近海风力机组塔架塔基载荷研究

综合考虑风、海浪、海流对近海风力机的作用和影响,建立了机组塔架塔基的负载模型.通过空气动力学、流体力学和结构力学相关理论对模型中风对风轮和海浪海流对塔架塔基的作用力的计算方法进行详细分析.结合我国海上风场情况,以1.5MW变桨距近海风力机为例进行极限负载计算,最终结果与用风力机设计软件BIAEDED仿真的数据相似,从而表明了所研究得到的负载模型和计算方法是科学可行的.     

基于随机森林分类的直流微电网孤岛检测

提出一种基于随机森林分类的直流微电网孤岛检测方法。该方法首先对原始数据进行清洗并提取特征,选择直流母线侧的电压、电流、输出有功功率及3者的一阶后向差分等6个孤岛特性指标作为检测特征,生成特征向量集,然后基于随机森林分类建立直流微电网的孤岛检测模型,实现了孤岛的准确检测,最后与决策树分类法进行比较,随机森林分类法在处理大量数据情况下可更加准确地检测孤岛。     

波浪能发电装置能量转换系统多级负载试验

根据波浪的特点,设计了一种应用在波浪能转换装置上的多级负载系统。对安装该系统的鹰式波浪能转换装置,按照功能和能量转换两种分类方法介绍机构组成和工作原理,并对液压系统的多级负载系统的设计进行了描述,包括系统的元件、试验原理及方法,最后通过试验对系统进行验证。试验结果表明,多级负载系统设计合理,能高效合理地利用及转换液压能。     

基于改进FC-IRKM的电力用户负荷分类研究

为了提升电力用户负荷分类精度,从而为电力企业有效掌握用户用电规律,制定电价,合理评估用电需求,进行了基于改进的FC-IRKM的电力用户负荷分类研究。首先,模糊聚类综合了模糊思想与聚类方法,粗糙集则解决了数据分类不清晰的情况,将两种方法的优点进行结合,提出了模糊粗糙K均值算法;其次,对IRKM算法记进行了理论分析,结合模糊粗糙算法,提出了一种FC-IRKM算法,并通过遗传算法对其参数进行优化,获得改进后的FC-IRKM算法;然后通过仿真对比实验,验证了在处理相同数据的时候,改进的FC-IRKM算法比其他常见传统算法的分类精度更高,适用性更广;最后将改进的FC-IRKM算法应用于电力用户负荷分类上,分类结果与预期结果相互印证,验证了该方法的适用性。该研究为电力企业把握电力用户的用电规律、合理规划供电计划提供了可靠的理论支持。     

基于曲线相似性的负荷分类方法及其应用研究

在电力需求侧管理中,负荷特性分类作为负荷控制工作的重要基础,深入研究其分类方法便于掌握负荷变化规律,实现负荷的科学管理。为了提高负荷特性分类的效率,同时使分类满足精度要求,提出一种基于曲线相似性的电力负荷特性分类法,通过引入日负荷曲线类相似精度,依据其阈值将日负荷曲线集划分成若干满足要求的负荷类别,无需依赖大量试算。经实测算例验证,此方法能较有效地提升负荷分类效率,并满足精度需求。     

抽油机电机的节能改造

本文分析了抽油机负载的特点,提出了一种经济实用的抽油机电机节能改造的方法,计算结果和油田现场试验结果都说明了这种方法是正确和切实可行的。     

基于负载电流的光伏系统MPPT方法的仿真试验

光伏系统实现MPPT的传统方法一般均需光伏系统输入电压、电流两个变量,且存在系统结构复杂、不利工程实现及不能直接反映负载的最大功率的缺陷,对此,提出一种以光伏系统的负载电流作为测量变量的单输入MPPT方法,以 Boost 拓扑为最大功率跟踪电路,选用简单有效的占空比扰动观察算法,分别建立了单、双输入法的MPPT 算法的Matlab仿真模型,并进行仿真试验验证。结果表明,以光伏系统的负载电流作为测量变量的单输入MPPT方法简化了系统的控制结构,便于控制器的集成,节约了系统成本,几乎可适用于全部的负载类型,具有一定的实际应用价值。     

基于自适应神经网络系统聚类的特殊负荷分类研究

根据特殊负荷调查统计信息,甄别不同聚类分析的优缺点,提出了一种基于自适应神经网络系统聚类的特殊负荷分类方法,分别从负荷特性、供电可靠性与电能质量3个维度建立聚类指标,并以78个特殊负荷为样本进行聚类分析。结果表明,该方法快速有效,同类特殊负荷相似度高,各类特殊负荷之间差异明显。该分类方法可推广至典型特殊行业负荷分类,为配电网对特殊负荷采用相应的供电措施提供理论依据,便于供电部门制定特殊负荷供电服务。     

