电力系统运行信息的数据挖掘研究
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摘要
随着信息技术的发展和电网数字化的进程,电力系统内采集、存储、待分析的数据量急剧膨胀,然而利用数据库查询统计和传统数据分析方法获得的有价值信息却很有限,因此讨论如何从大规模、高维数据中提取出隐藏的模式和规则,为电力系统决策者提供决策支持具有重要的研究价值。
数据挖掘技术正是在“海量数据”和“有限知识”的矛盾背景下快速发展,近年来得到各行业学者的广泛重视。该技术综合运用智能算法、模式识别、数理统计等理论,能够发现隐含在大量数据中、先前未知的、对决策有潜在价值的规律和知识。电力系统运行需要满足安全、经济和优质性的要求,针对这三个基本要求,本文围绕运行信息(节点电压、有功功率)的数据挖掘进行了深入研究,完成了以下主要工作：
(1)将相空间重构(PSR, Phase Space Reconstruction)方法引入电能质量研究领域,考虑电压暂降、电压瞬升、电压中断、脉冲、谐波及闪变六类扰动。利用相空间重构在相平面构造不同扰动的信号轨迹,并将其归一化编码为二进制图像。针对轨迹图像,定义了最大邻近距离、载体分量相似度、扰动覆盖区域和平均幅度四项指标。分析和仿真结果表明,通过上述四项指标可以有效地提取不同扰动的特性,为电能质量分析提供了新思路。
(2)本文的第一个数据挖掘任务是针对电网运行的优质性要求。对于PSR提取出的特性矢量,进一步构建支持向量机分类器对电能质量扰动事件进行分类研究。分类对象包括短期扰动(暂降、瞬升)和长期扰动(谐波、闪变)两两叠加的电能质量问题以及相应的单一扰动,结果显示该方法可以有效识别不同的扰动类型,为改善电能质量措施提供依据。研究中同样设计了基于小波变换和人工神经网络的分类方案,通过对比实验显示了本文所提基于相空间重构和支持向量机的分类系统的优越性。
(3)电力用户负荷曲线的聚类是形成合理电价体系和实施负荷管理措施的基础。本文的第二个数据挖掘任务是基于自组织映射(SOM, Self-Organizing Map)神经网络进行低压终端用户的负荷曲线聚类研究。首先定义并提取功率曲线、分时功率、功率频谱三类向量,分别作为SOM神经网络的输入进行可视化聚类。采用相对量化误差和拓扑误差两个指标表征聚类质量,选取聚类结果最好的SOM输出层结合k-均值法进行用户负荷曲线划分。根据Davies指标将本文研究的杭州地区终端用户的131条日负荷曲线划分为八类,对每类曲线进行描述。最后进行新用户的识别,结果表明该基于SOM神经网络的聚类方法有效可靠,可以为提高电网运行的经济性提供有价值的知识。
(4)电力系统运行信息的第三个数据挖掘研究是建立电网安全性评估的决策树规则。研究对象包括美国西部WSCC三机九节点简化模型和浙江地区某实际电网模型。该研究有两个目标,首先通过决策树算法实现故障前电网稳态运行参数和电网承受故障能力间的映射,为调度人员提供辅助的快速安全评估规则；其次在有无电压相角参数下分别生成决策树,通过对比两者的性能,验证相角测量单元PMU的作用。研究结果表明,决策树算法对于电网安全状态可以达到96.5%的识别精度,此外PMU的合理配置可以提高系统的可观测性。
本文的研究工作按照数据准备—数据挖掘—解释评估的顺序展开讨论,研究内容为数据挖掘技术在电力系统的应用进行了新的拓展,分析结果表明,本文采用的方法可靠、全面和实用,对今后提高大电网运行的优质、经济和安全性具有指导意义和应用价值。
The amount of data sampled, accumulated and to be analyzed in power system is expanding drastically, along with the development of information technology and the progress of power grid digitalization. However, limited valuable knowledge can be extracted using inquiry and statistics operation in database or traditional data anlysis method. Therefore it is highly significant to discuss how to obtain hidden pattern and rules from large-scale, multi-dimensional data, in order to provide decision support to power system decision maker.
Data mining technique advanced significaly in the past few years with the contradiction between "mass data" and "limited knowledge", and drew attention from diverse researchers. This technique combines various theories such as intellectual algorithms, pattern recognition and mathematics statistics, and is able to discover the rules or knowledge embeded in tremendous data, prior unknown but valuable in decision making. Power system should operate reliably, economically and securely. For these three basic requirements, this thesis conducted intensive study in data mining of power system operation information (voltage and active power flow). The research work accomplished is shown as follows:
(1) Phase Space Reconstruction (PSR) method was introduced to power quality research field. The types of concerned disturbances included voltage sags, voltage swells, voltage interruptions, impulsive transients, harmonics and flickers. Based on this method, trajectories of disturbance signals were constructed in phase plane and converted to binary images after normalization and coding process. Four indices of trajectory image which are Maximum Adjacent Distance, Carrier Component Similarity, Overlay Area and Mean Amplitude were presented. Analysis and simulation results showed that by these four indices, features of different disturbances can be extracted effectively. The proposed approach provides a new idea for power quality analysis.
