Enhanced Pv Solar Power System Design with An Mppt Controller as A Function of
Temperature
I.Rahul a, and Dr.R.Hariharan b
A Department of Electrical & Electronics Engineering, Saveetha School of Engineering, SIMATS, Chennai-602105, India. B Department of Electrical & Electronics Engineering, Saveetha School of Engineering, SIMATS, Chennai-602105, India. .
Article History: Received: 11 January 2021; Accepted: 27 February 2021; Published online: 5 April 2021 ______________________________________________________________________________________________________
Abstract: In order to achieve optimum point tracking (MPPT) for the selection of the highest power consumption, the photovoltaic (PV) system was improved. The output power of the PV effect depends on external solar irradiation and the ambient temperature. In the existing MPPT approaches, most take only radiation fluctuations into account, with limited consideration of the consequences of temperature shifts. But the temperature coefficients (TCs), especially in applications where changes in ambient temperature are relative high, play an important role in the PV system. A correct study for the MPPT method was performed with a PV system which considers the temperature change using a variable universe fuzzy logic control (VUFLC). Taking account of the changing atmosphere temperature in photovoltaic panels, the proposed control method will regulate VUFLC contraction and expansion, removing the effect of temperature fluctuations and improving MPPT efficiency and thus achieving a quick and accurate tracking control. The proposed strategy was tested under different environmental conditions for a PV module and its control efficiency is compared with simulation and experimental results to other MPPT strategies. Compared with fossil fuels, the best alternative is to generate photovoltaic energy every day. In future, because of the above developments the thermal and nuclear power plant will probably withdraw. The cost of development and installation are rising in comparison with other renewable options. The article addresses a better configuration with an MPPT controller for a PV solar power system compared to other strategies dealing with the non-shaded, partial shading of variable sun-irradiance. This helps designers and maintainers build maintenance processes and phases.
Keywords: Maximum power point tracking (MPPT), Photovoltaic (PV), Temperature coefficients (TC), Variable universe fuzzy logic control (VUFLC).
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1. IntroductionWith the advancement of photovoltaic (PV) technology, an growing number of PV generation systems for large-scale applications have been introduced. The PV module is a core component of PV electricity generation systems and directly affects the output and efficiency of the network as a whole. However, the electricity provided by PV modules largely depends on environmental factors such as solar insolation and ambient temperatures(Subudhi , 2013; Arunkarthikeyan, 2020; Latchoumi, 2020) In the process of extracting the maximum output power and increasing the performance of the entire PV network, several advanced MPP control methods were employed for PV systems(Reisi, 2013; Babu, 2017; Li, 2016; Aroulanandam, 2020). A variety of MPPT control algorithms have already been suggested and built in the recent years (Robles Algarín, 2018), including open circuit-voltage (OCV)/short-circuit-current (SAC) (Shebani, 2016; Arunkarthikeyan, 2021) incremental conductivity (IPC) (Deopare, 2015), destructive and tracking (P&O) (Amri,2015) and others. Owing to non-linear PV cell problems, a number of soft-computing techniques such as the ANN (Lin, 2011) and the Fused Logical Management System (FLC) (Lin , 2011; Kalogirou , 2014; Balamurugan, 2018) have been utilized to ensure the implementation of the MPPT in pv systems. The variable radiation level only exists in most MPPT solutions while the temperature effects are scarcely taken into account. Any of the latest MPPT methods based on temperature measurements is addressed in reference (Balamurugan, 2020). In order to generate MPPT as a feedback parameter, most control algorithms use the temperature. For instance, the reference studies (Narendiran, 2016; Da, 2016; Francisco, 2012) proposed the MPPT algorithm for the determination of the maximum power point voltage to control the machine's maximum power point ( MPP). The comparison(Park, 2004) shows a temperature effect and optimal device configuration for the solar tracking system. Temperature measurement-based MPPTs explicitly tackle changes in temperature that can result in change in MPPs that can achieve a faster tracking speed, especially in engineering applications with a fairly high temperature shift, compared to other methods in the same control algorithm. In order to improve tracking accuracy, several artificial Intelligence approaches have been used to incorporate the MPPT (Mustafa, 2016; Kreeumporn 2016; Garikipati, 2021). Fuzzy Logic Control (FLC) has been used to monitor MPP on pv systems (Eltamaly, 2016; Chin, 2011)and is common and mature. The findings in reference (Sampathkumar, 2020) shows, relative to some MPPT INC and P&O techniques and others under both predictable and fluid conditions, the FLC has better performance in normal operating range. Input and output universes can be modified to enhance the controls and to reach greater precision through the Fuzzy Logic Control Variable Universe. However, the choice of a universal scalable vector or
