Decentralized voltage control of wind farm based on gradient projection method

This paper proposes a distributed coordinated voltage control scheme for distribution networks with distributed generation (DG) and on-load tap changer (OLTC). In this scheme, static synchronous compensators (STATCOMs), DG units and OLTC are coordinated to regulate voltages of all buses to be close to the nominal value in the distribution network, mitigate voltage fluctuations, and minimize the number of operations of OLTC while considering different temporal characteristics of voltage regulation devices. The optimization problem of coordinating DG units and STATCOMs is decomposed by the gradient projection (GP) method. The local controller optimizes the reactive power outputs of DGs and STATCOMs according to local voltage and reactive power measurements, and still achieves the optimal coordination of DG units and STATCOMS in a decentralized manner without a central controller or communication between local controllers. The OLTC control scheme is designed to correct the long-term voltage deviations based on model predictive control (MPC) while minimizing the number of operations. The local controllers send the calculated reactive power references of DG and STATCOMs to the OLTC controller, which achieves distributed coordinated voltage control and mitigates the computation burden.

Distributed cooperative voltage control of wind farms based on consensus protocol

This paper deals with the simultaneous tuning of proportional integral controller of static synchronous compensator (STATCOM) associated with lead-lag controller (LL) in order to improve the stability performance of combined wind farm (CWF). This type of farms has been developed to summarize the main benefits of Squirrel-Cage Induction Generator (SCIG) and doubly fed induction generators (DFIG). The performance of CWF with the tuned STATCOM is studied during line-to-line and three-phase faults. Multi-objective Lightning Attachment Procedure Optimization Algorithm (LAPO) is used as an intelligent optimization technique to determine the control parameters which can achieve the specified objective functions; minimizing the error signal and maximizing the output signal. The multi-objective LAPO is used to obtain the optimized values of AC voltage regulator, DC voltage regulator, current regulator, gains and time constant of LL. The ability of STATCOM tuned by multi-objective LAPO is examined by studying the performance of three wind farms. The first one is CWF and two other wind farms; one of them is based only on SCIG (SCIG-WF) and the other based only on DFIG (DFIG-WF) when they associated with the tuned STATCOM LAPO during grid fault. The results show that CWF associated with the tuned STATCOM LAPO has the best performance during grid faults compared with the other wind farms.

According to the grid codes, on wind farm for reactive power compensation and voltage stability requirements, this paper analyzes the extent of a static synchronous compensator (STATCOM) to voltage stability of doubly fed induction generator (DFIG) wind farm, compared to a static VAR compensator (SVC), and proposes a new scheme for coordinated control of reactive power between DFIG and STATCOM, to do this, DFIG stator, grid-side converter (GSC) and STATCOM supply reactive power to the grid coordinately, where the advantages and disadvantages of rotor-side converter (RSC) and GSC are taken into more consideration, and the reactive power control ability of DFIG is fully utilized. Based on test system, research results show that, STATCOM can provide more reactive power than SVC for voltage stability under the same capacity, especially during the serious voltage sag; The coordinated control scheme can not only improve the transient voltage stability, but also help reducing the capacity of STATCOM so that save the cost of investments in wind farms.

The wind farm based on Doubly Fed Induction Generator (DFIG) is built. Not only it can be as active power source to provide active power to network, but also as reactive power source to stabilize voltage or supply Var compensation (reactive power absorbed or generated). Therefore, Var coordinated control strategy is proposed, instead of expensive equipments, such as Static Var Compensator(SVC) or Static Synchronous Compensator (STATCOM), in order to provide sufficient reactive power to maintain system stability. DFIG comprehensive model is built based on correlation models in Dig SILENT/Power Factory. The eigen value analysis method is employed to verify its impact on low-frequency oscillation modes and small signal stability of power system integrated with wind farm. The results from eigen value analysis are elucidated the effectiveness of Var coordinated control in DFIG system.

Although the wind farms based on squirrel cage induction generators (SCIG) is cheaper than the wind farms based on doubly fed induction generators (DFIG), it is always in desperate need for reactive power compensation. Nevertheless, the wind farms based on DFIG are expensive compared with the SCIG wind farm, it features by its ability to control the active power independent of reactive power. However, combined wind farm (CWF) has been developed to collect the benefits of SCIG and DFIG wind turbines in the same wind farm. In this article, artificial neural network (ANN) is used to evaluate gain parameters of static synchronous compensator (STATCOM) in order to improve the stability performance of CWF. The impact of tuned STATCOM on the performance of CWF during gust wind speed and during three-phase fault is comprehensively investigated. The performance of CWF with STATCOM tuned by ANN is compared with its performance when the STATCOM tuned by the multiobjective genetic algorithm (MOGA) and whale optimization algorithm (WOA). The results show that the performance of CWF can be enhanced using STATCOM tuned by ANN more than MOGA and WOA.

Since the doubly fed induction generator (DFIG) converters are designed by the slip power (a fraction 25-30% of the total power) to achieve full control of the generator, there is not enough capacity to provide reactive power when the voltage dips below the tolerable grid voltage level at the point of common coupling (PCC). We propose in this paper to use the static synchronous compensator (STATCOM) for compensating the grid voltage dip to improve the power quality in a weak grid or asymmetrical load. The simplified active and reactive power control strategies of a 6 MVA wind farm with DFIG are described. The maximal power point tracking (MPPT) algorithm is implemented. The simulation results using the platform between Matlab/Simulink and PLECS demonstrate the good performance of the wind farm with DFIG and STATCOM, validating the analytical conclusions.

