电能质量综合检测与录波数据压缩的研究
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摘要
现代电力系统的电网结构和负荷构成出现了新的变化趋势,随之带来的电能质量问题逐渐引起电力部门和用户的高度重视。电能质量监测是监督、改善电能质量的前提条件,对保证电力系统的安全经济运行与用户用电安全具有重要的理论与实际意义。现代电力系统运行与智能电网发展对电能质量监测技术提出了更高的要求。局限于稳态电能质量检测的传统仪器仪表已不再满足需要,暂态电能质量检测越来越成为关注的焦点,更多、更直观的关于电能质量的实时信息成为必须。而如何对海量测量数据进行有效地存储与传输成为电能质量监测技术发展过程中亟待解决的问题。本文针对目前电能质量监测技术中存在的问题展开了研究工作。
论文首先研究了稳态电能质量检测方法,通过测试各项稳态电能质量指标验证了方法的准确性。利用数学形态学实时性强的优点并根据各种暂态扰动波形的特征,提出了一种基于数学形态学的暂态电能质量检测方法,通过对常见暂态电能质量扰动信号进行定位与分类验证了方法的可行性。电能质量检测结果作为录波启动判据与电能质量监测系统的分析依据。
针对电能质量录波数据的存储与传输问题,本文借鉴图像压缩方法,提出了一种基于提升格式的2维离散小波变换、SPIHT编码与算术编码的数据压缩方法。通过对常见电能质量扰动数据进行压缩,证明了该方法具有良好的压缩性能且可通过控制压缩码率控制压缩比,有利于根据网络状况调节传输的数据量。
基于电能质量检测方法与录波数据压缩方法,本文设计了电能质量综合检测与数据管理系统,并详细阐述了系统设计与实现的过程。该系统以LabVIEW为开发平台,实现了稳态与暂态电能质量的综合检测、录波数据压缩、网络化、数据库访问等功能,以形象、直观的形式显示检测与分析结果,满足现代电能质量监测技术要求,具有较好的理论与实际意义。
There are new trends in the grid and load structure of modern power system, leading to various power quality problems which have aroused great attention of power departments and users. Power quality monitoring is a prerequisite for supervising and improving power quality, and a guarantee for power system’s safe and economic operation and users’security. Thus, it’s significant in both theory and practice. The operation of modern power system and the development of smart grid put forward higher requirement for power quality monitoring technology. Traditional instruments limited to steady-state power quality detection no longer meet the requirements, since dynamic quality detection becomes more and more concerned and more intuitive real-time information about power quality is necessary. How to solve the problem of saving and transmitting vast amounts of measurement data is urgent in the development process of power quality monitoring technology. To address problems existing in present power quality monitoring technology, researches are conducted in this paper.
Steady-state power quality detection algorithms are discussed in this paper, the accuracy of which is verified by measuring various power quality indices. Taking advantage of good real-time performance of mathematical morphology and according to waveform characteristics of various transient disturbances, an algorithm based on mathematical morphology is presented in this paper, the feasibility of which is verified by detecting usual dynamic quality disturbances. Power quality detection results function as data recording criterion and analysis basis in power quality monitoring system.
To handle the problem of storing and transmitting a large number of measurement data, a data compression method based on two-dimension discrete wavelet transform (2-D DWT) in lifting format, set partitioning in hierarchical tree (SPIHT) coding with arithmetical coding is presented in this paper. The high compression performance of which is verified by compressing several kinds of frequent power quality disturbance data. And the compression ratio can be regulated by changing the bit-rate, thus the amount of data can be adjusted to the network environment.
Based on the above power quality detection algorithms and data compression method, the power quality integrated detection and data management system is designed in this paper, and the design and implementation process are presented in detail. With LabVIEW as a development platform, the system has achieved functions of integrated detection of steady-state and dynamic quality, recorded data compression, network, database access and etc, and provided image and intuitive detection and analysis results. It’s significant both in theory and practice respects.
