Wind farm distributed PSO-based control for constrained power generation maximization

Abstract

A novel distributed approach to treat the wind farm (WF) power maximization problem accounting for the wake interaction among the wind turbines (WTs) is presented. Power constraints are also considered within the optimization problem. These are either the WTs nominal power or a maximum allowed power injection, typically imposed by the grid operator. The approach is model-based. Coupled with a distributed architecture it allows fast convergence to a solution, which makes it exploitable for real-time operations. The WF optimization problem is solved in a cooperative way among the WTs by introducing a new distributed particle swarm optimization algorithm, based on cooperative co-evolution techniques. The algorithm is first analyzed for the unconstrained case, where we show how the WF problem can be distributed by exploiting the knowledge of the aerodynamic couplings among the WTs. The algorithm is extended to the constrained case employing Deb's rule. Simulations are carried out on different WFs and wind conditions, showing good power gains and fast convergence of the algorithm.

Multi-Objective Optimization for Determining Trade-Off between Output Power and Power Fluctuations in Wind Farm System

“…The main purpose of BESS is to reduce the output power fluctuations and keep the large power output of the WF system. Constraints related to the operation of the WF system are presented in (11) to (15). The charging bounds are shown in (11) considering SOC at the previous interval and the charging rate of BESS.…”

“…The minimization of the power deviation for the set-points of WTGs helps the output power of WTGs much smoother and therefore avoids unnecessary internal power fluctuations. Finally, different case studies are also analyzed to show the effectiveness of the proposed method.Energies 2019, 12, 4242 2 of 18 studies have suggested different approaches to increase the output power and service reliability of the WF system, such as pitch misalignment detection [7], identification of defective anemometers [8], estimation of wind energy production considering varying air density [9], optimization of the position of WTGs in the WF system [10][11][12], optimization of the operation of WTGs to reduce power loss [13,14], or reduction of wake loss in WF systems [15,16]. The authors in [11] have proposed a new WF layout optimization model for maximizing the equivalent power of a WF system using particle swarm optimization algorithm.…”

“…The authors in [13] have proposed an optimal reactive power dispatch strategy to minimize the total electrical losses of a WF system, including losses inside WTGs, transformers, and losses in the transmission cables. The authors in [15] have presented a distributed approach to solve a WF power maximization problem considering the wake interaction among WTGs using a distributed particle swarm optimization algorithm. The authors in [16] have presented a new methodology for optimizing the wake effect performance by controlling the pitch angle and the tip speed ratio of each WTGs.…”

“…Several studies have suggested different methods to optimize the sizing of BESSs [27]. To reduce output power fluctuation of WF, several studies have also been proposed for filtering the grid harmonics due to the high-voltage direct-current line in WF [28,29].Based on the above literature review, it can be concluded that most existing methods for the operation of the WF system have only focused on a single operation objective, i.e., maximizing the output power [11][12][13][14][15][16] or minimizing the output power fluctuation [22][23][24][25][26]. However, many WF systems wish to generate maximum output power and preserve a low power fluctuation at the same time.…”

A non-centralized predictive control strategy for wind farm active power control: A wake-based partitioning approach

“…This idea has been applied to several large-scale systems, e.g., traffic systems, energy systems, smart grids and water systems [7,8,9]. The application of partitioning techniques for controlling wind farms in a decentralized manner has been started only recently [10,11,12]. One pioneering work in this regard was proposed in [12], in which by exploiting the problem structure a combination of online and offline computations are used to reduce the solving time.…”