感应电机定转子故障的微粒群诊断方法研究
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摘要
感应电机是工农业生产中应用最为广泛的驱动设备之一，它的正常运行是保证安全生产的关键。随着现代工农业系统的飞速发展，电机的单机容量不断增加，所驱动的负载也越来越复杂。驱动电机一旦发生故障，势必造成一定的经济损失，并且影响到生产线的安全以及产品质量，严重时还会造成灾难性后果。统计数据表明，定转子故障是感应电机最常见的故障，分别约占其全部故障种类的30%和10%。因此，对感应电机定转子故障进行早期检测和诊断具有重大的理论意义和社会经济效益。本文正是在这样的背景下，从微粒群优化理论入手，对感应电机定转子故障问题进行研究，提出了一种改进的微粒群算法和相应的定转子故障诊断新方法。具体研究内容和主要成果如下：
1）为改善微粒群算法的全局收敛性，使其更适合工程实际的应用，提出一种基于单纯形法和改进骨干微粒群算法的混合优化算法(SM-MBBPSO)。在该算法中，一方面利用惰性微粒的动态初始化策略保持种群的多样性，提高微粒的搜索效率；另一方面利用K-均值聚类混合策略结合微粒群算法强大的全局搜索能力和单纯形法精确的局部搜索能力。这两种改进策略使得整个混合算法的广度探索与深度开发能力得到了有效的均衡。最后，通过典型测试函数，验证了该混合算法的有效性。
2）在传统定子电流频谱分析中，感应电机转子断条故障特征经常被基波分量淹没而无法准确检测。针对这一问题，提出一种基于SM-MBBPSO算法的基波滤除方法。该方法根据故障电流信号特点，将波形参数识别问题转换为优化问题，然后利用求得的波形参数，直接将基波分量在时域中剔除，从而达到突出故障特征的目的。最后，对模拟数据和实测信号进行实验，结果验证了所提方法的有效性和优越性。
3）分析了定子故障对定子电流谐波和负序电流的影响，提出两种基于SM-MBBPSO算法的定子故障检测方法。第一种方法首先利用基于SM-MBBPSO算法的基波滤除方法消除基波分量对故障特征提取的影响；然后，利用小波包技术将残余电流信号中与故障相关的谐波分量分解到不同的频带；根据这些频带能量值的变化情况判别感应电机的定子故障。第二种方法根据基波在电流信号中所占比重最大的特点，利用SM-MBBPSO算法提取出三相定子电流的基波幅值和相位，进而直接计算出总的负序电流。由于在实际电机中，供电电压不平衡、电机先天不平衡和负载的变化等因素都会影响负序电流的大小，因此通过等效负序阻抗和支持向量机来消除这些非故障因素的影响，从而得到仅与定子故障相关的残余负序电流，实现感应电机定子故障的准确检测。最后，实验结果表明这2种方法都是可行、有效的。
4）为了准确识别感应电机定转子故障，提高方法的推广能力，提出一种基于SM-MBBPSO算法和支持向量机的故障识别方法，并给出了可行的识别步骤和分析。该方法首先利用基于SM-MBBPSO算法的基波滤除方法和小波包技术，滤除基波分量、强化故障特征；然后根据定转子故障电流的频谱特征，选出感应电机运行状态的本质特征，并以此作为支持向量机的输入向量。采用“二叉树”向量机进行分类，并利用SM-MBBPSO算法和交叉检验优化支持向量机模型参数。最后实验结果表明，该方法识别感应电机定转子断条故障能取得良好的效果。
Induction motor is one of the most widely used drive-equipments in industrial andagricultural production, its regular operation is essential for guaranteeing the safety inproduction. With the rapid development of modern industrial system, the capacity of asingle motor keeps increasing and the load also becomes more complicated. Once amotor failure happens, it will lead to economic loss, affect the production line safetyand product quality, and some times cause catastrophic failure. Statistical studies haveshown that the stator and rotor faults, which account for nearly30%and10%of totalfailures respectively, are the most frequent faults of induction motors. Consequently, itis of significant social and economic benefits to do the research on the stator and rotorfault diagnosis. In this dissertation, on the basis of the particle swarmoptimization(PSO) theory, stator and rotor fault diagnosis methods for inductionmotors were deeply studied. Then a modified PSO algorithm and its diagnosismethods were proposed systematically. The main works and the contributions aresummarized as follows.