脉冲负载下电网储能电池最优容量配置方法研究

针对传统脉冲负载下电网储能电池容量配置方法调频能力差、频率波动大的问题，研究了一种新的脉冲负载下电网储能电池最优容量配置方法。分析脉冲负载下电网储能电池多角度最优容量，判定储能电池的容量、有效功率、充放电速率，研究储能电池最优容量倍率特性，对脉冲负载下电网储能电池参与脉冲负载下电网进行两次调配，进而去除调频死区，实现电网储能电池最优容量配置。与传统方法进行实验对比，结果表明，研究的最优容量配置方法在负载脉冲下具有很强的电网调频能力，调频频率波动更小，为电网储能电池最优容量配置方法提供有利科学依据。     

受扰分数阶直驱风电机组混沌运动的H∞鲁棒控制

针对含有负载扰动的非线性分数阶直驱永磁同步风力发电机组(D-PMSG)存在混沌现象，提出一种抑制混沌运动的新型H∞鲁棒控制方法。在非线性D-PMSG混沌模型下，验证负载扰动会导致D-PMSG呈现混沌现象。采用Takagi-Sugeno(T-S)模型建立受扰分数阶D-PMSG模糊混沌模型。基于并行分布补偿(PDC)控制理论，提出一种新型模糊状态反馈H_∞鲁棒控制器设计方法。利用分数阶Lyapunov稳定性理论，依据Schur补引理和合同变换，以线性矩阵不等式(LMIs)形式推导出D-PMSG系统Mittag-Leffler稳定的充分条件。Matlab仿真结果表明，该控制器在分数阶阶次变化和外界负载扰动随机变化情况下具有良好的控制性能和较强的鲁棒性。     

Medium-term electric load forecasting using singular value decomposition

Medium-term load forecasting is an important stage in electric power system planning and operation. It is used in maintenance scheduling, and to plan for outages and major works in the power system. A new technique is proposed which uses hourly loads of successive years to predict hourly loads and peak load for the next selected time span. The proposed method implements a new combination of some existing and well established techniques. This is done by first filtering out the load trend, then applying the SVD (singular value decomposition) technique to de-noise the resulting signal. Hourly load is thus divided to three main components: a) a load trend-following component, b) a random component, and c) a de-noised component. Results of applying the technique to the Jordanian power system showed that good forecasting accuracies are attained. In addition, the proposed method outperforms the traditional exponential curve fitting method. The peak load error was found to be less than 5% using the proposed methodology. It was also found that a lag period of 4 years suits the load forecasting purposes of the Jordanian power system. The proposed method is generic and can be implemented to the hourly loads of any power system.     

热负荷预测的RBF神经网络预测方法研究

由于BP神经网络存在一些缺点，本文采用RBF神经网络方法用于热电厂热负荷预测。预测结果表明：用本文的方法进行热负荷预测得到了十分满意的结果。     

变压器之间负载的经济分配研究

变压器间存在着特性差异，而变压器的有功功率损失和无功功率消耗随负载又是非线性变化的，因此存在变压器负载合理分配，选择最佳点的问题，即变压器的负载经济分配。通过例子说明了它的节电效果。     

非线性时间序列的RBF神经网络预测方法及其应用

将一种基于自动增加隐节点数目训练算法的径向基函数（RBF）神经用于非线性时间序列预测。这种方法成功地解决了BP网络的局部极小、隐节点数目的选择和过拟合问题，并用于热电厂热负荷预测。预测结果表明，用本方法进行热负荷预测得到了十分满意的结果。     

Structural load mitigation control for wind turbines: A new performance measure

Structural loads of wind turbines are becoming critical because of the growing size of wind turbines in combination with the required dynamic output demands. Wind turbine tower and blades are therefore affected by structural loads. To mitigate the loads while maintaining other desired conditions such as the optimization of power generated or the regulation of rotor speed, advanced control schemes have been developed during the last decade. However, conflict and trade‐off between structural load reduction capacity of the controllers and other goals arise; when trying to reduce the structural loads, the power production or regulation performance may be also reduced. Suitable measures are needed when designing controllers to evaluate the control performance with respect to the conflicting control goals. Existing measures for structural loads only consider the loads without referring to the relationship between loads and other control performance aspects. In this contribution, the conflicts are clearly defined and expressed to evaluate the effectiveness of control methods by introducing novel measures. New measures considering structural loads, power production, and regulation to prove the control performance and to formulate criteria for controller design are proposed. The proposed measures allow graphical illustration and numerical criteria describing conflicting control goals and the relationship between goals. Two control approaches for wind turbines, PI and observer‐based state feedback, are defined and used to illustrate and to compare the newly introduced measures. The results are obtained by simulation using Fatigue, Aerodynamics, Structures, and Turbulence (FAST) tool, developed by the National Renewable Energy Laboratory (NREL), USA.     