This makes use of the PV modules' real time temperature variable as the limitations of the universal factor variable option. In order to efficient monitoring in external work environments (environment conditions) the new MPPT VUFLC temperature algorithm is developed. By integrating the tempo coefficient of the modules (TCs) to accelerate the MPPT cycle and improve tracking accuracy in compare with the traditional MPPT technique, the proposed VUFLC-temperature MPPT system selects the vector universal component. The suggested MPPT VUFLC temperature approach was validated with simulation and experimental tests. Sun, wind and mare are sufficient natural resources for renewable energy. These are now the options to produce huge electricity. Demand is on the rise for photovoltaic systems in the power systems delivery area. The earth's irradiance is 1345 w / m^2, but the sky is up to 1000 w / m^3. The MPPT algorithm controls the sun and temperature of the GMPP. Since solar plants have been very costly, various innovations have been developed in the past to help produce full power from the solar array so higher returns and profits are expected. Each of the previous strategies has its own drawbacks, many of which are unable to track GMPP quickly. This paper demonstrates MPPT controls advances as a temperature mechanism in which earlier methods of the MPPT controller tackle changing insolation and partial shading conditions. The algorithm transmits full power and reduces the time needed for tracking a global MPP. The electronic interface is used to monitor charge strength when a charge is directly attached to the PV array and many well- known algorithms are used to measure the maximum power point (MPPT).
2. Literature Review
The photovoltaic photovoltaic network (PV) is used for optimum performance of photovoltaic (PV) set in this analysis, (Bhasha, 2020; Pavan, 2020; Balamurugan, 2017). The power output of the PV effect depends on external solar irradiation and atmospheric conditions of temperature. In present MPPT approaches, often often changes in radiation rates are taken into consideration, seldom taking into account the consequences of temperature changes. However, temperature coefficients (TCs), especially in applications where ambient temperature fluctuations are relatively small, play a significant role in the PV system. In this article, the MPPT approach is defined for a PV system that uses a flat-rate VUFLC to take into consideration changes in temperature. Taking into account the changes in the ambient temperatures in PV modules, a proposed management system will monitor the VUFLC contraction and expansion factor, reducing the effect of the temperature variable and increasing MPPT efficiency, thereby obtaining a rapid and reliable tracking power. The suggested approach was tested for a PV module and compared to other PPT methods with simulation and experimental tests, under different environmental conditions. The approach is also tested.
In contrast with fossil-based power plants, the solar photovoltaic energy generation is now a day's best alternative. As a result of the growth of the above, the prospect of thermal and nuclear power plants is definitely being removed. Unlike green alternatives, processing costs and deployment costs decrease dramatically in the world. This helps build phases and repair procedures for planners and construction managers. The research involves the design, modeling and installation of a solar power control system for a battery power grid. (Li, 2016; ChinnamahammadBhasha, 2020). Seem, J.E. The architecture involves many methods of regulation, including regulation of energy management, mode switching and data sharing. To refine the configuration of the control system parameters a genetic algorithm was developed and the simulation and actual implementation of the algorithm showed comparable performance. The power supply control system has been installed for linking the grid. The energy supply simulated a solar panel and evaluated the characteristics of the energy transmission of the built device, connecting to the power grid through the energy hub equipment. The tests showed that the chosen algorithm met the objective criteria.
(Rajesh Kumar. 2017; ChinnamahammadBhasha, 2020) Solar technology is one of the key options for future power generation. The demand for energy is increasingly rising in photovoltaic power plants (PV). Specific strategies for optimizing Power Generation Power Points (MPPT) are used in the PV systems. This article discusses abrupt increases in irradiation and temperatures of the cells in the grid PV model of MATLAB SIMULINK (ChinnamahammadBhasha, 2020). The power generated in the PV system is maximized to with stood these strong disturbances by using the new adaptive controller Incremental Conduction MPPT. Two separate optimization techniques (Invasive weed and Harmony search) allow the new adaptive controller to be configured. The optimization results are compared with both techniques. A robustness test is performed to verify the system's reliability, to tolerate various random irradiance patterns and cell temperatures without the full power point being monitored. Finally, the video is compared quickly and the new adaptive system provides improved performance.
