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Design of Solar Charge Controller Using Maximum Power Point Tracking

Info: 4435 words (18 pages) Dissertation
Published: 10th Dec 2019

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Tagged: EngineeringEnergy


As renewable energy is becoming famous in the world due to increase in pollution, low maintenance cost, abundance of raw material, researchers and scholars are doing more research and development in this area to make it economical and efficient. Out of renewable energies solar energy is more useful based on the fact that it has low maintenance cost as well as environment-friendly. Maximum Power Point Transfer (MPPT) is a method which enhances PV array efficiency. Different algorithms are used to increase the efficiency of Maximum Power Point Transfer (MPPT) solar charge controller such as Perturb and observe (P&O), incremental conductance (INC), constant voltage (CV), short current pulse, fuzzy logic control (FLC) , artificial neural network (ANN) etc. out of which Hill-Climbing methods such as Perturb and Observe and Incremental Conductance are most widely used. The main objective of this review is to find and fill the research gaps in the design and implementation of solar charge controller with Maximum Power Point Transfer (MPPT). There are some lags in the design of algorithms and inverter which needs to be improved in future research which will be based upon this literature review and analysis. Review and evaluation are the foundation of future research work on MPPT solar charge controller.

1.      Introduction

Due to increase in pollution and extreme climate change, development of renewable energy sources is increasing continuously. PV arrays and wing generators are mostly used in all of the renewable energy systems. Today, solar panel are widely used in satellite power supply, water pumps, house power systems and battery charging etc. Solar energy generates electricity without any pollution(Zheng & Liu 2008)

The quick pattern of industrialization of countries and expanded enthusiasm for natural issues as of late drove us to investigate the utilization of inexhaustible resources, for example, sunlight based power system. The photovoltaic array is increasing as a sustainable source because of no fuel cost, low-cost maintenance, hassle-free, and so on. Specifically, energy transformation from sun-powered arrays got impressive consideration over the most recent two decades (Veerachary, Senjyu & Uezato 2002).

Solar energy demand is increasing day by day due to rise in energy usage and decline in non-renewable supplies. But the difficult with solar energy is that it fluctuates depending upon the weather conditions. Hence, a battery is connected with the load every time. Maximum power is achieved from PV cells using MPPT method. There are two advantages of using MPPT method protection of battery from being overcharged and energy metering (Pathare et al. 2017)

Solar energy is the most beneficial energy source in the world. Photovoltaic array converts solar energy into electrical energy depending upon the temperature of solar arrays, angle of rays with solar cells, insolation and load characteristics. As the solar energy changes with respect to changing weather conditions, so, a battery is used as a secondary source. The output power of the solar cells is not linear due to changing climate continuously. Hence, to increase the overall efficiency of solar charge controller maximum power point tracking (MPPT) technique is used (Joel, Saikumar & Patange 2016).

Semiconductor physics is advancing day by day, it is providing efficient techniques and methods to withdraw energy from renewable resources these days. Perturb and observe (P&O), incremental conductance (INC), constant voltage (CV), short current pulse , fuzzy logic control (FLC) , artificial neural network (ANN) and some other techniques have been developed to remove energy efficiently. Still, efficiency of energy harvesting process can be enhanced by using Current-Voltage characteristics of PV array (Rokonuzzaman & Hossam-E-Haider 2016).

The Current-Voltage characteristics of PV array is not linear and changes with the change of temperature and irradiation. There is a peak point on the Power-Voltage and Current- Voltage curve named as Maximum power point (MPP), at this point maximum power is extracted from solar cells and photovoltaic system works with maximum efficiency (Rokonuzzaman & Hossam-E-Haider 2016).

This paper describes various techniques and methods to improve the efficiency of solar charge controller. Each method has its own advantages and disadvantages depending upon the application. This review will help to understand the research gap and utilizing this base knowledge in future research.

2.    Literature Review

Photovoltaic array is very expensive currently but it is believed that PV solar power systems will replace systems that run on fossil fuels. The output power of solar panel depends mainly on rays and ambient temperature. So, to take maximum out from the PV array, power system must track the maximum power point of PV array considering different climate changes. There are three algorithms to track maximum power point (MPP) which are applied by most of the scientists and researchers such as Perturbation and Observation (P&O), Incremental Conductance and Constant Voltage Tracking. The first two methods also called hill-climbing because these are based on the characteristics of the PV array (Zheng & Liu 2008).

