Maximum Power Point Tracking Power Research Articles

An Optimization Algorithm for Embedded Raspberry Pi Pico Controllers for Solar Tree Systems

Solar photovoltaic (PV) systems stand out as a promising solution for generating clean, carbon-free energy. However, traditional solar panel installations often require extensive land resources, which could become scarce as the population grows. To address this challenge, innovative approaches are needed to maximize solar power generation within limited spaces. One promising concept involves the development of biological tree-like structures housing solar panels. These “solar trees” mimic the arrangement of branches and leaves found in natural trees, following patterns akin to phyllotaxy, which correlates with the Fibonacci sequence and golden ratio. By adopting an alternative 1:3 phyllotaxy pattern, three solar panels can be efficiently arranged along the stem of the solar tree structure, each rotated at a 120-degree displacement. Optimizing the performance of solar trees requires effective maximum power point tracking (MPPT), a crucial process for extracting the maximum available power from solar panels to enhance the overall efficiency. In this study, a novel metaheuristic algorithm called horse herd optimization (HHO) is employed for MPPT in solar tree applications. Moreover, to efficiently manage the generated power, a cascaded buck–boost converter is utilized. This converter is capable of adjusting the DC voltage levels to match the system requirements within a single topology. The algorithm is implemented using MATLAB and embedded within a Raspberry Pi Pico controller, which facilitates the generation of pulse-width modulation (PWM) signals to control the cascaded buck–boost converter. Through extensive validation, this study confirms the effectiveness of the proposed HHO algorithm integrated into the Raspberry Pi Pico controller for optimizing solar trees under various shading conditions. In essence, this research highlights the potential of solar tree structures coupled with advanced MPPT algorithms and power management systems to maximize solar energy utilization, offering a sustainable solution for clean energy generation within limited land resources.

A comparative assessment of popular tracking algorithms used in standalone photovoltaic systems

Working performance of a PV module or array is largely reliant on climate (temperature/irradiation) and is also non-linear. Maximum power point tracking (MPPT) must be used to guarantee that the PV array generates the greatest electricity under any conditions. Researchers have proposed many approaches to track peak performance. There are benefits and drawbacks to every approach. Some approaches might be difficult to apply, while others provide erroneous results. MPPT boosts photovoltaic (PV) system efficiency and electricity output. Current research focuses on designing, developing, and using fast-tracking algorithms with strong dynamic performance and tracking capabilities. Without a uniform test bench, defending the optimal algorithm and converter combination is difficult. MPPT uses artificial neural networks, fuzzy logic control, cuckoo search, perturb and observe, and particle swarm optimisation (PSO) approaches. This study suggests evaluating these well-known MPPT algorithms on a 120 Wp standalone PV system with a dc-dc boost converter MPPT power interface. Tracking efficiency, inaccuracy, relative power loss, and gain are best using the PSO algorithm. Tracking efficiency improves by about 1% compared to other methods and roughly 4.5-5% for previously reported values.

Technico and Economic Optimization of the Injection of a Photovoltaic System on a Low Voltage Network

AbstractThis paper investigates an experimental study of photovoltaic PV modules behavior (SM110-24) connected to the low-voltage grid (230 V / 50 Hz) with a three-phase inverter. In order to ensure maximum power point tracking (MPPT), The input voltage is delivered via a DC/DC converter that is controlled by the Fuzzy Logic Technical FLC to ensure maximum power MPPT. The control system of the setup is designed for the extraction of maximum power. The primary aim of this investigation is to synchronize the two sources namely PV-grid, using the Phase Locked Loop PLL command with the injection of an alternating current into the network whose aim is stabilizing the system and minimizing the cost of the energy consumed by the load. In this study, the authors have taken into account the real kWh price of the Algerian society of distribution of electricity (Sonelgaz) as the basis for their research. The simulation results obtained by utilizing the MATLAB software show that the proposed system exhibits high performance and flexibility, indicating its potential usefulness for practical applications in the future.

Simulation of distributed photovoltaic power generation system

Combined with the operation characteristics of photovoltaic power generation system, the mathematical model and simulation model are established, and the control strategy of bipolar inverter with wide output characteristics and the maximum power point tracking (MPPT) control method of conductance increment method are designed. The maximum power output and stable operation of distributed photovoltaic power generation system are realized. The simulation results show that the photovoltaic power generation system model established in this paper can effectively reflect the characteristics of distributed photovoltaic power generation and MPPT control. With the change of lighting conditions, the distributed photovoltaic power generation system can accurately adjust the system output, which is of great significance for the research and operation of distributed photovoltaic power generation system.