This principle is developed and simulated with a photovoltaic power point display to design and simulate a basic but efficient load control device. It provides computational analysis and modeling of photovoltaic networks by analogous electrical circuits. The structures are made up of multiple MPPT algorithms and control methods as they use the MPPT. DC-DC converter architecture and hardware implementation were tested by P-Spice and MATLAB simulation. The findings indicate that MPPT will substantially improve the efficiency and output of PV.
Every day, the consumption of electricity is growing. All fields are filled with electricity (Garikapati, 2020). Converting solar power into electricity is one of the easiest methods of rising fossil fuel consumption. The cost and performance of solar cells are not used in most electrical applications. The implementation of MPPT algorithms, however, improved solar cell performance. The paper explores the different MPPT algorithms. The applications assisted are discussed in these MPPT algorithms.
(AL-monier, 2012; Deepthi, 2019) MPPT (Maximum Power Point Tracking) is a process that typically uses grid-linked inverters, solar charging systems and related equipment to produce as much potential electricity from a single photovoltaic cell. MPPT curve P-V and I-V. A relative analysis of commonly used MPPT algorithms is described in this article. The efficiency is measured using Matlab / Simulink simulation methods, using the most efficient energy capacity, taking into account diverse variations in solar radiance. PV cells interconnect with total resistance and nonlinear output efficiency based on the typical V-I curve of the PV Module and are capable to investigate for better electricity generation.
3. Objectives
1) The highest electric panel system to build and automate the solar PV plant to maximize the performance of the pv plant
2) To design , simulate and track the MPT algorithm of 72 cell solar pv system;
3) To evaluate an advanced PV system with the vector universe fluent logic regulation taking the temperature fluctuations into account optimum power points monitoring process ·
4) To research power quality improvement in link pv grid with active filters and anfi-based mppt 5) Development of an improved photovoltaic network with a temperature control mppt
4. Problem Statement
In all countries India and other countries worldwide want solar systems. Therefore it should become apparent in the coming years that much more feasible electricity from solar panels also adds to the environmental gain of rising greenhouse gases. Shift of atmospheric temperature will derail and the sun 's life will be that. When choosing solar panels for applications, normally a certain wattage margin is chosen. In the location where the derailment is handled, check the temperature and atmospheric temperature levels. This will support designers and maintenance workers. Secondly, the PV network is dead at night and additional systems are supplied with an upgraded configuration on campus or grid during the day. And at night, when they are charged up, we will take grid supplies or campus batteries. Issue with electricity output, e.g. voltage shift, distortion of voltage and L.T harmonics. The line is assembled by source and load, the latest power quality issue evaluation using active filters and reduction techniques. Second, it is described as an Active Power Filter (SAPF) concept for grid-connected photovoltaic systems. This paper primarily aims at the design of highly effective grid built-in PV. PV performance is increased by using the MPPT technique. The ANFIS-based MPPT system runs rapidly and produces better performance under solar alteration in radiation. The ANFIS MPPT system functions easily. The MATLAB / SIMULINK Program is used for the simulation studies.
5. Theory on Grid Connected System
A big power source to satisfy global energy needs is planned for the photovoltaic network. In two major fields photovoltaic systems can be added to power networks: off-grid or isolated and utility applications. Stand-alone PV systems can be used for driving remote cargoes which have no grid connection, while grid-connected installations require power transmission to utility grids and to power local loads. Increased use for medium-sized grids for home services of the photovoltaic network. PV panels are connected in series and in parallel to produce useful voltage and current. It is possible to incorporate the serial connecting stress level and increase the current density through the parallel link (Eltamaly , 2015) Converter configurations can both be cost-effective and cost-effective. Through optimizing the feeders' voltage pattern and reducing loss of electricity, maintenance costs and loading transformer tape changes at peak hours PV systems are able to increase the operation of the power systems. In contrast with other green technology such as feeding stuff congestion, harmony noise , high cost of investments, poor performance and weak reliability, the photographic systems do face significant problems and other detrimental effects on the network. Changes in the amount of solar radiation can also result in changes in the flickering of power and agitation, creating an unwanted PV system penetration effect. Other control mechanisms, such as MPPT can be used to enhance PV systems performance. With these controllers, the induced voltage and PV array current should be regulated. The PV system configuration can be complicated by an increased risk of failure when full power is tracked in unintended weather conditions. After the machine is disconnected from the utility grid under faulty conditions, DGs will energize local loads. The main cause of harmonic distortion in a power distribution system appears to be nonlinear loads under these conditions.
