基于网格计算的电网智能化安全评估研究
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摘要
智能化安全评估是统一坚强电网建设的关键内容,是互联输电网的神经中枢,是维系电力生产过程的基础,是保障电网运行和发展的重要手段。随着广域量测系统和网格计算技术在我国电力系统中的广泛应用,使得利用系统广域信息和网格计算技术开展智能化安全评估系统研究与设计工作成为可能。
针对电力系统静态稳定运行的特点,提出了基于支持向量机的智能化电压稳定评估模型。在电压稳定评估方面,采用基于SVM分类机的电压稳定模型,将电压稳定评估处理成二分类问题,并充分利用广域测量信息以捕捉多种典型系统特征的学习样本。在电压稳定预测方面,采用基于SVM回归机的电压预测模型,根据电压相量的幅值、相角的历史数据预测出未来时刻电压的相量信息,并根据这些相量信息得到反映电压稳定的裕度k。
针对电力系统运行方式复杂、时变的特点,提出了智能化暂态稳定评估与控制模型。对于系统的详细模型,提出了基于在线学习的暂态稳定预测模型,通过LWPR在线学习算法对当前的运行方式和预想事故进行仿真,评估系统的稳定性,同时采用了CCCOI-RM变换,以提高计算速度以满足在线暂稳预测的需要。对于系统的简化模型,提出了基于抗原能量函数的暂态稳定评估模型,利用抗原能量函数对系统的加速能量和减速能量进行区分以获得临界能量估计值,并使用基于抗原能量函数裕度分析策略来指导快关汽门等控制措施维持系统稳定。
针对上海电网的实际特点,开发了基于网格计算的安全评估体系。系统的设计本着低成本、可靠性、适应性的原则,利用虚拟化的环境构架为其在电力系统中的应用提供了性能优化和竞争优势;利用Globus和SHPG的技术融合使电力系统网格技术构架更加灵活、可靠;利用代理服务模式和分区模版、可伸缩性设计使电力网格的可扩展性和适应性更加强大。上海电网的仿真结果显示:基于网格计算的安全评估系统,能够充分利用现有资源,使批量计算任务达到线性加速比,计算结果准确可靠,为电网运行方式人员提供强有力的工具。
As the nerve center of interconnection grid, smart security assessment is a unified key element of a strong power grid, which is the basis for the maintenance of power production process and the way to protect power grid operation and development. With the wide-area measurement systems and grid computing technology in China’s power system widely used, making use of these imformation for intelligent security assessment system research and design work possible.
For the characteristics of power system static stable, a smart voltage stability assessment and prediction model is proposed using support vector machine. In the voltage stability assessment, classification machine is used, and it makes full use of wide area measurement system information in order to capture the typical characteristics of a variety of learning samples. In the voltage stability prediction, regression machine is used, which predicts the phasor and amplitude of voltage, and then it is used to find the stability margin k.
For the complex, time-varying characteristics of power system, a smart model for transient stability assessment and control is proposed. A transient stability prediction model of on-line learning is used for a detailed system, which assesses the stability of the system through LWPR online learning algorithm. It also uses CCCOI-RM tramsform to improve the computing speed to meet the on-line transient stability predicted needs. Another transient stability assessment model for simplified model of the system is proposed based on antigen energy function. Using of antigen energy function to distintinct between acceleration energy and deceleration energy to obtain the critical energy estimateds, and then to guide the fast-valving and other control measures to maintain system stability.
For the actual characteristics of Shanghai power grid, a security assessment system based on grid computing is developed. Excepte the feature of low-cost, reliability and adaptability, the use of virtual environment framework provides a performance optimization and competitive advantage. The integration of Globus and SHPG makes the grid power system technical architecture more flexible and reliable. The design of proxy service models, geographical templates and scalable grid group so that the scalability and adaptablility of power grid more powerful. The simulation results of Shanghai power grid show that: based on grid comuputing for dynamic security assessment system can make full use of existing resoutces to achieve linear speedup and the calculation results are accurate and reliable, which proveds a powerful tool.
针对电力系统静态稳定运行的特点,提出了基于支持向量机的智能化电压稳定评估模型。在电压稳定评估方面,采用基于SVM分类机的电压稳定模型,将电压稳定评估处理成二分类问题,并充分利用广域测量信息以捕捉多种典型系统特征的学习样本。在电压稳定预测方面,采用基于SVM回归机的电压预测模型,根据电压相量的幅值、相角的历史数据预测出未来时刻电压的相量信息,并根据这些相量信息得到反映电压稳定的裕度k。
针对电力系统运行方式复杂、时变的特点,提出了智能化暂态稳定评估与控制模型。对于系统的详细模型,提出了基于在线学习的暂态稳定预测模型,通过LWPR在线学习算法对当前的运行方式和预想事故进行仿真,评估系统的稳定性,同时采用了CCCOI-RM变换,以提高计算速度以满足在线暂稳预测的需要。对于系统的简化模型,提出了基于抗原能量函数的暂态稳定评估模型,利用抗原能量函数对系统的加速能量和减速能量进行区分以获得临界能量估计值,并使用基于抗原能量函数裕度分析策略来指导快关汽门等控制措施维持系统稳定。
针对上海电网的实际特点,开发了基于网格计算的安全评估体系。系统的设计本着低成本、可靠性、适应性的原则,利用虚拟化的环境构架为其在电力系统中的应用提供了性能优化和竞争优势;利用Globus和SHPG的技术融合使电力系统网格技术构架更加灵活、可靠;利用代理服务模式和分区模版、可伸缩性设计使电力网格的可扩展性和适应性更加强大。上海电网的仿真结果显示:基于网格计算的安全评估系统,能够充分利用现有资源,使批量计算任务达到线性加速比,计算结果准确可靠,为电网运行方式人员提供强有力的工具。
As the nerve center of interconnection grid, smart security assessment is a unified key element of a strong power grid, which is the basis for the maintenance of power production process and the way to protect power grid operation and development. With the wide-area measurement systems and grid computing technology in China’s power system widely used, making use of these imformation for intelligent security assessment system research and design work possible.
