Wind Farm Layout Design Using Cuckoo Search Algorithms

ABSTRACT

Wind energy has emerged as a strong alternative to fossil fuels for power generation. To generate this energy, wind turbines are placed in a wind farm. The extraction of maximum energy from these wind farms requires optimal placement of wind turbines. Due to complex nature of micrositing of wind turbines, the wind farm layout design problem is considered a complex optimization problem. In the recent past, various techniques and algorithms have been developed for optimization of energy output from wind farms. The present study proposes an optimization approach based on the cuckoo search (CS) algorithm, which is relatively a recent technique. A variant of CS is also proposed that incorporates a heuristic-based seed solution for better performance. The proposed CS algorithms are compared with genetic and particle swarm optimization algorithms which have been extensively applied to wind farm layout design. Empirical results indicate that the proposed CS algorithms outperformed the genetic and particle swarm optimization algorithms for the given test scenarios in terms of yearly power output and efficiency.     

Wind Farm Layout Design Using Cuckoo Search Algorithms

Wind energy has emerged as a strong alternative to fossil fuels for power generation. To generate this energy, wind turbines are placed in a wind farm. The extraction of maximum energy from these wind farms requires optimal placement of wind turbines. Due to complex nature of micrositing of wind turbines, the wind farm layout design problem is considered a complex optimization problem. In the recent past, various techniques and algorithms have been developed for optimization of energy output from wind farms. The present study proposes an optimization approach based on the cuckoo search (CS) algorithm, which is relatively a recent technique. A variant of CS is also proposed that incorporates a heuristic-based seed solution for a better performance. The proposed CS algorithms are compared with genetic and particle swarm optimization (PSO) algorithms, which have been extensively applied to wind farm layout design. Empirical results indicate that the proposed CS algorithms outperformed the genetic and PSO algorithms for the given test scenarios in terms of yearly power output and efficiency.     

Optimum layout design of onshore wind farms considering stochastic loading

Renewable energy technologies are developing rapidly, while in the last decade great interest is encountered in the use of wind energy, especially due to the energy crisis and serious environmental problems appeared from the use of fossil fuels and therefore a large number of wind farms have been installed around the world. On the other hand the ability of nature inspired algorithms to efficiently handle combinatorial optimization problems was proved by their successful implementation in many fields of engineering sciences. In this study, a new problem formulation for the optimum layout design of onshore wind farms is presented, where the wind load is implemented using stochastic fields. For this purpose, a metaheuristic search algorithm based on a discrete variant of the harmony search method is used for solving the problem at hand. The farm layout problem is by nature a constrained optimization problem, and the contribution of the wake effects is significant; therefore, in two formulations presented in this study the influence of wind direction is also taken into account and compared with the scenario that the wake effect is ignored. The results of this study proved the applicability of the proposed formulations and the efficiency of combining metaheuristic optimization with stochastic wind loading for dealing with the problem of optimal layout design of wind farms.     

Wind turbine selection for wind farm layout using multi-objective evolutionary algorithms

Wind energy has become the world’s fastest growing energy source. Although wind farm layout is a well known problem, its solution used to be heuristic, mainly based on the designer experience. A key in search trend is to increase power production capacity over time. Furthermore the production of wind energy often involves uncertainties due to the stochastic nature of wind speeds. The addressed problem contains a novel aspect with respect of other wind turbine selection problems in the context of wind farm design. The problem requires selecting two different wind turbine models (from a list of 26 items available) to minimize the standard deviation of the energy produced throughout the day while maximizing the total energy produced by the wind farm. The novelty of this new approach is based on the fact that wind farms are usually built using a single model of wind turbine. This paper describes the usage of multi-objective evolutionary algorithms (MOEAs) in the context of power energy production, selecting a combination of two different models of wind turbine along with wind speeds distributed over different time spans of the day. Several MOEAs variants belonging to the most renowned and widely used algorithms such as SPEA2 NSGAII, PESA and msPEA have been investigated, tested and compared based on the data gathered from Cancun (Mexico) throughout the year of 2008. We have demonstrated the powerful of MOEAs applied to wind turbine selection problem (WTS) and estimate the mean power and the associated standard deviation considering the wind speed and the dynamics of the power curve of the turbines. Among them, the performance of PESA algorithm looks a little bit superior than the other three algorithms. In conclusion, the use of MOEAs is technically feasible and opens new perspectives for assisting utility companies in developing wind farms.     

