison to classical algorithms. In this paper, an advanced yaw control strategy based on reinforcement learning (RL) is designed and verified in simulation environment. The proposed RL algorithm considers multivariable states and actions, as well as the mechanical loads due to the yaw rotation of the wind turbine nacelle and rotor. Furthermore, a particle swarm optimization (PSO) and Pareto optimal front (PoF)‐based algorithm have been developed in order to find the optimal actions that satisfy the compromise between the power gain and the mechanical loads due to the yaw rotation. Maximizing the power generation and minimizing the mechanical loads in the yaw bearings in an automatic way are the objectives of the proposed RL algorithm. The data of the matrices Q (s,a) of the RL algorithm are stored as continuous functions in an artificial neural network (ANN) avoiding any quantification problem. The NREL 5‐MW reference wind turbine has been considered for the analysis, and real wind data from Salt Lake, Utah, have been used for the validation of the designed yaw control strategy via simulations with the aeroelastic code FAST.     

A model predictive control approach to the problem of wind power smoothing with controlled battery storage

The aim of this study is to design a controller, based on model predictive control (MPC), to smooth the wind power output, which is generated from a wind farm, and subject to a variety of constraints on the system model. In order to employ the model predictive controller, we propose a wind power prediction system, which is used by the controller within its predictive optimization. The proposed controller is capable of smoothing wind power by utilizing inputs from our prediction system, and optimizes the maximum ramp rate requirement and also the state of the charge of the battery under practical constraints. The proposed prediction model is capable of predicting the wind power several steps ahead which is used in the optimization part of the controller. We illustrate the effectiveness of the proposed controller with a simulation example, employing real wind farm data under a variety of hard constraints.     

A framework for isogeometric‐analysis‐based optimization of wind turbine blade structures

Early‐stage wind turbine blade design usually relies heavily on low‐fidelity structural models; high‐fidelity, finite‐element‐based structural analyses are reserved for later design stages because of their complex workflows and high computational expense. Yet, high‐fidelity structural analyses often provide design‐governing feedback such as buckling load factors. Mitigation of the issues of workflow complexity and computational expense would allow designers to utilize high‐fidelity feedback earlier, more easily, and more often in the design process. Thus, a blade analysis framework that employs isogeometric analysis (IGA), a simulation method that overcomes many of the aforementioned drawbacks associated with traditional finite element analysis (FEA), is presented. IGA directly utilizes the mathematical models generated by computer‐aided design (CAD) software, requiring less user interaction and no conversion of parametric geometries to finite element meshes. Furthermore, IGA tends to have superior per‐degree‐of‐freedom accuracy compared with traditional FEA. Issues unique to IGA in the context of wind turbine blade design, such as coupling of thin‐shell components, are addressed, and a design approach that combines reduced‐order aeroelastic analysis with IGA is outlined. Aeroelastic analysis is used to efficiently provide dynamic kinematic data for a wide range of wind load cases, while IGA is used to perform buckling analysis. The value of incorporating high‐fidelity analysis feedback into blade design is demonstrated through optimization of the NREL/SNL 5 MW wind turbine blade. A variety of potential designs are produced with reduced blade mass and material cost, and IGA‐based buckling analysis is shown to provide design‐governing constraint information.     

Linear models‐based LPV modelling and control for wind turbines

The non‐linear behaviour of wind turbines demands control strategies that guarantee the robustness of the closed‐loop system. Linear parameter‐varying (LPV) controllers adapt their dynamics to the system operating points, and the robustness of the closed loop is guaranteed in the controller design process. An LPV collective pitch controller has been developed within this work to regulate the generator speed in the above rated power production control zone. The performance of this LPV controller has been compared with two baseline control strategies previously designed, on the basis of classical gain scheduling methods and linear time‐invariant robust H_∞ controllers. The synthesis of the LPV controller is based on the solution of a linear matrix inequalities system, proposed in a mixed‐sensitivity control scenario where not only weight functions are used but also an LPV model of the wind turbine is necessary. As a contribution, the LPV model used is derived from a family of linear models extracted from the linearization process of the wind turbine non‐linear model. The offshore wind turbine of 5 MW defined in the Upwind European project is the used reference non‐linear model, and it has been modelled using the GH Bladed 4.0 software package. The designed LPV controller has been validated in GH Bladed, and an exhaustive analysis has been carried out to calculate fatigue load reductions on wind turbine components, as well as to analyse the load mitigation in some extreme cases. Copyright © 2014 John Wiley & Sons, Ltd.     