Perturb and Observe algorithm (P&O) is used to extract maximum power from the solar array. By change in voltage, power is measured and if the power rises, it has to be stopped by some adjustments. This is called Perturb and Observe method. A problem of this method is that it increases oscillation of the output power but this method is used commonly till now. The algorithm of Perturb and Observe (P&O) increases or decreases based upon the output terminal voltage of the PV array from time to time and then compares power gained in the present cycle and the past cycle. Suppose, the power is more than the previous value, it means it is going toward maximum power point. If the power is less than the previous one, the operating point is going away from MPP. So, in this case direction of perturbation should be altered towards the MPP. In this paper, a simple and cheap solar power generation system is made for small direct current loads. This circuit is simulated in Proteus software. Hall Effect current sensors are used which are a little bit expensive in some countries only. A microcontroller kit is necessary for the hardware otherwise circuitry will be very complex and expensive. 5A fuse protects the circuit from overcurrent and TVS diode is used for overvoltage protection at both panel and load side (Pathare et al. 2017).

Several peaks are seen in the power-voltage characteristic graph of solar cell, maximum power point tracking (MPPT) is a conventional method to achieve maximum power point but this method cannot track global maximum power point (GMPP). That’s why a rectified incremental conductance algorithm is described in this paper which will be able to track GMPP with half-darkness and changing load parameters. This algorithm increases the speed of MPPT process by modulating cycles of dc to dc converter. Design and implementation of hardware is done by considering the modified algorithm with changing load and half-darkness conditions. The results show that this algorithm is faster than the commonly used MPPT method and can also track GPPT precisely under different conditions (Tey & Mekhilef 2014).

The Constant Voltage method is based upon the voltage of the maximum power point (0.7v). Perturbation and Observation technique is good when the temperature is constant or changes slowly. Because of the slower response of tracking MPP, P&O method fails, to remove this problem of P&O, incremental conductance method is used which compares incremental conductance with instantaneous conductance. This method works properly but there is also drawback of this method, speed of tracking MPP is reduced because time of calculation is increased for complex control algorithm. It also decreases the sampling frequency of current and voltage of solar panel. The Constant Voltage method is used in this paper based on the fact that when irradiances changes MPP voltage increases or decreases to a small degree. The ratio VMPP/VOC is kept at 76%, however, it depends on the parameters of PV array. In this method, current is taken as zero to find out the open circuit voltage of array. After calculating VOC, voltage of maximum power point are measured. A difficulty of this method is ratio of the MPP voltage and open circuit voltage is not always 76% but it has more operating speed, simple, cheap and efficient as compared to the other methods explained above. Hence, this method is used in this paper. Saber software is used to simulate the MPPT solar charge controller and simulation results show that design is in working condition.(Zheng & Liu 2008)

Fuzzy logic method is used to extract the maximum power from PV arrays. The benefit of this method is that it is simple to design the system, can control variable which is not precise, doesn’t need of precise mathematical model and it can deal with non-liner behavior. As the solar cell show non-linear behavior, fuzzy logic can be used to track maximum power point. Algorithm of fuzzy logic method has three steps such as Fuzzification, Inference and Defuzzification. In fuzzification process, input signal is changed into variable and function of variables is defined. Fuzzy rules are defined in inference process, in the last, defuzzification is opposite of fuzzification (Joel, Saikumar & Patange 2016).

MPPT strategy with a straightforward calculation for photovoltaic (PV) power system. The strategy depends on utilization of a short-current pulse of the PV to decide an ideal working current where the greatest yield power can be achieved and totally contrasts from traditional hill climbing-based techniques. In the proposed energy system, the ideal working current is immediately decided basically by taking a result of the short-current and a parameter k on the grounds that the ideal working current is precisely relative to the short current under different states of illuminance and temperature. The above versatile MPPT calculation has been brought into a current-controlled chopper and various power converter systems made out of PV and chopper modules. Different working qualities have tentatively been analyzed on this various PV and chopper module systems, MPPT execution has been affirmed through the tests (Noguchi, Togashi & Nakamoto 2002).

