Feel free to edit this page and add sources and summaries.
Renewable energy sources like solar power are becoming increasingly popular and affordable, and the need for efficient and cost-effective solar charge controllers and MPPTs has also grown. But the cost of commercial MPPT devices is significantly higher than solar charge controllers, making them less accessible and affordable for users. So, our project aims to build a new open source MPPT design to reduce the price gap between MPPT and solar charge controllers. By utilizing low-cost components and simplified manufacturing processes, this project hopes to bring down the cost of MPPT devices to a level comparable to that of solar charge controllers.
Literature review on "Open source design of MPPT solar charge controller 30A, 24V"
1. A Comprehensive Review of Maximum Power Point Tracking (MPPT) Techniques Used in Solar PV Systems[1]
Musong L. Katche et. al discussed different methods for maximum power point tracking (MPPT) in photovoltaic systems categorized them in conventional, intelligent, optimization, and hybrid techniques. The methods are compared based on various criteria such as efficiency, cost, stability, and complexity of implementation.
The review suggests that hybrid techniques are highly efficient but more complex and expensive than conventional methods.
Conventional methods are less effective under partial shading and have slower response times, whereas intelligent, optimization, and hybrid techniques can handle partial shading.
They recommended to conduct further research to improve the efficiency and tracking stability of the existing conventional Perturb and Observe (P&O) algorithm used in MPPT. This can be achieved by exploring ways of selecting and adjusting step sizes used in the P&O method\
2. General review and classification of different MPPT Techniques[2]
Nabil Karami et. al. presented an overview of 40 different Maximum Power Point Tracking (MPPT) methods for power tracking in PV systems. The methods are mathematically modeled and presented also compared in a table to simplify the classification.
They categorized maximum power point tracking (MPPT) methods into 5 groups: (1) constant parameter tracking, (2) measurement and comparison tracking, (3) trial and error tracking, (4) mathematical calculation tracking, and (5) intelligent prediction tracking.
Based on their study the most popular MPPT method is the P & O method, but the IC method is also widely used on the other hand artificial intelligence-based methods are fast and stable but require digital applications and multiple sensors.
P&O method suffers from trade-offs between faster response and steady-state oscillations due to the step size of perturbation. To improve performance, four modified versions of P & O have been discussed, which uses converter duty ratio as the perturbed signal or adaptive perturbation values.
3. MPPT methods for solar PV systems: a critical review based on tracking nature[3]
Amit Kumer Podder et. al. reviewed of 50 different maximum power point tracking (MPPT) methods, categorizing them into eight categories based on their tracking characteristics.
The study analyzes the key characteristics and eleven selection parameters of the methods, which is a novel approach not seen in previous reviews.
The β method is suitable for smaller ripple voltage, better transient response, and less complexity.
Evolutionary methods like PSO, GA, DE, FLC are best for extracting power from PV panels under partial shading conditions.
Current-based methods are more accurate but require expensive hardware.
4. A Variable Step Size INC MPPT Method for PV Systems[4]
Fangrui Liu et. al. proposes a modified variable step size INC MPPT algorithm that automatically adjusts the step size to improve MPPT speed and accuracy.
The variable step size is determined based on the derivative of power to voltage of the PV array.
The step size is increasing when the operating point is far from the Maximum Power Point (MPP) and decreasing when near the MPP to reduce oscillation and improve efficiency
The scaling factor N, which determines the performance of the MPPT system, is manually tuned, and a simple method to determine N is proposed.
The proposed method is simple and can be easily implemented in digital signal processors.
5. A review on MPPT techniques of PV system under partial shading condition[5]
Alivarani Mohapatra et. al. reviewed various maximum power point tracking (MPPT) algorithms under partial shading conditions.
GWO is combined with Direct Duty Cycle Control (DCC) to keep duty cycle constant at Maximum Power Point (MPP) to reduce steady-state oscillations.
The Firefly Algorithm (FA) uses an updated β coefficient and a simplified version of the original algorithm to track the MPP of a PV system under partial shading conditions.
The Ant Colony Optimization (ACO) algorithm is also discussed, which uses a probabilistic approach to search for the MPP.
