With the high cost of grid extension and approximately 1.6 billion people still living without electrical services, the solar home system is an important technology in the alleviation of rural energy poverty across the developing world. The performance monitoring and analysis of these systems provide insights leading to improvements in system design and implementation in order to ensure high quality and robust energy supply in remote locations. Most small solar home systems now use charge controllers using pulse width modulation (PWM) to regulate the charge current to the battery. A rapid variation in current and voltage resulting from PWM creates monitoring challenges, which, if not carefully considered in the design of the monitoring system, can result in the erroneous measurement of photovoltaic (PV) power. In order to characterize and clarify the measurement process during PWM, a mathematical model was developed to reproduce and simulate measured data. The effects of matched scan and PWM frequency were studied with the model, and an algorithm was devised to select appropriate scan rates to ensure that a representative sample of measurements is acquired. Furthermore, estimation methods were developed to correct for measurement errors due to factors such as nonzero “short circuit” voltage and current/voltage peak mismatches. A more sophisticated algorithm is then discussed to more accurately measure PV power using highly programmable data loggers. The results produced by the various methods are compared and reveal a significant error in the measurement of PV power without corrective action. Estimation methods prove to be effective in certain cases but are susceptible to error during conditions of variable irradiance. The effect of the measurement error has been found to depend strongly on the duty cycle of PWM as well as the relationship between scan rate and PWM frequency. The energy measurement error over 1 day depends on insolation and system conditions as well as on system design. On a sunny day, under a daily load of about 20 A h, the net error in PV energy is found to be 1%, whereas a system with a high initial battery state of charge under similar conditions and no load produced an error of 47.6%. This study shows the importance of data logger selection and programming in monitoring accurately the energy provided by solar home systems. When appropriately considered, measurement errors can be avoided or reduced without investment in more expensive measurement equipment.

1.
Modi
,
V.
,
McDade
,
S.
,
Lallement
,
D.
, and
Saghir
,
J.
, 2005, “
Energy Services for the Millennium Development Goals
,” United Nations Development Programme, Technical Report.
2.
Kaundinya
,
D. P.
,
Balachandra
,
P.
, and
Ravindranath
,
N.
, 2009, “
Grid-Connected Versus Stand-Alone Energy Systems for Decentralized Power—A Review of Literature
,”
Renewable Sustainable Energy Rev.
1364-0321,
13
, pp.
2041
2050
.
3.
Schare
,
S.
, and
Smith
,
K. R.
, 1995, “
Particulate Emission Rates of Simple Kerosene Lamps
,”
Energy for Sustainable Development
,
2
, pp.
32
35
.
4.
Gustavsson
,
M.
, and
Ellegard
,
A.
, 2004, “
The Impact of Solar Home Systems on Rural Livelihoods. Experiences From Nyimba Energy Service Company in Zambia
,”
Renewable Energy
0960-1481,
29
, pp.
1059
1072
.
5.
International Energy Agency
, 2003, “
Guidelines for Monitoring Stand-Alone Photovoltaic Power Systems
,” International Energy Agency, Technical Report.
6.
Steca Solsum Solar Charge Controllers 5.6/6.6/5.0/8.8/8.0, User’s Manual.
7.
Luque
,
A.
, and
Hegedus
,
S.
, 2003,
Handbook of Photovoltaic Science and Engineering
,
Wiley
,
New York
.
8.
CR1000 Measurement and Control System Operation Manual.
9.
Muñoz
,
F.
,
Almonacid
,
G.
,
Nofuentes
,
G.
, and
Almonacid
,
F.
, 2006, “
A New Method Based on Charge Parameters to Analyse the Performance of Stand-Alone Photovoltaic Systems
,”
Sol. Energy Mater. Sol. Cells
0927-0248,
90
, pp.
1750
1763
.
10.
Reinders
,
A. H. M. E.
,
Pramusito
,
Sudradjat
,
A.
,
van Dijk
,
V. A. P.
,
Mulyadi
,
R.
, and
Turkenburg
,
W. C.
, 1999, “
Sukatani Revisited: On the Performance of Nine-Year-Old Solar Home Systems and Street Lighting Systems in Indonesia
,”
Renewable Sustainable Energy Rev.
1364-0321,
3
, pp.
1
47
.
11.
Nieuwenhout
,
F.
,
van de Rijt
,
P.
, and
Vervaart
,
M.
, 2001, “
Monitoring of Solar Home Systems in China: First Year Results
,”
17th European Photovoltaic Solar Energy Conference
.
12.
Gustavsson
,
M.
, 2007, “
With Time Comes Increased Load—An Analysis of Solar Home System Use in Lundazi, Zambia
,”
Renewable Energy
0960-1481,
32
, pp.
796
813
.
13.
Dunlop
,
J. P.
, 1997, “
Batteries and Charge Control in Stand-Alone Photovoltaic Systems
,” Florida Solar Energy Center, Technical Report.
14.
Commission of the European Communities
, 1997, “
Photovoltaic System Monitoring, Guidelines for the Assessment of Photovoltaic Plants
,” Document A, April.
15.
Commission of the European Communities
, 1997, “
Photovoltaic System Monitoring, Guidelines for the Assessment of Photovoltaic Plants
,” Document B, April.
16.
International Electrotechnical Commission
, 1998, IEC Standard 61724, “
Photovoltaic System Performance Monitoring Guidelines for Measurement, Data Exchange and Analysis
,” April.
17.
Muñoz
,
F.
,
Echbarthi
,
I.
,
Nofuentes
,
G.
,
Fuentes
,
M.
, and
Aguilera
,
J.
, 2009, “
Estimation of the Potential Array Output Charge in the Performance Analysis of Stand-Alone Photovoltaic Systems Without MPPT (Case Study: Mediterranean Climate)
,”
Sol. Energy
0038-092X,
83
, pp.
1985
1997
.
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