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基于支持向量机的燃气轮机故障诊断 总被引:7,自引:1,他引:7
分析燃气轮机的8种典型常见故障,建立了基于支持向量机的故障诊断模型,用实例计算证明其有效性。同时和神经网络方法对比后发现:在小样本情况下,支持向量机方法的计算结果比神经网络要好,推广能力更强,而且效率高于神经网络。本方法针对故障诊断样本少的特点,为建立智能化的燃气轮机状态监控和故障诊断提供了一种新的途径,具有广泛的实用价值。 相似文献
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As the use of wind power has steadily increased, the importance of a condition monitoring and fault diagnosis system is being emphasized to maximize the availability and reliability of wind turbines. To develop novel algorithms for fault detection and lifespan estimation, a wind turbine simulator is indispensible for verification of the proposed algorithms before introducing them into a health monitoring and integrity diagnosis system. In this paper, a new type of simulator is proposed to develop and verify advanced diagnosis algorithms. The simulator adopts a torque control method for a motor and inverter to realize variable speed-variable pitch control strategies. Unlike conventional motor–generator configurations, the simulator includes several kinds of components and a variety of sensors. Specifically, it has similarity to a 3 MW wind turbine, thereby being able to acquire a state of operation that closely resembles that of the actual 3 MW wind turbine operated at various wind conditions. This paper presents the design method for the simulator and its control logic. The experimental comparison between the behavior of the simulator and that of a wind turbine shows that the proposed control logic performs successfully and the dynamic behaviors of the simulator have similar trends as those of the wind turbine. 相似文献
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针对强噪声背景下风力机齿轮箱振动信号易被掩盖、难以提取的难题,基于频域谱负熵(Frequency-domain Spectral Negentropy,FSN)改进经验小波变换(Empirical Wavelet Transform,EWT)提出优化经验小波变换方法(Improved Empirical Wavelet Transform,IEWT),并采用改进灰狼算法(Improved Grey Wolf Optimization,IGWO)优化支持向量机(Support Vector Machine,SVM)惩罚系数α及核参数σ。基于NREL GRC风力机齿轮箱数据验证所提方法的有效性。结果表明:IEWT-IGWO-SVM可有效提取故障信息并进行故障识别,分类准确率高达99.66%。 相似文献
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针对齿轮箱故障信号的非线性和非平稳性特征,提出基于经验小波变换(Empirical WaveletTransform,EWT)、关联维数(Correlation Dimension,CD)和支持向量机(Support Vector Machine,SVM)的故障诊断方法。首先通过EWT对风力机齿轮箱信号进行分解,得到若干本征模态函数(Intrinsic Mode Function,IMF)分量,再采用G-P算法求取各组IMF分量的关联维数,并将各组关联维数特征集输入SVM中完成故障识别及分类。结果表明:振动信号关联维数与嵌入维数呈正相关,且正常信号与故障信号的关联维数区分度不明显,通过SVM能对其进行精确识别和分类;该方法能有效提取系统故障非线性特征,故障识别准确率高达100%。 相似文献
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为了提高制冷系统故障诊断速度及准确性,提出了基于最小二乘支持向量机(LS-SVM)的制冷系统故障诊断模型,并采用ASHRAE制冷系统故障模拟实验数据进行模型训练与验证.对一台90冷吨(约316 kW)的离心式冷水机组的7类制冷循环典型故障进行了实验.研究结果表明,LS-SVM模型对制冷系统七类故障的总体诊断正确率比支持向量机(SVM)诊断模型、误差反向传播(BP)神经网络诊断模型分别提高0.12%和1.32%;尽管对个别局部故障(冷凝器结垢、冷凝器水流量不足、制冷剂含不凝性气体)的诊断性能较SVM模型的略有下降,但对系统故障的诊断性能均有较大改善,特别是对制冷剂泄漏/不足故障;诊断耗时比SVM模型减少近一半,快速性亦有所改善.可见,LS-SVM模型在制冷系统故障诊断中具有良好的应用前景. 相似文献
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光伏发电功率预测是减小大规模光伏发电并网对电网造成不良影响的有效手段,对电网调度及光伏电站的优化运行具有重要意义。针对光伏发电功率序列的周期性和非平稳性,本文提出了基于小波变换和支持向量机(Support vector machine, SVM)的预测方法。文中对原始功率序列进行小波分解并单支重构,构成低频趋势信号和高频随机信号,利用具有小样本学习能力强和计算简单等特点的SVM对各小波数据序列分别预测,最终将各预测值合成得到预测功率值。某光伏发电站的实际数据仿真验证了该预测方法的可行性和有效性。 相似文献
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基于小波包能量分析及改进支持向量机的风机机械故障诊断 总被引:1,自引:2,他引:1
为了准确诊断风机的机械故障,提出了一种基于小波包能量特征和改进支持向量机的诊断方法.在某4-73No.8D风机实验台上对13种不同运行状态下的振动信号进行采集,利用小波包对振动信号进行消噪、分解与重构,提取其小波包能量特征,得到了各运行状态下风机多测点信息融合的小波包能量特征向量,并利用改进支持向量机对特征向量样本集进行训练与测试,实现了风机机械故障的分类诊断.结果表明:该诊断方法能够有效地诊断风机机械故障的类别、严重程度和发生部位,且诊断准确率高、测试时间短,适用于在线机械诊断. 相似文献
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基于小波消噪理论,采用改进小数据量法计算最大Lyapunov指数,对北碚水文站月径流时间序列进行混沌特性识别,利用C-C法重构相空间挖掘北碚站月径流时间序列中的信息,通过SCE-UA算法优化出惩罚因子、核宽度,并引入径向基核函数简化非线性问题的求解过程.实例结果表明,SVM径流预测模型实现了精度与实用性的统一,可较好处理复杂的水文序列,具有较高的泛化能力和预测精度,为资料匮乏地区预报研究提供了一种新方法. 相似文献
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For a solid oxide fuel cell (SOFC) integrated into a micro gas turbine (MGT) hybrid power system, SOFC operating temperature and turbine inlet temperature are the key parameters, which affect the performance of the hybrid system. Thus, a least squares support vector machine (LS-SVM) identification model based on an improved particle swarm optimization (PSO) algorithm is proposed to describe the nonlinear temperature dynamic properties of the SOFC/MGT hybrid system in this paper. During the process of modeling, an improved PSO algorithm is employed to optimize the parameters of the LS-SVM. In order to obtain the training and prediction data to identify the modified LS-SVM model, a SOFC/MGT physical model is established via Simulink toolbox of MATLAB6.5. Compared to the conventional BP neural network and the standard LS-SVM, the simulation results show that the modified LS-SVM model can efficiently reflect the temperature response of the SOFC/MGT hybrid system. 相似文献
