IMPLEMENTATION OF A REAL TIME MONITORING SYSTEM FOR A PHOTOVOLTAIC GENERATION SYSTEM

Advances in Engineering: an International Journal (ADEIJ), Vol.2, No.4
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IMPLEMENTATION OF A REAL TIME MONITORING
SYSTEM FOR A PHOTOVOLTAIC GENERATION
SYSTEM
Abd Essalam BADOUD
Automatic laboratory of Setif, electrical engineering department, university of Setif 1,
Algeria
ABSTRACT
Generally PV generators are considered reliable compared to other systems, but like all processes, a PV
system can be exposed to several failures causing the PV system to malfunction. Several studies have
found that the reliability of PV systems is highly dependent on the equipment used for the construction of
PV panels, temperature, humidity and solar radiation. A PV system can have several defects, be it defects
of construction types, or material and electrical defects caused by climatic conditions. As such, we can
cite the fault most commonly encountered in a PV generator which is the partial shading defect.
This paper is organized as follows. Section II will generally introduce PV systems and their
characteristics. Section III will introduce bond-graph models for PV systems. Section IV will discuss the
problem of optimal sensor placement. Sections V and VI will validate the method through simulations and
experiments. Finally, section VII will discuss the results.
KEYWORDS
Faults detection and location, PV generator, Sensor placement, Bond graph, Output power
1. INTRODUCTION
Recent studies have focused on remote monitoring for fault detection in PV plants [1], [2].
Some authors base their contribution on comparing the theoretical energy yield of the PV plant
with real measured data. Errors of about 10 % have been reported using these methods [3].
Other authors have developed new simulation tools for the diagnostic and fault detection of PV
systems [4], [5] or using simulation platforms such as SPICE [6], SABER [7], and EMTP [8].
Meta-heuristic models based on Neural Networks [9], Fuzzy logic [10] and Neural-Fuzzy
hybrid [11] have also been proposed.
Despite these recent developments, several challenges still remain in this field. Some techniques
may require an expensive solar simulator to be validated. Others do not have the flexibility to
study more advanced features of PV systems such as interconnection among modules.
Simulated time is also an issue in some software platforms. Even with improvements in the
calculation speed of the traditional mathematic models, some electrical characteristics of the PV
system still cannot be studied in detail. Other models have a trade-off between complexity and
simulation time making scalability of basic models very difficult.
All these problems lead to major difficulties in creating a generic PV model suitable for
studying defaults. On one hand, the available models offer limited flexibility for creating
defaults. On the other hand, they do not allow an overall study on system observability. This
works seeks to contribute on these two issues by presenting a flexible bond-graph based model

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for a PV plant and using this model to determine the optimal placement of sensors within the
plant.
2. MODELLING OF PV SYSTEM
Real-time simulation of the PV system consists of development of the mathematical model of
the PV module, boost converter and MPPT algorithm for analog DC input, analogous to
temperature and irradiance.
2.1. PV module
Several methods of modelling of PV cells [7]–[11] have been introduced over the years. Ideally,
PV cells are represented by a current source in parallel with an ideal diode.
Researchers have modified this ideal model in many ways to account for several factors. The
equivalent circuit of the PV model considered in this paper is shown in figure (1).
Figure 1. Equivalent circuit of PV model with series and parallel resistance
In this paper, the PV module is designed using equation (1) to (5). The values of Rs and Rp are
calculated using the iteration method.
1 2

    PV s
p
v iR
ph d d Ri I I I
(1)
Where
       
c
rf
G
ph sc i c rf GI I K T T
(2)
1 01 1
  
   
   
PV s
d
t
V IR
I I exp
nV
(3)
2 02
1
1
  
   
   
PV s
d
t
V IR
I I exp
nV
(4)
 
  01 02
1
 
 
    
sc i c rf
oc v c rf t
I K T T
I I
exp V K T T / V
(5)

3
Figure 2. PV Bond graph model with single diode
2.2. Typical Grid-Connedted PV Systems
A typical grid-connected PV system shown in figure (3) is the research target of this paper. It
consists of several major components, including solar PV arrays, centralized inverter with
MPPT algorithm, electrical connection wirings, and protection devices, such as over-current
protection devices (OCPDs) and ground fault protection devices (GFPDs). Note that the PV
system in the research is a grounded system, which has a system grounding point Gsys
according to National Electric Code (NEC) in the US.
The PV array typically contains m×n PV modules connected electrically in series and parallel
configuration. This array configuration is, nowadays, most common in PV technologies. There
are n numbers of PV strings in parallel. Each PV string consists of m number of modules in
series.

