基于暂态量的高压输电线路故障分类与定位方法研究
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摘要
随着综合国力的不断提升与电力工业的飞速发展,我国电力系统已经步入了高电压、大电网和大机组时代；但随着输电容量与电压等级的不断提高以及输电距离的不断增长,高压输电线路的故障将对电力系统的稳定运行、国民经济建设及人民日常生活带来更为严重的危害与影响。因此,研究快速、准确的输电线路故障分类与故障定位方法,不仅可以缩短停电时间、减小电力运行维护人员的工作强度,而且对保障电力系统的安全性与经济性具重要的意义。高压输电线路故障产生的暂态量虽然持续时间短,却蕴含了丰富的故障信息,这为实现快速、准确的故障分类与定位提供了可能性。基于此,本文以构筑高压输电线路的快速故障诊断系统为目标,对基于暂态量的高压输电线路故障分类与故障定位方法进行研究。
在故障分类方面,论文基于人工智能算法给出了三种高压输电线路故障分类方法。其一为基于粗神经网络的故障分类方法,该方法提取故障暂态电流信号13种不同的时域特征和时频域特征作为故障分类的特征量,以10个不同的粗神经网络构成故障分类器对十种常见的短路故障进行分类识别。其二为基于自适应神经模糊推理系统的故障分类方法,该方法以暂态电流故障分量的时域标准差和四分位距作为故障分类的特征量,根据特征量的特点构造两个不同的自适应神经模糊推理系统作为故障分类器,第一个用于分类识别单相故障、两相故障和三相故障,当其输出结果为两相故障时,利用第二个分类器来判断故障是否为接地故障。其三是基于阴性选择算法的故障分类方法,该方法以暂态电流信号的高频暂态能量作为故障分类的特征量,并依据改进的阴性选择算法设计故障分类的分类机制。利用PSCAD/EMTDC仿真数据对三种故障分类方法进行了测试与验证,仿真结果表明：论文给出的三种方法均能快速、准确、可靠地分类识别出高压输电线路的故障类型,且其分类效果不受故障电阻、故障距离、故障初始角等因素的影响,对噪声干扰具有较强地适应性。
其次,论文在研究高压输电线路故障暂态信号时频特征的基础上,提出了一种基于暂态信号时频特征的故障分类方法。该方法综合考虑故障暂态电流信号的时频相关系数和时频能量特征,定义了暂态信号的时频特征相关系数,以此来刻画不同的故障类型；并据此设计故障分类机制,避免了基于人工智能算法的故障分类方法中训练样本难以构造的问题。利用仿真数据对故障分类方法进行了仿真验证,结果表明：该方法能快速、准确、可靠地分类识别出高压输电线路的不同故障,且其分类效果不受故障电阻、故障距离和故障初始角等因素的影响。
在故障定位方面,为提高现有单端行波法和行波固有频率法的定位准确性,论文基于暂态行波的时频特征,提出了两种单端故障定位方法。方法一为考虑暂态行波频域特征的时域故障定位方法,该方法以传统单端行波法为出发点,在考虑暂态电流行波频率特征对行波传播速度及波头到达时刻影响的基础上,利用暂态行波信号的Lipschitz指数将暂态行波波头的时域特征和频域特征联系起来,得到更为准确的故障定位结果。方法二为考虑暂态行波时域特征的频域故障定位方法,该方法以单端行波固有频率法为出发点,利用信号的时域周期特征来修正暂态行波信号的固有频率值,进而得到更为准确的故障定位结果。大量的PSCAD/EMTDC仿真试验结果证明：提出的两种故障定位方法的定位准确性比现有的行波法和固有频率法都有明显的提高,且其定位的可靠性、准确性不受故障电阻、故障初始角以及故障类型的影响,同时具有较强抗噪能力。
论文最后将考虑暂态行波时域特征的频域定位方法拓展至高压输电网的故障定位中,在推导了暂态行波信号的固有频率与其在电网中的传播路径以及边界条件的数学关系的基础上,提出了基于暂态行波传播路径的高压输电网故障定位方法。该方先根据暂态行波信号的固有频率值或分布情况准确地判断故障线路,然后利用反映包含故障点的行波传播路径的固有频率对故障距离进行准确计算。大量仿真试验结果表明：在输电网拓扑结构确定的情况下,该方法不仅能准确地判断出故障线路,而且还能准确地计算出故障距离,且故障定位准确性不受故障电阻、故障初始角和故障类型的影响。
总的来说,本论文最终形成了一个从故障分类到故障定位的高压输电线路快速故障诊断体系。
本论文是国家自然科学基金——《基于信息理论的多信源电网故障诊断方法及应用》(No.50877068,2009-2011)和教育部博士点基金——《基于单端行波自然频率提取的输电线路故障测距新方法》(No.200806130004,2009-2011)的组成部分。
With the continuous improvement of comprehensive national strength and the rapid development of power industry, China's electric power system has entered a time of high-voltage, great gird and large generators. However, with the unceasing increase of transmission capacity and voltage level, high-voltage transmission line fault will also bring more serious harm and impact to electric power system and stable operation, national economic construction and people's daily life. Therefore, the study of fast and accurate fault classification and fault location methods for transmission lines, can not only shorten the power-off time and reduce the strength of maintenance staff for power operation, but also have a major practical significance in ensuring the security and economical efficiency of power system. Although the transient duration of fault on transmission line is short, it contains a wealth of fault information, which makes it possible to achieve a fast and accurate classification and location. Based on this, in this thesis, in order to build a rapid fault diagnosis system for high-voltage transmission lines, fault classification and fault location methods based on transient are studied.