Optimizing wind farm cable layout considering ditch sharing

Wind power is becoming an important source of electrical energy production. In an onshore wind farm (WF), the electrical energy is collected at a substation from different wind turbines through electrical cables deployed over ground ditches. This work considers the WF layout design assuming that the substation location and all wind turbine locations are given, and a set of electrical cable types is available. The WF layout problem, taking into account its lifetime and technical constraints, involves selecting the cables to interconnect all wind turbines to the substation and the supporting ditches to minimize the initial investment cost plus the cost of the electrical energy that is lost on the cables over the lifetime of the WF. It is assumed that each ditch can deploy multiple cables, turning this problem into a more complex variant of previously addressed WF layout problems. This variant turns the problem best fitting to the real case and leads to substantial gains in the total cost of the solutions. The problem is defined as an integer linear programming model, which is then strengthened with different sets of valid inequalities. The models are tested with four WFs with up to 115 wind turbines. The computational experiments show that the optimal solutions can be computed with the proposed models for almost all cases. The largest WF was not solved to optimality, but the final relative gaps are small.     

A hybrid particle swarm optimization algorithm for high-dimensional problems

In recent years, particle swarm optimization (PSO) emerges as a new optimization scheme that has attracted substantial research interest due to its simplicity and efficiency. However, when applied to high-dimensional problems, PSO suffers from premature convergence problem which results in a low optimization precision or even failure. To remedy this fault, this paper proposes a novel memetic PSO (CGPSO) algorithm which combines the canonical PSO with a Chaotic and Gaussian local search procedure. In the initial evolution phase, CGPSO explores a wide search space that helps avoid premature convergence through Chaotic local search. Then in the following run phase, CGPSO refines the solutions through Gaussian optimization. To evaluate the effectiveness and efficiency of the CGPSO algorithm, thirteen high dimensional non-linear scalable benchmark functions were examined. Results show that, compared to the standard PSO, CGPSO is more effective, faster to converge, and less sensitive to the function dimensions. The CGPSO was also compared with two PSO variants, CPSO-H, DMS-L-PSO, and two memetic optimizers, DEachSPX and MA-S2. CGPSO is able to generate a better, or at least comparable, performance in terms of optimization accuracy. So it can be safely concluded that the proposed CGPSO is an efficient optimization scheme for solving high-dimensional problems.     

Evolutionary computation approaches for real offshore wind farm layout: A case study in northern Europe

This paper presents the layout optimization of a real offshore wind farm in northern Europe, using evolutionary computation techniques. Different strategies for the wind farm design are tested, such as regular turbines layout or free turbines disposition with fixed number of turbines. Also, different layout quality models have been applied, in order to obtain solutions with different characteristics of high energy production and low interlink cost. In all the cases, evolutionary algorithms are developed and detailed in the paper. The experiments carried out in the real problem show that the free design with fixed number of turbines is more appropriate and obtains better quality layouts than the regular design.     

研华WebAccess组态软件在风力发电风场管理系统中的应用

本文介绍研华WebAccess组态软件在风力发电风场管理系统中的应用。在风电场现场控制级监控站点上,SCADA Node节点数据采集是通过标准的以太网接口直接采集现场的实时数据和参数,即采集各风轮机在线监测点控制器PLC的全部现场数据,如：有功功率、无功功率、效率、风机总数、运行数、停机数、待机、故障、运行等实时数据。     

Adjoint-based model predictive control for optimal energy extraction in waked wind farms

In this paper, a model predictive control (MPC) is proposed for wind farms to minimize wake-induced power losses. A constrained optimization problem is formulated to maximize the total power production of a wind farm. The developed controller employs a two-dimensional dynamic wind farm model to predict wake interactions in advance. An adjoint approach as an efficient tool is utilized to compute the gradient of the performance index for such a large-scale system. The wind turbine axial induction factors are considered as the control inputs to influence the overall performance by taking the wake interactions into account. A layout of a 2 × 3 wind farm is considered in this study. The parameterization of the controller is discussed in detail for a practical optimal energy extraction. The performance of the adjoint-based model predictive control (AMPC) is investigated with time-varying changes in wind direction. The simulation results show the effectiveness of the proposed approach. The computational complexity of the developed AMPC is also outlined with respect to the real time control implementation.     

Wind turbine micrositing by using the firefly algorithm

Traditionally energy has been a burning issue of mankind, however, this trend has changed with the advent of clean technologies such as wind power. It is common knowledge that wind turbines need to be installed in an open, unobstructed area to obtain the maximal power output. This document attempts to solve the problem of optimization of the layout of large wind farms by the use of nature inspired algorithms. Particular reference is made to the use of the firefly algorithm. A good comparison is made with the past approaches of the use of spread sheets and GA's for optimization.     

