Active Power Control for Wind Farms Using Distributed Model Predictive Control and Nearest Neighbor Communication

Distributed trajectory optimization for multiple solar-powered UAVs target tracking in urban environment by Adaptive Grasshopper Optimization Algorithm

This work focuses on a class of nonlinear control problems that arise when new control systems which may use networked sensors and/or actuators are added to already operating control loops to improve closed‐loop performance. In this case, it is desirable to design the pre‐existing control system and the new control system in a way such that they coordinate their actions. To address this control problem, a distributed model predictive control method is introduced where both the pre‐existing control system and the new control system are designed via Lyapunov‐based model predictive control. Working with general nonlinear models of chemical processes and assuming that there exists a Lyapunov‐based controller that stabilizes the nominal closed‐loop system using only the pre‐existing control loops, two separate Lyapunov‐based model predictive controllers are designed that coordinate their actions in an efficient fashion. Specifically, the proposed distributed model predictive control design preserves the stability properties of the Lyapunov‐based controller, improves the closed‐loop performance, and allows handling input constraints. In addition, the proposed distributed control design requires reduced communication between the two distributed controllers since it requires that these controllers communicate only once at each sampling time and is computationally more efficient compared to the corresponding centralized model predictive control design. The theoretical results are illustrated using a chemical process example. © 2009 American Institute of Chemical Engineers AIChE J, 2009

This paper presents distributed model predictive control methods for improving the transient response of feedback-controlled irrigation channels by managing the water-level reference set points across the irrigation season. The implemented distributed model predictive control methods exploit a two-level and single-level architectures for communication. For an automated irrigation channel, the satisfactory performance of the method with two-level architecture for communication in improving the transient response of automated irrigation channel is illustrated using computer simulation and compared with the performance of the distributed model predictive control method that exploits a single-level architecture for communication. It is illustrated that the distributed model predictive control method that exploits a two-level architecture for communication has a better performance in improving the transient response by better managing communication overhead.

In this paper, an observer‐based event‐triggered distributed model predictive control method is proposed for a class of nonlinear interconnected systems with bounded disturbances, considering unmeasurable states. First of all, the state observer is constructed. It is proved that the observation error is bounded. Second, distributed model predictive controller is designed by using observed value. Meanwhile, the event‐triggered mechanism is set by using the error between the actual output and the predicted output. The setting of event‐triggered mechanism not only ensures the error between the actual output and the predicted output within a certain range, but also reduces the calculation amounts of solving the optimization problem. The states of each subsystem enter the terminal invariant set by distributed model predictive control, and then are stabilized in the invariant set under the action of output feedback control law. In addition, sufficient conditions are given to ensure the feasibility of the algorithm and the stability of the closed‐loop system. Finally, the numerical example is given, and the simulation results verify the effectiveness of the proposed algorithm.

With an increasing number of vehicles on the road, multiple‐vehicle platoons have become a new solution under communication constraints. However, the existing studies seldom consider the effect of the tail vehicle on the information transfer and platoon control over long distances, and thus the control performance under time‐varying delays is unacceptable. In this study, a hierarchical multi‐platoon control architecture considering the backbone platoon and sub‐platoons is proposed under non‐ideal communication conditions, where the backbone platoon layer communication topology is designed to achieve backbone platoon control and appropriate coordination among sub‐platoons. Distributed model predictive controllers (DMPCs) for backbone‐ and sub‐platoons that integrate delay compensation have been proposed. Buffers and delay compensators in the proposed DMPC are designed to reduce the interference caused by non‐ideal communications. The asymptotic stability is analyzed at the sub‐ and multi‐platoon levels. The results show that compared with traditional DMPC without a compensator, the proposed DMPC can eliminate the velocity lag and steady‐state spacing errors and maintain the maximum velocity and spacing errors within 0.11 m/s and 0.17 m, respectively, under certain operating conditions. Thus, the proposed method guarantees the stability of multiple platoons under both the constant and time‐varying delay conditions.