论文首先研究了稳态电能质量检测方法,通过测试各项稳态电能质量指标验证了方法的准确性。利用数学形态学实时性强的优点并根据各种暂态扰动波形的特征,提出了一种基于数学形态学的暂态电能质量检测方法,通过对常见暂态电能质量扰动信号进行定位与分类验证了方法的可行性。电能质量检测结果作为录波启动判据与电能质量监测系统的分析依据。
针对电能质量录波数据的存储与传输问题,本文借鉴图像压缩方法,提出了一种基于提升格式的2维离散小波变换、SPIHT编码与算术编码的数据压缩方法。通过对常见电能质量扰动数据进行压缩,证明了该方法具有良好的压缩性能且可通过控制压缩码率控制压缩比,有利于根据网络状况调节传输的数据量。
基于电能质量检测方法与录波数据压缩方法,本文设计了电能质量综合检测与数据管理系统,并详细阐述了系统设计与实现的过程。该系统以LabVIEW为开发平台,实现了稳态与暂态电能质量的综合检测、录波数据压缩、网络化、数据库访问等功能,以形象、直观的形式显示检测与分析结果,满足现代电能质量监测技术要求,具有较好的理论与实际意义。
There are new trends in the grid and load structure of modern power system, leading to various power quality problems which have aroused great attention of power departments and users. Power quality monitoring is a prerequisite for supervising and improving power quality, and a guarantee for power system’s safe and economic operation and users’security. Thus, it’s significant in both theory and practice. The operation of modern power system and the development of smart grid put forward higher requirement for power quality monitoring technology. Traditional instruments limited to steady-state power quality detection no longer meet the requirements, since dynamic quality detection becomes more and more concerned and more intuitive real-time information about power quality is necessary. How to solve the problem of saving and transmitting vast amounts of measurement data is urgent in the development process of power quality monitoring technology. To address problems existing in present power quality monitoring technology, researches are conducted in this paper.
Steady-state power quality detection algorithms are discussed in this paper, the accuracy of which is verified by measuring various power quality indices. Taking advantage of good real-time performance of mathematical morphology and according to waveform characteristics of various transient disturbances, an algorithm based on mathematical morphology is presented in this paper, the feasibility of which is verified by detecting usual dynamic quality disturbances. Power quality detection results function as data recording criterion and analysis basis in power quality monitoring system.
To handle the problem of storing and transmitting a large number of measurement data, a data compression method based on two-dimension discrete wavelet transform (2-D DWT) in lifting format, set partitioning in hierarchical tree (SPIHT) coding with arithmetical coding is presented in this paper. The high compression performance of which is verified by compressing several kinds of frequent power quality disturbance data. And the compression ratio can be regulated by changing the bit-rate, thus the amount of data can be adjusted to the network environment.
Based on the above power quality detection algorithms and data compression method, the power quality integrated detection and data management system is designed in this paper, and the design and implementation process are presented in detail. With LabVIEW as a development platform, the system has achieved functions of integrated detection of steady-state and dynamic quality, recorded data compression, network, database access and etc, and provided image and intuitive detection and analysis results. It’s significant both in theory and practice respects.
引文
[1]肖湘宁.电能质量分析与控制[M].北京:中国电力出版社,2004.
[2]程浩忠,艾芊.电能质量[M].北京:清华大学出版社,2006
[3]雍静.供配电系统[M].北京:机械工业出版社,2003.
[4]周林,张凤,栗秋华.配电网中谐波源定位方法综述[J].高电压技术,2007,33(5):103-108.
[5]李勇,罗隆福,贺达江等.新型直流输电系统典型谐波分布特性分析[J].电力系统自动化,2009,33(10):59-63.
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[9]卢艳霞,张秀敏,蒲孝文.电动汽车充电站谐波分析[J].电力系统及其自动化学报,2006,18(3):51-54.
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[14]刘定安,肖先勇,邓武军.基于离散余弦变换和小波变换的电能质量扰动信号检测方法[J].电网技术,2005,29(10):70-74.
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[29] Phang Yoke Yin, Chilukuri M.V. Remote power quality monitoring and analysis system using LabVIEW software[C]. IMTC, 2009: 279-283.
