独立光伏系统控制器的研究
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摘要
在化石能源日益短缺的今天,不可再生能源——太阳能逐渐在能源消费市场上占据了一定的地位,而且太阳能的使用也越来越普遍。因此对于光伏系统的相关研究也一直是不可再生能源领域的一个重要研究课题。目前,由于对太阳能的利用还不是非常普遍,造成了民用光伏电池的价格偏高,而且电池的转换效率还比较低,只有15%~30%,因此光伏系统的性价比不高。一般而言,在光伏发电系统中,光伏电池占整个系统总成本的57%,蓄电池占30%,最大功率控制器占7%,其它占6%。以上数据表明,为了提高光伏系统的性价比,比较合理的方式是研发使系统转换效率更高的控制器。因此具有最大功率跟踪功能(MPPT)的光伏控制器就是本文所研究的重点内容。
本文重点研究了三款不同原理、不同性能的光伏控制器,其光伏系统转换效率是由低到高,在商业上也能满足不同消费层次和不同地域的需求。
第一款控制器是在古典控制器的基础上增加了蓄电池的温度补偿功能,其主要特点是能够在不同温度下安全地用光伏电池为蓄电池充电,不必考虑蓄电池会因过度充电而损害的问题。
第二款控制器是在使用传统电量增量算法的基础上提出了一种新算法:二阶电导增量比较法。此新算法除了继承传统算法快速反应的能力外,还具有跟踪时间快,精度高的特点。
第三款控制器与前一款控制器在原理上具有本质的不同,前一款在控制上属于开环控制系统,而这一款属于闭环控制系统。此款控制器借鉴了人工智能的原理,采用了径向基神经网络算法能够自动识别光伏电池的最大功率输出点,具有反应更快、跟踪时间更短、精度更高的特点,因此将大大增强光伏系统的适用区域。
For the growing shortage of fossil fuels at the present day, the non-renewable energy -solar energy gradually occupies a certain position in the energy consumption market, and the use of solar energy is becoming more and more common. Thence, the studies of photovoltaic system are always the significant field of non-renewable energy research. At present, due to the use of solar energy is not very common, that results in high price of photovoltaic cells in civilian, and cell conversion efficiency is still relatively low, only up to from 15% to 30%, so cost-effective of photovoltaic system is not high. Generally speaking, in the photovoltaic power generation system, PV cells account for the total cost of the entire system of 57%, 30% for battery, the maximum power controller takes 7% and 6% for others. The above data show that, in order to improve the cost-effective of photovoltaic system, a more reasonable approach is the study of a higher conversion efficiency controller. Therefore the photovoltaic controller with maximum power point tracking (MPPT) is the focus of the study in this paper.
This article focuses on researching photovoltaic controller with three different principles and different properties, and its efficiency of photoelectric conversion is from low to high, it can meet the needs of different geographical levels and consumer of different regions in the business.
The first controller is based on the classic controller to increase the function of battery temperature compensation, and its main characteristic is that can be safely used charging from photovoltaic cells to battery at different temperatures, and no need to consider the damage due to excessive charging of the battery.
For the second controller, this paper proposes a new algorithm- two-order Incremental Conductance Comparative method, which is based on the use of traditional electricity incremental algorithm. This algorithm not only inherits the rapid reaction capacity of the traditional algorithm, but also has the characteristics of the low tracking time and high precision.
The third controller is essentially different from the second one on principle. The second controller belongs to open-loop control system in control, but the third one is a closed-loop control system. This controller applies the principles of artificial intelligence, using a neural network algorithm of radial basis function to automatically identify the PV cells at the maximum power output, and with the characteristics of a faster response, a shorter tracking time, the higher accuracy. Therefore, it will magnify the application region of photovoltaic system.
