20-10-2016, 12:57 PM
DESIGN AND IMPLEMENTATION SOLAR PANEL POWER CONTROL USING MPPT ALGORITHM FOR SOLAR HOUSING
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ABSTRACT
Conventional solar panels available today simply use Photovoltaic cells to convert solar to electrical energy. The power output from such solar panels is unstable and inefficient due to their incompetence to change its operation with changes in temperature, irradiance and other environmental factors which affect their output. The fact that renewable sources are unstable and that they are difficult to interface with a real time application makes people opt for non renewable energy sources. By tracking the operating point at which the maximum power occurs for a particular irradiance, temperature and other factors, one can eliminate both the drawbacks faced currently. This tracking technique based on Maximum Power Point Tracking (MPPT) enables the direct use of the power in everyday application as it makes the source stable and efficient. This project is aimed at developing a MPP tracking algorithm based on Fuzzy logic sets.It has been proved that the system with MPPT using fuzzy logic controller increase the efficiency of energy production from PV. The comparison with the proposed symmetrical FLC-based MPPT method, the transient time and the MPPT tracking accuracy are further improved by 42.8% and 0.06%, respectively. The working prototype will boost the output power from the solar panel through MPPT algorithm stored in the micro-controller by changing the duty cycle of the power MOSFET.
INTRODUCTION
One of the major concerns in the power sector is the day-to-day increase in power demand but there is an unavailability of enough resources to meet the power demand using the conventional energy sources. Demand has increased for renewable energy sources to be utilized along with conventional systems to meet this increased energy demand. Renewable sources like solar energy, wind energy and tidal energy are the prime energy sources which are being utilized in this regard. The continuous use of non renewable energy has caused the fossil fuel deposits to be reduced and has drastically affected the environment, depleting the biosphere and cumulatively adding to global warming. Solar energy is abundantly available that has made it possible to harvest it and utilize it properly. Solar energy can be a standalone generating unit or can be a grid connected generating unit depending on the availability of a grid nearby. Thus it can be used to power rural areas where the availability of grids is very low. Another advantage of using solar energy is the portable operation whenever and wherever necessary.
In order to tackle the present energy crisis one has to develop an efficient method in which power can be extracted from the incoming solar radiation. The development in power electronics and material science has helped engineers to come up with very small but powerful systems to withstand the high power demand. Trend has been set for the use of multi-input converter units that can effectively handle any voltage fluctuations. But due to high production cost and the low efficiency of these systems they can hardly compete in the competitive markets as a prime power generation source. The constant increase in the development of the solar cells manufacturing technology would definitely make the use of these technologies possible on a wider basis than what it is presently. The use of the newest power control mechanisms called the Maximum Power Point Tracking (MPPT) algorithms has led to the increase in the efficiency of operation of the solar modules and thus is effective in the field of utilization of renewable sources of energy.
1.2 NEED FOR THE PROJECT
Photovoltaic power control is one of the burning research fields these days. Researchers are working round the clock to develop better solar cell materials and efficient control mechanisms. Renewable energy is the necessity of the day due to increasing negative impacts from non-renewable resources. The main issues in renewable energy sources are that they are unstable and inefficient and so they cannot be directly interfaced with any application. The control of solar panel power by MPPT has been done using several algorithms namely perturb and observe (P&O) algorithm, incremental conductance algorithm and so on. This project deals with the control of the same using fuzzy logic algorithm. The challenge of the project, the new area of study and its social necessity were the motivations behind the project.
1.3 OBJECTIVE OF THE PROJECT
The basic objective is to design and implement a control in solar panel using MPPT algorithm by varying the operating point of the panel according to the effects of temperature and irradiance changes. Thereby, successfully implement the control mechanism in MATLAB simulation and in hardware as well. Modeling the converter and the solar cell in Simulink and interfacing both with the MPPT algorithm using fuzzy logic to obtain the maximum power point operation would be of prime importance.