GWO has the highest tracking efficiency with elimination of transient and steady-state oscillations.
6.A direct control based maximum power point tracking method for photovoltaic system under partial shading conditions using particle swarm optimization algorithm[6]
Kashif Ishaque et. al used particle swarm optimization (PSO) algorithm which can track the global maximum point of the PV array under partial shading conditions.
The proposed method is a direct control scheme where duty cycle is computed directly in the algorithm, eliminating the need for control loops, such as PI controllers.
The algorithm uses a solution vector of duty cycles to find the personal best position of each particle. The variable Pbesti is used to memorize the best duty cycle found by the ith particle, and the variable Gbest is used to memorize the best duty cycle achieved among all particles.
The proposed method accurately tracks the global MPP for all given shading conditions and yields more than 99.5% efficiency except for two cases where the MPP is in the vicinity of VOC.
7. A Neural Network Based MPPT Technique Controller for Photovoltaic Pumping System[7]
Mohammed Yaichi et. al presented improved MPPT method for photovoltaic system consisting of a PV array, an inverter, an asynchronous motor, and a centrifugal pump, using an artificial neural network (ANN).
The proposed network topology for the MPPT technique includes an input layer, one hidden layer, and an output layer with a sigmoidal transfer function.
The study found that increasing the number of neurons in the hidden layer led to an increase in the regression coefficient R-square (R2), indicating higher accuracy in predicting the output speed response of the motor-pump
The architecture with five neurons in the hidden layer achieved the highest R2 value of 0.9999.
Furthermore, the paper presents simulation results for four different levels of solar radiation. The MPPT controller was found to be effective in tracking for all tested radiation levels, demonstrating the robustness of the proposed control strategy.
8. Optimization of Perturb and Observe Maximum Power Point Tracking Method[8]
Nicola Femia et. al. addressed two big problems associated with P&O method and analyze the optimal choice of two main parameters characterizing the P&O algorithm to overcome the problems.
P&O algorithm hovers around the MPP which leads to instability problem. Also this method deals confusingly with rapidly changing atmospheric conditions.
In this paper, instability problem is avoided by setting the sampling interval higher than a proper threshold of the MPPT algorithm for a dynamic system.
To cope up with rapidly changing irradiance condition, the optimization of the duty cycle is shown for a system.
The outcomes obtain through this method can be utilized to improve the design of MPPT regulators by adjusting it to suit the unique dynamic qualities of the system.
9. Adaptive perturb and observe algorithm for photovoltaic maximum power point tracking[9]
L. Piegari et. al. proposes an adaptive P&O method that exhibits faster dynamics and improved stability, which was established and verified through numerical simulations and experimental tests.
The fundamental concept of this algorithm is to adjust the perturbation amplitude according to the present operational conditions.
The proposed algorithm has the added benefit of being adaptive, making it less affected by changes in circuit parameters.
The effectiveness of the proposed algorithm has been experimentally verified through laboratory tests conducted on a low-power panel.
10. An Improved Perturbation and Observation Maximum Power Point Tracking Algorithm for PV Arrays[10]
Xuejun Liu et. al. suggested an updated P&O MPPT algorithm that speed up the system response and reduce oscillations around the Maximum Power Point (MPP).
The proposed P&O algorithm technique incorporates with high sampling rates and very fast response time.
The proposed algorithm uses peak current control and instantaneous values instead of averaged values to determine the direction of the subsequent perturbation.
11. Optimizing Duty-cycle Perturbation of P&O MPPT Technique[11]
N Femia et. al. demonstrates that the adverse impacts for rapidly changing atmospheric conditions can be significantly minimized by adjusting the magnitude of the duty-cycle perturbations.
This paper controls the magnitude of the duty-cycle in accordance to the dynamic characteristics of the particular converter utilized for implementing the P&O MPPT.
This technique can be utilized to improve the design of MPPT regulators by adjusting it to match the unique dynamic qualities of the system.
The optimization of the duty cycle is shown for a boost battery charger system.