本文重点研究了三款不同原理、不同性能的光伏控制器,其光伏系统转换效率是由低到高,在商业上也能满足不同消费层次和不同地域的需求。
第一款控制器是在古典控制器的基础上增加了蓄电池的温度补偿功能,其主要特点是能够在不同温度下安全地用光伏电池为蓄电池充电,不必考虑蓄电池会因过度充电而损害的问题。
第二款控制器是在使用传统电量增量算法的基础上提出了一种新算法:二阶电导增量比较法。此新算法除了继承传统算法快速反应的能力外,还具有跟踪时间快,精度高的特点。
第三款控制器与前一款控制器在原理上具有本质的不同,前一款在控制上属于开环控制系统,而这一款属于闭环控制系统。此款控制器借鉴了人工智能的原理,采用了径向基神经网络算法能够自动识别光伏电池的最大功率输出点,具有反应更快、跟踪时间更短、精度更高的特点,因此将大大增强光伏系统的适用区域。
For the growing shortage of fossil fuels at the present day, the non-renewable energy -solar energy gradually occupies a certain position in the energy consumption market, and the use of solar energy is becoming more and more common. Thence, the studies of photovoltaic system are always the significant field of non-renewable energy research. At present, due to the use of solar energy is not very common, that results in high price of photovoltaic cells in civilian, and cell conversion efficiency is still relatively low, only up to from 15% to 30%, so cost-effective of photovoltaic system is not high. Generally speaking, in the photovoltaic power generation system, PV cells account for the total cost of the entire system of 57%, 30% for battery, the maximum power controller takes 7% and 6% for others. The above data show that, in order to improve the cost-effective of photovoltaic system, a more reasonable approach is the study of a higher conversion efficiency controller. Therefore the photovoltaic controller with maximum power point tracking (MPPT) is the focus of the study in this paper.
This article focuses on researching photovoltaic controller with three different principles and different properties, and its efficiency of photoelectric conversion is from low to high, it can meet the needs of different geographical levels and consumer of different regions in the business.
The first controller is based on the classic controller to increase the function of battery temperature compensation, and its main characteristic is that can be safely used charging from photovoltaic cells to battery at different temperatures, and no need to consider the damage due to excessive charging of the battery.
For the second controller, this paper proposes a new algorithm- two-order Incremental Conductance Comparative method, which is based on the use of traditional electricity incremental algorithm. This algorithm not only inherits the rapid reaction capacity of the traditional algorithm, but also has the characteristics of the low tracking time and high precision.
The third controller is essentially different from the second one on principle. The second controller belongs to open-loop control system in control, but the third one is a closed-loop control system. This controller applies the principles of artificial intelligence, using a neural network algorithm of radial basis function to automatically identify the PV cells at the maximum power output, and with the characteristics of a faster response, a shorter tracking time, the higher accuracy. Therefore, it will magnify the application region of photovoltaic system.
引文
[1]钱金法,王雅萍,钱惠祥.光伏产业的发展与分析.电工电气,2009(1): 1-4.
[2]王慧,邵竹锋.太阳能电池概述.综述,2008(6):67-69.
[3]张耀明.中国太阳能光伏发电产业的现状与前景.新能源与新材料,2007(1):1-6.
[4] Dong Yun Lee, Hyeong Ju Noh, Dong Seok Hyun, Ick Choy. An improved MPPT converter using current compensation method for small scaled PV-applications. Applied Power Electronics Conference and Exposition, Eighteenth Annual IEEE, 2003, 1: 540-545.
[5] Omole, Adedamola O. Analysis, Modeling and Simulation Of Optimal Power Tracking of Multiple-Modules of Paralleled Solar Cell Systems.
[6] Jaboori MG, Saied M, Hanafy A A R. A Contribution to the Simulation and Design Optimization of Photovoltaic Systems, IEEE Trans. on Energy Conversion, 1991, 6(3): 401-406.
[7] A Al-Amoudi, L Zhang. Application of radial basis function networks for solar-array modelling and maximum power-point prediction, IEE power-generation, transmission and distribution, 2000, 147(5).
[8] Swiegers, W, Enslin, J H R. An Integrated Maximum Power Point Tracker for Photovoltaic Panels. Proc. IEEE Int. Symp. on Industrial Electronics, Pretoria, South Af-rica,1998:40-44.
[9] C Hua, C Shen. Comparative Study of Peak Power Tracking Techniques for Solar Storage System. 1998 IEEE Applied Power Electronics Conference and Exposition Proceedings, 1998,2: 679-683.
[10] K H Hussein, Muta, T Hoshino, M Osakada. Maximum Photovoltaic Power Tracking: An Algorithm for Rapidly Changing Atmospheric Conditions. IEEE Proceedings on Generation, transmission and Distribution. 1995, 142(1): 59-64.