Neural network based MPPT is designed and implemented on hybrid power system which comprises of solar power, wind control, diesel motor, and a power controller. MATLAB/Simulink was utilized to fabricate the dynamic model and recreate the power system. To accomplish a quick and stable reaction, the genius controller comprises a Radial Basis Function Network (RBFN) and an enhanced Elman Neural Network (ENN) for most extreme power point (MPPT). The pitch angle of wind turbine is controlled by the ENN, and the solar power system utilizes RBFN, where the yield output signal is utilized to control the dc/dc converters to accomplish the MPPT. The most widely recognized technique in this field is the P&O strategy. It intermittently increments or reductions the solar cell’s voltage as said before to look for the maximum power point. A variable strategy is proposed to look for the maximum power point, where the step length is balanced by the separations to the MPP. The proportion of the variety of energy P to voltage V is considered as the step length of duty ratio D, which is really the incline of each working point under short sampling time (Lin, Hong & Chen 2011).

An integrated low-power MPPT controller inside the solar panel is introduced to make it cheap and fast. This can bring about a 25% energy increment with improving battery voltage control and synchronization of the PV array with the load. Rather than utilizing a remotely associated MPPT, an embedded MPPT controller is used as a component of the PV board. It is suggested that this embedded MPPT utilizes a cheap controller also. This method is most efficient for small PV energy systems (Enslin et al. 1997).

Modification of perturb and observe algorithm is used in MPPT technique. A downside of P&O is that, at constant state, the operating point increases or decreases near MPP which wastes a significant amount of energy. In addition, P&O calculation can be upset when climate changes very fast. So, to confine the negative impacts related to the above downsides, the P&O MPPT parameters must be altered. A hypothetical examination permitting the ideal decision of such parameters is likewise done. Consequences of test estimations are in concurrence with the expectations of theoretical study (Femia et al. 2005).

The V-I characteristics of the PV module are non-linear. To control the problem of non-linearity, output feedback control is made using RBF Neural Networks method. Adaptive control and robust control is used in RBF Neural Networks (Seshagiri & Khalil 2000).

Maximum power operating time (MPOP) of solar panel systems varies with the changing solar radiations and ambient temperature. To track the MPOP accurately, a modified algorithm is designed. Though, modified algorithm is highly efficient in case of varying atmosphere rapidly. The work is done by experiment and simulation showing the higher efficiency than the simple algorithms (Hussein et al. 1995).

Switching frequency modulation technique for separating maximum power from photovoltaic (PV) arrays is described. The power transformation arrange, which is placed between a PV board and a load, is Cuk converter. A technique for finding the most extreme power point (MPP) depends on infusing a sinusoidal wave into the exchanging frequency and contrasting the AC segment and mean value of the solar board terminal voltage. The extra benefit of this method is that it can also track global MPP (Tse et al. 2002).

A dynamic procedure for achieving the maximum power point of a variable power source, a sunlight based solar cell, is presented. The procedure tracks maximum power about cycle-by-cycle during a momentary value. Data from the switching ripple rather than outside perturbation is utilized to help the amplifying procedure. The technique is internationally steady for DC-DC control converters (Midya et al. 1996).

PV system is works in different insolation conditions, and its VI curve shows nonlinear behavior. In order to solve this problem, a practical MPPT method is simulated which gets starting point of the operation using monitoring cells and incremental resistance method (dv/di) (Irisawa et al. 2000).

Photovoltaic systems typically utilize a most extreme power point following (MPPT) procedure to persistently extract energy to the load when changes in the sun rays and temperature happen. It solves the problem of mismatch between the solar arrays and the given load. A basic strategy of maximum power point compels the system to work near the maximum point. By utilizing the proposed design, the disadvantages of the state-space-averaging technique can be overcome. The TI320C25 advanced flag processor (DSP) was utilized to execute the proposed MPPT controller, which controls the DC/DC converter in the photovoltaic power system (Chihchiang, Jongrong & Chihming 1998).

This overview of various MPPT methods utilized as a part of the usage of photovoltaic power systems. It will talk about various 30 strategies utilized as a part of MPPT in photovoltaic cells. This paper can be considered as a finish and statement of the great endeavors made in, that talked about 19 MPPT strategies in PV cells, while outlines extra 11 MPPT techniques (Ali et al. 2012).