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为及时监测变压器潜伏性故障和准确诊断故障,提出基于优化惩罚因子C参数的支持向量机算法(C-SVC:C-support vector classification)和交叉验证算法相结合的变压器故障诊断方法。该方法利用变压器在故障时产生的氢气、甲烷、乙烷、乙烯、乙炔的体积分数数据建立训练集和测试集。在训练集中,该方法能自动优化出(寻找最佳)支持向量机的核函数的参数γ和惩罚因子C,利用优化的参数对训练集进行训练,可得到最佳的支持向量机模型,并用该模型对测试集进行分类,从而诊断出变压器的故障类型。变压器故障诊断实例分析结果证明,该方法可行,有效,且具有较高的故障诊断准确率。 相似文献
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This paper proposes a data driven model-based condition monitoring scheme that is applied to wind turbines. The scheme is based upon a non-linear data-based modelling approach in which the model parameters vary as functions of the system variables. The model structure and parameters are identified directly from the input and output data of the process. The proposed method is demonstrated with data obtained from a simulation of a grid-connected wind turbine where it is used to detect grid and power electronic faults. The method is evaluated further with SCADA data obtained from an operational wind farm where it is employed to identify gearbox and generator faults. In contrast to artificial intelligence methods, such as artificial neural network-based models, the method employed in this paper provides a parametrically efficient representation of non-linear processes. Consequently, it is relatively straightforward to implement the proposed model-based method on-line using a field-programmable gate array. 相似文献
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Guido Cocchi Leonardo Galli Giulio Galvan Marco Sciandrone Matteo Cantù Giuseppe Tomaselli 《风能》2018,21(5):357-371
In liberalized markets, there usually exists a day‐ahead session where energy is sold and acquired for the following production day. Owing to the high uncertainty of its production, renewable energy (wind in particular) can significantly influence the network imbalance of the following day. In this work, we consider the problem of predicting the sum of the bid volumes for wind energy of all the producers inside the day‐ahead energy market. This is a valuable tool to be used by an energy provider in order to determine the imbalance of a market zone and, thus, properly size its bids. In particular, we focus on the estimation of the possible relationship between the meteorological forecasts and the wind power offered on the market by the companies for a market zone. We propose a machine learning model which is used to compute a 1‐day‐ahead forecast. The input‐output mapping is obtained by support vector regression. The input feature vector is defined by a suitable feature extraction technique since the meteorological forecasts are given on a lattice of thousands of geographical points. The computational experiments are performed considering the Italian market as a case study (years 2012‐2016). The results show that the proposed feature extraction technique, selecting only some geographical zones, manages to reduce the error attained using all the features. Moreover, classical statistical methods are shown to be outperformed by machine learning models. The analysis reveals also some weaknesses of the model, which may be due to other nonmeteorological factors at play. 相似文献
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基于小波的多分辨率分析,针对风速序列拟周期性、非平稳性及非线性等特点,将风速序列按不同频率进行分解,对分解后的原始风速信号分别建立不同的预测模型;各个模型的最佳参数由贝叶斯证据3层推断得出,用以建立基于小波和贝叶斯证据推断框架下的最小二乘支持向量机(LS-SVM)回归短期风速预测模型。应用该模型对东北某风电场的风速进行了提前1 h的预测,预测的平均绝对百分比误差为7.63%,提高了预测精度。预测结果表明:基于贝叶斯证据推断框架下的LS-SVM和小波分析相结合的短期风速预测模型是一种有效、可行的风速预测模型,可为风力发电功率的预测提供一定的理论支持。 相似文献
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In Seop Lim Jin Young Park Eun Jung Choi Min Soo Kim 《International Journal of Hydrogen Energy》2021,46(2):2543-2554
The temperature of a fuel cell has a considerable impact on the saturation of a membrane, electrochemical reaction speed, and durability. So thermal management is considered one of the critical issues in polymer electrolyte membrane fuel cells. Therefore, the reliability of the thermal management system is also crucial for the performance and durability of a fuel cell system. In this work, a methodology for component-level fault diagnosis of polymer electrolyte membrane fuel cell thermal management system for various current densities is proposed. Specifically, this study suggests fault diagnosis using limited data, based on an experimental approach. Normal and five component-level fault states are diagnosed with a support vector machine model using temperature, pressure, and fan control signal data. The effects of training data at different operating current densities on fault diagnosis are analyzed. The effects of data preprocessing method are investigated, and the cause of misdiagnosis is analyzed. On this basis, diagnosis results show that the proposed methodology can realize efficient component-level fault diagnosis using limited data. The diagnosis accuracy is over 92% when the residual basis scaling method is used, and data at the highest operating current density is used to train the support vector machine. 相似文献