计及风、光消纳的风电-光伏-光热互补发电二层优化调度

以风电、光伏、光热构成新能源互补发电模式,计及风、光资源消纳,建立将高载能负荷作为可调度资源参与新能源互补发电系统的二层优化调度模型,采用NSGA-Ⅱ和二进制粒子群算法求解模型。上层优化模型以互补系统输出功率波动最小和并网效益最大为优化指标确定各机组的出力,并且计算出弃风弃光量。在此基础上,下层优化模型针对上层优化模型造成的弃风弃光量,选取能够有效消纳风、光的高载能负荷参与电网调度。最后,以甘肃地区实际数据为例,验证了所提方法的可行性和有效性。仿真结果表明,光热电站的参与增加了44.3%的经济效益,减小了65.8%的输出功率波动,高载能负荷的参与减小了86.3%的弃风弃光量。     

适应负荷速变的TCT式可控电抗器控制方法

针对负荷快速变化时无功功率的补偿问题,提出一种TCT式可控电抗器优化控制方法,首先分析了晶闸管触发角与补偿后功率因数、三相不平衡、谐波畸变率之间的关系,然后利用遗传算法寻找一组最优触发角来控制无功补偿装置,使得补偿效果最佳,最后利用BP神经网络对结果进行训练,建立了负荷功率与触发角之间的映射。仿真结果表明,该方法具有较高的效率,能够快速补偿功率因数和三相不平衡,同时可有效降低线路的谐波电流含量,尤其适用于负荷快速波动的场合。     

Design clustering of offshore wind turbines using probabilistic fatigue load estimation

In large offshore wind farms fatigue loads on support structures can vary significantly due to differences and uncertainties in site conditions, making it necessary to optimize design clustering. An efficient probabilistic fatigue load estimation method for monopile foundations was implemented using Monte-Carlo simulations. Verification of frequency domain analysis for wave loads and scaling approaches for wind loads with time domain aero-elastic simulations lead to 95% accuracy on equivalent bending moments at mudline and tower bottom. The computational speed is in the order of 100 times faster than typical time domain tools. The model is applied to calculate location specific fatigue loads that can be used in deterministic and probabilistic design clustering. Results for an example wind farm with 150 turbines in 30–40 m water depth show a maximum load difference of 25%. Smart clustering using discrete optimization algorithms leads to a design load reduction of up to 13% compared to designs based on only the highest loaded turbine position. The proposed tool improves industry-standard clustering and provides a basis for design optimization and uncertainty analysis in large wind farms.     

Fuel cell health prognosis using Unscented Kalman Filter: Postal fuel cell electric vehicles case study

The Proton Exchange Membrane Fuel Cell (PEMFC) health monitoring and management are of critical importance for the performance and cost efficiency of Fuel Cell Electric Vehicle (FCEV). Prognostics play an important role in improving the lifetime and reducing maintenance costs of PEMFC by predicting the degradation trend. In this paper, the degradation prediction of PEMFC is based on a novel model-driven method which combines the Unscented Kalman Filter (UKF) algorithm with the proposed voltage degradation model. The experimental data originated from the FCEVs which achieve postal delivery mission in the real road are used for construction and validation of the proposed model-driven prognostic method. At our best knowledge, this is the first application which uses field-based data for FC health prognosis. The influence of different lengths of measured voltage data on degradation prediction of PEMFC, and the degradation prediction performance of PEMFC in different FCEVs are also investigated by the proposed method. Test results show that the proposed model-driven method is able to accurately estimate the voltage degradation trend of PEMFC in the FCEV. When more data are applied to learning the degradation of PEMFC, the mean Relative Error (RE) in the prediction phase will decrease. Especially, when the learning data exceeds 45 h, the mean RE in prediction phase is reduced to 0.68%. Considering that the maximum mean RE in the prediction phase is 2.03% for 3 postal FCEVs, the proposed method can be applied in the degradation trend prediction of PEMFC in FCEV under real conditions.