1.4 LITERATURE REVIEW
1. Ahmed M. Othman, Mahdi M.M. El-arini, Ahmed Ghitas and Ahmed Fathy “Realworld maximum power point tracking simulation of PV system based on Fuzzy Logic control”, NRIAG Journal of Astronomy and Geophysics (2012) 1, 186–194
In the recent years, the solar energy becomes one of the most important alternative sources of electric energy, so it is important to improve the efficiency and reliability of the photovoltaic (PV) systems. Maximum power point tracking (MPPT) plays an important role in photovoltaic power systems because it maximize the power output from a PV system for a given set of conditions, and therefore maximize their array efficiency. This paper presents a maximum power point tracker (MPPT) using Fuzzy Logic theory for a PV system. The work is focused on the well-known Perturb and Observe (P&O) algorithm and is compared to a designed fuzzy logic controller (FLC). The simulation work dealing with MPPT controller; a DC/DC Buck converter feeding a load is achieved. The results showed that the proposed Fuzzy Logic MPPT in the PV system is valid.
2. Bendib.B, F. Krim, H. Belmili, M. F. Almi, and S. Boulouma “Advanced Fuzzy MPPT Controller for a stand-alone PV system”, The International Conference on Technologies and Materials for Renewable Energy, Environment and Sustainability, TMREES14.
This paper explain an intelligent method of maximum power point tracking (MPPT) using fuzzy logic control for stand-alone photovoltaic (PV) system . The PV system is composed of PV solar array, buck DC-DC converter, and MPPT controller. Fuzzy logic controller (FLC) is easy to implement, and does not need knowledge of the exact model of the system. Simulation results compared with those obtained by the conventional perturbation and observation (P&O) technique show the effectiveness of the fuzzy logic controller during steady-state and varying weather conditions
3. Bouchafaa.F, I.Hamzaoui and A.Hadjammar “Fuzzy Logic Control for the tracking of maximum power point of a PV system”, Energy Procedia 6 (2011) 633–642
Tracking of the maximum power point (MPPT) plays an important role in photovoltaic (PV) power systems because they maximize the power output from a PV system for a given set of conditions, and therefore maximize the array efficiency. This work presents a comparative study between different control strategies used most conventional digital namely perturbation and observation (P & O) and incremental Conductance (INC) with digital control by fuzzy logic (FLC). The introduction of fuzzy controller as a solution has given very good performance and whatever the parametric variation of the system
4. Bounechba.H, A. Bouzid, K. Nabti and H. Benalla “Comparison of perturb & observe and fuzzy logic in maximum power point tracker for PV systems”, The International Conference on Technologies and Materials for Renewable Energy, Environment and Sustainability, TMREES14
In this paper, an intelligent control method for the maximum power point tracking (MPPT) of a photovoltaic system under variable temperature and insolation conditions is discussed. The MPPT controller for boost converter based on fuzzy logic (FLC) is developed and compared to conventional tracking algorithm (P&O). The different steps of the design of these controllers are presented together with its simulation. Results of this simulation show that the system with MPPT using fuzzy logic controller increase the efficiency of energy production from PV.
5. Chun-Liang Liu, Jing-Hsiao Chen, Yi-Hua Liu and Zong-Zhen Yang “An Asymmetrical Fuzzy-Logic-Control-Based MPPT Algorithm for Photovoltaic Systems”, Energies 2014, 7, 2177-2193; doi:10.3390/en7042177
In this paper, a fuzzy-logic-control (FLC) based maximum power point tracking (MPPT) algorithm for photovoltaic (PV) systems is proposed. The power variation and output voltage variation are chosen as inputs of the proposed FLC, which simplifies the calculation. Compared with the conventional perturb and observe (P&O) method, the proposed FLC-based MPPT can simultaneously improve the dynamic and steady state performance of the PV system. To further improve the performance of the proposed method, an asymmetrical membership function (MF) concept is also proposed. Two design procedures are proposed to determine the universe of discourse (UOD) of the input MF. Comparing with the proposed symmetrical FLC-based MPPT method, the transient time and the MPPT tracking accuracy are further improved by 42.8% and 0.06%, respectively.