Optimization‐based load reduction during rapid shutdown of multi‐megawatt wind turbine generators

This paper describes an optimization‐based approach to reducing extreme structural loads during rapid or emergency shutdown of multi‐megawatt wind turbine generators. The load reduction problem is cast into an optimal control formulation, and a simple, low‐order model is developed in order for this optimization problem to be tractable in reasonable time using state‐of‐the‐art numerical methods. To handle the variations in wind speed and turbulence inherent to wind turbine operation as well as the presence of model mismatch, a real‐time optimization strategy based on fast sensitivity updates is also considered, whose online computational burden is limited to the repeated solution of quadratic programs that are designed offline. The low‐order model and both the open‐loop and closed‐loop optimal control strategies are validated against a high‐fidelity model in the simulation environment Bladed ? for an industrial 3 MW wind turbine. Under favorable shutdown scenarios, i.e. when the wind turbine is operating properly and the actuators and sensors are not faulty, large reductions of the first compressive peak and subsequent compressive/tensile peaks of the tower load pattern are obtained at various above‐rated wind speeds compared with normal pitch control shutdown. Extension to more challenging shutdown scenarios are also discussed. Copyright © 2013 John Wiley & Sons, Ltd.     

Aerodynamic shape optimization of wind turbine blades using a Reynolds‐averaged Navier–Stokes model and an adjoint method

Computational fluid dynamics (CFD) is increasingly used to analyze wind turbines, and the next logical step is to develop CFD‐based optimization to enable further gains in performance and reduce model uncertainties. We present an aerodynamic shape optimization framework consisting of a Reynolds‐averaged Navier Stokes solver coupled to a numerical optimization algorithm, a geometry modeler, and a mesh perturbation algorithm. To efficiently handle the large number of design variables, we use a gradient‐based optimization technique together with an adjoint method for computing the gradients of the torque coefficient with respect to the design variables. To demonstrate the effectiveness of the proposed approach, we maximize the torque of the NREL VI wind turbine blade with respect to pitch, twist, and airfoil shape design variables while constraining the blade thickness. We present a series of optimization cases with increasing number of variables, both for a single wind speed and for multiple wind speeds. For the optimization at a single wind speed performed with respect to all the design variables (1 pitch, 11 twist, and 240 airfoil shape variables), the torque coefficient increased by 22.4% relative to the NREL VI design. For the multiple‐speed optimization, the torque increased by an average of 22.1%. Depending on the CFD mesh size and number of design variables, the optimization time ranges from 2 to 24h when using 256 cores, which means that wind turbine designers can use this process routinely. Copyright © 2016 John Wiley & Sons, Ltd.     

Reactive power compensation of an isolated hybrid power system with load interaction using ANFIS tuned STATCOM

This paper presents an adaptive neuro fuzzy interference system (ANFIS) based approach to tune the parameters of the static synchronous compensator (STATCOM) with frequent disturbances in load model and power input of a wind-diesel based isolated hybrid power system (IHPS). In literature, proportional integral (PI) based controller constants are optimized for voltage stability in hybrid systems due to the interaction of load disturbances and input power disturbances. These conventional controlling techniques use the integral square error (ISE) criterion with an open loop load model. An ANFIS tuned constants of a STATCOM controller for controlling the reactive power requirement to stabilize the voltage variation is proposed in the paper. Moreover, the interaction between the load and the isolated power system is developed in terms of closed loop load interaction with the system. Furthermore, a comparison of transient responses of IHPS is also presented when the system has only the STATCOM and the static compensation requirement of the induction generator is fulfilled by the fixed capacitor, dynamic compensation requirement, meanwhile, is ful-filled by STATCOM. The model is tested for a 1% step increase in reactive power load demand at t = 0 s and then a sudden change of 3% from the 1% at t = 0.01 s for a 1% step increase in power input at variable wind speed model.     