1) To enhance global convergence capability of particle swarm optimization andmake it suitable to actual project application, a novel hybrid algorithm was proposed,called SM-MBBPSO, based on the Nelder-Mead simplex method(SM) and a modifiedbare-bones particle swarm optimization(MBBPSO). On the one hand, an adaptiveinitialization strategy on inactive particles was proposed to maintain diversity ofswarm and improve search efficiency of particles; on the other hand, a new hybridstrategy based on K-means clustering was proposed to combine the powerful globalsearch capability of MBBPSO and the high accurate local search capability of SM.These two strategys make the hybrid algorithm achieve a nice balance of exploitationand exploration capability. Finally, simulation results on benchmark functionsdemonstrate the effectiveness of the proposed algorithm.
2) In the traditional motor current signature spectrum analysis, the characteristiccomponents of broken rotor bars fault are often submerged by the fundamentalcomponent. In order to overcome this shortcoming, a fundamental-componet filteringmethod was proposed based on the SM-MBBPSO. According to the characteristic ofcurrent signal, the problem of waveform parameters identification was converted intoa optimization one. Using the waveform parameters estimated by the SM-MBBPSO,the fundamental component can be eliminated in the time domain, which can highlight the fault characteristic components. Finally, the results of simulation andlaboratory tests demonstrate the effectiveness and superiority of the proposed method.
3) The impact of stator fault on the stator current harmonics and negativesequence current was analyzed in detail; and two fault detection methods based on theSM-MBBPSO were proposed. In the first method, the fundamental component waseliminated by using the above fundamental-component filtering method firstly. Then,the harmonic components of residual current signal were decomposed into series offrequency bands by wavelet packet. The variation of subband energy was treated asthe fault feature to detect stator fault. According to the characteristic of current signals,the second method can estimate precisely the amplitude and initial phase of thefundamental component by using the powerful global search capability of theSM-MBBPSO; and then calculate the value of negative sequence current directly. In areal case, the unbalanced supply voltage sources, the inherent asymmetries of themotor and load variation will cause a change of the negative sequence current. Forthis reason, the portion of negative sequence current caused by the factors other thanstator fault was taken out by a negative sequence impedance and support vectormachine; and the residual negative sequence current was used to diagnose the statorfault of induction motor. Finally, the results of laboratory tests demonstrate theeffectiveness of these two methods.
4) To accurately recognize the stator and rotor faults of induction motors, a novelmethod for fault indentification was proposed based on the SM-MBBPSO and supportvector machine(SVM); and feasible diagnostic steps and analysis were alsointroduced. Firstly, the above fundamental-componet filtering method and waveletpacket were used to eliminate the influence of fundamental component and strengthenfault characteristics. Then according to the spectral characteristics of current signal infault condition, essential features of motor faults were chosen from all the frequencybands, and were considered as the input vector of SVM. The Binary tree vectormachine was used to solve the multi-class classification problem; and theSM-MBBPSO and cross-validation were taken to optimize model parameters. Finally,the experiment shows that the proposed method is effective to recognize the stator androtor faults of induction motors.
1）为改善微粒群算法的全局收敛性，使其更适合工程实际的应用，提出一种基于单纯形法和改进骨干微粒群算法的混合优化算法(SM-MBBPSO)。在该算法中，一方面利用惰性微粒的动态初始化策略保持种群的多样性，提高微粒的搜索效率；另一方面利用K-均值聚类混合策略结合微粒群算法强大的全局搜索能力和单纯形法精确的局部搜索能力。这两种改进策略使得整个混合算法的广度探索与深度开发能力得到了有效的均衡。最后，通过典型测试函数，验证了该混合算法的有效性。
2）在传统定子电流频谱分析中，感应电机转子断条故障特征经常被基波分量淹没而无法准确检测。针对这一问题，提出一种基于SM-MBBPSO算法的基波滤除方法。该方法根据故障电流信号特点，将波形参数识别问题转换为优化问题，然后利用求得的波形参数，直接将基波分量在时域中剔除，从而达到突出故障特征的目的。最后，对模拟数据和实测信号进行实验，结果验证了所提方法的有效性和优越性。
3）分析了定子故障对定子电流谐波和负序电流的影响，提出两种基于SM-MBBPSO算法的定子故障检测方法。第一种方法首先利用基于SM-MBBPSO算法的基波滤除方法消除基波分量对故障特征提取的影响；然后，利用小波包技术将残余电流信号中与故障相关的谐波分量分解到不同的频带；根据这些频带能量值的变化情况判别感应电机的定子故障。第二种方法根据基波在电流信号中所占比重最大的特点，利用SM-MBBPSO算法提取出三相定子电流的基波幅值和相位，进而直接计算出总的负序电流。由于在实际电机中，供电电压不平衡、电机先天不平衡和负载的变化等因素都会影响负序电流的大小，因此通过等效负序阻抗和支持向量机来消除这些非故障因素的影响，从而得到仅与定子故障相关的残余负序电流，实现感应电机定子故障的准确检测。最后，实验结果表明这2种方法都是可行、有效的。
4）为了准确识别感应电机定转子故障，提高方法的推广能力，提出一种基于SM-MBBPSO算法和支持向量机的故障识别方法，并给出了可行的识别步骤和分析。该方法首先利用基于SM-MBBPSO算法的基波滤除方法和小波包技术，滤除基波分量、强化故障特征；然后根据定转子故障电流的频谱特征，选出感应电机运行状态的本质特征，并以此作为支持向量机的输入向量。采用“二叉树”向量机进行分类，并利用SM-MBBPSO算法和交叉检验优化支持向量机模型参数。最后实验结果表明，该方法识别感应电机定转子断条故障能取得良好的效果。