6. Pongsakor Takun, Somyot Kaitwanidvilai and Chaiyan Jettanasen
“Maximum Power Point Tracking using Fuzzy Logic Control for Photovoltaic Systems”, The International MultiConference of Engineers and Computer Scientists 2011 Vol II IMECS 2011,March 16 - 18,2011,Hong Kong
In this paper, a fuzzy logic control (FLC) is proposed to control the maximum power point tracking (MPPT) for a photovoltaic (PV) system. The proposed technique uses the fuzzy logic control to specify the size of incremental current in the current command of MPPT. As results indicated, the convergence time of maximum power point (MPP) of the proposed algorithm is better than that of the conventional Perturb and Observation (P&O) technique.
1.5 CONVENTIONAL LOGIC VS FUZZY LOGIC CONTROL
Fuzzy logic is a form of many-valued logic in which the truth values of variables may be any real number between 0 and 1, considered to be "fuzzy". By contrast, in Boolean logic, the truth values of variables may only be 0 or 1, often called "crisp" values. Fuzzy logic has been extended to handle the concept of partial truth, where the truth value may range between completely true and completely false. Furthermore, when linguistic variables are used, these degrees may be managed by specific (membership) functions. Classical logic only permits conclusions which are either true or false. For example, the notion that 1+1=2 is a fundamental mathematical truth. However, there are also propositions with variable answers, such as one might find when asking a group of people to identify a colour. In such instances, the truth appears as the result of reasoning from inexact or partial knowledge in which the sampled answers are mapped on a spectrum.
Humans and animals often operate using fuzzy evaluations in many everyday situations. In the case where someone is tossing an object into a container from a distance, the person does not compute exact values for the object weight, density, distance, direction, container height and width, and air resistance to determine the force and angle to toss the object. Instead the person instinctively applies quick “fuzzy” estimates, based upon previous experience, to determine what output values of force, direction and vertical angle to use to make the toss.
Both degrees of truth and probabilities range between 0 and 1 and hence may seem similar at first. For example, let a 100 ml glass contain 30 ml of water. Then we may consider two concepts: empty and full. The meaning of each of them can be represented by a certain fuzzy set. Then one might define the glass as being 0.7 empty and 0.3 full. Note that the concept of emptiness would be subjective and thus would depend on the observer or designer. Another designer might, equally well, design a set membership function where the glass would be considered full for all values down to 50 ml. It is essential to realize that fuzzy logic uses degrees of truth as a mathematical model of vagueness, while probability is a mathematical model of ignorance.
BASIC THEORY OF SOLAR CELLS
The theory of solar cells explains the process by which light energy in photons is converted into electric current when the photons strike a suitable semiconductor device. The theoretical studies are of practical use because they predict the fundamental limits of solar cell, and give guidance on the phenomena that contribute to losses and solar cell efficiency.
Photons in sunlight hit the solar panel and are absorbed by semi-conducting materials. Electrons (negatively charged) are knocked loose from their atoms as they are excited. Due to their special structure and the materials in solar cells, the electrons are only allowed to move in a single direction. The electronic structure of the materials is very important for the process to work, and often silicon incorporating small amounts of boron or phosphorus is used in different layers. An array of solar cells converts solar energy into a usable amount of direct current (DC) electricity.
When a photon is absorbed, its energy is given to an electron in the crystal lattice. Usually this electron is in the valence band and is tightly bound in covalent bonds with neighboring atoms, and therefore unable to move far. The energy given to the electron by the photon “excites” it into the conduction band where it is free to move around within the semiconductor. The network of covalent bonds that the electron was previously a part of now has one fewer electron. This is known as a hole. The presence of a missing covalent bond allows the bonded electrons of neighboring atoms to move into the “hole,” leaving another hole behind, thus propagating holes through-out the lattice. It can be said that photons absorbed in the semiconductor create electron-hole pairs. A photon only needs to have energy greater than that of the band gap in order to excite an electron from the valence band into the conduction band. However, the solar frequency spectrum approximates a black body spectrum at about 5,800 K, and as such, much of the solar radiation reaching the Earth is composed of photons with energies greater than the band gap of silicon. These higher energy photons will be absorbed by the solar cell, but the difference in energy between these photons and the silicon band gap is converted into heat (via lattice vibrations called phonons) rather than into usable electrical energy. The photovoltaic effect can also occur when two photons are absorbed simultaneously in a process called two-photon photovoltaic effect. However, high optical intensities are required for this nonlinear process.