[11] Patcharaprakiti, N, Premrudeepreechacharn, S, Sriuthaisiri-wong, Y. Maximum power point tracking using adaptive fuzzy logic control for grid-connected photo-voltaic system. Renewable Energy, 2005, 30(11): 1771-1788.
[12] Takashi Hiyama, Ken Kitabayashi. Neural Network Based Estimation of Maximum Power Generation from PV Module Using Environmental Information. IEEE Transactions on Energy Conversion, 1997, 12(3).
[13]刘辉,吴麟章,江小涛,等.太阳能最大功率跟踪技术研究.武汉科技学院学报,2005,18(8):12- 15.
[14] Bose BK, Szczesny PM, Steigerwald RL. Microcomputer control of a residential photovolatic ower conditioning system. IEEE Trans Ind Appl, 1985, 21(5): 1182-1191.
[15] Huynh P, Cho BH. Design and analysis of microprocessor controlled peak power tracking system, Proceedings of the 27th IECEC, 1992, 1: 67-72.
[16] Yongji H, Deheng L. A new method for optimal output of a solar cell array. Proceeding of the IEEE International Symposium on Industrial Electronics, 1992, 1: 456-459.
[17] Salameh Z, Dagher F, Lynch WA. Step-down maximum power point tracker for photovoltaic system. Sol Energy, 1991, 46(5): 279-282.
[18]侯聪玲.太阳能最大功率跟踪技术研究.现代商贸工业,2008,20(3):285-286.
[19] Alghuwainem SM. Matching of a DC motor to a photovoltais generator using a step-up converter with a current-locked loop. IEEE Trans Energy Conver, 1994, 9(1):192-198.
[20] Chiang SJ, Chang KT, Yen CY. Residential photovoltaic energy storage system. IEEE Trans Ind Electron, 1998, 45(3): 385–394.
[21] Middlebrook RD, Cuk SA. General unified approach to modeling switching-converter power stages. IEEE Power Electronics Specialists Conference. 1976: 18-34.
[22]杨彦军,陆正元,姚菊莲,田建锋.利用径向基函数改进算法实现函数逼近.桂林工学院学报,2006,26(1):136-139.
[23]王长贵,王斯成.太阳能光伏发电实用技术.北京:化学工业出版社,2005:64-65.
[24]崔岩,李大勇,胡宏勋.光伏充电控制器温度补偿研究.太阳能学报,2004,25(1):92-94.
[25]崔岩.太阳能光伏电源控制器弱电控制线路的进一步研究.太阳能学报,2000,21(4):461-464.
[26] James P.Dunlop,P.E. Batteries and Charge Control in Stand-Alone Photovoltaic Systems Fundamentals and Application. http://www.localenergy.org/pdfs/Document%20Library/Fundamentals%20of%20batteries%20and%20charge%20control.pdf.
[27] N Vela,J Aguilera. Charavterisation of Charge Voltage of Lead-Acid Batteries:Application to the Charge Control Strategy in Photovoltaic Systems. Progress in Photo Voltaics, 2006:721-732.
[28]王晓晶,班群.硅太阳电池材料的研究进展.能源工程,2002,04:28-31.
[29]赵可斌,陈国雄.电子电力变流技术.上海交通出版社,1993:72-86.
[30] Xiao, W, Dunford, W G, Palmer, P R, Capel, A. Regulation of photovoltaic voltage. IEEE Trans Ind Electron, 2007, 54(3): 1365-1374.
[31] Xiao W, Ozog N, Dunford, W G. Topology study of photovoltaic interface for maximum power point tracking. IEEE Trans Ind Electron, 2007 54(3):1696-1704.
[32] Samangkool K, Premrudeepreechacharn S. Maximum Power Point Tracking Using Neural Networks for Grid-Connected Photovoltaic System. Future Power System, 2005: 1-4.
[33] Mukerjee, A K, Dasgupta, N. DC power supply used as photovoltaic simulator for testing MPPT algorithms. Renewable Energy. 2007, 32(4):587-592.
[34] H Ravishankar Kamath, Aditya Kumar, Devi Prasad Mishra. Application of Radial Basis Function for MPPT. PROCEEDINGS OF INTERNATIONAL CONFERENCE ON ENERGY AND ENVIRONMENT, 2009: 880-884.