Nonlinear charging harmonic currents are recovered via the PCC injection into power delivery networks. Such (harmonic) fluctuations cause various problems and affect the power supply. Many solutions were introduced to improve power efficiency by taking into account current distortion limitations for non-linear freight. This paper analyzes and simulates SAPF related PV systems for harmonics elimination and reactive power compensation. The SAPF injects the same amplitude and the opposite step of the charging current to balance the source direction. The APF shunt is for nonlinear loading purposes. The PCC is linked to the delivery network. For the latest comparison measure, we used the P-Q principle. The value of the PV output system voltage can also be adjusted through a DC / DC converter. Fuzzy Logic Control (FLC) is the powerful MPPT controller.
The biggest challenge is to use the PV systems to deal with the non-linear properties of the PV series. The PV characteristics depend on the degree of radiation and temperature. The PV array is irradiated by waves, adjacent objects or by trees at various rates. Environmental variables including solar radiation and temperature levels affect the energy-voltage (P-V) and the current-voltage (I-V) output of a photovoltaic cell. The maximum extractible power on the nonlinear output function of the PV system is one of the key factors which affect the efficiency and overall cost of a control panel within the photovoltaic system. MPPT based on the angry rationale that does not need the PV panel's expertise which results in dramatic increases in radiation.
6. Existing System
The ANFIS-based MPPT approach is ideal for quick irradiation and partial shade, but is appropriate for these systems that provide enough paper to improve the duty cycle of the DC-DC transformer optimally and equate it to IC-based techniques. The non-linear properties of panels of photovoltaic (PV) are well known. At certain voltage, therefore (MPP), for a certain temperature and irradiation there is a specific limit power. MPP stress shifts especially with numerous irradiations and temperatures. The unit must then be operating on the PV Array MPP by setting the inverter to irradiation, temperature or other conditions. In addition, the supplied system supplies primarily to the distribution grid should not only be sinusoidal, but should also meet energy grid specifications such as no DC portion of the inverter output present, reducing damage due to a lack of harmonic power grid emissions etc[28]. These criteria place a high degree of inverter power. The challenge is for most designers to comply with the above requirements for a low cost. The photovoltaic power system usually requires an MPPT to get the photovoltaic array adequate electricity. To evaluate the maximum power point (MPP), the P&O process will measure dP/dV. Implement where the irradiance changes exponentially, it can not control the MPP and oscillates around the MPP rather than tracking it. The advancing action method is able to quickly obey MPP, but increases the AI sophistication measured by means of dI / dV. It is easy to keep track of MPPs by the constant voltage system, which has an open circuit voltage of 76 percent as MPP voltage.
AI-based approaches ideally suited for full power points control to maximize the dynamic performance. Taking into account the lack of features of the Solar PV panel, the AI processes provide a compact and computer-intensive solution to the MPPT problem. The neural logic and artificial network are two key AI methods in MPPT. This paper addresses the architecture and implementation of the ANFIS-based MPPT Scheme.
7. Proposed System
The ANFIS reference model is used as feedback for irradiance and working temperature. At a specific temperature and irradiance point, the ANFIS reference model gives a crisp value to the full PV module power available. PV module's current power output. The temperature and irradiance degree are determined by multiplication algorithm on the perceived working voltage and currents. Two power values are measured and an error is given to the proportional integral (PI) controller to produce control signals. The IGBT pulse is provided to activate the PI controller control signal. The signals produced regulates the duty cycle of the almost z-source inverter in order to adjust the operating point of the PV module. The goals of the control system proposed are
1. Complete identification of power points. 2. Wanted reliable grid power production.
On the basis of the demand of the customer the output power of the inverter must be regulated and adjustable.
8. Applications
1) They increase the appropriateness of transmission lines
2) Fast recovery of lines and feeder after sudden breakage / disruption. 3) Cost management
5) They have the ability to be integrated into large renewable sources of energy, leading to load sharing and a large-scale load reduction.
9. Methodology
The simulation consists of following blocks i. 72 cell solar module (BPSX150s) ii. Cuk Converter
iii. P and O based MPPT controller
9.1 72 cell solar module (BPSX150s)
72 cell solar module is simulated with help of BPSX150s.Building blocks of solar module is described below;
Figure 1. Simulation model of BPSX150s
The BP SX 150S Photovoltaic Module provides superior value and efficiency for both domestic and company use. It generates cost-effective electricity for DC loads or AC loads with tested silicon solar cells using an inverter. With a fixed nominal power of 150 watts, it can be used mainly for supplementary grid systems for homes, industrial facilities, and distributed electric power generation (including in additional grid systems for domestic applications).
