Online Optimal Feedback Voltage Control of Wind Farms: Decentralized and Asynchronous Implementations

Decentralized voltage control of wind farm based on gradient projection method

Distributed cooperative voltage control of wind farms based on consensus protocol

Large renewable power plants connected to the high-voltage network have the potential to make an important contribution to the grid's reactive power management. This is especially true when rather than using a predetermined Q characteristic, the system operator can control the power plant's reactive power output via a direct interface. However, the intermittent nature of the primary resources and today's reactive power requirements inhibit the permanent availability of reactive power from wind and photovoltaic (PV) power plants. This study shows that a synchronized, so-called symbiotic control of PV and wind power plants can vastly increase their capability of fulfilling reactive power demands. This is demonstrated by simulating the properties of a real PV and wind farm located in Germany. Furthermore, generating units coupled to the grid via power electronics have an even larger potential of providing reactive power when their P-Q characteristics are adapted. On the downside, this can lead to an undesired coupling of reactive and active power control in the grid. A symbiosis of wind farms and PV parks can once again improve this situation when smart power allocation methods are employed. An appropriate algorithm has been developed for this study and is tested on the above-mentioned simulation model.

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

Voltage and Frequency control are the two fundamental control problems in power systems. Unlike frequency control, voltage control is complicated by the fact that reactive power can't travel far distances from its source of generation. Due to this distributed nature of reactive power, voltage control is usually performed in decentralized manner. Typically, voltage control problem is divided into a three-level hierarchical structure namely primary, secondary and tertiary voltage control.;The aim of this thesis is to present an optimal secondary voltage control by decomposing a large power system into small subsystems called voltage control areas (VCAs) using the fast community detection algorithm. Each VCA is self-sufficient in satisfying its reactive power demand. A load bus, called pilot point/bus, is selected in each VCA as a representative of the voltage profile of the whole area. Singular value decomposition of Fast Decoupled Load Flow (FDLF) Jacobian is used to optimally control the voltages of these pilot buses.;The presented approach is tested on two standard IEEE test power systems i.e. 9-Bus and 39-Bus systems. The computational time comparison of the fast community detection algorithm with another algorithm called original-GN algorithm is also presented. Through simulation results, it is shown that the presented optimal voltage control (Opt-VC) is a better approach compared to sensitivity based voltage control (Sen-VC).

This paper proposes a grouped, reactive power optimization control strategy to maximize the active power output of a doubly-fed induction generator (DFIG) based on a large wind farm (WF). Optimization problems are formulated based on established grouped loss models and the reactive power limits of the wind turbines (WTs). The WTs in the WF are grouped to relieve computational burden. The particle swarm optimization (PSO) algorithm is applied to optimize the distribution of reactive power among groups, and a proportional control strategy is used to distribute the reactive power requirements in each group. Furthermore, the proposed control strategy optimizes the reactive power distribution between the stator and the grid side converter (GSC) in each WT. The proposed control strategy greatly reduces the number of variables for optimization, and increases the calculation speed of the algorithm. Thus, the control strategy can not only increase the active power output of the WF but also enable the WF to track the reactive power dispatching instruction of the power grid. A simulation of the DFIG WF is given to verify the effectiveness of the proposed control strategy at different wind speeds and reactive power references.

In this article, an optimal steady-state voltage control framework is developed based on a novel linear voltage-power dependence deducted from Gaussian process (GP) learning. Different from other point-based linearization techniques, this GP-based linear relationship is valid over a subspace of operating points and, thus, suitable for a system with uncertainties such as those in power injections due to renewables. The proposed optimal voltage control algorithms, therefore, perform well over a wide range of operating conditions. Both centralized and distributed optimal control schemes are introduced in this framework. The least-squares estimation is employed to provide analytical forms of the optimal control, which offer great computational benefits. Moreover, unlike many existing voltage control approaches deploying fixed voltage references, the proposed control schemes not only minimize the control efforts but also optimize the voltage reference setpoints that lead to the least voltage deviation errors with respect to such setpoints. The control algorithms are also extended to handle uncertain power injections with robust optimal solutions, which guarantee compliance with the voltage regulation standards. As for the distributed control scheme, a new network partition problem is cast, based on the concept of effective voltage control source (EVCS), as an optimization problem which is further solved using convex relaxation. Various simulations on the IEEE 33-bus and 69-bus test feeders are presented to illustrate the performance of the proposed voltage control algorithms and EVCS-based network partition.

The development and utilization of wind power to meet the electrical demand has recently received significant consideration. Additionally, one of the substantial roles of transmission system operators is to balance reactive power within a network in the sense that with the development of wind energy, wind turbines are expected to contribute towards reactive power generation. So far, there is not a wide range of attention being paid to reactive power in reliability evaluation including wind farms contribution. In this article, wind farms with several identical wind turbines are incorporated in reliability assessment while considering reactive power shortage. Fuzzy C-Means clustering method is used for the output power of wind turbines for analyzing power system reliability with wind farms integration. The application of this concept has been also utilized in developing multistep load levels in the illustrated load buses. Reactive power deficiency and the relevant voltage violations caused by the failure of reactive power generations are studied in this article. Load shedding and power injection techniques are employed to determine possible reactive power shortage required for alleviating network violations with and without wind power integration. Composite system reliability analysis in the existence of wind farms is implemented to assess indices affiliated with curtailed energy at various load points. The RBTS 6-bus system has been proportionally modified and studied to demonstrate the procedure. The results indicate the importance of wind power integration in improving both active and reactive reliability indices which in turn provide system planners with long-range planning for system development.

