matlab simulink model for energy harvesting system using … · 2019-08-26 · sources of energy...

International Journal on Future Revolution in Computer Science & Communication Engineering ISSN: 2454-4248 Volume: 5 Issue: 6 331 –337

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331 IJFRCSCE | June 2019, Available @ http://www.ijfrcsce.org

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Matlab Simulink Model for Energy Harvesting System using Integrated Input

Poonam1, Dr. Shamsher Malik

2

M. Tech Scholar1 – U. I. E. T, Department ECE, Rohtak, Haryana, India

Professor2– U. I. E. T, Department ECE, Rohtak, Haryana, India

[email protected], [email protected]

2

Abstract – Ambient Energy harvesting is one of the alternatives in replacing the use of batteries and wiring where small amounts of energy from

environmental sources such as solar, air flow or vibration is harvested to form an electrical energy. In the recent years, obtaining a sustainable

form of energy to power various autonomous wireless & portable devices is increasingly becoming a matter of concern & various alternate

sources of energy have been explored. The concept of power harvesting works towards developing self-powered devices that do not require

replaceable power supplies. This paper represents energy harvesting using various energy renewable resources which include solar, wind,

thermoelectric and piezoelectric. The system designed in Matlab Simulink environment and their energy combined and then transferred to small

grid. In this system in near future RF module can also be integrated so that if out of these module none of them able to provide sufficient amount

of energy then through RF link adequate amount of energy can be received so that low power devices can be driven.

Key Words – Solar Photovoltaic, Fuzzy Logic, Wind Turbine, Thermoelectric, Piezoelectric, MPPT

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I. INTRODUCTION

The field of power harvesting has experienced significant

growth over the past few years due to the ever-increasing

desire to produce portable & wireless electronics with

extended lifespan [4]. Need for energy is ever lasting and it is

definitely true regarding electrical energy, because electrical

energy is huge part of total energy consumption worldwide.

The consumption of electrical energy increasing exponentially

with pace of time therefore demand increased to twice in

recent times and it is estimated that demand can go up to 76%

by 2030. As we know conventional electric power generation

systems based on hydro power, nuclear power and fossil fuel.

Figure 1 Energy consumption of world per year

The nuclear and fossil fuels comes under category of not

renewable and these resources will run out in coming days as

the population increasing exponentially so definitely these

resources will dry up [2-3]. When we talk about hydropower,

there are many challenges which we have to face like limited

sites are available and those available are far from where

required. On the other hand when we get power with help of

conventional means then it is also harmful to the environment,

due to large scale combustion of hydrocarbon-rich fossil fuels,

huge amount of dangerous particles added to the atmosphere

like carbon dioxide and carbon monoxide which are

responsible for global warming [1].

II. LITERATURE SUREVY

Johan J. Estrada-López et al: IoT paradigm is under

constant development and is being enabled by the latest

research work from both industrial and academic

communities.

Figure 2 Block diagram of an IoT end-node

The paper starts by discussing a general structure for IoT end-

nodes and the main characteristics of PMUs for energy


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harvesting. Then, an overview is given of different published

works for multisource power harvesting, observing their main

advantages and disadvantages and comparing their

performance. Finally, some open areas of research in

multisource harvesting are observed [6].

Hiba Najini et al: This paper presents a technical simulation

based system to support the concept of generating energy from

road traffic using piezoelectric materials. The simulation based

system design replicates a real life system implementation. It

investigates practicality and feasibility using a real-time

simulation platform known as MATLAB Simulink. The

system design structure was proposed considering factors

involved with the field of material sciences for piezoelectric

generator modeling and field of power electronics for

additional components in producing a realist outcome. It also

ensures ease of vehicle performance, as this system utilizes

energy source derived as kinetic energy released from vehicles

into electrical power output, that is, obtained by harnessing

kinetic energy due to strain of vehicles over asphalt road

surface [7].