9.2 Cuk Converter
Dr Cuk has discovered the integrated magnetic theory Dc-Transformer many years ago, in which the number of Dc flows generated by L1 and Transformer T induction currents equals the Dc flow of the L2 inductors. Then the Dc streams are reciprocally terminated and the Main streams are skipped. Cuk converter provides some benefits similar to a buck con cuk transformer (as opposed to buck topology) such as capacitive transfer failure isolation[8]. Another benefit is that a steady CUK input current allows a fluid-free current to be drawn from a pv array critical to optimum output.
Figure 2. Number and details is missing in phase 1
The Cuk converter 's circuit interface with MOSFET button is revealed. The Cuk converter's output voltage is opposite the input voltage. D is forward-sensitive and the C1 condenser is charged by L1–D when the input
Figure 3. Number and details is missing in phase 2 9.3 P and O MPPT Algorithm
Figure 4. Pattern of MPPT algorithm
The P and O process is the MPPT algorithm and it is embedded into solar panels. The MPPT above is indicated; the resistive load is connected to the model developed for validation of the tests. A separate m file (matlab algorithm) has been created for the current implementation of the P and O MPPT process.
10. Results
The OS is applied and the analysis is accompanied by control algorithms. The effects of simulation and study of software and hardware are discussed here. The simulating program results in an appropriate output resulting from the chosen algorithm and control parameters. You will track the drift of MPP from the MPPT controller. The DC Connection voltage stabilizes the energy balance controller.
Figure 5. Simulation result of 3 phase’s grid voltage
The following charts show the results of a simulation of power control equipment with an input voltage of 200V DC which transfers separately 700W and 1000W. The red curve is the current of set DC power. The AC's present is Green. Green. The violet and yellow curves are both positive and negative DC voltages respectively
Figure 6. Transfer 1000-Watt Power from 200V DC power supply
The DC link is paid flatly, as shown in the figure. This is due to the optimization of the PID algorithm and the integration factor. Since the current stress in the pre-charge time is far from the set point, a significant error is produced. If an integration factor is not constrained, it saturates very easily and produces a high peak when the voltage of the DC link is below the setting point. The findings are identical to the simulated tests. The power supply is stable in this situation, so that the feed forward is proportionate to the input current. The PID controller effectively each the peak after the preload of the DC link. After inserting the PID controller, the production has applied noise. One potential explanation is that the input current values of the energy controller are estimated rather than determined. The present sensor is not protected by the dissertation and requires more calibration. Effective calibration of the new sensor decreases the noise of the device signal dramatically
11. Conclusion
This paper describes an innovative MPPT VUFLC-temperature cycle for a photovoltaic system which can adjust the environment of fluid controls dynamically and take account of the effects of a change in temperature. PV cell output was addressed, and universe temperature regulation factors were proposed and designed according to the effect of the temperature. A updated dynamic control factor based on the temperature shift value was implemented into the current MPPT VUFLC temperature system, improving the speed and precision of the MPPT monitoring over the fixed universe of conventional fugitive driving. Various experiments were carried out. The impact and advantages of the VUFLC MPPT method were checked by simulation and test tests. The proposed controller had better monitoring performance in photovoltaic generation systems compared to traditional approaches, in particular with temperature shifts. When temperature rises, experimental tests of the control system are generally similar to theoretical forecasts. The proposed control process not only improves the MPP tracking efficiency, but offers even better tracking performance, which in contrast control algorithms under the same
12. Future Scope
The development of this prototype did not end. Multiple modes of service exist. A PV simulator power supply was used in the previous test. The performance of actual solar panels can be tested. A user interface design has to be designed and developed in order to complete this project.
References
1. AL-monier, H., EL-halim, H.A., Kotb Salem, M.E. (2012) Maximum tracking point
with Fuzzy Logic Control." Electr. Electr. Power-energy system. 39:21-28.
2. Aroulanandam VV, Latchoumi TP, Balamurugan K, Yookesh TL. (2020) Improving
the Energy Efficiency in Mobile Ad-Hoc Network Using Learning-Based Routing,
Revue d'Intelligence Artificielle, Vol 34(3), pp. 337-343, 2020.