This paper describes an estimation algorithm for velocity and power output signals in a wind farm under changing wind direction. A graph-theoretic definition describes the wind farm as a collection of nodes (turbines) and time-varying weighted edges (inter-turbine wake propagation) that change as a function of incoming wind direction. The velocity at each turbine is determined through a discrete input-output model. Changes in wind direction serve as the input and the output is defined in terms of a time-varying weighted adjacency matrix that depends on the time-delay of information propagation between turbines. These delays, which are defined in terms of the advection speed of the wind and the distance between the turbines, capture the delayed effect of wind direction changes on the inter-connectivity of the graph as the wind conditions at the farm inlet propagate through the turbine array. An event-based update framework is employed to capture time-dependent topology changes due to shifts in wind direction. Simulation results for dynamically changing wind inlet directions to a circular wind farm are compared to predictions from both the static and dynamic versions of the FLOw Redirection and Induction in Steady State (FLORIS) model. The approach is shown to enable real-time tracking of dynamic changes to wind farm power output within a framework that can be easily integrated into real-time, horizon-based, control strategies that typically do not account for wind direction changes.

Abstract. This paper studies a closed-loop wind farm control framework for active power control (APC) with a simultaneous reduction of wake-induced structural loads within a fully developed wind farm flow interacting with the atmospheric boundary layer. The main focus is on a classical feedback control, which features a simple control architecture and a practical measurement system that are realizable for real-time control of large wind farms. We demonstrate that the wake-induced structural loading of the downstream turbines can be alleviated, while the wind farm power production follows a reference signal. A closed-loop APC is designed first to improve the power-tracking performance against wake-induced power losses of the downwind turbines. Then, the nonunique solution of APC for the wind farm is exploited for aggregated structural load alleviation. The axial induction factors of the individual wind turbines are considered control inputs to limit the power production of the wind farm or to switch to greedy control when the demand exceeds the power available in the wind. Furthermore, the APC solution domain is enlarged by an adjustment of the power set-points according to the locally available power at the waked wind turbines. Therefore, the controllability of the wind turbines is improved for rejecting the intensified load fluctuations inside the wake. A large-eddy simulation model is employed for resolving the turbulent flow, the wake structures, and its interaction with the atmospheric boundary layer. The applicability and key features of the controller are discussed with a wind farm example consisting of 3×4 turbines with different wake interactions for each row. The performance of the proposed APC is evaluated using the accuracy of the wind farm power tracking and the wake-induced damage equivalent fatigue loads of the towers of the individual wind turbines.

This paper proposes a distributed model predictive control (DMPC) method with guaranteed stability for system with control coupled information and coupled constraints among the subsystems, which are described by piecewise affine (PWA) model. According to the condition of control input coefficient matrix of the PWA model, two frameworks are established. The DMPC optimal problem of each subsystem is converted into mixed integer quadratic programming (MIQP) to solve and obtain the control actions. To ensure the stability, equality constraint is applied.

As installed capacity of wind power increases significantly around the world, the power from wind energy starts to replace conventional generation and the active power control of wind farms has become a major concern to the stability and security of the entire power system. It is expected that the wind farm should operate upon operator's request and control its active power according to the dispatch command, which means the wind farm should behavior in some ways more similar to the conventional power plants. This paper mainly focuses on the control system and control capability of the double fed induction generator (DFIG)-based wind farm as well as the wind turbine. Besides, the requirements and different control modes of active power in wind farm are also presented. The control performance demonstrated by the simulation result validates the feasibility of active power control methods and control system presented in this paper.

In a typical traffic scenario, automated vehicles are required to interact with surrounding traffic participants, e.g. human-driven vehicles, pedestrians, etc. In this paper, a novel adaptive authority allocation strategy considering the social behaviours of surrounding vehicles is proposed for the shared steering control (SSC) of automated vehicles. First, a Koopman-based potential-field-driven distributed model predictive control (K-PF-DMPC) method is proposed for the modelling of vehicle interaction to describe surrounding vehicle's social behaviour. This method effectively deals with the nonlinearity embedded in the non-cooperative game-based vehicle interaction model, so that the analytical form of Nash equilibrium can be derived for a fast online solution. Then, the SSC system of the automated vehicle is implemented by a weighted summation method. To balance the driving performance and intervention degree while considering the surrounding vehicles' social behaviours, a hybrid fuzzy strategy (HFS) is proposed for allocating the control authority between the driver and automation. The value of authority allocation coefficient is calculated by fusing the outputs from two shared fuzzy controllers, i.e. the enhanced and weakened shared fuzzy controllers, in accordance to the belief of the surrounding vehicle's type. Several numerical simulations and driver-in-the-loop simulator experiments are conducted for validation. Results show that the proposed strategy can provide appropriate steering intervention to the driver owing to the consideration of the surrounding vehicle's social behaviour and obtains the best driver-automation collaboration performance compared with the comparative groups.