(2) The first data mining task in this thesis was conducted for the high-quality requirement of power grid operation. The extracted features using PSR method were utilized as inputs to the support vector machines (SVM) classifier to realize the automatic classification of power disturbances. The types of disturbances discussed included a combination of short-term disturbances (voltage sags, swells) and long-term disturbances (flickers, harmonics), as well as their homologous single ones. Numerical results showed that the method proposed can effectively classify different disturbance patterns and provide basis for power quality mitigation measures. In this research, Comparison studies based on Wavelet Transform (WT) and Artificial Neural Network (ANN) were also reported, to show the advantage of the classification system based on PSR and SVM algorithm.
(3) Load profile clustering of power customers is the basis to construct proper tariff system and apply load manage measures. The second data mining task was to study on load profile clustering of low-voltage terminal customers based on Self-Organizing Map (SOM) neural networks. First, three types of vectors which are power curve, time sharing power and power spectrum were defined, and then used as inputs of SOM neural networks to visualize clustering. Two indices, namely relative quantization error and topology error were introduced to evaluate clustering quality. We chose SOM output layer of best performance and allocate load profiles with k-means method. The 131 profiles concerned in this paper were allocated into eight clusters according to Davies index, and each group of profiles is described. Finally, the ability of SOM neural network to identify new customers was examined. The result showed that the method proposed is effective and reliable, and able to supply useful knowledge to enhance power grid operation economy aspect.
(4) The third research job for power system operation information was establishing decision tree rules for power grid security assessment. The studied models included Western Systems Coordinating Council (WSCC) three plants nine nodes simplified model and a realistic power grid model in Zhejiang Province. This case study had two goals. First, using knowledge database that covers all possible pre-fault operating conditions, decision rules in the form of hierarchical trees were developed for on-line assessment. Second, Phasor Measurement Units (PMU) were taken into consideration to better decision tree's performance. The results demonstrated that the proposed machine learning scheme was able to identify crucial security indicators and gave reliable security predictions to 96.5% accuracy. Furthermore, voltage phase angle difference that obtained by PMU was proved helpful in improving DT's identification accuracy.
The research contents are presented according to steps of data preparation-data mining-interpretation and evaluation. This work expands the application of data mining techniques in power system. The analysis results show that the methods proposed are reliable, comprehensive and practical, and can provide directive significance and application value to enhance the quality, economy and security level of large scale power grid.
数据挖掘技术正是在“海量数据”和“有限知识”的矛盾背景下快速发展,近年来得到各行业学者的广泛重视。该技术综合运用智能算法、模式识别、数理统计等理论,能够发现隐含在大量数据中、先前未知的、对决策有潜在价值的规律和知识。电力系统运行需要满足安全、经济和优质性的要求,针对这三个基本要求,本文围绕运行信息(节点电压、有功功率)的数据挖掘进行了深入研究,完成了以下主要工作：
(1)将相空间重构(PSR, Phase Space Reconstruction)方法引入电能质量研究领域,考虑电压暂降、电压瞬升、电压中断、脉冲、谐波及闪变六类扰动。利用相空间重构在相平面构造不同扰动的信号轨迹,并将其归一化编码为二进制图像。针对轨迹图像,定义了最大邻近距离、载体分量相似度、扰动覆盖区域和平均幅度四项指标。分析和仿真结果表明,通过上述四项指标可以有效地提取不同扰动的特性,为电能质量分析提供了新思路。
(2)本文的第一个数据挖掘任务是针对电网运行的优质性要求。对于PSR提取出的特性矢量,进一步构建支持向量机分类器对电能质量扰动事件进行分类研究。分类对象包括短期扰动(暂降、瞬升)和长期扰动(谐波、闪变)两两叠加的电能质量问题以及相应的单一扰动,结果显示该方法可以有效识别不同的扰动类型,为改善电能质量措施提供依据。研究中同样设计了基于小波变换和人工神经网络的分类方案,通过对比实验显示了本文所提基于相空间重构和支持向量机的分类系统的优越性。
(3)电力用户负荷曲线的聚类是形成合理电价体系和实施负荷管理措施的基础。本文的第二个数据挖掘任务是基于自组织映射(SOM, Self-Organizing Map)神经网络进行低压终端用户的负荷曲线聚类研究。首先定义并提取功率曲线、分时功率、功率频谱三类向量,分别作为SOM神经网络的输入进行可视化聚类。采用相对量化误差和拓扑误差两个指标表征聚类质量,选取聚类结果最好的SOM输出层结合k-均值法进行用户负荷曲线划分。根据Davies指标将本文研究的杭州地区终端用户的131条日负荷曲线划分为八类,对每类曲线进行描述。最后进行新用户的识别,结果表明该基于SOM神经网络的聚类方法有效可靠,可以为提高电网运行的经济性提供有价值的知识。
(4)电力系统运行信息的第三个数据挖掘研究是建立电网安全性评估的决策树规则。研究对象包括美国西部WSCC三机九节点简化模型和浙江地区某实际电网模型。该研究有两个目标,首先通过决策树算法实现故障前电网稳态运行参数和电网承受故障能力间的映射,为调度人员提供辅助的快速安全评估规则；其次在有无电压相角参数下分别生成决策树,通过对比两者的性能,验证相角测量单元PMU的作用。研究结果表明,决策树算法对于电网安全状态可以达到96.5%的识别精度,此外PMU的合理配置可以提高系统的可观测性。
本文的研究工作按照数据准备—数据挖掘—解释评估的顺序展开讨论,研究内容为数据挖掘技术在电力系统的应用进行了新的拓展,分析结果表明,本文采用的方法可靠、全面和实用,对今后提高大电网运行的优质、经济和安全性具有指导意义和应用价值。
The amount of data sampled, accumulated and to be analyzed in power system is expanding drastically, along with the development of information technology and the progress of power grid digitalization. However, limited valuable knowledge can be extracted using inquiry and statistics operation in database or traditional data anlysis method. Therefore it is highly significant to discuss how to obtain hidden pattern and rules from large-scale, multi-dimensional data, in order to provide decision support to power system decision maker.