3. Amri, B.—Ashari, M. (2015) Buck-boost, Cuk, Sepic and Zeta converters for full power
point photovoltaic monitoring using P&O system" 2nd International Conference on
Information Technology , Software and Electrical Engineering (ICITACEE), Semarang,
Indonesia; 327–332.
4. Arunkarthikeyan K., and Balamurugan K. (2021) Experimental Studies on Deep Cryo
Treated Plus Tempered Tungsten Carbide Inserts in Turning Operation. In:
Arockiarajan A., Duraiselvam M., Raju R. (eds) Advances in Industrial Automation and
Smart Manufacturing. Lecture Notes in Mechanical Engineering. Springer, Singapore.
https://doi.org/10.1007/978-981-15-4739-3_26
5. Arunkarthikeyan, K. and Balamurugan, K., (2020) July. Performance improvement of
Cryo treated insert on turning studies of AISI 1018 steel using Multi objective
optimization. In 2020 International Conference on Computational Intelligence for
Smart Power System and Sustainable Energy (CISPSSE) (pp. 1-4). IEEE.
6. Balamurugan K, Uthayakumar M, Sankar S, Hareesh US, Warrier KG. (2018)
Preparation, characterisation and machining of LaPO4-Y2O3 composite by abrasive
water jet machine. International Journal of Computer Aided Engineering and
Technology, 10(6), pp.684-697.
7. Balamurugan K. (2020) Metrological changes in surface profile, chip, and temperature
on end milling of M2HSS die steel. International Journal of Machining and
Machinability of Materials, 22(6):pp. 443-453.
8. Babu, T.S.; Rajasekar, N (2017) A thorough study of solar photovoltaic optimum
tracking techniques." Repeat. Repeat. Sustain. Sustain. Rev. 67: 826–847.
9. Balamurugan K and Uthayakumar M, (2017) Preparation And Machining Studies Of
Lapo4 Y2o3 Ceramic Matrix Composite, http://hdl.handle.net/10603/166221.
10. Bhasha, A.C., Balamurugan, K. (2020) End mill studies on Al6061 hybrid composite
prepared by ultrasonic-assisted stir casting. Multiscale and Multidiscip. Model. Exp.
and Design, https://doi.org/10.1007/s41939-020-00083-1
11. ChinnamahammadBhasha, A., Balamurugan, K. (2020) Studies on Al6061nanohybrid
Composites
Reinforced
with
SiO2/3x%
of
TiC
-a
Agro-Waste. Silico,.
https://doi.org/10.1007/s12633-020-00758-x
12. Chinnamahammad Bhasha and Balamurugan K, (2020) Multi-objective optimization of
high-speed end milling on Al6061/ 3% RHA/ 6% TiC reinforced hybrid composite using
Taguchi coupled GRA, International Conference on Computational Intelligence for
Smart Power System and Sustainable Energy (CISPSSE), Keonjhar, India, 2020, pp.
1-6, doi: 10.1109/CISPSSE49931.2020.9212295.
13. Chin, C.S., Neelakantan, P., Yoong, H.P., Teo, K.T.K. (2011) Fuzzy Logic Based
MPPT Influenced by Solar Irradiation and Cell Temperature" The 2011 UkSim 13th
International Conference on Computer Modeling and Simulation, Cambridge, UK; pp.
376–381.
14. Deepthi T, and Balamurugan K. (2019) Effect of Yttrium (20%) doping on mechanical
properties of rare earth nano lanthanum phosphate (LaPO4) synthesized by aqueous
sol-gel process. Ceramics International. 45(15), pp.18229-18235.
15. Deopare, Deshpande, A. (2015) Modeling and simulation of Incremental Power Point
Monitoring." In the 2015 International Conference on Energy Systems and
Applications, Pune, India, pp. 501–505.
16. Da Rocha, N.M.M., Martins, D.C. (2016) Photovoltaic cell temperature regulation
system MPPT." In the 2016 IEEE International Conference on Business Applications
(INDUSCON), Curitiba, Brazil, November 20–23; pp. 1–8.
17. Eltamaly, A.M.(2015) A thorough study of various photovoltaic MPPT techniques."
Sol. Sol. Power,112:1-11.
18. Francisco, Cruz, D. (2012) Optimized Power Point Tracking Method based on PV
Surface Temperature Measurement," Recent studies; Gebremedhin, A., Ed.; InTech:
Vienna, Austria.