3.    Evaluation

Photovoltaic (PV) arrays have produced huge market and research premiums in recent days because of the availability of raw materials and their quiet and nature-friendly power producing process. Since the power produced by PV modules relies upon sun oriented illumination level, MPPT controller is required to guarantee that the most maximum power is produced (Tey & Mekhilef 2014). There are almost 30 methods which are part of the maximum power point tracking (MPPT) (Ali et al. 2012). Hill climbing algorithm is most commonly used in MPPT (Noguchi, Togashi & Nakamoto 2002). Perturbation and Observation (P&O) and Incremental Conductance are called as hill-climbing methods (Zheng & Liu 2008). Perturbation and Observation (P&O) algorithm is used to get the maximum power point as part of the MPPT method, it has drawback of increasing output power oscillations but this algorithm is widely used. Renewable energy system is simple and cheap using P&O method which works well with small loads (Pathare et al. 2017). The writer also described P&O benefit and problem that it works well at constant temperature but it has slow speed of extracting power from solar panels. So, to overcome this problem incremental conductance algorithm is used (Zheng & Liu 2008). The writer demonstrated that Incremental conductance method tracks maximum power point fast and can also track global power point track (GPPT) under different conditions by modulating waves of dc to dc converter. The author claimed that Incremental conductance has also a drawback of increasing or decreasing sampling frequency of current and voltage of solar cells. There is a chain can be seen in literature review that solving a problem creates a new problem. So, after explaining problems in P&O and incremental conductance, suggested constant voltage method. In constant voltage technique suppose that current is zero and ratio of maximum power point voltage and open circuit voltage is 76%. While finding open circuit voltage, maximum power point voltage is measured. This method is proved better than the P&O and incremental conductance because it has fast speed of tracking maximum power point and doesn’t change sampling frequency of the wave. Moreover, a problem of this algorithm is that ratio of maximum power point voltage and open circuit voltage is not always 76% (Zheng & Liu 2008). A method called fuzzy logic based MPPT controller, which controls the non-linearity behavior of the V-I characteristics better than the other algorithm using steps such as fuzification, inference and defuzification (Joel, Saikumar & Patange 2016). Short current pulse strategy is far better than hill climbing because it extracts more power from the solar cells or arrays. It is proved by theoretical as well as experimental results explained briefly (Noguchi, Togashi & Nakamoto 2002). A hybrid power system consists of solar panels, wind generator and diesel motor. Every component is operated by different neural network technique like solar panel is controlled by Radial Basis Function Network and the pitch angle of wind generator is controlled by an enhanced Elman Neural Network. Neural network based MPPT controller is showing better results than Perturbation and Observation (Lin, Hong & Chen 2011). Integrated MPPT controller, embedded in PV module, which is cheap, simple and 25% more efficient than the other methods but limited to only small PV systems (Enslin et al. 1997). A new modified algorithm, Modified Perturb and Observe algorithm, while considering the disadvantages of P&O strategy (Femia et al. 2005). A solution for non-linearity of the Voltage-Current characteristics of the solar panel to improve the efficiency of solar charge controller because output voltage and current are continuously changing, therefore, most of the energy is wasted (Seshagiri & Khalil 2000). On the other hand, the writer also removed non-linear behavior of V-I characteristics curve by combining monitoring cells with incremental resistance method (Irisawa et al. 2000). A modified algorithm to trace Maximum Power Operating Time (MPOP) under varying irradiances and temperature, faster than the simple algorithms (Hussein et al. 1995). Switching frequency modulation technique for separating maximum power from solar arrays. A Cuk converter is fundamental component of this experiment. Moreover, switching frequency modulation can track Global Maximum Power Point (Tse et al. 2002). The author proposed dynamic procedure to get maximum power point cycle-by-cycle during a transient value (Midya et al. 1996). Worked on a design to overcome the drawbacks of state-space-averaging strategy (Chihchiang, Jongrong & Chihming 1998). In the last, an overview of all the techniques (about 30 methods and solutions) to separate maximum possible power from PV cells efficiently (Ali et al. 2012).

During literature review and analysis, it can be seen clearly that removing a problem creates another problem while trying to rectify it, a new difficulty arises. Each and every method or solution has its own advantage and disadvantages, only operates accurately and precisely with specified application and last and the foremost, simulates under different conditions. Each algorithm is simulated with software like Simulink, matlab etc. Most of the algorithms reviewed above have drawbacks, lack of accuracy and have certain limitations. Based upon literature review and analysis, research questions are:

  1. How to modify the present algorithms to remove the drawbacks?
  2. How to increase the efficiency of MPPT solar charge controller by introducing intelligent inverters?

4.    Conclusions

In this paper, algorithms and strategies have been reviewed to improve the efficiency of solar charge controller using MPPT method. Maximum Power Point Tracking (MPPT) is a method to extract maximum power from the PV arrays efficiently. Hil-climbing technique is found useful but it has some disadvantages. To remove the drawbacks an improved algorithm is needed.