[35] Aberbour M, Mehrez H, Maximum Architecture and design methodology of the RBF-DDA neural network. Circuits and Systems, 1998, 3: 199-202.
[36] H.Ravishankar Kamath, R S Aithal, P K.Singh Ashis Kumar, Sinha, Atit R Danak. modeling of photovoltaic array and maximum power point tracker using ann, http://journal.esrgroups.org/jes/papers/4_3_4.pdf.
[37] Chun hua LI, Xin jian ZHU, Guang yi CAO, Wan qi HU, Sheng SUI, Ming-ruo HU. A maximum power point tracker for photovoltaic energy systems based on fuzzy neural networks. Zhejiang Univ Sci A, 2009, 10(2): 263-270.
[38]宫亚琼,径向基函数神经网络在过程建模中的研究与应用,北京化工大学,硕士:21-22.
[39] Chuan Yu Chang and Shih Yu Fu. Image Classification using a Module RBF Neural Network, Proceedings of the First International Conference on Innovative Computing, Information and Control. 2006.
[40]张建辉,K-means聚类算法研究及应用,武汉理工大学,硕士:34-35.
[2]王慧,邵竹锋.太阳能电池概述.综述,2008(6):67-69.
[3]张耀明.中国太阳能光伏发电产业的现状与前景.新能源与新材料,2007(1):1-6.
[4] Dong Yun Lee, Hyeong Ju Noh, Dong Seok Hyun, Ick Choy. An improved MPPT converter using current compensation method for small scaled PV-applications. Applied Power Electronics Conference and Exposition, Eighteenth Annual IEEE, 2003, 1: 540-545.
[5] Omole, Adedamola O. Analysis, Modeling and Simulation Of Optimal Power Tracking of Multiple-Modules of Paralleled Solar Cell Systems.
[6] Jaboori MG, Saied M, Hanafy A A R. A Contribution to the Simulation and Design Optimization of Photovoltaic Systems, IEEE Trans. on Energy Conversion, 1991, 6(3): 401-406.
[7] A Al-Amoudi, L Zhang. Application of radial basis function networks for solar-array modelling and maximum power-point prediction, IEE power-generation, transmission and distribution, 2000, 147(5).
[8] Swiegers, W, Enslin, J H R. An Integrated Maximum Power Point Tracker for Photovoltaic Panels. Proc. IEEE Int. Symp. on Industrial Electronics, Pretoria, South Af-rica,1998:40-44.
[9] C Hua, C Shen. Comparative Study of Peak Power Tracking Techniques for Solar Storage System. 1998 IEEE Applied Power Electronics Conference and Exposition Proceedings, 1998,2: 679-683.
[10] K H Hussein, Muta, T Hoshino, M Osakada. Maximum Photovoltaic Power Tracking: An Algorithm for Rapidly Changing Atmospheric Conditions. IEEE Proceedings on Generation, transmission and Distribution. 1995, 142(1): 59-64.
[11] Patcharaprakiti, N, Premrudeepreechacharn, S, Sriuthaisiri-wong, Y. Maximum power point tracking using adaptive fuzzy logic control for grid-connected photo-voltaic system. Renewable Energy, 2005, 30(11): 1771-1788.
[12] Takashi Hiyama, Ken Kitabayashi. Neural Network Based Estimation of Maximum Power Generation from PV Module Using Environmental Information. IEEE Transactions on Energy Conversion, 1997, 12(3).
[13]刘辉,吴麟章,江小涛,等.太阳能最大功率跟踪技术研究.武汉科技学院学报,2005,18(8):12- 15.
[14] Bose BK, Szczesny PM, Steigerwald RL. Microcomputer control of a residential photovolatic ower conditioning system. IEEE Trans Ind Appl, 1985, 21(5): 1182-1191.
[15] Huynh P, Cho BH. Design and analysis of microprocessor controlled peak power tracking system, Proceedings of the 27th IECEC, 1992, 1: 67-72.
[16] Yongji H, Deheng L. A new method for optimal output of a solar cell array. Proceeding of the IEEE International Symposium on Industrial Electronics, 1992, 1: 456-459.