Coordinated Reactive Power Control of a Large Wind Farm and a STATCOM Using Heuristic Dynamic Programming

A novel interface neurocontroller (INC) is proposed for the coordinated reactive power control between a large wind farm equipped with doubly fed induction generators (DFIGs) and a static synchronous compensator (STATCOM). The heuristic dynamic programming (HDP) technique and radial basis function neural networks (RBFNNs) are used to design this INC. It effectively reduces the level of voltage sags as well as the over-currents in the DFIG rotor circuit during grid faults, and therefore, significantly enhances the fault ride-through capability of the wind farm. The INC also acts as a coordinated external damping controller for the wind farm and the STATCOM, and therefore, improves power oscillation damping of the system after grid faults. Simulation studies are carried out in PSCAD/EMTDC and the results are presented to verify the proposed INC.      

Model‐based receding horizon control of wind farms for secondary frequency regulation

In this study, we propose the use of model‐based receding horizon control to enable a wind farm to provide secondary frequency regulation for a power grid. The controller is built by first proposing a time‐varying one‐dimensional wake model, which is validated against large eddy simulations of a wind farm at startup. This wake model is then used as a plant model for a closed‐loop receding horizon controller that uses wind speed measurements at each turbine as feedback. The control method is tested in large eddy simulations with actuator disk wind turbine models representing an 84‐turbine wind farm that aims to track sample frequency regulation reference signals spanning 40 min time intervals. This type of control generally requires wind turbines to reduce their power set points or curtail wind power output (derate the power output) by the same amount as the maximum upward variation in power level required by the reference signal. However, our control approach provides good tracking performance in the test system considered with only a 4% derate for a regulation signal with an 8% maximum upward variation. This performance improvement has the potential to reduce the opportunity cost associated with lost revenue in the bulk power market that is typically associated with providing frequency regulation services. Copyright © 2017 John Wiley & Sons, Ltd.     

Application of STATCOM for damping torsional oscillations in seriescompensated AC systems

This paper presents the results of a study on the application of the recently developed FACTS device, the static compensator (STATCOM), for the damping of torsional oscillations that occur in a series compensated AC system. The IEEE first benchmark system is considered for this study. In order to suppress unstable torsional mode oscillations, a STATCOM with a PI controller to regulate the bus voltage, and with an auxiliary signal derived from the generator speed deviations is employed at the generator terminal. The eigenvalue analysis technique is used for small signal analysis and optimization of the control system parameters is done through step response studies. In addition, dynamic performance of the nonlinear system with optimized STATCOM controller is evaluated under a three-phase fault. Results from the analytical and digital simulation studies reveal the technical feasibility of using STATCOM for damping of turbine-generator torsional oscillations in series compensated AC systems     

New design of robust PID controller based on meta‐heuristic algorithms for wind energy conversion system

This paper proposes a new robust control method for a wind energy conversion system. The suggested method can damp the deviations in the generator speed because of the penetration of wind speed and load demand fluctuations in the electrical grid. Furthermore, it can overcome the uncertainties of the plant parameters because of load demand fluctuations and the errors of the implementation. The new method has been built based on new simple frequency‐domain conditions and the whale optimization algorithm (WOA). This method is utilized to design a robust proportional‐integral‐derivative (PID) controller based on the WOA in order to enhance the damping characteristics of the wind energy conversion system. Simulation results confirm the superiority and robustness of the proposed technique against the wind speed fluctuations and the plant parameters uncertainties compared with other meta‐heuristic algorithms.