Abstract. Wind farm control strategies are being developed to mitigate wake losses in wind farms, increasing energy production. Wake steering is a type of wind farm control in which a wind turbine's yaw position is misaligned from the wind direction, causing its wake to deflect away from downstream turbines. Current modeling tools used to optimize and estimate energy gains from wake steering are designed to represent wakes for fixed wind directions. However, wake steering controllers must operate in dynamic wind conditions and a turbine's yaw position cannot perfectly track changing wind directions. Research has been conducted on robust wake steering control optimized for variable wind directions. In this paper, the design and analysis of a wake steering controller with wind direction variability is presented for a two-turbine array using the FLOw Redirection and Induction in Steady State (FLORIS) control-oriented wake model. First, the authors propose a method for modeling the turbulent and low-frequency components of the wind direction, where the slowly varying wind direction serves as the relevant input to the wake model. Next, we explain a procedure for finding optimal yaw offsets for dynamic wind conditions considering both wind direction and yaw position uncertainty. We then performed simulations with the optimal yaw offsets applied using a realistic yaw offset controller in conjunction with a baseline yaw controller, showing good agreement with the predicted energy gain using the probabilistic model. Using the Gaussian wake model in FLORIS as an example, we compared the performance of yaw offset controllers optimized for static and dynamic wind conditions for different turbine spacings and turbulence intensity values, assuming uniformly distributed wind directions. For a spacing of five rotor diameters and a turbulence intensity of 10 %, robust yaw offsets optimized for variable wind directions yielded an energy gain equivalent to 3.24 % of wake losses recovered, compared to 1.42 % of wake losses recovered with yaw offsets optimized for static wind directions. In general, accounting for wind direction variability in the yaw offset optimization process was found to improve energy production more as the separation distance increased, whereas the relative improvement remained roughly the same for the range of turbulence intensity values considered.

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.

With the high penetration of renewable energy, the active distribution network (ADN) and multi‐microgrids (MMGs), as emerging multi‐layered energy management systems, face challenges such as voltage violations and conflicts of interest among multiple agents. To address these distributed coordinating optimization issues in the ADN‐MMGs system, this paper proposes a coordinating optimization method for ADN‐MMGs systems that balances voltage robustness and system economics. In the day‐ahead stage, the method considers MMGs' participation in the voltage regulation ancillary service market and decentralized peer‐to‐peer (P2P) trading market, and baseline plans are developed for voltage stability and economic operation. In the intra‐day stage, ADN and MMGs are treated as subsystems. Using voltage sensitivity matrices and consensus algorithms, a state‐space‐based distributed model predictive control (Di‐MPC) coordination model is constructed, effectively mitigating short‐term power fluctuations from renewable energy and load variability. An analysis on a modified IEEE 33‐bus system demonstrates that the proposed method enhances economic efficiency and voltage robustness by coordinating subsystem energy flows. Compared to the distributed economic model predictive control (MPC) scheme, the proposed method reduces the state of charge (SOC) tracking error of MMGs by 51.21% and ensures that the deviation in planned daily operating cost for the ADN‐MMGs system does not exceed 1%.

Wind power plays a vital role in the global effort towards net zero. A recent figure shows that 93GW new wind capacity was installed worldwide in 2020, leading to a 53% year-on-year increase. The control system is the core of wind farm operations and has an essential influence on the farm’s power capture efficiency, economic profitability, and operation and maintenance cost. However, the inherent system complexities of wind farms and the aerodynamic interactions among wind turbines cause significant barriers to control system design. The wind industry has recognized that new technologies are needed to handle wind farm control tasks, especially for large-scale offshore wind farms. This paper provides a comprehensive review of the development and most recent advances in wind farm control technologies. It covers the introduction of fundamental aspects of wind farm control in terms of system modeling, main challenges and control objectives. Existing wind farm control methods for different purposes, including layout optimization, power generation maximization, fatigue load minimization and power reference tracking, are investigated. Moreover, a detailed discussion regarding the differences and similarities between model-based, model-free and data-driven wind farm approaches is presented. In addition, we highlight state-of-the-art wind farm control technologies based on reinforcement learning—a booming machine learning technique that has drawn worldwide attention. Future challenges and research avenues in wind farm control are also analyzed.

In this paper, we propose a distributed model predictive control method with chance constraints for tracking reference signals. More specifically, considering a network of systems with independent dynamics, the proposed control scheme allows each subsystem to reach feasible setpoints, while respecting local and joint probabilistic constraints. Both a simulation example consisting of an academic temperature control problem and experimental results concerning the motion and coordination of moving agents are illustrated.