The most commonly known solar cell is configured as a large-area p-n junction made from silicon. As a simplification, one can imagine bringing a layer of n-type silicon into direct contact with a layer of p-type silicon. In practice, p-n junctions of silicon solar cells are not made in this way, but rather by diffusing an n-type dopant into one side of a p-type wafer (or vice versa). If a piece of p-type silicon is placed in close contact with a piece of n-type silicon, then a diffusion of electrons occurs from the region of high electron concentration (the n-type side of the junction) into the region of low electron concentration (p-type side of the junction). When the electrons diffuse across the p-n junction, they recombine with holes on the p-type side. However (in the absence of an external circuit) this diffusion of carriers does not go on indefinitely because charges build up on either side of the junction and create an electric field. The electric field promotes charge flow, known as drift current, that op-poses and eventually balances out the diffusion of electrons and holes. This region where electrons and holes have diffused across the junction is called the depletion region because it contains practically no mobile charge carriers. It is also known as the space charge region, al-though space charge extends a bit further in both directions than the depletion region.X
3.4.4 Isolated DC-DC Converters
In many DC-DC applications, multiple outputs are required and output isolation may need to be implemented depending on the application. In addition, input to output isolation may be required to meet safety standards and / or provide impedance matching. The above discussed DC-DC topologies can be adapted to provide isolation between input and output.
3.4.5 Flyback Converter
The flyback converter can be developed as an extension of the Buck-Boost converter. The buck-boost converter works by storing energy in the inductor during the ON phase and releasing it to the output during the OFF phase. With the transformer the energy storage is in the magnetisation of the transformer core. To increase the stored energy a gapped core is often used.
4.1 MAXIMUM POWER POINT TRACKING – AN OVERVIEW
The efficiency of a solar cell is very low. In order to increase the efficiency, methods are to be undertaken to match the source and load properly. One such method is the Maximum Power Point Tracking (MPPT). This is a technique used to obtain the maximum possible power from a varying source. In photovoltaic systems the I-V curve is non-linear, thereby making it difficult to be used to power a certain load. This is done by utilizing a boost converter whose duty cycle is varied by using a mppt algorithm. Few of the many algorithms are listed below.
A boost converter is used on the load side and a solar panel is used to power this converter.
4.2 METHODS FOR MPPT
There are many methods used for maximum power point tracking a few are listed below:
Perturb and Observe method
Incremental Conductance method
Parasitic Capacitance method
Constant Voltage method
Constant Current method
4.2.1 Perturb and Observe method
The P&O algorithm is also called “hill-climbing”, but both names refer to the same algorithm depending on how it is implemented. Hill-climbing involves a perturbation on the duty cycle of the power converter and P&O a perturbation in the operating voltage of the DC link between the PV array and the power converter. In the case of the Hill-climbing, perturbing the duty cycle of the power converter implies modifying the voltage of the DC link between the PV array and the power converter, so both names refer to the same technique.
In this method, the sign of the last perturbation and the sign of the last increment in the power are used to decide what the next perturbation should be. On the left of the MPP incrementing the voltage increases the power whereas on the right decrementing the voltage increases the power. If there is an increment in the power, the perturbation should be kept in the same direction and if the power decreases, then the next perturbation should be in the opposite direction. Based on these facts, the algorithm is implemented. The process is repeated until the MPP is reached. Then the operating point oscillates around the MPP.
4.2.2 Incremental conductance method
The incremental conductance algorithm is based on the fact that the slope of the curve power vs. voltage (current) of the PV module is zero at the MPP, positive (negative) on the left of it and negative (positive) on the right.
∆V/∆P = 0 (∆I/∆P = 0) at the MPP
∆V/∆P > 0 (∆I/∆P < 0) on the left
∆V/∆P < 0 (∆I/∆P > 0) on the right
By comparing the increment of the power vs. the increment of the voltage (current) between two consecutives samples, the change in the MPP voltage can be determined.
