Title:ANN-based Maximum Power Point Tracking for a Large Photovoltaic Farm Through Wireless Sensor Networks
VOLUME: 14 ISSUE: 1
Author(s):K. Annaraja*, S. S. Sundaram, S. Selvaperumal and G. Prabhakar
Affiliation:Department of Electrical and Electronics Engineering, PTR College of Engineering & Technology, Madurai, Tamilnadu, Department of Electrical and Electronics Engineering, Hindustan College of Engineering and Technology, Coimbatore, Tamilnadu, Department of Electrical and Electronics Engineering, Syed Ammal Engineering College, Ramanathapuram, Tamilnadu, Department of Electrical and Electronics Engineering, V.S.B. Engineering College, Karur, Tamilnadu
Keywords:Solar photo voltaic farm, wireless sensor network, unequal solar insulation on PV panels, artificial neural network,
maximum power point tracking, sliding mode controller.
Abstract:Background: A novel system for the usage of Maximum Power Point Tracking of an
expansive Solar Photo Voltaic (SPV) farm subjected to conceivable incomplete shading is displayed
in this paper. The SPV farm being spread over an expansive territory a remote sensor organize
is utilized for checking the sun based protection in the region of each board. The motivation
behind the remote sensor organize is to screen the sunlight based protection at various areas near
each of the PV board from the tremendous region of the photograph voltaic homestead comprising
of countless voltaic boards. The observed protection information is utilized by a prepared. Artificial
Neural Network to locate the ideal DC terminal voltage to be kept up over the general DC
terminals of the photograph voltaic ranch. All the PV boards are associated in arrangement association
with the fundamental bye pass diodes. The DC control accessible at the yield terminals of
the SPV cultivate is first DC to DC changed over with a Positive Output Luo Converter (POLC)
and bolstered to a heap. A MATLAB Simulink based reproduction was created to approve the
proposed system.
Methods: Maximum Power Point Tracking based on Artificial Neural Network through wireless
sensor networks.
Results: As the result of the proposed idea and its implementation in MATLAB we have two sets
of results. In either case the input is a vector of 40 elements and the output of the first segment of
the work is the estimation of the threshold PV terminal voltage that will guarantees maximum
power point operation. In the first case we have the MATLAB SIMULINK implementation of the
basic configuration of the forty PV panels arranged in series connection and we have provided a
facility to edit the solar insulation levels pertaining to the individual PV panels. In this first configuration
we have set a continuously variable PV current for all the panels and the PV current for
all the panel are the same. Using this setup, for any combination of solar insulation pattern of the
forty panels the overall PV curve and the overall VI curve can be drawn in MATLAB. As the
simulation runs the PV current is changed from 0 to the maximum or the short circuit current level
in a slowly rising manner implemented using a ramp signal.
During this period the total power output and the terminal voltage of the PV farm are sent to the
work space and the data is thus collected in the workspace of MATLAB. Using basic MATLAB
commands the maximum power output and the PV terminal voltage corresponding to the maximum
power output are obtained. The PV current at maximum power output condition, the corresponding
PV farm terminal voltage, the maximum power output recorded at this condition all correspond
to the present insulation vector condition. This way, by changing the elements of the
insulation for all the forty panels in a random manner we obtain for each case the Ipmax[i],
Pmax[i], Vpmax[i] and this corresponds to insulation[n,i]. Where n is the number of panels, in this
case 40 and i the ith experiment. In each experiment the solar insulation level of all the forty panels
can be changed and the parameters Vpmax[i], Ipmax[i] and Pmax[i] can be obtained. The value of
the harvested power as found from the characteristics for any given set of insulation is denoted as
the estimated power. The value of power as obtained from the proposed ANN SMC POLC
combination is denoted as the Actual Power.
Conclusion: A wireless network based insulation monitoring has been done. An ANN based
MPPT algorithm has been developed that gives the reference MPP voltage. The sliding mode control scheme uses the reference voltage and produces the switching pulses for the POLC. The ANN had been trained with a number of combinations of different insulation values falling on each of
the forty panels and the ANN gives the correct reference voltage for any combination of insulation
levels that were not used while training. The sliding mode controller uses this reference voltage
and gives the switching pulses to the POLC that harvests the maximum power output to the RL
load. The proposed system has been implemented in the MATLAB SIMULINK environment and
has thus been validated. The obtained results have been compared against the maximum power
output values that could be derived from the characteristic curves obtained for the given combination
of insulation levels. The proposed system gives results very close to the values obtained from
the characteristics. As a future work the proposed idea can be validated using hardware based experimental
setup.