19. Garikipati P, and Balamurugan K. (2021) Abrasive Water Jet Machining Studies on
AlSi 7+ 63% SiC Hybrid Composite. InAdvances in Industrial Automation and Smart
Manufacturing, pp. 743-751, Springer, Singapore.
20. Garikapati P, Balamurugan K, Latchoumi TP, Malkapuram R. (2020) A Cluster-Profile
Comparative Study on Machining AlSi 7/63% of SiC Hybrid Composite Using
Agglomerative
Hierarchical
Clustering
and
K-Means.
Silicon.
https://doi.org/10.1007/s12633-020-00447-9
21. Kalogirou, S.A. (2014) MPPT-based photovoltaic device artificial intelligence
techniques and deployment into field-programmable gate array chips: analysis of
current status and future perspectives." Energy, 70-1-21. [CrossRef] [CrossRef]
22. Kreeumporn, W., I-Ngamroo. (2016) Integrated in PV generators for smoothing power
and regulation of voltage in the distribution system with PHEVs," Trans IEEE. Appl.
Super second. 5402805.
23. Lin, W.-M.; C.-M.; Chen, C.-H. (2011)Neural-network - based MPPT Stand-alone
hybrid power generation device control." Trans IEEE. Electron Control. 26, 3571–
3581.
24. Latchoumi TP, Reddy MS, Balamurugan K. (2020) Applied Machine Learning
Predictive Analytics to SQL Injection Attack Detection and Prevention. European
Journal of Molecular & Clinical Medicine.;7(02), pp. 3543-3553.
25. Li, X., Li, Y., Seem, J.E., Lei, P. (2016) Internal Resistance Shift Detection for
Photovoltaic Arrays Using Extremum-Seeking MPPT Signals." Trans IEEE. Control
device. Technol. Technol. 24-325-333.
26. Mustafa, S.S., Mutlag, A.H. (2016) Mod solar power system optimization for
temperature and sun monitoring." In the 2016 Al-Sadeq International Conference on
Multidisciplinary Research and Applications in IT and Connectivity (AIC-MITCSA),
Baghdad, Iraq; pp. 1–5.
27. Narendiran, S., Sahoo, S.K., Das, R. (2016) Fuzzy PV device based maximum power
point monitoring" In Proceedings of the 3rd International Electrical Energy Systems
Conference 2016 (ICEES), Tiruchengode, India, pp. 29–34.
28. Pavan MV, and Balamurugan K. (2020) Compressive Property Examination on Poly
Lactic Acid-Copper Composite Filament in Fused Deposition Model–A Green
Manufacturing Process. Journal of Green Engineering.10, pp.843-852.
29. Park, M., I.A. (2004) Study on optimal MPPT voltage from solar cell surface
temperature. In Proceedings of IEEE Industrial Electronics Society's 30th Annual
Conference, Busan, South Korea; 30, pp. 2040–2045.
31. Reisi, A.R., Moradi, M.H. (2013) Classification and comparison of maximum
photovoltaic power point monitoring techniques: analysis." Repeat. Repeat. Sustain.
Sustain. Rev. Energy, 19: 433–443.
32. Rajesh Kumar, T., Suresh, G.R., Kanaga Suba Raja, S. (2017) Conversion of Non
Audible Murmur to Normal Speech Based on Full-Rank Gaussian Mixture Model’,
Journal of Computational and Theoretical Nanoscience, ISSN: 1546-1955, 14, 12, pp.
01–06.
33. Shebani, M.M., Quaicoe, J.E. (2016) Comparing numerical bisection algorithm with
short-circuit fractional and open-circuit voltage methods for photovoltaic MPPT
systems." The 2016 IEEE Electrical Power and Energy Conference (EPEC), Ottawa,
ON, Canada, October 12–14, pp. 1–5.
34. Subudhi, Pradhan, R. (2013) A Comparative Report on Photovoltaic PowerPoint
Monitoring Techniques," Trans IEEE. Sustain. Sustain. Energy, 4:89-98.
35. Sampathkumar, A., Murugan, S., Rastogi, R., Mishra, M. K., Malathy, S., Manikandan, R. (2020).
Energy Efficient ACPI and JEHDO Mechanism for IoT Device Energy Management in Healthcare. In Internet of Things in Smart Technologies for Sustainable Urban Development (pp. 131-140). Springer, Cham.