4
Figure 3. Typical faults in solar PV arrays
2.3. Faults in Solar PV Arrays
Typical faults in PV arrays consist of ground faults, line-line faults, and mismatch faults among
PV modules [25-26]. Among these faults, line-line faults and mismatch faults are studied in this
paper, since they are more difficult to detect by conventional protection devices than ground
faults [27].
 A ground fault is an accidental electrical short circuit involving ground and one or more
normally designated current-carrying conductors.
 A line-line fault is an accidental short-circuit connection between two points of different
potential in PV arrays.
 Mismatch faults occur when the electrical parameters of module(s) is significantly
changed from those of the remaining modules. Mismatch fault could be temporary,
such as partial shading on PV modules. Also, it could be permanent, such as open
circuit in PV modules/strings, degradation, or defective modules.
3. PROPOSED ALGORITHM
The DT model will be built according to four key steps in the process. The first step is data
acquisition, which obtains the training and test set from experiments. The second step is to pre-
process the experimental data, including data cleaning, sampling, creating new attributes and
attribute selection. The third step is to train the DT model by using 66% of randomly chosen
pre-processed data. The last step is using remainder of pre-processed data to test the model.
For example, in figure (4), faults (e.g. line-line faults) in PV arrays usually cause changed
current vs. voltage (I-V) curves and reduced maximum power points (MPPs). Fault MPPs may
vary from normal MPPs in the I-V curves.

5
Figure 4. I-V curves of normal and faulted PV arrays
Once the DT model is built and tested, it can operate on-line for fault monitoring, as shown in
figure (5). The DT model could be either programmed as “if-then” statements in a separate
microcontroller.
Figure 5. The flowchart of proposed fault detection model
4. MODEL IMPLEMENTATION
The block diagram showing the implementation of the PV array model in RT-LAB simulator is
depicted in figure (6). The computation of the I-V characteristic of the array based on the bond
graph approach was coded in Embedded Matlab in order to get full compatibility with RT-LAB.

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This computation is performed periodically and takes into account at each time the updated
parameters from the weather conditions (irradiance G and ambient temperature T) and from the
array configuration. The computed I-V characteristic is continuously fed into the look up table
which in turn output the control signal according to the measured signal.
Figure 6. Implementation of PV array model
5. EXPERIMENTAL RESULTS
To validate the proposed PV simulator, the load mentioned in figure (6) was replaced with a
commercial PV inverter connected to the power grid. The inverter used in this test is an AXUN
2100.
The PV array used in the model consists of one string of 10 modules. Each module has 72 cells
in series protected by 4 bypass diodes. The data sheet values of the module used in the model
are given in Table (1)
Table 1. PV module data sheet
Maximum power 165 W
Maximum power voltage 34.4 V
Maximum power current 4.8 A
Open circuit voltage 43.2 V
Short circuit current 5.1 A
5.1. Variable weather conditions
In this case, the solar irradiance was set to switch between G =1000W/m2
and G =500W/m2
during the simulation.
The temperature was kept constant at 300 °K. The I-V and P-V characteristics of the array
obtained from offline simulation at these two irradiance levels and the expected maximum
power points (MPP) are shown in figure (7).

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Figure 7. I-V and P-V characteristics from offline simulation (case1)
Figure (8) shows the current and voltage measured at the output of the simulator. The solar
irradiance was initially set to G =1000W/m2
and then changed to G =500W/m2
at t =110s and
backward at t =190s. During start up, the inverter requires approximately 40s to synchronize
with the power system. During this period, the DC power supply operates at the voltage equals
to the open circuit voltage of the array since there is no demand in current from the inverter.
After the connection with the power system is established, the inverter starts to seek the MPP of
the corresponding irradiance and oscillates around that point once found. The power at the
simulator output can be obtained by multiplying the measured current and voltage. It oscillates
around 1230W when G=1000W/m2
and around 690W when G =500W/m2
.