Design and implementation of a novel multi-faceted-efficient pneumatic dual-axis solar tracker

PurposePhotovoltaic (PV) systems are experiencing exponential growth due to environmental concerns, unlimited and ubiquitous solar energy, and starting-to-make-sense panel costs. Alongside designing more efficient solar panels, installing solar trackers and special circuitry for optimizing power delivery to the load according to a maximum power point tracking (MPPT) algorithm are other ways of increasing efficiency. However, it is critical for any efficiency increase to account for the power consumption of any amendments. Therefore, this paper aims to propose a novel tracker while using MPPT to boost the PV system's actual efficiency accounting for the involved costs.Design/methodology/approachThe proposition is an experimental pneumatic dual-axis solar tracker using light-dependent resistor (LDR) sensors. Due to its embedded energy storage, the pneumatic tracker offers a low duty-cycle operation leading to tracking energy conservation, fewer maintenance needs and scalability potential. While MPPT assures maximum load power delivery, the solar PV's actual delivered power is calculated for the first time, accounting for the solar tracking and MPPT power costs.FindingsThe experiments' results show an increase of 37.6% in total and 35.3% in actual power production for the proposed solar tracking system compared to the fixed panel system, with an MPPT efficiency of 90%. Thus, the pneumatic tracking system offers low tracking-energy consumption and good actual power efficiency. Also, the newly proposed pneumatic stimulant can significantly simplify the tracking mechanism and benefit from several advantages that come along with it.Originality/valueTo the best of the authors’ knowledge, this work proposes, for the first time, a single-motor pneumatic dual-axis tracker with less implementation cost, less frequent operation switching and scalability potential, to be developed in future works. Also, the pneumatic proposal delivers high actual power efficiency for the first time to be addressed.

Inter-harmonics Mitigation for PV-based Converters Using INHARE MPPT Algorithm

The need for environmentally-friendly energy generation and massive exploitation of fossil fuels are leading to the increased demand for renewable energy sources like photovoltaic (PV) and wind energy, etc. The fluctuating behaviour of PV causes various disadvantages, of which the generation of Inter-harmonics is the major problem. The production of Inter-harmonics by the load-interfacing inverters is primarily due to the Maximum Power Point Tracking (MPPT) techniques employed in PV system. The requirement of multiple perturbations in tracking peak points is the primary cause behind the inter-harmonic generation. Since solar irradiation varies throughout a specific period, there can be situations, where the operating irradiance changes abruptly from one state to the other. These abrupt changes generate enormous fluctuations in the MPPT power output, resulting in an inter-harmonics generation. This paper proposes an evolved INter-HARmonic-Elimination (INHARE) algorithm for minimizing the PV fluctuating behaviour, leading to lower voltage ripple and settling time. The proposed INHARE algorithm is examined for a 0.52 kW PV system, where the Inter-harmonics content is reduced considerably in load current for the case of varying irradiances at a constant temperature.

A model predictive control strategy based on energy storage grid‐connected quasi‐z‐source inverters

The battery energy stored quasi-Z-source (BES-qZS) based photovoltaic (PV) power generation system combines advantages of the qZS inverter and the battery energy storage (BES) system. To realize multi-objective cooperative control, a model predictive control (MPC) strategy for the PV grid-connected system based on an energy-storage quasi-Z source inverter (ES-qZSI) is proposed. The energy storage battery is added to the traditional quasi-Z source inverter (qZSI). The MPC strategy is adopted to realize maximum power point tracking (MPPT), battery, and point of common coupling (PCC) tripartite energy control in a single-stage conversion system. The discrete-time model of ES-qZSI is derived, and the cost function was constructed by using the system state variables. The PV power MPPT module, the battery power management module, and the PCC power module are designed to generate the reference signal of the prediction function. Finally, the proposed algorithm was validated by simulation. The theoretical analysis and simulation results were consistent.

