三电平风电变流器故障诊断策略研究
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摘要
目前，能源短缺和环境污染已经成为全球性的两大难题，而风能的开发利用将是缓解这两大难题的有效途径。作为双馈式风力发电系统的核心部件，风电变流器主要用于保证在风速变化时系统依然能够发出频率、幅值稳定的优质电能。然而，由于风电变流器所处现场往往环境恶劣，高温湿热、油水脏污以及交变的电磁干扰，再加上自身构造复杂且需要承受高电压、大电流及高频开关状态，导致其极易发生故障。因此，加强风电变流器的故障诊断研究，开展高效可靠的状态监测和故障诊断显得尤为重要。
论文的主要研究工作包括：
（1）在深入研究双馈式三电平风电变流器的拓扑结构和数学模型基础上，分析了变流器的典型故障及内部机理。通过比较常用的时频分析技术，选用小波包完全分解应用于变流器故障诊断，根据选定的小波基函数和分解尺度对故障原始信号进行变换，提取故障信号在各频带的能量作为特征信息，构建有效的特征向量作为故障分类器的训练和测试样本。
（2）针对变流器故障样本数据的特点，同时也为降低参训样本规模，提高训练和决策速度，提出了基于邻界样本的稀疏最小二乘支持向量机有监督学习算法（BNSS-LSSVM）。为进一步扩展其多类分类能力，提出了基于Huffman决策树的变流器多故障诊断方法。实验结果显示该算法在测试速度、分类效果等方面优于OVO和OVR方法。通过加入不同强度的白噪声干扰，分类结果表明其具备一定的抗干扰能力。
（3）算法BNSS-LSSVM的惩罚因子C和核函数高宽σ的选取直接影响系统诊断的精度。为进一步优化故障诊断结果，将多种群协同进化思想和人工免疫原理相结合，提出了一种改进的多种群免疫协同进化粒子群优化算法（MICPSO）。算法充分考虑种群间既相互竞争又协同合作的对立统一，采用阴性免疫算法划分解空间，最大限度地实现空间覆盖，增加初始种群的多样性；利用混合策略选取各个Common种群的精英组建Elite种群，充当高层优良库；通过疫苗提取和疫苗接种的双向机制促进种群间信息共享和协同进化；针对勘探能力和开采能力之间的矛盾，依据抗体的适应度和浓度采用双重概率变异算子，通过大步长的柯西变异提高算法的发散式全局勘探能力，而通过小步长的高斯变异增强算法的精细局部开采能力。
（4）为将大量未标签样本与少量已标签样本结合起来提高分类器的泛化性能，在研究半监督学习和直推式支持向量机的基础上，提出了一种改进的渐进直推LSSVM半监督故障诊断算法（IPTS-LSSVM）。该算法对满足条件的无标签样本进行区域标注，扩大了搜索范围，减少了迭代次数；重置迭代中不一致的准标定样本，加快了收敛速度；引入基于Moore广义逆矩阵的增量正学习和减量逆学习来避免直接大矩阵复杂逆运算，在降低计算复杂度的同时又减少了存储空间需求。
Currently, energy shortage and environmental pollution have become two majorglobal problems, and wind energy development and utilization is an effectiveapproach to relieve them. As the key equipment in doubly-fed wind power generationsystem, wind power converter assures the generator to emit electric energy meetingthe requirements of power grid. Due to hot and humid, oil and dirt, high voltage andlarge current, the converter is vulnerable to failure. Thus, it is necessary to strengthenthe fault diagnosis research on the wind power converter and to construct the efficientand reliable state monitoring and fault diagnosis system.
The main research work in the dissertation includes:
(1) By comparison of commonly used time-frequency analysis technology, thewavelet packet decomposition is applied to the fault diagnosis of converter. Theselected wavelet function and scale are adopted to transform the original fault signalsinto eigenvectors acting as training and testing sample for the fault classfier.
(2) According to the characteristics of the converter fault data, a sparse LSSVMalgorithm based on boundary neighbor sample is presented. In order to expand themulti-class classification ability, a multi-fault diagnosis method based on Huffmantree is provided. The experimental results show the method advantages in testingspeed and accuracy.
(3) The paper proposes an improved multi-population immune co-evolutionparticle swarm optimization algorithm. The algorithm takes full into account of bothcompetition and collaboration between the populations. At the same time, the negativeimmune operator is used to divide the whole population into sub-populations toincrease their diversity and improve the global search ability. Futhermore, twomutation operators are adopted, which can increase the early exploration ability andimprove later exploitation ability. The experiments show that the proposed algorithmcan not only effectively solve problem of lack of local search ability, but alsosignificantly speed up the convergence and improve the stability.
