Advanced Scence and Technology Letters Vol.83 (ISA 05), pp.7-75 http://dx.do.org/0.457/astl.05.83.4 A Short Term Forecastng Method for Wnd Power Generaton System based on BP Neural Networks Shenghu Wang, Xaonan Lu, Yuexn Jn, Kedng Qu Shenyang Insttute of Engneerng, Shenyang 036, Chna; NorthEast Chna Grd Company Lmted, Shenyang 036, Chna luxn00@63.com Abstract. Ths paper analyze and summarze the current stuaton as well as methods of forecastng wnd power from home and aboard based on wnd power development of Chna. Due to the BP neural network can approxmate any nonlnear mappng wth any arbtrary precson and ts generalzaton ablty s strong. Ths paper used BP neural network for power predcton, set up a model wth numercal weather predcton data and wnd power of a wnd farm n Inner Mongola Autonomous Regon. Then used MATLAB to smulate and verfy the feasblty of ths predcton model The precson meet the requrements. In the last of ths paper, the author development and desgn a smple system of wnd power forecastng by usng vsual basc. The system has made the forecastng process to be smple and convenent. And also made easy to operaton for the dspatcher. Keywords: Wnd power, Wnd power forecastng, BPNN, Forecastng system. Introducton Wth the rapd consumpton of world energy and the deteroraton of ecologcal envronment, wnd power as the major renewable energy has acheved a rapd development. And ts development also attracted all countres attenton. Chnese wnd energy has a large reserve and wde dstrbuton. The wnd energy reserve n land has got about.53 0 8 KW. In the past decades, the global wnd power cumulatve nstalled capacty has eght-fold ncrease. Chnese wnd power cumulatve nstalled ncrease has ncreased 58 tmes and ranked frst n the world. Accordng to the statstcs that the Global Wnd Energy Councl (GWEC) has ssued, the global cumulatve nstalled capacty of wnd power reached 3.8 08 KW at the end of 03. Rankng the top fve countres are Chna, Amerca, Germany, Span, Inda. Rankng the top fve countres of the newly nstalled capacty are Chna, Germany, Brtan, Inda, Canada. In 03 year, the new nstalled capacty of Chna reached.609 07mllon KW and the cumulatve nstalled capacty reached 9.74 0 7 KW.The new grd connected capacty of wnd power reached.449 0 7 KW, the cumulatve grd connected capacty of 7.76 0 7 KW[]. Table. shows the ISSN: 87-33 ASTL Copyrght 05 SERSC
Advanced Scence and Technology Letters Vol.83 (ISA 05) stuaton of global accumulated and newly ncreased wnd power capacty n 03 of the frst fve countres. The wnd power has a strong randomness and volatlty. The unt capacty of wnd turbne and the scale of grd wnd farms has contnuous expanson, so the wnd power penetraton s also ncreasng year by year. The attendant problems of wnd power have become ncreasngly promnent. Therefore, the accurate forecastng of wnd power s necessary to ensure the power system to be safe, relable, economcal and stable. It s also a problem that to be overcome urgently at ths stage of Chna. The wnd power forecastng research abroad started very early, there are many mature commercal software has appled n power generaton scheme and electrc power market transacton, such as Predktor of Denmark Rsoe laboratory, WPPT and Zephyr of Techncal Unversty of Denmark and WPMS and Prevento of Germany.ther wnd power short-term forecast error can reach 0%-5%[].Chnese wnd power predcton started later than foregn. Chna has dstnct clmatc condton and geographc factors, we need to research and development our own wnd power forecastng system. The frst wnd power forecastng system s developed by Chnese Electrc Power Research Insttute, named WPFS Ver.0.It has been put nto tral operaton snce 008, ts forecastng error reaches less than 0%.The accuracy s n the domestc leadng level, reached the level of smlar foregn products[3]. Another forecastng system of wnd power named WPFS was developed by North Chna Electrc Power Unversty, ts root-mean-square error of Ultra short term forecastng has reached 0% wthn 6h. Ths system has been put nto operaton. In Longyuanchuanjng wnd farms and Bayn wnd farm of Chna Guodan Corporaton[4]. In 04,"VeStore-WPFS centralzed wnd power forecastng system V.0" of Bejng Huadan Tanren Power Control Technology Co.,Ltd won the computer software copyrght regstraton certfcate whch ssued by the Natonal Copyrght Admnstraton of the People's Republc of Chna. Accordng to the dfferent forecastng tme scale, Wnd power predcton methods can be dvded nto ultra short term forecast, short-term predcton and long term predcton [5].Accordng to the dfferent forecastng models t can be dvded nto statstcal model and physcal model. Accordng to dfferent types