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Frontiers in Energy

ISSN 2095-1701

ISSN 2095-1698(Online)

CN 11-6017/TK

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Front. Energy    2016, Vol. 10 Issue (3) : 277-285    https://doi.org/10.1007/s11708-016-0400-3
RESEARCH ARTICLE
A new method for estimating the longevity and degradation of photovoltaic systems considering weather states
Amir AHADI1,*(),Hosein HAYATI1,Joydeep MITRA2,Reza ABBASI-ASL3,Kehinde AWODELE4
1. Young Researchers and Elite Club, Ardabil Branch, Islamic Azad University, Ardabil 5615731567, Iran
2. Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48823, USA
3. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA
4. Department of Electrical Engineering, University of Cape Town, Cape Town 7701, South Africa
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Abstract

The power output of solar photovoltaic (PV) systems is affected by solar radiation and ambient temperature. The commonly used evaluation techniques usually overlook the four weather states which are clear, cloudy, foggy, and rainy. In this paper, an ovel analytical model of the four weather conditions based on the Markov chain is proposed. The Markov method is well suited to estimate the reliability and availability of systems based on a continuous stochastic process. The proposed method is generic enough to be applied to reliability evaluation of PV systems and even other applications. Further aspects investigated include the new degradation model for reliability predication of PV modules. The results indicate that the PV module degradation over years, failures, and solar radiation must be considered in choosing an efficient PV system with an optimal design to achieve the maximum benefit of the PV system. For each aspect, a method is proposed, and the complete focusing methodology is expounded and validated using simulated point targets. The results also demonstrate the feasibility and applicability of the proposed method for effective modeling of the chronological aspects and stochastic characteristics of solar cells as well as the optimal configuration and sizing of large PV plants in terms of cost and reliability.
Keywords photovoltaic (PV) systems      solar cell      Markov model      weather effects     
Corresponding Author(s): Amir AHADI   
Just Accepted Date: 18 February 2016   Online First Date: 11 April 2016    Issue Date: 07 September 2016
 Cite this article:   
Amir AHADI,Hosein HAYATI,Joydeep MITRA, et al. A new method for estimating the longevity and degradation of photovoltaic systems considering weather states[J]. Front. Energy, 2016, 10(3): 277-285.
 URL:  
https://academic.hep.com.cn/fie/EN/10.1007/s11708-016-0400-3
https://academic.hep.com.cn/fie/EN/Y2016/V10/I3/277
Fig.1  Pictorial chart of reliability evaluation based on FTA
Fig.2  Four weather state models
Fig.3  Oneline diagram of large-scale PV systems [10]
Power/kW PV modules DC switch AC switch Inverter Connector SP DCB BS ACCB GP CC
100 437 3 1 1 874 23 1 16 1 1 1
200 874 6 1 2 1748 46 1 30 2 1 1
500 2166 15 1 5 4332 114 1 76 5 1 1
1000 4351 27 1 9 8702 229 1 150 9 1 1
1500 6517 42 1 14 13034 343 1 224 14 1 1
2000 8702 57 1 19 17404 458 1 298 19 1 1
2500 10868 72 1 24 21736 572 1 372 24 1 1
Tab.1  Number of components per each PV system [10]
Component Failure rate/(10–6 failures·h–1)
PV modules 0.0152
String protection 0.313
DC switch 0.2
Inverter 40.29
AC circuit breaker 5.712
Grid protection 5.712
AC switch 0.034
Differential circuit breaker 5.712
Connector (couple) 0.00024
Battery system 12.89
Charge controller 6.44
Tab.2  Failure rate of component [10]
Fig.4  Reliability of total component after one year of operation (in percentage)
Power/kW Normal Rainy Cloudy Foggy
100 97.96 95.31 97.02 97.42
200 95.96 90.84 94.13 94.91
500 90.28 78.82 86.08 87.85
1000 81.44 62.01 74.00 77.10
1500 73.54 48.88 63.70 67.74
2000 66.34 38.45 54.76 59.45
2500 59.89 30.31 47.14 52.23
Tab.3  Reliability of PV module for a period of one year (Unit: %)
Fig.5  Reliability of total component after 20 years of operation (in percentage)
Power/kW Normal Rainy Cloudy Foggy
100 66.22 38.29 54.62 59.31
200 43.85 14.66 29.83 35.18
500 12.96 0.85 4.99 7.51
1000 1.65 0.0071 0.24 0.55
1500 0.2141 0.0001 0.01 0.04
2000 0.02 0 0.0006 0.003
2500 0.0035 0 0 0.0002
Tab.4  Reliability of PV module for a period of 20 years (Unit: %)
Fig.6  Unavailability for a period of one year
Power/kW Normal Rainy Cloudy Foggy
100 78.37 76.25 77.62 77.94
200 64.92 61.46 63.69 64.21
500 36.98 32.29 35.26 35.99
1000 16.68 12.70 15.15 15.79
1500 6.52 4.33 5.65 6.0088
2000 2.54 4.33 2.10 2.28
2500 0.99 0.50 0.78 0.86
Tab.5  Reliability of overall system for a period of one year (Unit: %)
Power/kW Normal Rainy Cloudy Foggy
100 0.7641 0.4419 0.6303 0.6845
200 0 0 0 0
500 0 0 0 0
1000 0 0 0 0
1500 0 0 0 0
2000 0 0 0 0
2500 0 0 0 0
Tab.6  Overall system’s reliability for a period of 20 years (Unit: %)
Fig.7  Unavailability for a period of 20 years
Fig.8  Reliability of PV modules for a period of one year of operation considering weather effects
Fig.9  Reliability of PV modules for a period of 20 years of operation considering weather effects
Fig.10  Reliability of overall system for a period of one year of operation
Fig.11  Reliability of overall system for a period of 20 years of operation
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