Induction motor is one of the most widely used drive-equipments in industrial andagricultural production, its regular operation is essential for guaranteeing the safety inproduction. With the rapid development of modern industrial system, the capacity of asingle motor keeps increasing and the load also becomes more complicated. Once amotor failure happens, it will lead to economic loss, affect the production line safetyand product quality, and some times cause catastrophic failure. Statistical studies haveshown that the stator and rotor faults, which account for nearly30%and10%of totalfailures respectively, are the most frequent faults of induction motors. Consequently, itis of significant social and economic benefits to do the research on the stator and rotorfault diagnosis. In this dissertation, on the basis of the particle swarmoptimization(PSO) theory, stator and rotor fault diagnosis methods for inductionmotors were deeply studied. Then a modified PSO algorithm and its diagnosismethods were proposed systematically. The main works and the contributions aresummarized as follows.
1) To enhance global convergence capability of particle swarm optimization andmake it suitable to actual project application, a novel hybrid algorithm was proposed,called SM-MBBPSO, based on the Nelder-Mead simplex method(SM) and a modifiedbare-bones particle swarm optimization(MBBPSO). On the one hand, an adaptiveinitialization strategy on inactive particles was proposed to maintain diversity ofswarm and improve search efficiency of particles; on the other hand, a new hybridstrategy based on K-means clustering was proposed to combine the powerful globalsearch capability of MBBPSO and the high accurate local search capability of SM.These two strategys make the hybrid algorithm achieve a nice balance of exploitationand exploration capability. Finally, simulation results on benchmark functionsdemonstrate the effectiveness of the proposed algorithm.
2) In the traditional motor current signature spectrum analysis, the characteristiccomponents of broken rotor bars fault are often submerged by the fundamentalcomponent. In order to overcome this shortcoming, a fundamental-componet filteringmethod was proposed based on the SM-MBBPSO. According to the characteristic ofcurrent signal, the problem of waveform parameters identification was converted intoa optimization one. Using the waveform parameters estimated by the SM-MBBPSO,the fundamental component can be eliminated in the time domain, which can highlight the fault characteristic components. Finally, the results of simulation andlaboratory tests demonstrate the effectiveness and superiority of the proposed method.
3) The impact of stator fault on the stator current harmonics and negativesequence current was analyzed in detail; and two fault detection methods based on theSM-MBBPSO were proposed. In the first method, the fundamental component waseliminated by using the above fundamental-component filtering method firstly. Then,the harmonic components of residual current signal were decomposed into series offrequency bands by wavelet packet. The variation of subband energy was treated asthe fault feature to detect stator fault. According to the characteristic of current signals,the second method can estimate precisely the amplitude and initial phase of thefundamental component by using the powerful global search capability of theSM-MBBPSO; and then calculate the value of negative sequence current directly. In areal case, the unbalanced supply voltage sources, the inherent asymmetries of themotor and load variation will cause a change of the negative sequence current. Forthis reason, the portion of negative sequence current caused by the factors other thanstator fault was taken out by a negative sequence impedance and support vectormachine; and the residual negative sequence current was used to diagnose the statorfault of induction motor. Finally, the results of laboratory tests demonstrate theeffectiveness of these two methods.
4) To accurately recognize the stator and rotor faults of induction motors, a novelmethod for fault indentification was proposed based on the SM-MBBPSO and supportvector machine(SVM); and feasible diagnostic steps and analysis were alsointroduced. Firstly, the above fundamental-componet filtering method and waveletpacket were used to eliminate the influence of fundamental component and strengthenfault characteristics. Then according to the spectral characteristics of current signal infault condition, essential features of motor faults were chosen from all the frequencybands, and were considered as the input vector of SVM. The Binary tree vectormachine was used to solve the multi-class classification problem; and theSM-MBBPSO and cross-validation were taken to optimize model parameters. Finally,the experiment shows that the proposed method is effective to recognize the stator androtor faults of induction motors.
引文
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