4.2.3 Parasitic Capacitance method
This method is an improved version of the incremental conductance method, with the improvement being that the effect of the PV cell's parasitic union capacitance is included into the voltage calculation.
4.2.4 Constant Voltage method
This method which is a not so widely used method because of the losses during operation is dependent on the relation between the open circuit voltage and the maximum power point voltage. The ratio of these two voltages is generally constant for a solar cell, roughly around 0.76. Thus the open circuit voltage is obtained experimentally and the operating voltage is adjusted to 76% of this value.
4.2.5 Constant Current method
Similar to the constant voltage method, this method is dependent on the relation between the open circuit current and the maximum power point current. The ratio of these two currents is generally constant for a solar cell, roughly around 0.95. Thus the short circuit current is obtained experimentally and the operating current is adjusted to 95% of this value.
The methods have certain advantages and certain disadvantages. Choice is to be made regarding which algorithm to be utilized looking at the need of the algorithm and the operating conditions. For example, if the required algorithm is to be simple and not much effort is given on the reduction of the voltage ripple then P&O is suitable. But if the algorithm is to give a definite operating point and the voltage fluctuation near the MPP is to be reduced then the IC method is suitable, but this would make the operation complex and more costly.
4.3 FLOW CHART OF MPPT ALGORITHMS
Two of the most widely used methods for maximum power point racking are studied here. The methods are
Perturb & Observe Method.
Incremental Conductance Method.
The flow charts for the two methods are shown below.
Flow chart for perturb & observe:
FUZZY LOGIC – AN OVERVIEW
Fuzzy logic (FL) is a problem-solving control system methodology that lends itself to implementation in systems ranging from simple, small, embedded micro-controllers to large, networked, multi-channel PC or workstation-based data acquisition and control systems. It can be implemented in hardware, software, or a combination of both. FL provides a simple way to arrive at a definite conclusion based upon vague, ambiguous, imprecise, noisy, or missing input information. FL's approach to control problems mimics how a person would make decisions, only much faster.
FL requires some numerical parameters in order to operate such as what is considered significant error and significant rate-of-change-of-error, but exact values of these numbers are usually not critical unless very responsive performance is required in which case empirical tuning would determine them. For example, a simple temperature control system could use a single temperature feedback sensor whose data is subtracted from the command signal to compute "error" and then time-differentiated to yield the error slope or rate-of-change-of-error or also called "error-dot". Error might have units of degs F and a small error considered to be 2F while a large error is 5F. The "error-dot" might then have units of degs/min with a small error-dot being 5F/min and a large one being 15F/min. These values don't have to be symmetrical and can be "tweaked" once the system is operating in order to optimize performance. Generally, FL is so forgiving that the system will probably work the first time without any tweaking.
The use of fuzzy logic control has become popular over the last decade because it can deal with imprecise inputs, does not need an accurate mathematical model and can handle nonlinearity. Microcontrollers have also helped in the popularization of fuzzy logic control. The fuzzy logic consists of three stages: fuzzification, inference system and defuzzification. The structure of fuzzy logic controller
FUZZIFICATION & DEFUZZIFICATION
Fuzzification comprises the process of transforming numerical crisp inputs into linguistic variables based on the degree of membership to certain sets. Membership functions are used to associate a grade to each linguistic term. The number of membership functions used depends on the accuracy of the controller, but it usually varies between 3, 5 and 7. In Fig.5.2 five fuzzy levels are used: NB (Negative Big), NS (Negative Small), ZE (Zero), PS (Positive Small), and PB (Positive Big). The values a and b are based on the range values of the numerical variable. In some cases the membership functions are chosen less symmetric or even optimized for the application for better accuracy. The inputs of the fuzzy controller are usually an error (E) and the change of the error (∆E). The error can be chosen by the designer, but usually it is chosen as ∆P/∆V because it is zero at the MPP.