For the characteristics of power system static stable, a smart voltage stability assessment and prediction model is proposed using support vector machine. In the voltage stability assessment, classification machine is used, and it makes full use of wide area measurement system information in order to capture the typical characteristics of a variety of learning samples. In the voltage stability prediction, regression machine is used, which predicts the phasor and amplitude of voltage, and then it is used to find the stability margin k.
For the complex, time-varying characteristics of power system, a smart model for transient stability assessment and control is proposed. A transient stability prediction model of on-line learning is used for a detailed system, which assesses the stability of the system through LWPR online learning algorithm. It also uses CCCOI-RM tramsform to improve the computing speed to meet the on-line transient stability predicted needs. Another transient stability assessment model for simplified model of the system is proposed based on antigen energy function. Using of antigen energy function to distintinct between acceleration energy and deceleration energy to obtain the critical energy estimateds, and then to guide the fast-valving and other control measures to maintain system stability.
For the actual characteristics of Shanghai power grid, a security assessment system based on grid computing is developed. Excepte the feature of low-cost, reliability and adaptability, the use of virtual environment framework provides a performance optimization and competitive advantage. The integration of Globus and SHPG makes the grid power system technical architecture more flexible and reliable. The design of proxy service models, geographical templates and scalable grid group so that the scalability and adaptablility of power grid more powerful. The simulation results of Shanghai power grid show that: based on grid comuputing for dynamic security assessment system can make full use of existing resoutces to achieve linear speedup and the calculation results are accurate and reliable, which proveds a powerful tool.
引文
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[2]张伯明,孙宏斌,吴文传,郭庆来.智能电网控制中心技术的未来发展[J]. 2009,33(17):21-28.
[3]常康,薛峰,杨卫东.中国智能电网基本特征及其技术进展评述[J]. 2009,33(17):10-15.
[4]王琼.网格计算综述[J ] .洛阳大学学报,2003 ,18 (2)
[5]刘鹏,网格应用研究现状.清华大学计算机系高性能所网格研究组.
[6] Gabrielle Allen, Thomas Dramlitsch Ian Foster Nicholas T. Karonis Mtei Ripeanu Edward Seidel Brian Toonen. Supporting Efficient Execution in Heterogeneous Distributed Computing Environments with Cactus and Globus.
[7]中国国家网格http://www.cngrid.org
[8] Globus http://www.Globus.org
[9] Condor-G http://www.cs.wisc.edu/Condor/Condorg/
[10] GridGain http://www.gridgain.com
[11]边江,杜杰,翁楚良等.一个面向服务的电力网格应用平台[J].华中科技大学学报(自然科学版),2007,35(2):79-81.
[12]刘劲风,王述洋.WAMS在电力系统分析和控制中应用的新进展[J].高电压技术,2007,33(7): 182-184.
[13]胡益胜,张明.广域测量系统在电力系统中的应用[J].电工技术,2007,20(5): 19-21 .
[14]许树楷,谢小荣,辛耀中.基于同步相量测量技术的广域测量系统应用现状及发展前景[J].电网技术.2005,29(2):44一49
[15]白茂金,陈刚,刘青等.一种基于LS-SVR的电网在线暂态稳定性预测新方法[J].电力系统保护与控制.2008,36(19):9-14.
[16]宋方方,毕天姝,杨奇逊.基于WAMS的电力系统受扰轨迹预测[J].电力系统自动化.2006,30(23):27-32
[17]刘新东,江全元,曹一家.基于功角受扰轨迹拟合的暂态稳定快速预测[J].电力系统自动化.2008,32(19):5-9.
[18]李大虎,曹一家.基于同步相量测量的实时电压稳定分析方法[J].电力系统自动化,2006,30(12):17～22.
[19]段俊东,孙彦楷,尹秀刚.广域测量系统用于电压稳定在线预测[J].高电压技术.2009,35(7):1748-1752.
[20] Kamwa I, Grondin R, Hebert Y. Wide-area measurement based stabilizing control of large power systems– a cecentralized/hierarchical approach. IEEE Transactions on Power Delivery, 2001, 16(1):136-153.
[21]边肇祺,张学工等.模式识别[M].北京:清华大学出版社,2000
[22]徐泰山,薛禹胜,韩祯样.关于电力系统电压稳定性分析方法的综述[J].电力系统自动化,1996,20(5):62-67.
[23] BJeyasurya. Artificial neural network for on-line voltage stability assessment[C]. IEEE Power Engineering Soeiety Summer Meeting,2000:2014-2018
[24]付英,钟德成.自组织映射神经网络用于动态电压稳定分析的新方法[J].电力系统自动化,2000,24(1):27-31
[25]许涛,贺仁睦,王鹏等.基于统计学习理论的电力系统暂态稳定评估[J].中国电机工程学报,2003,23(11):51-55.
[26] V.N.Vapnik.统计学习理论[M].许建华,张学工译.北京:电子工业出版社,2004
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