Using the FACTS controllers, such as static synchronous compensator (STATCOM), as it provides continuous reactive power, in the grid including wind turbine (WT) equipped with doubly fed induction generator, for improving voltage profile (under normal circumstances) and providing a transition ability from inductor generator transition state has been proposed. In this paper, in order to control the controllers and explained goals, nonlinear controller, as a substitute for the traditional controller, is presented. Replacing STATCOM controller in wind farm, which is equipped with doubly fed induction generator (DFIG) using input-output feedback linearization controller, the needed reactive power in order to stable wind farm equipped with DFIG is considered when error is occurred. The proposed control method has been simulated for IEEE-9 Bus, bus No 5, and the achievability to the desired targets in STATCOM efficiency for its reactive power has been investigated. The reasons of using these controllers in bus 5 are voltage dropping and reducing reactive power in this bus. It can be seen by compensating for voltage and reactive power in this bus that these two parameters have improved in other buses. The results show that with the proposed controller, STATCOM has done its duty well and the network bus voltage and reactive power to sustain the wind farm equipped with DFIG in transient mode is provided.

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.

Decentralized optimal voltage control for wind farm with deep learning-based data-driven modeling

This paper presents an autonomous wind farm voltage controller based on model predictive control. The reactive power compensation and voltage regulation devices of the wind farm include static Var compensators, static Var generators, wind turbine generators and on-load tap changing transformer, and they are coordinated to keep the voltages of all the buses within the feasible range. Moreover, the reactive power distribution is optimized throughout the wind farm in order to maximize the dynamic reactive power reserve. The sensitivity coefficients are calculated based on an analytical method to improve the computation efficiency and overcome the convergence problem. Two control modes are designed for both voltage violated and normal operation conditions. A wind farm with 20 wind turbines was used to conduct case studies to verify the proposed coordinated voltage control scheme under both normal and disturbance conditions.

Low-voltage problem emerges in cases of symmetrical and asymmetrical fault in power systems. This problem can be solved out by ensuring low-voltage ride-through capability of wind power plants, through a static synchronous compensator (STATCOM). The purpose of this study is to reveal that the system can be recovered well by inserting a STATCOM with energy storage to a bus where a double-fed induction generator (DFIG)-based wind farm has been connected, so the bus voltage is maintained within desired limits during a fault. Moreover, a supercapacitor is used as an energy storage element. Modeling of the DFIG and STATCOM along with the nonlinear supercapacitor was carried out in a MATLAB/Simulink environment. The behaviors of the system under three- and two-phase faults have been compared with and without STATCOM-supercapacitor, by observing the parameters of DFIG output voltage, active power, speed, electrical torque variations, and d–q axis stator current variations. It was found that the system became stable in a short time when the STATCOM-supercapacitor was incorporated into the full-order modeled DFIG.

The paper considers a previously unreported control approach for the control of off-shore DFIG-based wind farm grid with line commutated HVDC connection. The method is applicable for wind farms based-on doubly fed induction generators (DFIGs) or generators interfaced with full-rated power electronic converters. The paper focused on DFIG- based generation. The proposed control scheme allows each wind turbine to share in the voltage and frequency control of the offshore grid. The classical approach of using droop characteristics for adjusting the voltage and frequency according to the power flow of each turbine is employed. In result, the offshore grid is controlled by each turbine using only the electrical variables of its local connection point. The proposed control approach is validated via PSCAD simulations of normal operation regime and of ride-through operation under loss of the main onshore AC grid. The results confirm very good active and reactive power sharing between wind-farm DFIGs while frequency and voltage deviations in the off-shore grid are very small.

As wind power is the fastest growing renewable energy, it is essential to determine problems associated with maintaining a stable power system when it is connected to grid. In this article a simple strategy is described to stabilize a grid-connected wind farm by controlling reactive power and minimizing voltage fluctuation using a simple 6-pulse Static Synchronous Compensator (STATCOM). The STATCOM is controlled by using voltage control loop with PI controller and Pulse Width Modulation with carrier frequency of 9 times fundamental and varying dc voltage. This paper also presents a comparative study of stabilizing a wind farm using this STATCOM or using only a capacitor bank during wind speed change. Simulation results show that the wind farm could be more effectively stabilized by using STATCOM. The choice of this study is justified by simulation in PSCAD/EMTDC program.

The scaled gradient projection (SGP) method, which can be viewed as a promising improvement of the classical gradient projection method, is a quite efficient solver for real-world problems arising in image science and machine learning. Most recently, Bonettini and Prato [Inverse Probl. 2015;31:095008. 20 p] proved that the SGP method with the monotone Armijo line search technique has the convergence rate, where k counts the iteration. In this paper, we first show that the SGP method could be equipped with the nonmonotone line search procedure proposed by Zhang and Hager [SIAM J Optim. 2004;14:1043–1056]. To some extent, such a nonmonotone technique might improve the performance of SGP method, because its effectiveness has been verified for unconstrained optimization by comparing with the traditional monotone and nonmonotone strategies. Then, we prove that the new SGP method also has the convergence rate under the condition that the objective function is convex. Furthermore, we derive the linear convergence of the SGP algorithm under the strongly convexity assumption of the involved objective function.