(4) Based on the incremental study and region labeling, an improved progressivetransductive semi-supervised LSSVM algorithm is proposed. Experimental resultsdisplay that the algorithm has high generalization capability.
论文的主要研究工作包括：
（1）在深入研究双馈式三电平风电变流器的拓扑结构和数学模型基础上，分析了变流器的典型故障及内部机理。通过比较常用的时频分析技术，选用小波包完全分解应用于变流器故障诊断，根据选定的小波基函数和分解尺度对故障原始信号进行变换，提取故障信号在各频带的能量作为特征信息，构建有效的特征向量作为故障分类器的训练和测试样本。
（2）针对变流器故障样本数据的特点，同时也为降低参训样本规模，提高训练和决策速度，提出了基于邻界样本的稀疏最小二乘支持向量机有监督学习算法（BNSS-LSSVM）。为进一步扩展其多类分类能力，提出了基于Huffman决策树的变流器多故障诊断方法。实验结果显示该算法在测试速度、分类效果等方面优于OVO和OVR方法。通过加入不同强度的白噪声干扰，分类结果表明其具备一定的抗干扰能力。
（3）算法BNSS-LSSVM的惩罚因子C和核函数高宽σ的选取直接影响系统诊断的精度。为进一步优化故障诊断结果，将多种群协同进化思想和人工免疫原理相结合，提出了一种改进的多种群免疫协同进化粒子群优化算法（MICPSO）。算法充分考虑种群间既相互竞争又协同合作的对立统一，采用阴性免疫算法划分解空间，最大限度地实现空间覆盖，增加初始种群的多样性；利用混合策略选取各个Common种群的精英组建Elite种群，充当高层优良库；通过疫苗提取和疫苗接种的双向机制促进种群间信息共享和协同进化；针对勘探能力和开采能力之间的矛盾，依据抗体的适应度和浓度采用双重概率变异算子，通过大步长的柯西变异提高算法的发散式全局勘探能力，而通过小步长的高斯变异增强算法的精细局部开采能力。
（4）为将大量未标签样本与少量已标签样本结合起来提高分类器的泛化性能，在研究半监督学习和直推式支持向量机的基础上，提出了一种改进的渐进直推LSSVM半监督故障诊断算法（IPTS-LSSVM）。该算法对满足条件的无标签样本进行区域标注，扩大了搜索范围，减少了迭代次数；重置迭代中不一致的准标定样本，加快了收敛速度；引入基于Moore广义逆矩阵的增量正学习和减量逆学习来避免直接大矩阵复杂逆运算，在降低计算复杂度的同时又减少了存储空间需求。
Currently, energy shortage and environmental pollution have become two majorglobal problems, and wind energy development and utilization is an effectiveapproach to relieve them. As the key equipment in doubly-fed wind power generationsystem, wind power converter assures the generator to emit electric energy meetingthe requirements of power grid. Due to hot and humid, oil and dirt, high voltage andlarge current, the converter is vulnerable to failure. Thus, it is necessary to strengthenthe fault diagnosis research on the wind power converter and to construct the efficientand reliable state monitoring and fault diagnosis system.
The main research work in the dissertation includes:
(1) By comparison of commonly used time-frequency analysis technology, thewavelet packet decomposition is applied to the fault diagnosis of converter. Theselected wavelet function and scale are adopted to transform the original fault signalsinto eigenvectors acting as training and testing sample for the fault classfier.
(2) According to the characteristics of the converter fault data, a sparse LSSVMalgorithm based on boundary neighbor sample is presented. In order to expand themulti-class classification ability, a multi-fault diagnosis method based on Huffmantree is provided. The experimental results show the method advantages in testingspeed and accuracy.
(3) The paper proposes an improved multi-population immune co-evolutionparticle swarm optimization algorithm. The algorithm takes full into account of bothcompetition and collaboration between the populations. At the same time, the negativeimmune operator is used to divide the whole population into sub-populations toincrease their diversity and improve the global search ability. Futhermore, twomutation operators are adopted, which can increase the early exploration ability andimprove later exploitation ability. The experiments show that the proposed algorithmcan not only effectively solve problem of lack of local search ability, but alsosignificantly speed up the convergence and improve the stability.
(4) Based on the incremental study and region labeling, an improved progressivetransductive semi-supervised LSSVM algorithm is proposed. Experimental resultsdisplay that the algorithm has high generalization capability.
引文
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