[30] Lee R.P.K, Lai L.L, Tse N. A web-based multi-channel power quality monitoring system for a large network[J]. The 5th International Conference on Power System Management and Control, 2002: 112-117.
[31]孙晓明,李绍生,赵桂平.基于虚拟仪器的电能质量综合监测系统的开发与研究[J].电测与仪表,2006,43(483):27-30.
[32]林涛,刘林,曹健等.智能电网技术框架下的电能质量监测与分析技术综述[J].低压电器,2009(21):1-6, 33.
[33]李振杰,袁越.智能微网——未来智能配电网新的组织形式[J].电力系统自动化,2009,33(17):42-48.
[34]施婕,艾芊.智能电网实现的若干关键技术问题研究[J].电力系统保护与控制,2009,37(19):1-4, 55.
[35]陈平,徐丙垠,葛耀中等.离散小波变换用于输电线路故障暂态行波信息压缩[J].电力系统自动化,2000,24(4):31-35.
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[37]郭彬彬,黄纯.基于小波包变换的电能质量扰动数据压缩[J].电力自动化设备,2005,25(11):34-37.
[38] Hsieh C, Huang S. Disturbance data compression of a power system using the huffman coding approach with wavelet transform enhancement[J]. IEE Proceedings on Generation, Transmission and Distribution, 2003, 150(1): 7-14.
[39]王永玉,孙衢,袁超伟.基于整数小波变换和SPIHT编码的录波数据压缩算法[J].继电器,2008,36(11):50-54.
[40]高培生,陈新建,吴为麟等.二维表示的电能质量故障数据压缩[J].浙江大学学报(工学版),2008,42(4):686-690.
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[2]程浩忠,艾芊.电能质量[M].北京:清华大学出版社,2006
[3]雍静.供配电系统[M].北京:机械工业出版社,2003.
[4]周林,张凤,栗秋华.配电网中谐波源定位方法综述[J].高电压技术,2007,33(5):103-108.
[5]李勇,罗隆福,贺达江等.新型直流输电系统典型谐波分布特性分析[J].电力系统自动化,2009,33(10):59-63.
[6]王育飞,姜建国.用于电能质量研究的新型交流电弧炉混沌模型[J].中国电机工程学报,2008,28(10):106-110.
[7]王建,李兴源,邱晓燕.含有分布式发电装置的电力系统研究综述[J].电力系统自动化,2005,29(24):90-97.
[8] Huang Wei, Zhang Jiahua, Xie Qinghua, Wu Ziping. The impact on power quality by PWM converter in micro-grid[C]. ICSET, 2008: 239-243.
[9]卢艳霞,张秀敏,蒲孝文.电动汽车充电站谐波分析[J].电力系统及其自动化学报,2006,18(3):51-54.
[10]林海雪.现代电能质量的基本问题[J].电网技术,2001,25(10):5-12.
[11]粟时平,刘桂英.现代电能质量检测技术[J].北京:中国电力出版社,2008.
[12] Santoso S, Power E.J, Grady W. M. Electric power quality disturbance detection using wavelet transform analysis[C]. The IEEE SP International Symposium on Time-frequency and Time-scale Analysis, 1994: 166-169.
[13]胡铭,陈珩.基于小波变换模极大值的电能质量扰动检测与定位[J].电网技术,2001,25(3):12-16.
[14]刘定安,肖先勇,邓武军.基于离散余弦变换和小波变换的电能质量扰动信号检测方法[J].电网技术,2005,29(10):70-74.
[15] Gaouda A.M, Salama M.M.A, Sultan M.R, Chikhani A.Y. Power quality detection and classification using wavelets-multiresolution signal decomposition[J]. IEEE Trans on Power Delivery, 1999, 14(4): 1459-1476.
[16] Morsi W.G, El-Hawary M.E. Wavelet packet transform-based power quality indices for balanced and unbalanced three-phase systems under stationary or nonstationary operating conditions[J]. IEEE Transactions on Power Delivery, 2009, 24(4): 2300-2310.