Design and development of extract maximum power from single-double diode PV model for different environmental condition using BAT optimization algorithm

To minimize real-time errors in a Photovoltaic (PV) system performances must be forecasted through precise simulation design before continuing into a practical application. However, due to the scarcity of data in datasheets and the inherent transcendental connections are between PV current and PV voltage, to determining the Single Diode Model (SDM) parameters becomes a more challenging problems. This paper offers a simulated study of a SDM and Double Diode Model (DDM) solar PV system under various irradiation represents, and the performance was developed by incorporating an optimization-based Maximum Power Point (MPP) tracking techniques. According to the present simulation presented in this article, a mathematical model for a SDM/DDM as well as optimization methodologies has been estimated MATLAB platform. The present MPP circuit model designed and compared with BAT optimization algorithms. The nonlinear relationship between Voltage (V) - Current (I) and Voltage (V) –Power (W) acknowledged as characteristic curves for different temperature (∘c) and irradiance (W/m2) values are verified in numerical simulation results. MPP tracking power and efficiency are examined for maximum power (Pmax) to test the optimization based system. The simulation results show that the BAT optimization model was achieved the highest tracking efficiency better than other heuristic algorithms.

BOSS-D-RBFN: BOosted Salp Swarm optimization based Deep RBFN for MPPT under partial shading condition in photovoltaic systems

PhotoVoltaic (PV) systems play a promising role in renewable energy resources (REs) as it provides environmentally friendly energy without pollution. PV systems under Partial Shading conditions (PSc) can significantly drop off their productivity of power where the arrays are shaded for various reasons, which lead to the output power that has many peaks, so there is a chance for local minima. Maximum Power Point Tracking (MPPT) methods will reduce the loss created over the PSc. Even though many conventional and soft computing approaches are commonly employed for the MPPT problem, conventional approaches show inadequate efficiency owing to fixed step size, and most of the existing soft computing approaches are limited by complicated rules implementation and require reinitialization during changing environmental conditions. So in this work, Deep learning based Radial Basis Function Network (D-RBFN) is proposed for the MPPT method as this neural network does not oscillate nearby the maximum power point region and performs well in nonlinear and rapid changing condition. Also, RBFN is optimized with the proposed BOosted Salp Swarm optimization (BOSS) approach to attaining higher accuracy and convergence speed by removing the random weights. Moreover, this proposed BOSS-D-RBFN method uses a DC–DC boost converter for faster transient response. The proposed method is evaluated against the existing classical MPPT, neural network-based MPPT, and fuzzy logic-based MPPT method to validate the performance. The results from the simulation study proved that our proposed BOSS-D-RBFN performs better compared to other existing methods with respect to global MPPT and MPPT power efficiency.

MPPT optimisation techniques and power electronics for renewable energy systems: wind and solar energy systems

This study proposes the challenges faced by the present renewable energy scenario and the contribution of power electronics and maximum power point tracking (MPPT) optimisation techniques. India is one of the leading energy harvester country in the world. Power electronics keep power system operation stable, harvest electric power from renewable energy sources (RESs), and reduce energy consumption. This study has focused on power electronics and MPPT optimisation techniques for wind energy and photovoltaic energy and points out some aspects related to configuration for integration, energy storage technologies, reliability, and grid connection. Apart from this, modern optimisation techniques for MPPT control using artificial intelligence like fuzzy control and neural network control are also presented in this study.

Evaluation of Power Maximization and Curtailment Control Methods for Converters in Wearable Photovoltaic Energy Harvesting Applications

As wearables become more widely adopted, powering them from ambient energy sources can improve reliability and reduce reliance on batteries. Solar photovoltaic (PV) power is a viable power source for such emerging applications. However, because wearable applications bend and move with the user&#x0027;s motion, the PV panels used in these applications experience varying light intensities over multiple PV cells that reduce power generation in traditional series-string configurations. To address the PV power reduction problem, a configuration of boost converters with parallel-connected outputs are utilized, which is effective in uneven lighting conditions. Depending on the load demand and available solar power, a converter control system should quickly transition between maximum power point tracking (MPPT) and power curtailment operation. This work evaluates MPPT (perturb &amp; observe and constant-voltage) algorithms and power curtailment (over-voltage shut-off and flexible power point tracking) methods on their effectiveness in wearable applications. Experimental results verify that the perturb &amp; observe with flexible power point tracking effectively adapts to changes in the load and light conditions while maintaining 30&#x0025; and 31&#x0025; higher output power, respectively. It also maintains the maximum component temperature below <inline-formula><tex-math notation="LaTeX">$29{}^{\circ }$</tex-math></inline-formula>C which is a safe temperature for wearable applications.

