big data analysis and renewable energy extraction: a …€¦ · big data analysis and renewable...

IJEEE, Volume 3, Spl. Issue 2 (2016) National Conference on Recent Trends in Renewable Energy Sources & Electronics

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Big Data Analysis and Renewable EnergyExtraction: A New Path For Research

Chitra Sharma1, C. K. Dwivedi2

1Dept. of EEE, Kruti Institute of Technology and Engineering, Raipur, CG, India2Dept. of EEE, Columbia Institute of Engineering Technology, Raipur, CG, India

[email protected], [email protected]

Abstract— Big data analysis is future for the upcominginnovation and invention. In this paper a renewableapplication of big data analysis have been presented usingsolar PV power. United Nation has now considered big dataanalytics for various healthcare applications applied withhealth care analysis. In this paper a survey of application ofbig data analysis has been presented. Open source softwareRETScreen is used to monitor the data taken from NASA.This data will help to design the solar power system withMPPT for stand alone and grid connected system.

Keywords-Big Data analysis, solar power, renewableenergy, Hadoop

I. INTRODUCTION

Big Data bring new opportunities to modern society andchallenges to data scientists. In the age of digital world, allthe information is being stored in the cloud. Also withdigitization of various power sector has increased the flowof sensor data and the cloud computing. The first definitionof Big Data comes from Merv Adrian, “Big data exceedsthe reach of commonly used hardware environments andsoftware tools to capture, manage, and process it within atolerable elapsed time for its user population”[1]. In year2014 IBM has introduced the HyRef (Hybrid RenewableEnergy Forecasting) system for the wind and solar power.By utilizing local weather forecasts, HyRef can predict theperformance of each individual wind turbine and estimatethe amount of generated renewable energy. This level ofinsight will enable utilities to better manage the variablenature of wind and solar, and more accurately forecast theamount of power that can be redirected into the power gridor stored. It will also allow energy organizations to easilyintegrate other conventional sources such as coal andnatural gas.

However, this area of research is very new andpromising and carries lot of financial potential. Manyresearch communities are trying to develop a sophisticatednetwork comprised with big data analytics. In addition,solar and wind power forecasting is developed according tothe soft-modeling of the system with the sensor data withvarious level of weather analysis. Several researches haveproven the effectiveness of big data in health care andfinancial forecasting. International Data Corporation havebeen paid by Xerox, Univac and Burrough companies forpredicting future of computer technology in year 1999.[2]Various survey and analysis has been done by the IDC from1964 to till date.

In addition, Min Chen in year 2014 [3] presented adetailed survey on the big data. In this work state-of-the-art

of big data is presented with its pros and cons. Now a days,big data related to the service of Internet companies growrapidly. For example, Google processes data of hundreds ofPetabyte (PB), Face book generates log data of over 10 PBper month, Baidu, a Chinese company, processes data oftens of PB, and Taobao, a subsidiary of Alibaba, generatesdata of tens of Terabyte (TB) for online trading per day.

Figure 1 Stand alone Photo voltaic System

In 2010, Apache Hadoop defined big data as “datasetswhich could not be captured, managed, and processed bygeneral computers within an acceptable scope.” On thebasis of this definition, in May 2011, McKinsey &Company, a global consulting agency announced Big Dataas the next frontier for innovation, competition, andproductivity.

II. DEVELOPMENT OF BIG DATA

In the late 1970s, the concept of “database machine”emerged, which is a technology specially used for storingand analyzing data. With the increase of data volume, thestorage and processing capacity of a single mainframecomputer system became inadequate. However, manychallenges on big data arose. With the development ofInternet services, indexes and queried contents were rapidlygrowing. Therefore, search engine companies had to facethe challenges of handling such big data. Google createdGFS [4] and Map Reduce [5] programming models to copewith the challenges brought about by data management andanalysis at the Internet scale.


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I. INTRODUCTION










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I. INTRODUCTION










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Now big data is being applied for the future location ofcharging station and the IoT (Internet of Thing), V2G(Vehicle to Grid) communication etc. Image processing,voice processing and signal analysis tool has shown thebenefit of big data from various sources.

III. APPLICATION OF BIG DATA IN RENEWABLEENERGY

Continuous data have been generated from the weatherforecasting network for upcoming 24hr system. In this caseone system applied for distributed generation for solar orwind is needed to capture and process these data. Severalassessments are always required before utilizing it at fieldlevel. In figure 1 fundamental arrangement of solar PV cellis given for a standalone system with dc dc converter to getregulated power supply. In fig 2 a collected data has beenshown from the NASA for Raipur region. The above data isone day forecasting for complete year.