Literature review on "Open source MPPT controllers for PV systems"
12. Hardware Design of PIC Microcontroller based Charge controller and MPPT for the Standalone PV Battery charging System[12]
Rahul Santhosh et. al. designed the prototype of MPPT charge controller using microcontroller PIC16F877A.
They have used incremental conductance MPPT method and found 98% of efficiency.
Compared to commercial MPPT this efficiency is low because of the use of diode instead of synchronous rectifier MOSFET.
The MPPT is designed for 10W power which is very low and not suitable for commercial use.
No protection system has been implemented to protect the system against overcurrent, over voltage and overheating.
Also the algorithm used in the system is vulnerable against partial shading and tracts local maxima instead of global peak.
13. Hardware Prototype for Portable Automatic MPPT Solar Charger Using Buck Converter and PSO Technique[13]
Mahesh Parandhaman et. al. designed the prototype of MPPT charge controller using PSO technique to track maximum power under partial shading.
They have used Arduino uno as microcontroller to control the system, which is large in size and costly and doesn’t have built in wifi module for wireless telemetry.
The charge controller designed is for use of 40W solar power which can be considered as a prototype.
Only heat sink is used for overheat protection but no electrical protection system has been implemented to protect the system against overcurrent, over voltage etc.
Also they used conventional buck converter which reduces efficiency significantly due to the presence of diode.
Also the final prototype doesn’t have the features and protection system as a commercial MPPT.
14. 1kW Arduino MPPT Solar Charge Controller (ESP32 + WiFi)[14]
Angelo Casimiro has designed the project on ESP 32 micro controller with features and protection system like a commercial MPPT. Also the device in compact and small in size with 99% of efficiency because of synchronous buck converter and back flow current controlling.
But the algorithm used in the project fails to track MPP under partial shading.
In the project the designer has claimed of using range of input and output voltages but it is impossible to achieve with only buck converter.
Also the MPPT can charge the battery only. DC loads can’t be supplied directly, where this feature is available in commercial MPPT.
Although this project replicates the commercial MPPT in a large extent, this project can be improved using sophisticated algorithms and converters.
15. Monitoring System for Tracking a PV Generator in an Experimental Smart Microgrid: An Open-Source Solution[15]
José María Portalo et. al designed a proper monitoring in smart microgrids, specifically for SMG that combines photovoltaics and hydrogen energy
The system consists of open-source hardware and software responsible for sensing, data acquisition, and visualization.
The proposed solution is flexible, modular, and scalable, and the low-cost nature of the proposal lies in the inexpensive budget required for its implementation.
The data acquisition layer includes an Arduino microcontroller responsible for conditioning and processing the output signals of the sensors.
The data storage layer accumulates the information received from the previous layer in a structured manner using MariaDB, a database management system based on MySQL.
The data visualization and analytics layer displays real-time information to the user/operator for the proper tracking of the physical facility using Grafana.
Finally, the network communication layer covers the data exchange through the network between the previous layers using an Ethernet-based network for communication between the Raspberry Pi and Arduino boards.
16. Open-Source Hardware Platforms for Smart Converters with Cloud Connectivity[16]
Massimo Merenda et. al. design and implemented an open-source hardware platform for smart converters that are equipped with controllers able to online impedance match for maximum power transfer.
The platform can be used for the development and testing of various maximum power point tracking algorithms for different renewable energy systems.
It features bi-directional radio frequency communication that enables real-time reading of measurements and parameters, as well as remote modification of both algorithm types and settings.
The MLB has three main components: a microcontroller that acquires currents and voltages from the power circuit, an FPGA that generates PWM signals for controlling MOSFETs and implements the control logic, and a Wi-Fi
They used P&O algorithm for maximum power point tracking (MPPT) using FPGA on the open-source hardware smart converter platform. The algorithm is translated into VHDL code and synthesized using Lattice Diamond software.
The power conversion efficiency was considered, with efficiency values higher than 97% for input powers in the considered range and higher than 98% for power above 300 W. The efficiency decreased as the switching frequency increased.
The platform's limitations include a maximum power of 450 W, maximum input and output voltage of 70 V, and maximum current of 10 A not simultaneously.