[17] Wijayakulasooriya J.V, Putrus G.A, Minns P.D. Electric power quality disturbance classification using self-adapting artificial neural networks[J]. IEE Proceedings on Generation, Transmissionand Distribution, 2002, 149(1): 98-101.
[18] Whei-Min Lin, Chien-Hsien Wu, Chia-Hung Lin, Fu-Seng Cheng. Detection and classification of multiple power quality disturbances with wavelet multicalss SVM[J]. IEEE Transactions on Power Delivery, 2008, 23(4): 2575-2582.
[19] P. Janik, T. Lobos. Automated classification of power quality disturbances using SVM and RBF networks[J]. IEEE Transactions on Power Delivery, 2006, 21(3): 1663-1669.
[20] Chia-Hung Lin, Chia-Hao Wang. Adaptive wavelet networks for power quality detection and discrimination in a power system[J]. IEEE Transactions on Power Delivery, 2006, 21(3): 1106-1113.
[21] Kang Shanling, Song Yuhai, Kang Yuzhe. Application of adaptive wavelet network for power qualtiy disturbance recognition and analysis[C]. ICEMI, 2007: 3-438 - 3-441.
[22]凌玲,徐政.基于数学形态学的动态电能质量扰动的检测与分类方法[J].电网技术,2006,30(5):62-66.
[23] Tomas Radil, Pedro M. Ramos, Fernando M. Janeiro, A. Cruz Serra. PQ monitoring system for real-time detection and classification of disturbances in a single-phase power system[J]. IEEE Trans on Instrumentation and Measurement, 2008, 57(8): 1725-1733.
[24] Yong Huang, Yongqiang Liu, Zhiping Hong. Detection and location of power quality disturbances based on mathematical morphology and hilbert-huang transform[C]. ICEMI, 2009, 2-319 - 2-324.
[25]王丽霞,何正友,赵静.基于数学形态学的电能质量扰动检测和定位[J].电网技术,2008,32(10):63-68, 88.
[26] Lakshmikanth A, Morcos M.M. A power quailty monitoring system: a case study in DSP-based solutions for power electronics[J]. IEEE Transactions on Instrumentaion and Measurement, 2001, 50(3): 724-731.
[27] Yang G..H, Wen B.Y. A device for power quality monitoring based on ARM and DSP[C]. ICIEA, 2006:1-5.
[28]林广明,黄义锋,欧阳森等.基于DSP和CPLD电能质量监测装置的设计[J].电力系统保护与控制,2009,37(18):97-101.
[29] Phang Yoke Yin, Chilukuri M.V. Remote power quality monitoring and analysis system using LabVIEW software[C]. IMTC, 2009: 279-283.
[30] Lee R.P.K, Lai L.L, Tse N. A web-based multi-channel power quality monitoring system for a large network[J]. The 5th International Conference on Power System Management and Control, 2002: 112-117.
[31]孙晓明,李绍生,赵桂平.基于虚拟仪器的电能质量综合监测系统的开发与研究[J].电测与仪表,2006,43(483):27-30.
[32]林涛,刘林,曹健等.智能电网技术框架下的电能质量监测与分析技术综述[J].低压电器,2009(21):1-6, 33.
[33]李振杰,袁越.智能微网——未来智能配电网新的组织形式[J].电力系统自动化,2009,33(17):42-48.
[34]施婕,艾芊.智能电网实现的若干关键技术问题研究[J].电力系统保护与控制,2009,37(19):1-4, 55.
[35]陈平,徐丙垠,葛耀中等.离散小波变换用于输电线路故障暂态行波信息压缩[J].电力系统自动化,2000,24(4):31-35.
[36]剧高峰,罗安.离散小波变换用于电能质量扰动数据实时压缩[J].电力系统自动化,2002,26(19):61-63.
[37]郭彬彬,黄纯.基于小波包变换的电能质量扰动数据压缩[J].电力自动化设备,2005,25(11):34-37.
[38] Hsieh C, Huang S. Disturbance data compression of a power system using the huffman coding approach with wavelet transform enhancement[J]. IEE Proceedings on Generation, Transmission and Distribution, 2003, 150(1): 7-14.
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