Figure 3 photovoltaic system (a) Basic Circuit and (b)Characteristic Curve

ph

s

exp 1 (1)

Where ;

I = Current to the load

I = Photo current

I = Reverse saturation current of the diode

q = Electron charge

V= Voltage across the diode

K= Boltzmann con

S Sph S

sh

q V R I V R II I I

KT R

s

sh

stant

T= Junction temperature

=Ideality factor of the diode

R =Series resistors

R = Shunt resistor

Equation mentioned above is fundamental equation ofcurrent. Since solar power behaves as the current sourcefor different irradiance capacity and different irradiancelevel. In figure 1 operation of DC DC converter is based onthe MPPT algorithm from the various sensor data asvoltage or current. If the solar irradiance is available fromthe data forecasting and big data assessment, algorithmbecomes more effective. Various topologies of DC-DCconverter have been proposed till date to provide the bestassessment of power and to get the high efficiency powerconversion. It is found that the DC-DC conversionefficiency is very high for the application other than the PVpanel. Since the effect of partial shading and variation intemperature does show the variation on the actual voltagelevel and the DC DC converter does show its optimal

Figure 2 RETScreen data collected for the Raipur, Chhattisgarh


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ph

s

exp 1 (1)

Where ;


I = Photo current


q = Electron charge


K= Boltzmann con

S Sph S

sh

q V R I V R II I I

KT R

s

sh

stant



R =Series resistors

R = Shunt resistor




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ph

s

exp 1 (1)

Where ;


I = Photo current


q = Electron charge


K= Boltzmann con

S Sph S

sh

q V R I V R II I I

KT R

s

sh

stant



R =Series resistors

R = Shunt resistor




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behavior for such variation. Fig-4 shows a setup for 3kWPV array with partial cloud shading and the partialvariation of the parameter.

Figure 4 A common setup for the 3kW PV system

In figure 4 effect of cloud makes a great impact on theconversion efficiency of converter. To achieve good levelof power conversion one need to consider other variable ofsolar system.

Figure 5 P-V characteristic for different irradiance

Fig-5 shows the PV characteristic of a system for differentirradiance level during a day. In a day irradiance variesaccordingly for the various position of sun. There arevarious mathematical expressions for position of sun forthe day and night.

Figure 6 P-V characteristic for different Temperature.

Figure 7 Log characteristic of Voltage at different battery current.

In fig 6 power vs output voltage is presented for thedifferent temperature level. In this curve it is very muchclear that the temperature variation greatly affect the outputvoltage of solar PV panel.

Figure 8 Optimal application of PV using MPPT.

It is noteworthy that effectiveness of any power trackingalgorithm is function of different variable as voltagecurrent and power. However, for better control andextraction of power from the solar panel requires the nearto exact estimation of temperature, irradiance and the windspeed.

IV. CONCLUSION

This paper ideate the future of renewable energy sourcesfor the stand alone and large scale system where the


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analysis for control action is performed using the Big dataanalysis. Using big data analysis one can estimate the windspeed accordance with the irradiance and temperature level.Big data can be assessed at various levels to get bettercontrol and the fast forecasting for power scheduling.

REFERENCES

[1] Gantz J, Reinsel D (2011) Extracting value from chaos. IDC iView,pp 1–12

[2] Fact sheet: Big data across the federal government (2012).http://www.whitehouse.gov/sites/default/files/microsites/ostp/bigdata fact sheet 3 29 2012.pdf

[3] Chen, Min, Shiwen Mao, and Yunhao Liu. "Big data: A survey."Mobile Networks and Applications 19.2 (2014): 171-209.

[4] Ghemawat S, Gobioff H, Leung S-T (2003) The google file system.In: ACM SIGOPS Operating Systems Review, vol 37. ACM, pp 29–43

[5] Dean J, Ghemawat S (2008) Mapreduce: simplified data processingon large clusters. Commun ACM 51(1):107–113

[6] I. S. Jacobs and C. P. Bean, “Fine particles, thin films and exchangeanisotropy,” in Magnetism, vol. III, G. T. Rado and H. Suhl, Eds.New York: Academic, 1963, pp. 271–350.

[7] S. R. Wenham, M. A. Green, and M. Watt, Applied Photovoltaics.Sydney, Australia: Univ. New South Wales, 1994.