Figure 3 Process flow diagrams

Anjali Prabhakaran et al: There is a difficulty in tracking the

maximum power point of the photovoltaic system due to

nonlinearity of the I-V characteristics which is dependent of

the temperature and irradiation conditions. This paper

proposes the controlling of the photovoltaic system by sliding

mode control. Here, open circuit voltage MPPT technique is

used to track maximum power point. The system involves a

PV panel, dc/dc boost converter, a load and a control that

generates PWM signal that goes to the boost converter. The

open circuit voltage based MPPT uses open circuit voltage to

calculate maximum power output voltage. The input to the

sliding mode controller is the change in reference voltage and

PV voltage and the output of the SMC is the change in duty

ratio. The SMC is used to track the maximum power point by

changing the duty cycle of the boost converter. Using this

method, the output power of PV array directly controls the

dc/dc converter, hence reduces the complexity of the system.

The advantages of this method are high efficiency, best

accuracy, good convergence speed, and is robust to weather

condition changes [8].

Figure 4 Block diagram of simple PV system with SMC-

MPPT

Aryuanto Soetedj et al: This paper presents the modeling of

wind energy systems using MATLAB Simulink. The model

considers the MPPT technique to track the maximum power

that could be extracted from the wind energy, due the non-

linear characteristic of the wind turbine. The model consists of

wind generation model, converter model, and MPPT

controller. The main contribution of our work is in the model

of DC-DC converter (buck converter) which is developed in

rather details, which allows the MPPT controller output

adjusts the voltage input of the converter to track the

maximum power point of the wind generator. The simulation

results show that the developed model complies with the

theoretical one. Further the MPPT control shows a higher

power output compared to the system without MPPT [9].

Figure 5 Simulink model of the buck converter

Huan-Liang Tsai et al: This paper presents implementations

and verification of models of thermo-electric cooler and

generator modules using Matlab/Simulink. The proposed

models are designed with a user-friendly icon and a dialog


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box, like Simulink block libraries, making them easy to use for

simulation, analysis, and optimization of further applications

[10].

Figure 6 Simulink model of TEG system

III. NEED OF ENERGY

HARVESTING

Availability of advance transducers: Transducers are the

devices that turn one form of energy into another. Steady

advances in transducer technology, especially recent

breakthroughs in materials and semiconductors, have made

new types available and increased the efficiency of all of them.

Availability of Low-Power Circuitry: Just as the power

available from transducers has been increasing, the power

needed to run electronic circuits has been decreasing. Power

improvements do not totally depend on semiconductor

advances. Power consumption has become a major concern

with larger chips this motivated circuit designers to develop

low-power designs for digital and analog circuits. The

improvements made for large chips are being carried over to

smaller circuits, resulting in devices that can be powered

entirely from harvested energy [12].

IoT is Driving Devices Closer to the Edge: The Internet of

Things (IoT) is here and expanding rapidly. Depending on

which analyst you refer to, predictions of connected “things”

range from 20 billion to 50 billion in three years. Our

computers, smart phones, and web servers are already

connected, so the next connectivity wave will focus on

devices. As connected devices get smaller and further out on

the edge, powering them with batteries becomes more of a

problem. The prospect of replacing batteries, even as

infrequently as every five to 10 years, is simply a non-starter.

Consider sensors embedded within structural members or

walls. Battery replacement is simply not an option in such

locations [6].

Economical than Battery

The price of the electronics is so low, if a battery is needed, it

can represent a major fraction of the cost of the device cost.

This is a major reason behind the move to devices relying on

energy harvesting for power. Battery technology has not kept

pace, so modules that require batteries may not satisfy design

requirements because modules would be too large. One

possibility for addressing the reliability, management, and size

issues associated with batteries is to remove them all together.

Energy harvesting is making this possibility a reality.

Devices Reliability: As wireless devices are deployed in

larger numbers, end users are discovering that battery

reliability is not usually up to the task. A typical goal for the

low-power networking protocols described above is often a

10-year battery life on a single coin cell. Several approaches

are claiming success. These estimates often rely on simple

calculations that compare steady-state power consumption to

battery capacity specifications [14]. Real-world deployments,

however, often show unacceptable failure rates. The problem

is that battery lifetime is a multi-dimensional problem. Battery

reliability begins with the manufacturing process. Unlike

electronic components that have easy-to-predict lifetimes,

batteries are based on chemical processes that have many

different failure modes. Even manufacturers who weed out

infant mortalities still have field failures, and the unavailability

of data makes it impossible to include those failures in models

and calculations.

