共查询到20条相似文献,搜索用时 62 毫秒
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配电网状态估计的目的是根据获取的配电网的各种量测信息,估计配电网系统的运行状态,是配电管理系统(DMS)的重要核心功能之一。根据配电网特点,以电压幅值和相角为状态变量,以量测值和估计值误差最小为目标函数,以潮流方程和估计值的上、下限为约束条件,构建配电网状态估计的优化模型,采用果蝇优化算法对该模型进行求解。算例表明,基于视觉和嗅觉的觅食行为启发的果蝇优化算法可有效求解该优化模型,具有易于实现、控制参数少和计算精度高的特点,为配电网状态估计提供了新的途径。 相似文献
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基于改进粒子群算法含双馈风电机组
配网无功优化研究 总被引:1,自引:0,他引:1
针对风电机组并网后对配电网无功补偿产生的影响,从双馈风电机组自身的有功、无功输出特性出发,基于场景概率的方法计算风力机组出力情况,以网损最小为目标函数寻求优化求解方法。利用改进粒子群算法来实现系统接入双馈风电机组后的无功优化,在Matlab 2013b软件中构造IEEE33节点模型并利用该算法求解。结果表明,双馈风电机组在参与系统无功优化时具有良好的性能,验证了该改进算法的有效性 相似文献
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针对风电机组齿轮箱传统故障诊断方法以全局误诊断率最小化为目标,忽略了误分类型之间的差别的问题,提出基于代价敏感最小二乘支持向量机(Cost-sensitive Least Squares Support Vector Machine,CLSSVM)的风电机组齿轮箱故障诊断方法。该方法在最小二乘支持向量机原始最优化问题中二次损失函数中嵌入不同样本的误分类代价,建立以误分类代价最小化为目标的CLSSVM故障诊断模型,并同最小二乘支持向量机和代价敏感支持向量机比较。实验结果表明,该方法能提高误分类代价高的故障类样本的诊断正确率,具有代价敏感性,其训练速度也足以满足风电机组齿轮箱故障诊断实时性的需求。 相似文献
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基于改进共轭梯度优化BP神经网络的风电机组变桨距控制 总被引:1,自引:0,他引:1
根据共轭梯度算法和传统BP神经网络的变桨距控制器的原理,针对兆瓦级风电机组变桨距控制设计了一种改进共轭梯度优化BP神经网络的变桨距PID参数自整定控制器,此控制器采用改进共轭梯度法修正BP神经网络的权值和阈值,实现BP神经网络变桨距PID控制器的在线整定。在Matlab/Simulink中仿真,仿真结果表明,采用此变桨距控制器可以在额定风速之上快速响应,在相同风速状况下使发电机桨距角调节命令更加准确,风轮转速更加平稳,输出功率维持在额定功率附近,取得了很好的变桨距控制效果。 相似文献
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Wind turbine (WT) power curves effectively reflect the generation performance of WTs and depict the relationship between the wind speed and the WT power output. This paper aims at developing an effective method for learning the intrinsic representations of WT power curves, which are robust to external environmental changes. Based on the obtained representations, WT generation performance is monitored. In the proposed approach, data of the supervisory control and data acquisition (SCADA) system is employed to derive the representations. Parametric models of WT power curves are developed using the two‐parameter and four‐parameter logic models. The parameters of these model are identified via Jaya algorithm. To detect the changes of WT power curve model parameters over different time, multivariate control charts are employed. The effectiveness of the proposed WT generation performance monitoring approach is validated based on SCADA data collected from real commercial WTs. 相似文献
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风力机的选型是风电场建设的重要内容,它对风电场建设造价、投产后的发电量以及运行维护成本等有直接影响。文章在给定风资源的情况下,综合考虑风电场的容量系数和实际发电量,以风力机性能指数作为选型的依据,针对采用常规方法进行风力机参数线性化求解的缺陷,采用智能化的改进粒子群算法对风力机参数进行寻优。与常规计算方法相比,该方法寻得的风力机性能指数更优。结合具体实例计算候选机型的风速加权标准差,选出最优风力机。该研究结果为风电场的风力机选型提供了一种有效可行的方法,具有一定的应用参考价值。 相似文献
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Fault diagnosis for wind turbine transmission systems is an important task for reducing their maintenance cost. However, the non-stationary dynamic operating conditions of wind turbines pose a challenge to fault diagnosis for wind turbine transmission systems. In this paper, a novel fault diagnosis method based on manifold learning and Shannon wavelet support vector machine is proposed for wind turbine transmission systems. Firstly, mixed-domain features are extracted to construct a high-dimensional feature set characterizing the properties of non-stationary vibration signals from wind turbine transmission systems. Moreover, an effective manifold learning algorithm with non-linear dimensionality reduction capability, orthogonal neighborhood preserving embedding (ONPE), is applied to compress the high-dimensional feature set into low-dimensional eigenvectors. Finally, the low-dimensional eigenvectors are inputted into a Shannon wavelet support vector machine (SWSVM) to recognize faults. The performance of the proposed method was proved by successful fault diagnosis application in a wind turbine's gearbox. The application results indicated that the proposed method improved the accuracy of fault diagnosis. 相似文献
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Based on SCADA data, this study aims at fitting three performance curves (PCs), power curve, pitch angle curve, and rotor speed curve, to accurately describe the normal behaviour of a wind turbine (WT) for performance monitoring and identification of anomalous signals. The fitting accuracy can be undesirably affected by erroneous SCADA data. Hence, outliers generated from raw SCADA data should be removed to mitigate the prediction inaccuracy, so various outlier detection (OD) approaches are compared in terms of area under the curve (AUC) and mean average precision (mAP). Among them, a novel unsupervised SVM-KNN model, integrated by support vector machine (SVM) and k nearest neighbour (KNN), is the optimum detector for PC refinements. Based on the refined data by the SVM-KNN detector, several common nonparametric regressors have largely improved their prediction accuracies on pitch angle and rotor speed curves from roughly 86% and 90.6%, respectively, (raw data) to both 99% (refined data). Noticeably, under the SVM-KNN refinement, the errors have been reduced by roughly five times and 10 times for pitch angle and rotor speed predictions, respectively. Ultimately, bootstrapped prediction interval is applied to conduct the uncertainty analysis of the optimal predictive regression model, reinforcing the performance monitoring and anomaly detection. 相似文献
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《International Journal of Hydrogen Energy》2023,48(69):26951-26963
In this article, the black box dynamic model is presented for forecasting the performance of the PEM (Proton-exchange membrane) fuel cell (FC). An optimized deep artificial neural network has been used to build the experimental nonlinear model of the polymer membrane FC series that functions with hydrogen and oxygen. This research investigates predictability for a gate recurrent unit (GRU) optimized by a modified Prairie Dog Optimizer in PEMFCs. The results obtained have been validated by applying a case study and then a comparison is conducted among the outcomes of the offered technique and 2 other published methods: modified relevance vector machine and Lattice Gated Recurrent Unit (LGRU). The voltage clearly changes significantly, as demonstrated by simulations, even though the FC is handled with a low starting temperature and current. Also, the voltage point distribution has become more concentrated when the current and temperature are high. In both the training and prediction phases, the MAPE is reduced to approximately 0.0043 and 0.0047, respectively, showing that the proposed GRU technique produces superior prediction results when the operational settings approach the optimum operating conditions. According to simulations, the proposed IPDO/GRU with a 0.004 root mean square has the least error, followed by the mRVM and GRU with 0.009 and 0.010 root mean square values. The outcomes show that using the offered procedure does provide the finest verification of the empirical data. 相似文献