This document proposes a method to detect partial shading on solar panels in real-time by monitoring the output power using a CUSUM algorithm. The method was tested on real PV panel data and achieved a 81% detection rate with a 3.4% false alarm rate and 70% sensitivity. The system includes sensors to monitor current and voltage from the solar panels, uploads data to the cloud for CUSUM analysis, and sends real-time alerts via a mobile application. Experimental results demonstrate the method can accurately detect partial shading with low delay.

1. Partial Shade Detection
for PV Solar Panels via CUSUM Algorithm
Uzair Akbar; Moeez Akmal; Syed M. Ali Qasim Naqvi
National University of Sciences & Technology (NUST), Islamabad
INTRODUCTION
CONCLUSION
EXPERIMENTAL RESULTSMETHOD
REFERENCES
Figure 2. PCB Prototype. Figure 3. Mobile Application.
ABSTRACT
CONTACT
Uzair Akbar
National University of Sciences &
Technology (NUST), Islamabad
Email: uzairakbar@outlook.com
Phone: (+92)-334-9590511
Website:
The quality of power supply from
photovoltaic solar panels is very
sensitive to shading effects of single
or multiple cells. The energy yield of
a partially shaded photovoltaic
system is much lower than we could
assume from the mean solar
irradiance. Some of the power loss
due to partial shading can however
be reduced by removing any shading
objects that might appear when
shade is detected on the solar
panels.
The present work studies a method
for real-time detection of partial
shade on solar panels by monitoring
the output power. We use the
sequential change point detection
algorithm CUmulative SUM (CUSUM)
to detect any sudden deviation in
the output power time series, and to
raise an alarm for the user.
The experimental results on the
output power of real photovoltaic
panels show that our proposed
approach can detect partial shading
with low delay and high accuracy.
Our proposed system is composed of 3 modules:
• Current and voltage sensing module.
• A could based database and CUSUM analysis.
• A mobile application for real-time alert reception (Figure 3.).
Figure 4. shows the PCB design of the sensor module; ACS 712 was
used as the current sensor and a voltage divider was formed by 56
MΩ & 10 MΩ resistors. The sensor data is fed to STM Board which
uploads it to the cloud via ESP 8266 wifi module.
A simplified form of the CUSUM algorithm [3] is given below. The
algorithm reads the signal 𝑥 and estimates a change point 𝑛 𝑐 before
and after which hypothesis 𝐻0 and 𝐻1 are true respectively. A
user defined threshold ℎ determines the sensitivity of the algorithm.
The PV panel output power is normalized with respect to solar
insolation obtained from a weather or metrological database as
shown in Figure 5. This is done to achieve better accuracy of shade
detection.
The normalized power curve is fed into the CUSUM algorithm. Figure
6. shows alarms being sounded at the points of relatively rapid
change depending upon the threshold value ℎ.
The Gowrie Solar database from PVOutput [4] was used to
evaluate our proposed approach. This database consists of
output power data at 5 min. interval rate from 32 panels 250 W
peak wattage rating.
We applied our algorithm on a dataset of 3 consecutive days
and used the Receiver Operating Characteristic (ROC) curve to
represent the fraction of True Positive Rate (TPR) and
sensitivity vs. the False Positive Rate (FPR) with various
values of the threshold h, as shown in Figure 7.
𝐷𝑒𝑡𝑒𝑐𝑡𝑖𝑜𝑛 𝑅𝑎𝑡𝑒 = 𝑇𝑃 (𝑇𝑃 + 𝐹𝑁) = 81%
𝑆𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦 = 𝑇𝑃 (𝑇𝑃 + 𝐹𝑃) = 70%
𝐹𝑎𝑙𝑠𝑒 𝐴𝑙𝑎𝑟𝑚 𝑅𝑎𝑡𝑒 = 𝐹𝑃 (𝐹𝑃 + 𝑇𝑁) = 3.4%
In this report, we proposed a new approach for detection of partial
shade on solar panels in real time manner.
The approach is based on detection of sudden changes in normalized
solar panel output power, with respect to solar insolation, via
CUSUM algorithm.
Application on a real PV database (the PVOutput database) shows
that our proposed approach can achieve a detection rate of 81% with
a false alarm rate of 3.4% and sensitivity of 70%.
In a solar photovoltaic module, efficiency is adversely affected if all
the cells of the solar PV module are not equally illuminated [2]. All
the cells in a series array are forced to carry the same current even
though a few cells under shade produce less current. The shaded
cells may get reverse biased, acting as loads, draining power from
fully illuminated cells. This leads to loss of power and decrease in the
performance of the system.
Partial shade may cause as much as more than 50% power losses, as
stated in [1]. We hence propose a method for real time detection of
shade on solar PV panels by monitoring the output current and
voltage of the panel so that the shading objects, such as dust
accumulation, may be removed on detection to conserve power.
Upon the successful completion of this project, we were able to
detect partial shading with low delay and high accuracy. The
hardware components were put together so that real-time data from
solar PV module could be obtained and stored on the cloud where
CUSUM algorithm was applied to the data so that real-time
notifications can be sent to the consumer via an Android application.
The Android application can also be used to view real-time graphs of
power output from solar PV module.
1. R.Ramaprabha and Dr. B. L. Mathur, ‘Impact of Partial Shading on Solar PV Module Containing Series
Connected Cells’, International Journal of Recent Trends in Engineering, Vol 2, No. 7, November 2009.
2. R. E. Hanitsch, Detlef Schulz and Udo Siegfried, ’ Shading Effects on Output Power of Grid Connected
Photovoltaic Generator Systems’, Rev. Energ. Ren. : Power Engineering (2001).
3. Pierre Granjon, ‘The CUSUM algorithm, a small review’, June 22, 2012.
4. Gowrie Solar database from PVOutput.org.
Initialization
if necessary
end
while algorithm running
measure 𝑥[𝑘]
decide between 𝐻0 and 𝐻1
if 𝐻1
𝑛 𝑑 = 𝑘
estimate 𝑛 𝑐
stop or reset
end if
end while
IP+
2/1
IP-
3 /4
VIOUT
7
VCC
8
GND
5
FILTER
6
CURRENT
SENSOR
ACS712ELCTR-05B-TR1
56 M
R2
10 M
Vcc
Solar
Current
Sense
Solar
Voltage
Sense
1
2
PANEL
INPUT
CONN-H2
Figure 4. Schematic Design. Figure 5. Power Normalization.
Figure 6. CUSUM Alarms. Figure 7. Receiver Operating
Characteristics.
Algorithm 1. Simplified CUSUM Algorithm Pseudo Code.
Figure 1. Block Diagram.