5.    References

Ali, A.N.A., Saied, M.H., Mostafa, M.Z. & Moneim, T.M.A.-. 2012, ‘A survey of maximum PPT techniques of PV systems’, 2012 IEEE Energytech, pp. 1-17.

Chihchiang, H., Jongrong, L. & Chihming, S. 1998, ‘Implementation of a DSP-controlled photovoltaic system with peak power tracking’, IEEE Transactions on Industrial Electronics, vol. 45, no. 1,pp. 99-107.

Enslin, J.H.R., Wolf, M.S., Snyman, D.B. & Swiegers, W. 1997, ‘Integrated photovoltaic maximum power point tracking converter’, IEEE Transactions on Industrial Electronics, vol. 44, no. 6,pp. 769-73.

Femia, N., Petrone, G., Spagnuolo, G. & Vitelli, M. 2005, ‘Optimization of perturb and observe maximum power point tracking method’, IEEE Transactions on Power Electronics, vol. 20, no. 4,pp. 963-73.

Hussein, K.H., Muta, I., Hoshino, T. & Osakada, M. 1995, ‘Maximum photovoltaic power tracking: an algorithm for rapidly changing atmospheric conditions’, IEE Proceedings – Generation, Transmission and Distribution, vol. 142, no. 1,pp. 59-64.

Irisawa, K., Saito, T., Takano, I. & Sawada, Y. 2000, ‘Maximum power point tracking control of photovoltaic generation system under non-uniform insolation by means of monitoring cells’, Conference Record of the Twenty-Eighth IEEE Photovoltaic Specialists Conference – 2000 (Cat. No.00CH37036), pp. 1707-10.

Joel, Y.S., Saikumar, H.V. & Patange, S.S.R. 2016, ‘Design & performance analysis of Fuzzy based MPPT control using two-switch non inverting Buck-Boost converter’, 2016 International Conference on Electrical Power and Energy Systems (ICEPES), pp. 414-9.

Lin, W.M., Hong, C.M. & Chen, C.H. 2011, ‘Neural-Network-Based MPPT Control of a Stand-Alone Hybrid Power Generation System’, IEEE Transactions on Power Electronics, vol. 26, no. 12,pp. 3571-81.

Midya, P., Krein, P.T., Turnbull, R.J., Reppa, R. & Kimball, J. 1996, ‘Dynamic maximum power point tracker for photovoltaic applications’, PESC Record. 27th Annual IEEE Power Electronics Specialists Conference, vol. 2, pp. 1710-6 vol.2.

Noguchi, T., Togashi, S. & Nakamoto, R. 2002, ‘Short-current pulse-based maximum-power-point tracking method for multiple photovoltaic-and-converter module system’, IEEE Transactions on Industrial Electronics, vol. 49, no. 1,pp. 217-23.

Pathare, M., Shetty, V., Datta, D., Valunjkar, R., Sawant, A. & Pai, S. 2017, ‘Designing and implementation of maximum power point tracking(MPPT) solar charge controller’, 2017 International Conference on Nascent Technologies in Engineering (ICNTE), pp. 1-5.

Rokonuzzaman, M. & Hossam-E-Haider, M. 2016, ‘Design and implementation of maximum power point tracking solar charge controller’, 2016 3rd International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), pp. 1-5.

Seshagiri, S. & Khalil, H.K. 2000, ‘Output feedback control of nonlinear systems using RBF neural networks’, IEEE Transactions on Neural Networks, vol. 11, no. 1,pp. 69-79.

Tey, K.S. & Mekhilef, S. 2014, ‘Modified Incremental Conductance Algorithm for Photovoltaic System Under Partial Shading Conditions and Load Variation’, IEEE Transactions on Industrial Electronics, vol. 61, no. 10,pp. 5384-92.

Tse, K.K., Ho, M.T., Chung, H.S.H. & Hui, S.Y. 2002, ‘A novel maximum power point tracker for PV panels using switching frequency modulation’, IEEE Transactions on Power Electronics, vol. 17, no. 6,pp. 980-9.

Veerachary, M., Senjyu, T. & Uezato, K. 2002, ‘Voltage-based maximum power point tracking control of PV system’, IEEE Transactions on Aerospace and Electronic Systems, vol. 38, no. 1,pp. 262-70.

Zheng, s. & Liu, W. 2008, ‘Research and implementation of photovoltaic charging system with maximum power point tracking’, 2008 3rd IEEE Conference on Industrial Electronics and Applications, pp. 619-24.

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