IV. RESEARCH METHODOLOGY

AND SOFTWARE

SOFTWARE: MATLAB 2015b

It is powerful software that provides an environment for

numerical computation as well as graphical display of outputs.

In Matlab the data input is in the ASCII format as well as

binary format. It is high-performance language for technical

computing integrates computation, visualization, and

programming in a simple way where problems and solutions

are expressed in familiar mathematical notation. Using

MATLAB, you can solve technical computing problems very

easily and time saving as compared to traditional

programming languages, such as C, C++, and FORTRAN. The

name MATLAB stands for matrix laboratory.

After going through diverse research paper, merits and

demerits of the algorithm and thought come up with some

adaptive solution. In this paper four renewable energy

resources are used which are listed below:

Solar Energy Model

Wind Model using MPPT

Piezoelectric Model

Thermo Electric Model


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Solar PV Panel Model

Photovoltaic generators have a nonlinear voltage current

characteristic with a unique Maximum Power Point (MPP),

which depends on the temperature and irradiance condition.

When these conditions are changed, the operating point and

MPP will be changed. Therefore, MPPT control method is

required to ensure that the maximum available power is

obtained from the panel.

Solar System Configuration:

Voc = 44.1 volt

Vmp= 35.7

Isc=8.6

Imp=7.99

4*8 Solar Arrays

Solar Type= Poly Crystalline

Solar irradiance = 1000w/m^2

Maximum voltage = 24 V volts

Maximum current = 5 Amp

Temperature = 25’c

Power= 120 W

Table 1 Power v/s Irradiance

Sr. No Irradiance (w/m2) Power (W)

1 1000 120

2 800 80

3 600 50

Figure 7 Simulink model for solar PV panel

Wind Turbine using MPPT

Nowadays, demands for the renewable energy resources are

increase significantly. The most popular ones are wind energy

and solar energy resources. Both offer advantages such as free

and clean. But, the wind energy has a lower installation costs

compared to the solar energy.

Figure 8 Simulink model for wind turbine model using MPPT

Wind Turbine Configuration

Wind speed = 8-12 m/sec

Blade angle = 0’

Mechanical output power =8.5e3 W

Electrical generator base Power =8.5e3/0.9 VA

Permanent Magnet Synchronous Machine (PMSM)

In PMSM stator windings are connected in wye to an internal

neutral point. There are various configuration exist of rotor for

sinusoidal machine for example salient pole or round.

Predetermined models are accessible for sinusoidal back EMF

devices.

Number of phases=1

Back EMF waveform= Sinusoidal

Rotor Type= Salient Pole

Mechanical Input= Torque Tm

Stator phase resistance Rs (ohm)=0.425

Inductances [ Ld(H) Lq(H) ]= [0.0082 0.0082]

Flux Linkage= 0.433

Inertia, viscous damping, pole pairs, static friction= [0.01197

0.001189 5]

Table 2 Wind Speed v/s Power

Sr. No Wind Speed

(m/s)

Power (W)

1 12 80-120

2 10 70-110

3 8 50-105

The wind energy system extracts the wind energy and converts

it to the electrical energy. The output power of wind energy

system varies depend on the wind speed. Due the nonlinear

characteristic of the wind turbine, it is a challenging task to

maintain the maximum power output of the wind turbine for


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all wind speed conditions. There are extensive researches

concerning with the approaches to track the maximum power

point of the wind turbine called as MPPT control.

Thermo Electric Model

Thermoelectric power generators have emerged as a promising

alternative green technology due to their distinct advantages.

Thermoelectric power generation offer a potential application

in the direct conversion of waste-heat energy into electrical

power where it is unnecessary to consider the cost of the

thermal energy input.

Thermal System Configuration:

Gain=0.5

Height of thermoelectric module tolerated +/- 0.2mm Maximum compressive load: 1MPa Hot Side Temperature = 100

Flatness +/- 0.05mm

Cold Side Temperature = 25

Temperature Difference = 75

Table 3 Temperature at hot junction and cold

junction v/s Power

Sr. No Temperature

Difference

Power (W)

1 120 292

2 75 131

3 50 56

4 25 13

Figure 9 Simulink model for thermo electric system

The application of this alternative green technology in

converting waste-heat energy directly into electrical power can

also improve the overall efficiencies of energy conversion

systems.