In fault classification, the thesis first presents three different fault classification methods based on artificial intelligence algorithms for high-voltage transmission line. One is based on rough membership neural network (RMNN), which takes13different time domain and time-frequency domain characteristics of fault transient current signals as feature values for fault classification, using the classifier for fault classification based on ten different RMNN to classify and identify the ten common short circuit faults for high-voltage transmission lines. The second one is based on adaptive-network-based fuzzy inference system (ANFIS), which takes both time domain standard deviation and interquartile range of transient current fault components as the feature values for fault classification, and the fault classifier is based on two different ANFIS, which are designed according to the characteristics of the feature values. The third one is based on negative selection algorithm, which takes high-frequency transient energy of transient current signals as the feature values, and uses improved negative selection algorithm as the transmission line fault classifier. The simulation data from PSCAD/EMTDC is used to verify and test the proposed three kinds of fault classification methods for high-voltage transmission line, indicating that: these three methods of fault classification can classify and identify different fault of transmission line fast, accurately and reliably. Moreover, their classification effects are unaffected by fault resistance, fault distance, fault inception angle, and they also have a good adaptability to noise interference.
In addition, the thesis proposes a method for transmission line fault classification based on time-frequency characteristics of the fault transient signal. The method considers time-frequency correlation coefficient and characteristics of time-frequency energy of fault transient current signals to define time-frequency characteristic correlation coefficient, through which different fault can be characterized. Moreover, the classification criterion is designed based on time-frequency characteristic correlation coefficient directly, which avoids structural problems of training samples contained by fault classification methods based on artificial intelligence algorithms. The proposed method is verified and tested by large numbers of simulation data. The simulation results show that this method can classify the different fault of transmission line fast, accurately and reliably. In addition, fault resistance, fault distance and fault inception angle cannot affect the classification effects.
In the aspect of fault location, this thesis proposes two kinds of single-end fault location methods based on the time-frequency characteristics of transient traveling wave to improve the accuracy of current fault location methods using single-end traveling wave and traveling wave natural frequency. One of them is time-domain location method that considers frequency domain characteristics of transient traveling wave. It uses single-end traveling wave method as a springboard. Then on the basis of considering the influence of frequency characteristics of transient current traveling wave on the wave propagation velocity and the arrival of the first wave-front, Lipschitz exponent of transient signal is used to link the time domain and frequency domain characteristics of transient wave-front. Thus a more precise and reasonable method is achieved. Another is a frequency-domain fault location method, which considers the time-domain characteristics of transient traveling wave. The starting point of this method is traveling wave natural frequency. A new method of extracting natural frequency is proposed, which compensates for the natural frequency of transient traveling wave by time-domain period characteristic of signal to result in more accurate results of fault location. A large number of PSCAD/EMTDC simulation results show that the location accuracy of the two methods raised in this thesis have a significantly improvement over existing traveling wave method and traveling wave natural frequency method. Moreover, their reliability and accuracy will not be affected by fault resistance, fault distance, fault inception angle or fault type. It also has a good adaptability to noise interference.
Finally, the frequency-domain location method considering transient traveling wave time-domain characteristics is extended to the grid fault location. Based on the mathematical relationship between the natural frequency of transient traveling wave signal with its propagation path and boundary conditions, a grid fault location method based on propagation path of transient traveling wave is proposed. First, the method determines the fault line according to natural frequency or the distribution of the transient traveling wave signal accurately, and then the natural frequency that reflects traveling wave propagation path including fault point is used to compute the fault distance accurately. Large numbers of simulation show that, in the situation of certain power network topology, the method can not only figure out fault line, but also calculate the distance accurately, and the location accuracy is not affected by fault resistance, fault inception angle, fault type and fault distance.
