This PPT includes all the different types of MPPT techniques with Conventional & AI based for reviewing all techniques as comparison.

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Artificial Intelligence based MPPT Algorithm
for Grid Connected Solar PV System
1
6th International Conference for Convergence in
Technology (I2CT)
Darshil Shah, M. Tech Student at Nirma University
Dr. Manisha Shah, PhD, Asst. Professor at Nirma University

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 Introduction about Project
 Problem Statement & Project Design
 Basic Difference between AI & Without AI
 Conventional MPPT Method
 System Parameter Design
 P&O & Different AI Techniques with Simulations
 Captured Simulation Comparison in different Methods
 Comparison Analysis
 Conclusion
2
TABLE OF CONTENT

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INTRODUCTION TO PROJECT
▸Nowadays, energy sectors are trying to improve them generation capacity and also the installed energy
capacity. Moreover, use of renewable energy sources are on demand for generation plants such as
Solar, Wind, Biomass and so on.
▸Here, for this Project major concern and focused on use and development of Artificial Intelligence
along with Renewable Energy (Solar) to come up with efficient, sustain, steady, long term,
economically good and most importantly environmental friendly energy generation.
▸Artificial Intelligence becomes more and more important in the energy industry and is having great
potential for the future design of the energy system. Typical areas of application are electricity trading,
smart grids, or the sector coupling of electricity, heat and transport. Prerequisites for an increased use
of AI in the energy system are the digitalization of the energy sector and a correspondingly large set
of data that is evaluable. AI helps make the energy industry more efficient and secure by analyzing and
evaluating the data volumes.
▸ Perturb and Observe calculation is straightforward and doesn’t require past information on the PV
generator qualities or the estimation of sun powered force and cell temperature and is anything but
difficult to execute with simple and computerized circuits.

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PROBLEM STATEMENT & PROJECT DESIGN
▸Is it possible to use AI
with renewable energy?
Is it feasible?
▸If it is feasible, then
which method of AI
works better?

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BASIC DIFFERENT FOR WITH AND WITHOUT AI
Without use of AI With use of AI
Methods: P&O, IC, variable Step size P&O,
Fractional OC Voltage
Methods: Fuzzy Logic Control, Artificial Neural
Network, Adaptive Neuro Fuzzy Interface System,
Particle Swarm Optimization
Complexity in Algorithm: Very low Complexity in Algorithm: Very high
Time Consuming: Very high due to higher
iterations
Time Consuming: Very low due to iterations in
some fractions of time
Stability and Oscillations: Very low steady state
response with higher Oscillations
Stability and Oscillations: Very high steady state
response with negligible Oscillations
Feedback: Not so easy to take Feedback: Easily can be modify
Target matching: Near about Target Matching: Absolute

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CONVENTIONAL MPPT METHOD (P&O)
▸Perturb & Observe (P&O) is the simplest method. In
this we use only one sensor, that is the voltage sensor,
to sense the PV array voltage and so the cost of
implementation is less and hence easy to implement.
▸In this method the controller adjusts the voltage by a
small amount from the array and measures power; if
the power increases, further adjustments in that
direction are tried until power no longer increases.
▸This is called the perturb and observe method and is
most common, although this method can result in
oscillations of power output. The main advantages of
P&O algorithm are simple structure and ease of
implementation, with both stand-alone and grid-
connected systems, MPP tracking can be done with
very high efficiency. PV &VI Characteristics of P&O Method MPPT

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SYSTEM PARAMETER DESIGN
 Load Design: 410 W
 Boost Converter Design: Input: Voltage: 58-60 V
Current: 6.67 A
Power: 392-407 W
: Output: Voltage: 83-86 V
Current: 4.2-4.7 A
Power: 395-410 W
 PV Array Design: Input: Irradiance: 1000 W/m2
Temperature: 25 ˚C
Output: Voltage: 83-86 V
Current: 4.2-4.7 A
Power: 395-410 W

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SIMULATION DESIGN FOR CONVENTIONAL MPPT (P&O)
P&O Algorithm Method for Solar MPPT system

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SIMULATION DESIGN FOR AI BASED MPPT (ANN)
ANN Algorithm Method for Solar MPPT system

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SIMULATION DESIGN FOR AI BASED MPPT (ANFIS)
ANFIS Algorithm for Solar MPPT System

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INPUT & OUTPUT DATASETS FOR ANFIS
Vpv
61
61
59
54
51
47
43
39
33
31
25
20
18
12
10
0
Input datasets for ANN
Pout
0
10
12
18
20
25
31
35
41
49
54
60
65
72
79
84
Output datasets for ANN
‘nftool’ toolbox is applied to the
MATLAB for ANN work.
Which opt this input data for nftool and
train data for the same in ‘Levenberg-
Marquardt’ method.

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TRAINED DATA FOR ANFIS
Train data against error in FIS system

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CHANGES IN INPUTS BETWEEN ANN & ANFIS
ANFIS Model Structure of Inputs: Vpv, Ipv and Output: Duty
Scaled Conjugate Gradient in ANN

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CHANGES CAPTURED BETWEEN ANN & ANFIS for INPUT
VOLTAGE
Input Voltage is up to
58.6 V
Input Voltage is at 58 V

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CHANGES CAPTURED BETWEEN P&O & ANFIS FOR
OUTPUT POWER
Output Power at 400 W with
Oscillations
Output Power at 410 W with
Steady State Condition

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DIFFERENT IRRADIANCE & LOAD CONDITIONS
Different Irradiance Conditions for P&O and ANFIS method

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Different load Conditions for P&O and ANFIS method

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COMPARISON ANALYSIS OF DIFFERENT MPPT TECHNIQUES
MPPT
Techniques
Input
Voltage
(V)
Input
Current
(A)
Input
Power
(W)
Output
Voltage
(V)
Output
Current
(A)
Output
Power
(W)
Change in
Duty
P&O 61.3 6.67 409 90.27 4.513 407.83 0.72-0.31
IC 58.6 6.85 402.1 83.43 5.02 401.12 0.65-0.33
ANN 57.8 7.11 401.71 157.6 2.76 419.2 0.52-0.32
ANFIS 58.0 7.60 440.8 84.66 5.32 450.391 0.12-0.31
Fuzzy Logic 60.45 6.96 420.73 86.66 4.84 419.43 0.07-0.32

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CONCLUSION
▸ After all the discussion, this project is mainly focused on the different MPPT techniques
which are based on AI. Also, P&O, ANN, ANFIS and Fuzzy Logic method is described in
MATLAB Simulink.
▸ According to comparative study, ANFIS based AI technique is reach towards target due
to its analysis power in different conditions of irradiations and loads to the standalone
system. ANFIS fetched data from its given range of the different inputs of the voltage and
power from the PV panel.
▸ This proves that, ANFIS method is very much superior than using other methods because
of ANN and FUZZY Logic using comparison technique apart from using sustain and
reliable output given. Where, ANFIS provides smoothness and sharpness in output with
higher steady state condition along with higher rate of convergence in different layers and
complexity become smooth by itself.

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THANK YOU ALL