Piezoelectric Model

Piezoelectric energy generation utilizes the strain caused by

vehicles over asphalt road surface due to gravity and

harnessing kinetic energy or vibrations from moving vehicles.

These vibrations from moving vehicles are nothing but

imbalance caused by strain of a tire on gravel road. In order to

capture and harness such energy, a piezoelectric transducer by

nature is a perfect device as piezoelectric materials react to

“compression” to produce electrical output.

Figure 10 Simulink model for piezoelectric

Random Source = Output a random signal with uniform or

Gaussian distribution. Set output repeatability to Non

repeatable (block randomly selects initial seed every time

simulation starts), Repeatable (block randomly selects initial

seed once and uses it every time simulation starts), or Specify

seed (block uses specified initial seed every time simulation

starts, producing repeatable output).

Piezoelectric Model Configuration: Stiffness of the piezoelectric material of the sensor (k) =

2.211681228127215e+03

Electromechanical Coupling Factor = 0.65

Elastic modulus of the piezoelectric element (Kp) = 110

Piezoelectric coefficient of the piezoelectric element

(D33) = 250

Equivalent capacitance of the piezoelectric element (Ca) =

1/2.412743157956961e+04

Gain1 =0.02

Gain 2=20

Figure 11 Specification of random source for piezoelectric

power


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Figure 11 Simulation output for solar power, wind power,

piezoelectric power, thermo electric power and load power

Figure 12 Wind turbine fan and solar array of 4 sets

Figure 13 Configuration of Piezoelectric system

V. CONCLUSION

Energy harvesting can be viewed as a maintenance-free

alternative to battery technology. It may involve some initial

installation cost but in the long run it is cost effective and

beneficial as the sources for EH are available naturally. Since

the source availability is not continuous, the problem of

getting a consistent output remains a challenge. Further

improvements in the storage efficiency will make it possible to

increase the reliability of the EH over the battery and plug-

based connections. This paper represents energy harvesting

using various energy renewable resources which include solar,

wind, thermoelectric and piezoelectric. The system designed in

Matlab Simulink environment and their energy combined and

then transferred to small grid. In our research entire module

designed and integrated successfully and also capable of low

power devices. Further in this implemented module, RF

module can also be integrated to make our module totally

independent.

REFERENCES

[1] M Nalini, J V Nirmalkumar, R Muthukumar, M

VigneshEnergy harvesting and management from ambient RF

radiation, IEEE International Conference on Innovations in

Green Energy and Healthcare Technologies(ICIGEHT’17)

[2] Supriya Gaikwad, Ms. MunmunGhosal, Energy efficient

storage-less and Converter-less renewable energy harvesting

system using MPPT, 2017 2nd International Conference for

Convergence in Technology (I2CT)

[3] Akshaj Arora, Sahitya Singh, Neel Chatterjee, Malay

RanjanTripathy and Sujata Pandey, Design and Implementation

of Hybrid Energy Harvesting System for Low Power Devices,

Indian Journal of Science and Technology, Vol 9(47), DOI:

10.17485/ijst/2016/v9i47/106887, December 2016

[4] Prateek Asthana, Gargi Khanna, A Comparative Study of

Circuit for Piezo-electric Energy Harvesting, 2016

International Conference on Computing for Sustainable Global

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[5] Arif Obaid and Xavier Fernando, Wireless Energy Harvesting

from Ambient Sources for Cognitive Networks in Rural

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[6] Johan J. Estrada-López , Amr Abuellil, Zizhen Zeng and

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Power Electronics and Applications, 2018, 8, 6;

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[7] Hiba Najini and Senthil Arumugam Muthukumaraswamy,

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[8] Anjali Prabhakaran, Arun S Mathew, “Sliding Mode MPPT

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Matlab/Simulink”, Journal of Electronic Materials, ISSN 0361-

5235, Volume 39, Number 9, September 2010


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[11] Md. Rokonuzzaman and Md Hossam-E-Haider, “Design and

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Journal of Electrical and Computer Engineering Vol. 3, No. 1,

January 2019

matlab simulink model for energy harvesting system using … · 2019-08-26 · sources of energy...

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