of nput data t can be dvded nto forecastng model based on hstorcal data and forecastng model based on numercal weather predcton. Realty of BPNN models Ths paper establsh the BPNN model to forecastng day-ahead wnd power based on the numercal weather predcton data suppled by Shennengyhe wnd farm of Shenzhen Energy North Holdngs Co., Ltd n Inner Mongola, Chna. The data ncludes numercal weather predcton data and real output of the wnd farm between Aprl to 30.The data sample nterval s 5mn.There are 784 groups of data n total, frst 688 data serve as tranng samples and the rest 96 samples serve as testng samples. Ths wnd farm nstalls one hundred FD77B-500 wnd turbnes produced by Dongfang turbne Co. Ltd. The unt capacty of the wnd turbne s.5mw and the 7 Copyrght 05 SERSC
Advanced Scence and Technology Letters Vol.83 (ISA 05) total nstalled capacty s 50MW. Its theoretcal output power characterstcs curve 3 0 under the standard condton ( =.5 kg / m T p 0, =88.5K, 0 =0.33 KPa ). There s a problem need to pay more attenton When establsh the BPNN model to forcast the wnd power. The excessve ampltude value of the nput varable ampltude wll make the neuron output saturaton and nfluence the accuracy of the model. In order to mprove the learnng accuracy and effcency of BPNN, all the sample data must be normalzed before nput to model. The network nput and output sample value should lmt nto [0,] or [-,]. The mapmnmax functon of MATLAB can normalze the data nto [-,].Use ths functon to normalzed the wnd speed, temperature, pressure and actual power data. The wnd drecton normalzes wth sne functon and cosne functon. After get the normalzed forecastng value, we should reduct t to the orgnal dmenson. Normalze the data wth formula (), lmt the data nto [-,]. x ' x ( x x ) max mn. () ( x x ) max mn Use formula () to reduct the normalzaton forecastng value to ts orgnal dmenson. y ' ( x x ) y ( x x ). () max mn max mn x and x s the maxmum and mnmum of the data, x s the normalzaton max mn value of nput data and y s the normalzaton value of output data. In addton, the nput data should be tested ts accuracy before nput the forecastng model. The data cannot exceed ts normal range. The manly nput data n ths forecastng model s the normalzed numercal weather predcton data ncludng wnd speed, wnd drecton, temperature, Relatve Humdty, and pressure. The wnd speed s represented as sne and cosne value of the wnd drecton angle so the node number of nput layer can be confrmed as 6.Accordng to the emprcal formula, the node number of the hdden can be confrmed as 3 whch equals the number of nput nodes double tmes plus one. The node number of output layer can be confrmed as.the model show n fgure. Copyrght 05 SERSC 73
Advanced Scence and Technology Letters Vol.83 (ISA 05) Fg.. The mode of BPNN Ths paper constructs a BPNN model accordng to a practcal problem. The transfer functon of hdden layer and output layer adopts Tansg functon, the tranng algorthm adopts tranlm functon, the adjustment of the ntal weghts and thresholds adopts learngdm learnng functon and the error performance functon adopts the MSE functon. The maxmum tranng tme of ths BPNN s 5000, the tranng error s 0.005, the learnng rate s 0.05. 3 Concluson Ths paper presents the BPNN model and ts algorthm. And also verfes the accuracy and practcal value of BPNN forecastng model. The forecastng result of the BPNN model n ths paper comples wth the relevant requrements. Although artfcal neural can approxmate any nonlnear mappng wth any arbtrary precson, but the tradtonal BP algorthm has a slow convergence rate and easly falls nto local mnmum, so many scholars have put forward more excellent forecastng method n wnd power forecastng. Wth the contnuous development of wnd power forecastng technology, the forecastng method has changed from uncty to dversfcaton. The combnaton forecastng method can make full use of each sngle method to reduce the error and mprove the accuracy of forecastng results. References. Chna Power News Net.http://www.cpnn.com.cn/zdzgtt/040/t0406_65758.html. Natonal Electrc Power Dspatchng And Communcaton Center of Chna State Grd Corp. Wnd power forecastng system functon norm. Bejng: Chna Electrc Power Press, (00) 74 Copyrght 05 SERSC
Advanced Scence and Technology Letters Vol.83 (ISA 05) 3. Fu, J. W., J Ma. Revew of Wnd Power Forecastng Methods. East Chna Electrc Power. 45, (0) 4. Chen, Q.: Revew on Wnd Power Predcton Technology Development. Shanx Electrc Power. 7, (0) 5. Ahlstrom, M., Zavadl, RM: The Role of Wnd Forecastng n Grd Operatons and Relablty [A].Transmsson and Dstrbuton Conference and Exhbton. Asa and Pacfc. (005) Copyrght 05 SERSC 75