17. Design of Maximum Power Point Tracking Photovoltaic System Based on Incremental Conductance Algorithm using Arduino Uno and Boost Converter[17]
Efendi S Wirateruna et. al. designed a maximum power point tracking (MPPT) controller based on the Incremental Conductance algorithm.
The system consists of a solar panel module, DC-DC boost converter, voltage divider, current sensor, Arduino Uno, and load resistor.
MPPT needs an algorithm with low-medium complexity and easy hardware implementation in case of converter topology, so they have used IC method which is simple for hardware implementation.
The results show that the PV system with MPPT controller based on the IC algorithm can control PV power output at the maximum power point and has an average output power of about 7.34 watts, while a PV system without an MPPT controller has an average output power of about 6.07 watts.
18. Smart Monitoring System of DC to DC converter for Photovoltaic Application[18]
Jameel Kadhim Abed presented a new smart monitoring system for photovoltaic applications using a DC to DC converter.
The system has two parts: a control system using an Arduino NANO to read voltage and current data and send it wirelessly to a monitoring system application designed using open source software.
The monitoring system application software is designed to be installed on an Android smartphone instrument using MIT App builder.
The article describes the final hardware system of a DC to DC converter with photovoltaic application, which consists of DC/DC converter circuit, Arduino Nano, HC-5 Bluetooth communication system, voltage and current sensors, and LCD.
The monitoring data were taken by the smartphone application from a distance of less than 10 meters due to the HC-5 Bluetooth device.
19. Design of an Efficient Maximum Power Point Tracker Based on ANFIS Using an Experimental Photovoltaic System Data[19]
Sadeq D. Al-Majidi et. al discussed the use of the adaptive neural-fuzzy inference system (ANFIS) as the most powerful method for maximum power point tracking (MPPT) in photovoltaic systems.
The proposed method is simulated using a MATLAB/Simulink model and is shown to accurately track the optimized maximum power point with efficiencies of over 99.3% under varying climatic conditions.
The proposed ANFIS technique used the irradiance and temperature data collected by a weather station to predict the power generated by the photovoltaic system accurately. The ANFIS model was trained using 40 days of data, with about 6200 data sets.
The proposed ANFIS-MPPT method outperformed other MPPT techniques such as P&O-MPPT and FLC-MPPT under varying climatic conditions, achieving efficiencies greater than 99.3%.
They concluded that ANFIS-MPPT techniques can improve the efficiency of PV systems, but their implementation may become overly complex due to additional step units.
References
↑ M. L. Katche, A. B. Makokha, S. O. Zachary, and M. S. Adaramola, “A Comprehensive Review of Maximum Power Point Tracking (MPPT) Techniques Used in Solar PV Systems,” Energies, vol. 16, no. 5, Art. no. 5, Jan. 2023, doi: 10.3390/en16052206.
↑ N. Karami, N. Moubayed, and R. Outbib, “General review and classification of different MPPT Techniques,” Renew. Sustain. Energy Rev., vol. 68, pp. 1–18, Feb. 2017, doi: 10.1016/j.rser.2016.09.132.
↑ A. K. Podder, N. K. Roy, and H. R. Pota, “MPPT methods for solar PV systems: a critical review based on tracking nature,” IET Renew. Power Gener., vol. 13, no. 10, pp. 1615–1632, 2019, doi: 10.1049/iet-rpg.2018.5946.
↑ F. Liu, S. Duan, F. Liu, B. Liu, and Y. Kang, “A Variable Step Size INC MPPT Method for PV Systems,” IEEE Trans. Ind. Electron., vol. 55, no. 7, pp. 2622–2628, Jul. 2008, doi: 10.1109/TIE.2008.920550.
↑ A. Mohapatra, B. Nayak, P. Das, and K. B. Mohanty, “A review on MPPT techniques of PV system under partial shading condition,” Renew. Sustain. Energy Rev., vol. 80, pp. 854–867, Dec. 2017, doi: 10.1016/j.rser.2017.05.083.