8
Figure 8. Current and voltage measured at the input of inverter (case1)
5.2. Abnormal operation of the array
In this case, a fault is introduced in the array during the simulation. This fault is due to partial
shading on the modules which results in many local maximum power points. The irradiance and
temperature were kept constant and equal to 1000W/m2
and 300 o
K respectively. Figure (9)
shows the P-V characteristic of the array obtained from offline simulation. The inverter is
expected to operate at MPP1 during normal operation and at MPP3 when there is shading.

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Figure 9. P-V characteristic from offline simulation (case2)
6. CONCLUSION
This paper presented a method for developing a real time simulator of a PV array based on
Power Hardware In the Loop (PHIL) simulation. Variable weather conditions and major
electrical faults were taken into consideration in the proposed simulator. The steady state
performance of the proposed simulator is well established. Though, depending on the dynamic
of the applications with which this simulator will be used, their dynamic performances need to
be thoroughly examined.
The future research plans to address some of the previously discussed limitations, which include
model optimization, cost reduction of fault data acquisition, and integration with PV inverters.
REFERENCES
[1] A. Drews , A.C. de Keizer, H.G. Beyer E. Lorenz ,J. Betcke ,W.G.J.H.M. van Sark, W.
Heydenreich, E. Wiemken, S. Stettler ,P. Toggweiler, S. Bofinger , M. Schneider, G. Heilscher and
D. Heinemann, Monitoring and remote failure detection of grid-connected PV systems based on
satellite observations, Solar Energy 81 (2007) 548.
[2] M. Muselli, G. Notton, J. L. Canaletti and A. Louche, Utilization of METEOSAT Satellite-Derived
Radiation Data for Integration of Autonomous Photovoltaic Solar Energy Systems in Remote Areas,
Energy Conversion and Management 39 No. 1/2 (1998) pp. 1-19.
[3] Kuei-Siang Chao, Sheng-Han Ho, Meng-Hui Wang. Modeling and Fault Diagnosis of a
Photovoltaic System. Electric Power Systems Research, 2007, 78(2), pp. 97-105.
[4] M. Park, Y. In-Keun, A novel real-time simulation technique of photovoltaic generation system
using RTDS, IEEE Trans. Energy Convers. 19 (1) (2004), 164–169.
[5] F.D. No, T. D. Morgan, SPICE-modeling of cascade solar cells, in: Proceedings of the IEEE
Southeast Conference, 1991, pp. 776–780.

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[6] J. A. Gow, C. D. Manning, Development of photovoltaic array model foruse inpower-electronics
simulation studies, IEE Proc. Electric Power Appl.146 (2)(1999)193–200.
[7] K. W. Horng, Measurement and EMTP modeling of photovoltaic cells applied to the analysis of
partial shading faults, Master Thesis, National Yunlin University of Science & Technology, Taiwan,
2003.
[8] J. H. R. Enslin, D. B. Snyman, Combined low-cost, high-3 efficient inverter, peak power tracker and
regulator for PV applications, IEEE Trans. Power Electron. 6(1)(1991)73–82.
[9] A.AI-Amooudi, L. Zhang, Application of radial basis function networks for solar-array modeling
and maximum power-point prediction, IEE Proc. Gener. Transm. Distrib. 147(5)(2000).
[10] Th. F. Elshatter, M. T. Elhagry, E. M. Abou-Elzahab, A. A. T. Elkousy, Fuzzy modeling of
photovoltaic panel equivalent circuit, in: Proceedings of the Photovoltaic Specialists Conference,
vol. 15, no. 22, 2000, pp. 1656–1659.
[11] B. M. T. Hoand H.S.-H. Chung, “An integrated inverter with maximum power tracking for grid-
connected PV systems,” IEEE Trans. Power Electron., vol. 20,no. 4, pp. 953–962, Jul.2005.
AUTHORS
Abd Essalam Badoud was born in Ait Muhli, Algeria; he received the
Engineer, Magister and Ph.D. degrees in automatic from the University of Sétif
1, in 2006, 2009 and 2014 respectively. He is member at the Automatic
Laboratory of Sétif, currently he is professor at the electrical engineering
department, university of Sétif1, Algeria. His main research areas include
modeling, control, design and diagnosis for renewable energy and nuclear
systems. His is the author of more than 80 papers in journals and conferences.

IMPLEMENTATION OF A REAL TIME MONITORING SYSTEM FOR A PHOTOVOLTAIC GENERATION SYSTEM

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