[17] Salameh Z, Dagher F, Lynch WA. Step-down maximum power point tracker for photovoltaic system. Sol Energy, 1991, 46(5): 279-282.
[18]侯聪玲.太阳能最大功率跟踪技术研究.现代商贸工业,2008,20(3):285-286.
[19] Alghuwainem SM. Matching of a DC motor to a photovoltais generator using a step-up converter with a current-locked loop. IEEE Trans Energy Conver, 1994, 9(1):192-198.
[20] Chiang SJ, Chang KT, Yen CY. Residential photovoltaic energy storage system. IEEE Trans Ind Electron, 1998, 45(3): 385–394.
[21] Middlebrook RD, Cuk SA. General unified approach to modeling switching-converter power stages. IEEE Power Electronics Specialists Conference. 1976: 18-34.
[22]杨彦军,陆正元,姚菊莲,田建锋.利用径向基函数改进算法实现函数逼近.桂林工学院学报,2006,26(1):136-139.
[23]王长贵,王斯成.太阳能光伏发电实用技术.北京:化学工业出版社,2005:64-65.
[24]崔岩,李大勇,胡宏勋.光伏充电控制器温度补偿研究.太阳能学报,2004,25(1):92-94.
[25]崔岩.太阳能光伏电源控制器弱电控制线路的进一步研究.太阳能学报,2000,21(4):461-464.
[26] James P.Dunlop,P.E. Batteries and Charge Control in Stand-Alone Photovoltaic Systems Fundamentals and Application. http://www.localenergy.org/pdfs/Document%20Library/Fundamentals%20of%20batteries%20and%20charge%20control.pdf.
[27] N Vela,J Aguilera. Charavterisation of Charge Voltage of Lead-Acid Batteries:Application to the Charge Control Strategy in Photovoltaic Systems. Progress in Photo Voltaics, 2006:721-732.
[28]王晓晶,班群.硅太阳电池材料的研究进展.能源工程,2002,04:28-31.
[29]赵可斌,陈国雄.电子电力变流技术.上海交通出版社,1993:72-86.
[30] Xiao, W, Dunford, W G, Palmer, P R, Capel, A. Regulation of photovoltaic voltage. IEEE Trans Ind Electron, 2007, 54(3): 1365-1374.
[31] Xiao W, Ozog N, Dunford, W G. Topology study of photovoltaic interface for maximum power point tracking. IEEE Trans Ind Electron, 2007 54(3):1696-1704.
[32] Samangkool K, Premrudeepreechacharn S. Maximum Power Point Tracking Using Neural Networks for Grid-Connected Photovoltaic System. Future Power System, 2005: 1-4.
[33] Mukerjee, A K, Dasgupta, N. DC power supply used as photovoltaic simulator for testing MPPT algorithms. Renewable Energy. 2007, 32(4):587-592.
[34] H Ravishankar Kamath, Aditya Kumar, Devi Prasad Mishra. Application of Radial Basis Function for MPPT. PROCEEDINGS OF INTERNATIONAL CONFERENCE ON ENERGY AND ENVIRONMENT, 2009: 880-884.
[35] Aberbour M, Mehrez H, Maximum Architecture and design methodology of the RBF-DDA neural network. Circuits and Systems, 1998, 3: 199-202.
[36] H.Ravishankar Kamath, R S Aithal, P K.Singh Ashis Kumar, Sinha, Atit R Danak. modeling of photovoltaic array and maximum power point tracker using ann, http://journal.esrgroups.org/jes/papers/4_3_4.pdf.
[37] Chun hua LI, Xin jian ZHU, Guang yi CAO, Wan qi HU, Sheng SUI, Ming-ruo HU. A maximum power point tracker for photovoltaic energy systems based on fuzzy neural networks. Zhejiang Univ Sci A, 2009, 10(2): 263-270.
[38]宫亚琼,径向基函数神经网络在过程建模中的研究与应用,北京化工大学,硕士:21-22.
[39] Chuan Yu Chang and Shih Yu Fu. Image Classification using a Module RBF Neural Network, Proceedings of the First International Conference on Innovative Computing, Information and Control. 2006.
[40]张建辉,K-means聚类算法研究及应用,武汉理工大学,硕士:34-35.