Data mining technique advanced significaly in the past few years with the contradiction between "mass data" and "limited knowledge", and drew attention from diverse researchers. This technique combines various theories such as intellectual algorithms, pattern recognition and mathematics statistics, and is able to discover the rules or knowledge embeded in tremendous data, prior unknown but valuable in decision making. Power system should operate reliably, economically and securely. For these three basic requirements, this thesis conducted intensive study in data mining of power system operation information (voltage and active power flow). The research work accomplished is shown as follows:
(1) Phase Space Reconstruction (PSR) method was introduced to power quality research field. The types of concerned disturbances included voltage sags, voltage swells, voltage interruptions, impulsive transients, harmonics and flickers. Based on this method, trajectories of disturbance signals were constructed in phase plane and converted to binary images after normalization and coding process. Four indices of trajectory image which are Maximum Adjacent Distance, Carrier Component Similarity, Overlay Area and Mean Amplitude were presented. Analysis and simulation results showed that by these four indices, features of different disturbances can be extracted effectively. The proposed approach provides a new idea for power quality analysis.
(2) The first data mining task in this thesis was conducted for the high-quality requirement of power grid operation. The extracted features using PSR method were utilized as inputs to the support vector machines (SVM) classifier to realize the automatic classification of power disturbances. The types of disturbances discussed included a combination of short-term disturbances (voltage sags, swells) and long-term disturbances (flickers, harmonics), as well as their homologous single ones. Numerical results showed that the method proposed can effectively classify different disturbance patterns and provide basis for power quality mitigation measures. In this research, Comparison studies based on Wavelet Transform (WT) and Artificial Neural Network (ANN) were also reported, to show the advantage of the classification system based on PSR and SVM algorithm.
(3) Load profile clustering of power customers is the basis to construct proper tariff system and apply load manage measures. The second data mining task was to study on load profile clustering of low-voltage terminal customers based on Self-Organizing Map (SOM) neural networks. First, three types of vectors which are power curve, time sharing power and power spectrum were defined, and then used as inputs of SOM neural networks to visualize clustering. Two indices, namely relative quantization error and topology error were introduced to evaluate clustering quality. We chose SOM output layer of best performance and allocate load profiles with k-means method. The 131 profiles concerned in this paper were allocated into eight clusters according to Davies index, and each group of profiles is described. Finally, the ability of SOM neural network to identify new customers was examined. The result showed that the method proposed is effective and reliable, and able to supply useful knowledge to enhance power grid operation economy aspect.
(4) The third research job for power system operation information was establishing decision tree rules for power grid security assessment. The studied models included Western Systems Coordinating Council (WSCC) three plants nine nodes simplified model and a realistic power grid model in Zhejiang Province. This case study had two goals. First, using knowledge database that covers all possible pre-fault operating conditions, decision rules in the form of hierarchical trees were developed for on-line assessment. Second, Phasor Measurement Units (PMU) were taken into consideration to better decision tree's performance. The results demonstrated that the proposed machine learning scheme was able to identify crucial security indicators and gave reliable security predictions to 96.5% accuracy. Furthermore, voltage phase angle difference that obtained by PMU was proved helpful in improving DT's identification accuracy.
The research contents are presented according to steps of data preparation-data mining-interpretation and evaluation. This work expands the application of data mining techniques in power system. The analysis results show that the methods proposed are reliable, comprehensive and practical, and can provide directive significance and application value to enhance the quality, economy and security level of large scale power grid.
引文
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