Example for Fuzzification & Deffuzification
Suppose a simplified implementation for an air-conditioning system with a temperature sensor. The temperature might be acquired by a microprocessor which has a fuzzy algorithm to process an output to continuously control the speed of a motor which keeps the room in a “good temperature,” it also can direct a vent upward or downward as necessary. The figure illustrates the process of fuzzification of the air temperature. There are five fuzzy sets for temperature: COLD, COOL, GOOD, WARM, and HOT. The membership function for fuzzy sets COOL and WARM are trapezoidal, the membership function for GOOD is triangular, and the membership functions for COLD and HOT are half-triangular with shoulders indicating the physical limits for such process (staying in a place with a room temperature lower than 8 degrees Celsius or above 32 degrees Celsius would be quite uncomfortable).
MATLAB – AN OVERVIEW
MATLAB is a high-performance language for technical computing. It integrates computation, visualization, and programming in an easy-to-use environment where problems and solutions are expressed in familiar mathematical notation. Typical uses include
• Math and computation.
• Algorithm development.
• Data acquisition.
• Modeling, simulation, and prototyping.
• Data analysis, exploration, and visualization.
• Scientific and engineering graphics.
• Application development, including graphical user interface building.
MATLAB is an interactive system whose basic data element is an array that does not require dimensioning. This allows you to solve many technical computing problems, especially those with matrix and vector formulations, in a fraction of the time it would take to write a program in a scalar non interactive language such as C or FORTRAN. The name MATLAB stands for matrix laboratory. MATLAB was originally written to provide easy access to matrix software developed by the LINPACK and EISPACK projects. Today, MATLAB engines incorporate the LAPACK and BLAS libraries, embedding the state of the art in software for matrix computation. MATLAB has evolved over a period of years with input from many users. In university environments, it is the standard instructional tool for introductory and advanced courses in mathematics, engineering, and science. In industry, MATLAB is the tool of choice for high-productivity research, development, and analysis.
MATLAB features a family of add-on application-specific solutions called toolboxes .Very important to most users of MATLAB, toolboxes allow you to learn and apply specialized technology. Toolboxes are comprehensive collections of MATLAB functions (M-files) that extend the MATLAB environment to solve particular classes of problems. Areas in which toolboxes are available include signal processing, control Systems, neural networks, fuzzy logic, wavelets, simulation, and many others.
7.2 ROLE OF SIMULATION
Electrical power systems are combinations of electrical circuits and electromechanical devices like motors and generators. Engineers working in this discipline are constantly improving the performance of the systems. Requirements for drastically increased efficiency have forced power system designers to use power electronic devices and sophisticated control system concepts that tax traditional analysis tools and techniques. Further complicating the analyst's role is the fact that the system is often so nonlinear that the only way to understand it is through simulation .Land-based power generation from hydroelectric, steam, or other devices is not the only use of power systems. A common attribute of these systems is their use of power electronics and control systems to achieve their performance. Sim Power Systems is a modern design tool that allows scientists and engineers to rapidly and easily build models that simulate power systems. Sim Power Systems uses the Simulink environment, allowing you to build a model using simple click and drag procedures. Not only can you draw the circuit topology rapidly, but your analysis of the circuit can include its interactions with mechanical, thermal, control, and other disciplines. This is possible because all the electrical parts of the simulation interact with the extensive Simulink modeling library. Since Simulink uses MATLAB® as its computational engine, designers can also use MATLAB toolboxes and Simulink block sets.
7.3 SIMSCAPE AND SIMPOWER SYSTEM LIBRARIES
The libraries contain models of typical power equipment such as transformers, lines, machines, and power electronics. The capabilities of Sim Power Systems for modeling a typical electrical system are illustrated in demonstration files. And for users who want to refresh their knowledge of power system theory, there are also self-learning case studies. The Sim Power Systems main library, power lib, organizes its blocks into libraries according to their behavior. The power library window displays the block library icons and names. Double-click a library icon to open the library and access the blocks. The main Sim Power Systems power library window also contains the Powergui block that opens a graphical user interface for the steady-state analysis of electrical circuits. This is possible because all the electrical parts of the simulation interact with the extensive Simulink modeling library. Since Simulink uses MATLAB as its computational engine, designers can also use MATLAB toolboxes and Simulink block sets. Sim Power Systems and Sim Mechanics share a special Physical Modeling block and connection line interface.