Overall, this thesis eventually forms a rapid fault diagnosis system for high-voltage transmission line from fault classification to fault location.
This thesis is supported by National Natural Science Foundation of China:'Information theory based power network fault diagnosis of multi-sourced signal'(No.50877068,2009-2011) and Doctoral Fund of Ministry of Education of China:'A new transmission line fault location method based on the extracted natural frequency of single-ended traveling wave'(No.200806130004,2009-2011).
在故障分类方面,论文基于人工智能算法给出了三种高压输电线路故障分类方法。其一为基于粗神经网络的故障分类方法,该方法提取故障暂态电流信号13种不同的时域特征和时频域特征作为故障分类的特征量,以10个不同的粗神经网络构成故障分类器对十种常见的短路故障进行分类识别。其二为基于自适应神经模糊推理系统的故障分类方法,该方法以暂态电流故障分量的时域标准差和四分位距作为故障分类的特征量,根据特征量的特点构造两个不同的自适应神经模糊推理系统作为故障分类器,第一个用于分类识别单相故障、两相故障和三相故障,当其输出结果为两相故障时,利用第二个分类器来判断故障是否为接地故障。其三是基于阴性选择算法的故障分类方法,该方法以暂态电流信号的高频暂态能量作为故障分类的特征量,并依据改进的阴性选择算法设计故障分类的分类机制。利用PSCAD/EMTDC仿真数据对三种故障分类方法进行了测试与验证,仿真结果表明：论文给出的三种方法均能快速、准确、可靠地分类识别出高压输电线路的故障类型,且其分类效果不受故障电阻、故障距离、故障初始角等因素的影响,对噪声干扰具有较强地适应性。
其次,论文在研究高压输电线路故障暂态信号时频特征的基础上,提出了一种基于暂态信号时频特征的故障分类方法。该方法综合考虑故障暂态电流信号的时频相关系数和时频能量特征,定义了暂态信号的时频特征相关系数,以此来刻画不同的故障类型；并据此设计故障分类机制,避免了基于人工智能算法的故障分类方法中训练样本难以构造的问题。利用仿真数据对故障分类方法进行了仿真验证,结果表明：该方法能快速、准确、可靠地分类识别出高压输电线路的不同故障,且其分类效果不受故障电阻、故障距离和故障初始角等因素的影响。
在故障定位方面,为提高现有单端行波法和行波固有频率法的定位准确性,论文基于暂态行波的时频特征,提出了两种单端故障定位方法。方法一为考虑暂态行波频域特征的时域故障定位方法,该方法以传统单端行波法为出发点,在考虑暂态电流行波频率特征对行波传播速度及波头到达时刻影响的基础上,利用暂态行波信号的Lipschitz指数将暂态行波波头的时域特征和频域特征联系起来,得到更为准确的故障定位结果。方法二为考虑暂态行波时域特征的频域故障定位方法,该方法以单端行波固有频率法为出发点,利用信号的时域周期特征来修正暂态行波信号的固有频率值,进而得到更为准确的故障定位结果。大量的PSCAD/EMTDC仿真试验结果证明：提出的两种故障定位方法的定位准确性比现有的行波法和固有频率法都有明显的提高,且其定位的可靠性、准确性不受故障电阻、故障初始角以及故障类型的影响,同时具有较强抗噪能力。
论文最后将考虑暂态行波时域特征的频域定位方法拓展至高压输电网的故障定位中,在推导了暂态行波信号的固有频率与其在电网中的传播路径以及边界条件的数学关系的基础上,提出了基于暂态行波传播路径的高压输电网故障定位方法。该方先根据暂态行波信号的固有频率值或分布情况准确地判断故障线路,然后利用反映包含故障点的行波传播路径的固有频率对故障距离进行准确计算。大量仿真试验结果表明：在输电网拓扑结构确定的情况下,该方法不仅能准确地判断出故障线路,而且还能准确地计算出故障距离,且故障定位准确性不受故障电阻、故障初始角和故障类型的影响。
总的来说,本论文最终形成了一个从故障分类到故障定位的高压输电线路快速故障诊断体系。
本论文是国家自然科学基金——《基于信息理论的多信源电网故障诊断方法及应用》(No.50877068,2009-2011)和教育部博士点基金——《基于单端行波自然频率提取的输电线路故障测距新方法》(No.200806130004,2009-2011)的组成部分。
With the continuous improvement of comprehensive national strength and the rapid development of power industry, China's electric power system has entered a time of high-voltage, great gird and large generators. However, with the unceasing increase of transmission capacity and voltage level, high-voltage transmission line fault will also bring more serious harm and impact to electric power system and stable operation, national economic construction and people's daily life. Therefore, the study of fast and accurate fault classification and fault location methods for transmission lines, can not only shorten the power-off time and reduce the strength of maintenance staff for power operation, but also have a major practical significance in ensuring the security and economical efficiency of power system. Although the transient duration of fault on transmission line is short, it contains a wealth of fault information, which makes it possible to achieve a fast and accurate classification and location. Based on this, in this thesis, in order to build a rapid fault diagnosis system for high-voltage transmission lines, fault classification and fault location methods based on transient are studied.