自适应变步长最大风能捕获算法

风能具有随机性、不稳定性的特点,为了提高风力发电系统中风能的利用效率,在比较各种最大风能捕获算法的基础上,分析了爬山搜索法和叶尖速比法的不足,提出了自适应变步长搜索算法来捕获最大风能.通过改进爬山搜索法的变步长策略,明显加快了搜索速度,通过引入初始估计叶尖速比值,大大缩小了搜索范围.该算法不需要实时检测准确风速,不依赖风力机最佳功率曲线,有效地降低了成本,提高风力发电的效率.文中重点分析了算法的自适应性和变步长策略,仿真结果表明,该算法能够使风力机更快速到达最大功率点,动态响应快,收敛性好.     

煤矿大型风机设备磨损监控系统设计

针对煤矿大型风机设备主轴磨损而影响风机工作效率的问题,在对大型风机设备磨损故障的生成原因分析后,根据系统需求设计实现了系统的硬件模块和软件模块,通过传感器电路对大型风机设备的输入输出功率、出风量、主轴转速等参数进行采集,系统软件在对数据进行综合分析后将检测结果显示在液晶屏上,实时输出大型风机设备的运行状态以及主轴磨损状况,从而保证了风机的运行效率,避免设备故障的发生;在Matlab7.0平台上完成对风机设备的主轴磨损程度进行测试,仿真时间定为3 000s,测试结果表明:该系统对风机的主轴转速的预测值与实测值接近,误差在3.19%以内,满足实际需要,具有较高的鲁棒性和运算性能,取得了令人满意的效果,有很高的推广价值。     

Wind characteristics and mapping for power production in the Island of Lesvos, Greece

This study investigated the wind characteristics of the island of Lesvos, Greece, with the objective of providing the necessary data for identifying the wind power production capabilities of the island. Weather patterns were examined using weather data from four Remote Automatic Weather Stations. Specific tools were used to produce the necessary windroses, Weibull curves and charts that helped to understand the prevailing wind characteristics. By using the tools of Geographic Information Systems (GIS) and the Wind Atlas Analysis and Application Program (WAsP) as the basic calculation platform, a wind map was produced portraying the wind speeds that prevail at a height of 10 m above ground level. The results of the analysis were tested and evaluated with measurements from 15 wind turbine sites by creating six alternative scenarios. The optimum scenario was used to investigate the installation of a small wind farm with five wind turbines, of 3 MW total capacity.     

含风电电力系统的主从博弈经济调度

风力发电具有显著的随机性和波动性,对电力系统原有调度模式提出挑战.采用鲁棒优化处理风电不确定性,利用鲁棒优化蕴含的博弈思想,将风电场看作调度中心的一个虚拟博弈者,利用双层规划法建立了二者的主从博弈模型,将调度中心看作领导层,其决策目标为电网运行的成本最低,将风电场看作下属层,其决策目标是能保证系统实时安全运行的最大风电出力区间.由于考虑了火电机组的阀点效应,主从博弈模型呈现出非线性双层规划的数学特点,提出一种改进教与学算法与线性规划相嵌套的求解方法.最后,采用改进的10机39节点系统对模型以及求解方法的有效性进行了验证.     

基于混核LSSVM的批特征风功率预测方法

针对风电场风功率预测问题, 利用历史风功率、气象数据和测风塔实时数据等相关信息, 提出了带有批特征的混核最小二乘支持向量机(Hybrid kernel least squares support vector machine, HKLSSVM)方法, 建立风电场风功率预测模型.为了增强模型的适应性, 设计改进的差分进化算法对模型参数进行优化, 并利用稀疏选择方法来选取合适的训练样本集, 缩短建模时间, 保证预测模型精度.根据风场风机的地理位置分布情况, 提出批划分的建模策略, 对相近地理位置的风机进行组批, 替代传统风场风功率预测方法.通过风场中实际数据进行测试, 实验结果表明与其他预测方法相比, 本文提出的方法能够提高预测精度和效率, 减少风电波动性对电网的影响, 从而提高电网的安全性和可靠性.     