Distributed Optimal Output Feedback Control of Heterogeneous Multi-agent Systems under a Directed Graph

A stochastic minimax optimal time-delay state feedback control strategy for uncertain quasi-integrable Hamiltonian systems is proposed. First, a stochastic optimal state feedback control problem of uncertain quasi-integrable Hamiltonian system with time delay in feedback control subjected to Gaussian white noise is formulated. Then, the time-delayed state feedback control forces are approximated by the control forces without time delay and the original problem is converted into a stochastic optimal state feedback control problem of an uncertain system without time delay. After that, by following a procedure based on the stochastic averaging method and stochastic differential game, the worst-case disturbances and the optimal controls are obtained from solving a Hamiltonian–Jacobi–Isaacs (HJI) equation. As an example, the stochastic minimax optimal state feedback control of a Duffing oscillator with parametric disturbances and time-delayed feedback control is worked out in detail to illustrate the procedure and effectiveness of the proposed control strategy.

The ESS is considered as an effective tool for enhancing the flexibility and controllability of a wind farm, and the optimal control scheme of a wind farm with distributed ESSs is vital to the stable operation of wind power generation. In this paper, a coordinated active and reactive power control strategy based on model predictive control (MPC) is proposed for doubly fed induction generator (DFIG)-based wind farm (WF) with distributed energy storage systems (ESSs). The proposed control scheme coordinates the active and reactive power output among DFIG wind turbines (WTs), grid-side converters (GSCs), and distributed ESSs inside the WF, and the aim is to decrease fatigue loads of WTs, make the WT terminal voltage inside the extent practicable, and take the WF economic operation into consideration. Moreover, the best reactive power references of DFIG stator and GSC are produced independently based on their dynamics. At last, the control scheme generates optimal power references for all ESS to make the SOC of each ESS converge to their average state. With the distributed ESSs, the WF controller regulates the WTs inside WF more flexibly. A WF composed of 10 DFIG WTs was utilized to verify the control performance of the proposed coordinated active and reactive power control strategy.

Integration of large-scale distributed generators (DGs) (such as wind turbines, solar photo-voltaic, fuel cells, and so on) into the microgrid triggers voltage deviation which can give rise to desynchronization and distortion of power flow. To solve this problem, this article presents a distributed optimal secondary voltage control to synchronize the output voltage deviations of the DGs to zero. Instead of designing a distributed linear optimal control for the DGs linearized near an operating point as in the existing literature, a two-loop control structure comprising an inner-loop and outer loop is put in place. The inner-loop linearizes the nonlinear dynamics of each of the DGs. Then, the outer-loop which consists of the optimal distributed controller solves an optimization problem with the aim to minimize the voltage deviation of each DG by exchanging information with neighbouring DGs through a communication network. The proposed control technique will force the voltage deviations of the DGs to zero based on consensus. The efficacy of the proposed technique is confirmed by simulation studies.

This paper presents a now method of optimal digital PID feedback control for a linear multivariate system by using a, parameter optimization technique. The most difficult points for the design of optimal digital PID feedback control system by the modern control theory are : (1) the control structure of it is specified in advance, (2) unless all the state variables are fed back, the optimal control cannot be obtained by dynamic programming or the maximum principle. However, the optimal digital PID feedback gains are determined by using the parameter optimization technique in this paper. When there are some states which are not fed back in the control system, the optimal feedback gains depend on the initial states. First, the optimal solution is presented in the case where all the initial Btates are known. Next, it is extended to the case where the initial states are random variables and their statistical properties are known,

In this paper, we study the optimal control problem for Takagi–Sugeno (T-S) fuzzy systems with disturbances. Due to the presence of disturbances in the T-S fuzzy systems, a fuzzy optimal state feedback control approach is presented by employing a two-player zero-sum game. Since the analytical optimal control solutions and the worst-case disturbance policies of T-S fuzzy systems can be boiled down to solving the algebraic Riccati equations (AREs), which are difficult to be obtained directly, a model-free policy iteration (PI) learning algorithm is proposed to obtain their approximation solutions. It is proved that the developed fuzzy optimal state feedback controller can ensure the fuzzy systems to be asymptotically stable and satisfy the disturbance attenuation condition simultaneously. Also, the designed PI learning algorithm can converge to their optimal solutions. Finally, we apply the developed fuzzy optimal state feedback control method to the truck-trailer system and the simulation results demonstrated the effectiveness of the developed scheme.

A new and efficient methodology for optimal reactive power and voltage control of distribution networks with distributed generators based on fuzzy adaptive hybrid particle swarm optimisation (PSO) is proposed. The objective is to minimise comprehensive cost, consisting of power loss and operation cost of transformers and capacitors, and subject to constraints such as minimum and maximum reactive power limits of distributed generators, maximum deviation of bus voltages and maximum allowable daily switching operation number. PSO is used to solve the corresponding mixed integer non‐linear programming problem and the hybrid PSO (HPSO) method, consisting of three PSO variants, is presented. In order to mitigate the local convergence problem, fuzzy adaptive inference is used to improve the searching process and the final fuzzy adaptive inference‐based HPSO is proposed. The proposed algorithm is implemented in VC++ 6.0 program language and the corresponding numerical experiments are finished on the modified version of the IEEE 33‐node distribution system with two newly installed distributed generators and eight newly installed capacitors banks. The numerical results prove that the proposed method can search a more promising control schedule of all transformers, all capacitors and all distributed generators with less time consumption, compared with other listed artificial intelligent methods.