In fault classification, the thesis first presents three different fault classification methods based on artificial intelligence algorithms for high-voltage transmission line. One is based on rough membership neural network (RMNN), which takes13different time domain and time-frequency domain characteristics of fault transient current signals as feature values for fault classification, using the classifier for fault classification based on ten different RMNN to classify and identify the ten common short circuit faults for high-voltage transmission lines. The second one is based on adaptive-network-based fuzzy inference system (ANFIS), which takes both time domain standard deviation and interquartile range of transient current fault components as the feature values for fault classification, and the fault classifier is based on two different ANFIS, which are designed according to the characteristics of the feature values. The third one is based on negative selection algorithm, which takes high-frequency transient energy of transient current signals as the feature values, and uses improved negative selection algorithm as the transmission line fault classifier. The simulation data from PSCAD/EMTDC is used to verify and test the proposed three kinds of fault classification methods for high-voltage transmission line, indicating that: these three methods of fault classification can classify and identify different fault of transmission line fast, accurately and reliably. Moreover, their classification effects are unaffected by fault resistance, fault distance, fault inception angle, and they also have a good adaptability to noise interference.
In addition, the thesis proposes a method for transmission line fault classification based on time-frequency characteristics of the fault transient signal. The method considers time-frequency correlation coefficient and characteristics of time-frequency energy of fault transient current signals to define time-frequency characteristic correlation coefficient, through which different fault can be characterized. Moreover, the classification criterion is designed based on time-frequency characteristic correlation coefficient directly, which avoids structural problems of training samples contained by fault classification methods based on artificial intelligence algorithms. The proposed method is verified and tested by large numbers of simulation data. The simulation results show that this method can classify the different fault of transmission line fast, accurately and reliably. In addition, fault resistance, fault distance and fault inception angle cannot affect the classification effects.
In the aspect of fault location, this thesis proposes two kinds of single-end fault location methods based on the time-frequency characteristics of transient traveling wave to improve the accuracy of current fault location methods using single-end traveling wave and traveling wave natural frequency. One of them is time-domain location method that considers frequency domain characteristics of transient traveling wave. It uses single-end traveling wave method as a springboard. Then on the basis of considering the influence of frequency characteristics of transient current traveling wave on the wave propagation velocity and the arrival of the first wave-front, Lipschitz exponent of transient signal is used to link the time domain and frequency domain characteristics of transient wave-front. Thus a more precise and reasonable method is achieved. Another is a frequency-domain fault location method, which considers the time-domain characteristics of transient traveling wave. The starting point of this method is traveling wave natural frequency. A new method of extracting natural frequency is proposed, which compensates for the natural frequency of transient traveling wave by time-domain period characteristic of signal to result in more accurate results of fault location. A large number of PSCAD/EMTDC simulation results show that the location accuracy of the two methods raised in this thesis have a significantly improvement over existing traveling wave method and traveling wave natural frequency method. Moreover, their reliability and accuracy will not be affected by fault resistance, fault distance, fault inception angle or fault type. It also has a good adaptability to noise interference.
Finally, the frequency-domain location method considering transient traveling wave time-domain characteristics is extended to the grid fault location. Based on the mathematical relationship between the natural frequency of transient traveling wave signal with its propagation path and boundary conditions, a grid fault location method based on propagation path of transient traveling wave is proposed. First, the method determines the fault line according to natural frequency or the distribution of the transient traveling wave signal accurately, and then the natural frequency that reflects traveling wave propagation path including fault point is used to compute the fault distance accurately. Large numbers of simulation show that, in the situation of certain power network topology, the method can not only figure out fault line, but also calculate the distance accurately, and the location accuracy is not affected by fault resistance, fault inception angle, fault type and fault distance.
Overall, this thesis eventually forms a rapid fault diagnosis system for high-voltage transmission line from fault classification to fault location.
This thesis is supported by National Natural Science Foundation of China:'Information theory based power network fault diagnosis of multi-sourced signal'(No.50877068,2009-2011) and Doctoral Fund of Ministry of Education of China:'A new transmission line fault location method based on the extracted natural frequency of single-ended traveling wave'(No.200806130004,2009-2011).
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