[8] J. Larminie and A. Dicks, Fuel Cell Systems Explained. New York:Wiley, 2000.

[9] D. A. J. Rand, R. Woods, and R. M. Dell, Batteries for ElectricVehicles. New York: Wiley, 1998.

[10] M. Calais, J. M. A. Myrzik, and V. G. Agelidis, “Inverters for singlephase grid connected photovoltaic systems—overview andprospects,” in Proc. 17th PV Solar Energy Conf. and Exhibition,Münich, Germany, Oct. 2001.

[11] T. Noguchi, S. Togashi, and R. Nakamoto, “Short-circuit pulsebased maximum-power-point tracking method for multiple

[12] photovoltaic-and-converter module system,” IEEE Trans. Ind.Electron., vol. 49, pp. 217–223, Feb. 2002.

[13] L. M. Tolbert and F. Z. Peng, “Multilevel converters as a utilityinterface for renewable energy systems,” in IEEE PES SummerMeeting, Seattle, WA, 2000.

[14] M. Calais and V. G. Agelidis, “Multilevel converters for singlephase grid connected photovoltaic systems-an overview,” in Proc.IEEE International Symp. Industrial Electronics, Pretoria, SouthAfrica, 1998.

[15] J. H. R. Enslin, M. S. Wolf, D. B. Snyman, and W. Swiegers,“Integrated photovoltaic maximum power point tracking converter,”IEEE Trans. Ind. Electron., vol. 44, pp. 769–773, Dec. 1997.

[16] R. Giri, R. Ayyana, and N. Mohan, “Common duty ratio control ofinput series connected modular dc–dc converters with active inputvoltage and load current sharing,” in Proc. 18th Annu. IEEE AppliedPower Electronics Conf. Expo. (APEC’03), vol. 1, Feb. 2003, pp.322–326.

[17] G. Walker, “Evaluating MPPT converter topologies using aMATLAB PV model,” J. Elect. Electron. Eng. Australia, vol. 21,pp. 49–56, 2001.

[18] F. Hamma, T. Meynard, F. Tourkhani,and P. Viarouge, “Characteristics and designof multilevel choppers,” in Power ElectronicsSpecialists Conf. (PESC’95), vol. 2, 1995, pp.1208–1214.

[19] D. A. J. Rand, R. Woods, and R. M. Dell, Batteries for Electric[20] Vehicles. New York: Wiley, 1998.

BIBLOGRAPHYChitra Sharma, Completed her bachelorof Engineering in Electrical & ElectronicsEngineering, in year 2012, fromChhattisgarh Swami Vivekananda

Technical University. She is pursuing her Masters ofTechnology in Power System and Control from RKDFCollege of Engineering, affiliated to RGPV. Currently, sheis with Dept. of Electrical & Electronics Engineering,KITE-Raipur. Her area of interest is Testing-Commissioning of Electrical Equipments, High VoltageEngineering, Power System and Apparatus, and RenewableEnergy sources.

Chandra Kant Dwivedi received the B.Sc (Engg.) Degreein Electrical Engineering from B.I.T. Sindri, RanchiUniversity, India in 1971 & M.E. High VoltageEngineering from RTM University, Nagpur. He hastelescopic experience of more than 32 years in the field ofElectrical testing & commissioning of electricalequipments, H.T. & L.T.switch gears at floor level insteel industries as well as 9 years of teaching experienceto UG Students. He guided the team of electricalengineers for testing and commissioning of electrics atBokaro, Bhilai, Rourkela and Visage steel plant. SinceFeb, 2008 to October 2011, he was Sr.Lecturer inElectrical & Electronics Engineering at DIMAT, RAIPUR(C.G). From November 2011 to March 2013 he wasassociated with Columbia Istitute of Engineering &Technology, Raipur as Head of Department, EEE. FromJuly 2013 to June’14 he was HOD of ElectricalEngineering at MGEC Jaipur, Rajastan. From July’14 toJune’15 he was Associate Professor cum HOD of EEE atSBCET Jaipur, Rajasatan. At present, he is working asHOD of EEE in Columbia Institute of Engineering &Technology, Raipur. His main areas of interest are Highvoltage engineering, Testing & commissioning ofelectrical installations. Several research papers have beenpublished in International Journals. He is a member ofInstitute of Engineers, India & IEEE.

big data analysis and renewable energy extraction: a …€¦ · big data analysis and renewable...

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