风电场电能质量特性及治理措施仿真分析

基于甘肃河西地区大规模风电接入电网工程,建立了典型风电机组仿真模型,仿真分析了风电并网引起的电压偏差、电压波动、谐波等电能质量特性;在对风电场电能质量特性分析的基础上,综合考虑补偿效果等因素,对应用STATCOM治理风电场电能质量的效果进行了仿真。仿真结果表明,STATCOM对风电场的各种电能质量问题具有较好的治理效果,可为风电场电能质量特性分析和治理提供参考。     

基于经验模态分解与多分支神经网络的超短期风功率预测

风功率预测是实现风电场监控及信息化管理的重要基础,风功率超短期预测常用于平衡负荷、优化调度,对预测精度有较高的要求。由于风电场环境复杂、风速不确定性因素较多,风功率时序信号往往具有非平稳性和随机性。循环神经网络（RNN）适用于时间序列任务,但无周期、非平稳的时序信号会增加网络学习的难度。为了克服非平稳信号在预测任务中的干扰,提高风功率预测精度,提出了一种结合经验模态分解与多分支神经网络的超短期风功率预测方法。首先将原始风功率时序信号通过经验模态分解（EMD）以重构数据张量,然后用卷积层和门控循环单元（GRU）层分别提取局部特征和趋势特征,最后通过特征融合与全连接层得到预测结果。在内蒙古某风场实测数据集上的实验结果表明,与差分整合移动平均自回归（ARIMA）模型相比,所提方法在预测精度方面有将近30%的提升,验证了所提方法的有效性。     

Distributed Model Predictive Load Frequency Control of Multi-area Power System with DFIGs

Reliable load frequency control (LFC) is crucial to the operation and design of modern electric power systems. Considering the LFC problem of a four-area interconnected power system with wind turbines, this paper presents a distributed model predictive control (DMPC) based on coordination scheme. The proposed algorithm solves a series of local optimization problems to minimize a performance objective for each control area. The generation rate constraints (GRCs), load disturbance changes, and the wind speed constraints are considered. Furthermore, the DMPC algorithm may reduce the impact of the randomness and intermittence of wind turbine effectively. A performance comparison between the proposed controller with and without the participation of the wind turbines is carried out. Analysis and simulation results show possible improvements on closed-loop performance, and computational burden with the physical constraints.      

粒子置换的双种群综合学习PSO算法

针对粒子群算法(PSO)种群多样性低和易于陷入局部最优等问题,提出一种粒子置换的双种群综合学习PSO算法(PP-CLPSO)。根据PSO算法的收敛特性和Logistic映射的混沌思想,设计并行进化的PSO种群和混沌化种群,结合粒子编号机制,形成双种群系统中粒子的同号结构和同位结构,其中粒子的惯性权重根据适应度值自适应调节;当搜索过程陷入局部最优时,PSO种群同位结构下适应度值较差的粒子,根据与混沌化种群间的同号结构执行粒子置换操作,实现了双种群系统资源的合理调度,增加了种群的多样性;进而综合双向搜索的同位粒子学习策略和线性递减搜索步长的局部学习策略,进行全局探勘和局部搜索,提高了算法的求解精度。实验选取9个基准测试函数,同时与4个改进的粒子群算法和4个群智能算法进行对比验证,实验结果表明,PP-CLPSO算法在求解精度和收敛速度等方面具备较好的综合性能。     

H∞ position transfer and regulation for floating offshore wind turbines

This paper proposes ${\rm H}_\infty$ controller design for platform position transfer and regulation of floating offshore wind turbines. The platform movability of floating wind turbines can be utilized in mitigating the wake effect in the wind farm, thereby maximizing the wind farm''s total power capture and efficiency. The controller is designed so that aerodynamic force is adjusted to meet the three objectives simultaneously, that is, 1) to generate the desired electrical power level, 2) to achieve the desired platform position, and 3) to suppress the platform oscillation. To acquire sufficient aerodynamic force to move the heavy platform, the pitch-to-stall blade pitching strategy is taken instead of the commonly-used pitch-to-feather strategy. The desired power level is attained by the standard constant-power strategy for the generator torque, while ${\rm H}_\infty$ state-feedback control of blade pitch and nacelle yaw angles is adopted for the position regulation and platform oscillation suppression. Weighting constants for the ${\rm H}_\infty$ controller design are adjusted to take the trade-off between the position regulation accuracy and the platform motion reduction. To demonstrate the efficiency of the proposed controller, a virtual 5-MW semi-submersible wind turbine is considered. Simulation results show that the designed ${\rm H}_\infty$ controller successfully accomplishes the platform position transfer and regulation as well as the platform oscillation reduction against wind and wave disturbances, and that it outperforms a previously-proposed linear quadratic controller with an integrator.