↑ K. Ishaque, Z. Salam, A. Shamsudin, and M. Amjad, “A direct control based maximum power point tracking method for photovoltaic system under partial shading conditions using particle swarm optimization algorithm,” Appl. Energy, vol. 99, pp. 414–422, Nov. 2012, doi: 10.1016/j.apenergy.2012.05.026.
↑ M. Yaichi, M.-K. Fellah, and A. Mammeri, “A Neural Network Based MPPT Technique Controller for Photovoltaic Pumping System,” Int. J. Power Electron. Drive Syst. IJPEDS, vol. 4, no. 2, Art. no. 2, Jun. 2014
↑ N. Femia, G. Petrone, G. Spagnuolo, and M. Vitelli, “Optimization of perturb and observe maximum power point tracking method,” IEEE Trans. Power Electron., vol. 20, no. 4, pp. 963–973, Jul. 2005, doi: 10.1109/TPEL.2005.850975.
↑ L. Piegari and R. Rizzo, “Adaptive perturb and observe algorithm for photovoltaic maximum power point tracking,” IET Renew. Power Gener., vol. 4, no. 4, pp. 317–328, Jul. 2010, doi: 10.1049/iet-rpg.2009.0006.
↑ X. Liu and L. A. C. Lopes, “An improved perturbation and observation maximum power point tracking algorithm for PV arrays,” in 2004 IEEE 35th Annual Power Electronics Specialists Conference (IEEE Cat. No.04CH37551), Jun. 2004, pp. 2005-2010 Vol.3. doi: 10.1109/PESC.2004.1355425.
↑ N. Femia, G. Petrone, G. Spagnuolo, and M. Vitelli, “Optimizing duty-cycle perturbation of P&O MPPT technique,” in 2004 IEEE 35th Annual Power Electronics Specialists Conference (IEEE Cat. No.04CH37551), Jun. 2004, pp. 1939-1944 Vol.3. doi: 10.1109/PESC.2004.1355414.
↑ R. Santhosh, S. U. Sabareesh, R. Aswin, and R. Mahalakshmi, “Hardware Design of PIC Microcontroller based Charge controller and MPPT for the Standalone PV-Battery charging System,” in 2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT), Aug. 2021, pp. 172–175. doi: 10.1109/RTEICT52294.2021.9573523.
↑ M. Parandhaman, L. T. S. Annambhotla, and P. Parthiban, “Hardware Prototype for Portable Automatic MPPT Solar Charger Using Buck Converter and PSO Technique,” in 2022 IEEE Delhi Section Conference (DELCON), Feb. 2022, pp. 1–6. doi: 10.1109/DELCON54057.2022.9764362.
↑ J. M. Portalo, I. González, and A. J. Calderón, “Monitoring System for Tracking a PV Generator in an Experimental Smart Microgrid: An Open-Source Solution,” Sustainability, vol. 13, no. 15, Art. no. 15, Jan. 2021, doi: 10.3390/su13158182.
↑ M. Merenda et al., “Open-Source Hardware Platforms for Smart Converters with Cloud Connectivity,” Electronics, vol. 8, no. 3, Art. no. 3, Mar. 2019, doi: 10.3390/electronics8030367.
↑ “Design of Maximum Power Point Tracking Photovoltaic System Based on Incremental Conductance Algorithm using Arduino Uno and Boost Converter,” Appl. Technol. Comput. Sci. J., vol. 4, no. 2, pp. 101–112, 2022, doi: https://doi.org/10.33086/atcsj.v4i2.2450.
↑ J. K. Abed, “Smart Monitoring System of DC to DC Converter for Photovoltaic Application,” Int. J. Power Electron. Drive Syst. IJPEDS, vol. 9, no. 2, Art. no. 2, Jun. 2018, doi: 10.11591/ijpeds.v9.i2.pp722-729.
↑ S. D. Al-Majidi, M. F. Abbod, and H. S. Al-Raweshidy, “Design of an Efficient Maximum Power Point Tracker Based on ANFIS Using an Experimental Photovoltaic System Data,” Electronics, vol. 8, no. 8, Art. no. 8, Aug. 2019, doi: 10.3390/electronics8080858.
