暂态电能质量问题研究
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摘要
电能质量问题已引起电力部门及用户的高度关注。在治理电能质量问题时,对电能质量问题识别、定位和分类具有十分重要的意义。本文针对暂态电能质量问题的自动识别、定位和分类等方面开展研究工作,取得了如下成果:
根据电力系统中加入不同性质的负荷后,系统谐振频率会向不同方向改变的特性,通过对低电压时投电容器的弱扰动信号提取小波变换特定分解层的小波熵特征值,成功实现了对低电压投电容器扰动的识别。并在此基础上,成功地对此种扰动实现了准确定位。
扰动功率和能量法是定位电能质量问题常用的方法,但此法易受到扰动发生期间其他扰动的影响,且在定位向系统注入能量的扰动类型时,会发生原理性的错误。本文引入了扰动能量初始时刻极性变化和扰动初始电压的改变作为判据,它只考虑扰动初始时刻的变化,因此极大减少了其他扰动的影响,且不需要在定位前对暂态扰动分类,就可以对暂态扰动进行更为准确和可靠的定位,本文在总结各种暂态扰动的情况后,给出了科学的定位方法。
在对扰动分类时,现有的分类线法和K-近邻法的共同缺点是计算量大和计算存储量大,由此也造成了分类准确率的下降。本文改进了贝叶斯最优分类法,使其从有参数识别法转变为非参数识别方法,并可用于样本数量有限并有交叉的分类,扩大了它的应用范围。以此结论为基础,改善了分类线法和K-近邻法计算量大和计算存储量大的缺点,并对计算准确率有所提高;当应用在概率神经网络分类时,可对其固有的两个缺点都有所改善。
The problem of power quality has aroused great concern of power companies and consumers. Recognizing, locating and classifying the problem are of great significance. The paper analyzes automatic recognition, location and classification of transient power quality problem, and some improvements have been achieved.
Different types of load will change the resonant frequency of power systems to different ways. According to this characteristic, a recognition algorithm using the entropy feature vectors of the wavelet transform special layer is developed for capacitor switching disturbance at low voltage, furthermore, the location of the disturbance can be determined accurately.
The disturbance power and energy is a commonly used method to locate the power quality disturbances. But it is susceptive to other disturbances occurring at the same time and it makes mistake when the disturbance injects energy to the system. This paper considers only the initial disturbance energy and voltage as criterion, which lessens greatly the impact of other disturbance and obviates the need for the classification before the location. All types of disturbances are summarized and a right location method is proposed.
When classifying the disturbances, the linear decision method and K-nearest neighbor method have the same shortcomings: enormous amounts of computation and storage as well as impaired classification accuracy. This paper improves the Bayes optimal method, which changing it from the parameter classification method to a non-parameter one and making it applicable to the classification of limited intercrossed samples. Based on the conclusion, this paper improves the methods of linear decision and K-nearest neighbor, overcoming their shortcomings and improving the computation accuracy. When applied to the Probabilistic Neural Networks, the approach improves its two inherent shortcomings.
根据电力系统中加入不同性质的负荷后,系统谐振频率会向不同方向改变的特性,通过对低电压时投电容器的弱扰动信号提取小波变换特定分解层的小波熵特征值,成功实现了对低电压投电容器扰动的识别。并在此基础上,成功地对此种扰动实现了准确定位。
扰动功率和能量法是定位电能质量问题常用的方法,但此法易受到扰动发生期间其他扰动的影响,且在定位向系统注入能量的扰动类型时,会发生原理性的错误。本文引入了扰动能量初始时刻极性变化和扰动初始电压的改变作为判据,它只考虑扰动初始时刻的变化,因此极大减少了其他扰动的影响,且不需要在定位前对暂态扰动分类,就可以对暂态扰动进行更为准确和可靠的定位,本文在总结各种暂态扰动的情况后,给出了科学的定位方法。
在对扰动分类时,现有的分类线法和K-近邻法的共同缺点是计算量大和计算存储量大,由此也造成了分类准确率的下降。本文改进了贝叶斯最优分类法,使其从有参数识别法转变为非参数识别方法,并可用于样本数量有限并有交叉的分类,扩大了它的应用范围。以此结论为基础,改善了分类线法和K-近邻法计算量大和计算存储量大的缺点,并对计算准确率有所提高;当应用在概率神经网络分类时,可对其固有的两个缺点都有所改善。
The problem of power quality has aroused great concern of power companies and consumers. Recognizing, locating and classifying the problem are of great significance. The paper analyzes automatic recognition, location and classification of transient power quality problem, and some improvements have been achieved.
Different types of load will change the resonant frequency of power systems to different ways. According to this characteristic, a recognition algorithm using the entropy feature vectors of the wavelet transform special layer is developed for capacitor switching disturbance at low voltage, furthermore, the location of the disturbance can be determined accurately.
The disturbance power and energy is a commonly used method to locate the power quality disturbances. But it is susceptive to other disturbances occurring at the same time and it makes mistake when the disturbance injects energy to the system. This paper considers only the initial disturbance energy and voltage as criterion, which lessens greatly the impact of other disturbance and obviates the need for the classification before the location. All types of disturbances are summarized and a right location method is proposed.
When classifying the disturbances, the linear decision method and K-nearest neighbor method have the same shortcomings: enormous amounts of computation and storage as well as impaired classification accuracy. This paper improves the Bayes optimal method, which changing it from the parameter classification method to a non-parameter one and making it applicable to the classification of limited intercrossed samples. Based on the conclusion, this paper improves the methods of linear decision and K-nearest neighbor, overcoming their shortcomings and improving the computation accuracy. When applied to the Probabilistic Neural Networks, the approach improves its two inherent shortcomings.
引文
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