Wind Farm Fast Response Contribution in Power Frequency Control, Using a New Configuration and Control System Based on MPPT and Fine Tune Power Algorithm

Nowadays, with the increasing expansion of the power grid and the use of wind energy systems, the issue of frequency control of the power system in their presence is very important. In traditional power systems, the control of frequency is generally performed by hydroelectric power plants that are the slack bus of the grid. They usually have fast dynamic responses, capable of changing the power output rapidly. This can be difficult in cases such as drought, lack of large hydropower plants, or the expansion of the power grid. In this article, a new topology and control system for a wind farm connected to a four-area grid through an high voltage dc (HVdc) link is presented, which can participate in the issue of frequency control of the power system. The proposed system is based on maximum power point tracking (MPPT) and fine tune control of the permanent magnet synchronous generator (PMSG)-based wind farm. The simulation results were evaluated on a four-area power grid, they were compared with the absence of wind farm in frequency control, and the desired results with appropriate and acceptable dynamic responses were achieved. The simulation results were performed on the MATLAB/Simulink environment.

Fuzzy Based MPPT and Solar Power Forecasting Using Artificial Intelligence

Solar energy is the radiant heat and light energy harvested by ultra violet rays to convert into electrical Direct Current (DC). The solar energy stood ahead of other renewable energy as it can produce a constant level of alternating current over the year with minimal harmonic distortions. The renewable energy attracts the energy harvesters as there is rise of deficiency of carbon and reduction of efficiency in thermal energy generation. The concerns associated with the solar power generation are the fluctuation in the generated direct current due to the displacement of sun and deviation in the quantity of solar rays from place to place. This apprehension is overcome by following the technical methods of employing latest technology is determining the optimal position to harvest the solar power at the high rate and forecasting the power generation effectively. This paper proposes a novel hybrid methodology of employing fuzzy based controller to determine the Maximum Power Point Tracking (MPPT) in solar power generation and employing Artificial Intelligence (AI) technology to perform high precision forecasting of power generation. The K-Nearest Neighbor algorithm is a least assumption algorithm is employed in predicting the energy level harvested in the solar Photovoltaic cells. The Artificial Intelligence considers the vital parameters of displacement direction of the sun, temperature, clearness index and humidity in the air. The performance analysis of the proposed methodology is compared with the IEEE standard bus and the prediction is proved to be more precision with a maximum standard deviation of 0.06%.

Wide-Range Light Harvesting Module for Autonomous Sensor Nodes

A large number of autonomous devices is nowadays supported by renewable and green energy sources. A vital sub-circuit in such systems is the power converter circuit, which should efficiently transform and store the available energy. In order to obtain the maximum efficiency under varying energy conditions, various maximum power point tracking (MPPT) methods are used. In this work a complete harvesting module with battery management and MPPT is presented, suitable for a plethora of autonomous applications. A novel, low-complexity and ultra-low power consumption design is proposed, which offers very wide operating voltage and power range with high MPPT efficiency and very low power consumption. It can be combined with different harvesters, such as thermoelectric generators or photovoltaic panels and is able to work under widely varying energy conditions. As supported by experimental results, the proposed module covers a very wide working input power range, from 40 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> up to 4 W, as well as a very wide input voltage range, from 650 mV up to 2.8 V with 96.5% average MPPT efficiency and a total power consumption of 3.9 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> at 3.6 V. The module relies on an embedded ultra-low power microcontroller unit (MCU) to perform the power management and MPPT operations, which can also be used for extra tasks (e.g., sensor reading). Using the proposed module, an autonomous sensor node was built, able to acquire acceleration measurements, and wirelessly communicate with a remote user in order to send an alert or stream the acquired sensor data in real time.

An improved control strategy for charging solar batteries in off-grid photovoltaic systems

In off-grid photovoltaic (PV) systems, a battery charge controller is required for energy storage. However, due to unstable weather conditions as well as the frequent variations in load demand, the PV power flow delivered to the load could be fluctuated while the battery charging efficiency will be reduced. To overcome these issues, this paper presents an improved power balance control strategy based on two proportional and integral (PI) compensators only, which can adequately balance the PV power flow delivered to the DC load and the battery, so that the PV power is effectively utilized and the battery is properly charged. First, a modeling of the entire system based on the linear PV array model is performed to simplify the design of the PI compensators. In addition, four operating modes are adapted to deal with the aforementioned issues regarding any variation in weather conditions and load demand. Namely, Maximum Power Point Tracking (MPPT)-mode, Non-MPPT mode, Night-mode, and Off-mode. Afterwards, a digital anti-windup control strategy associated with PI compensators is also developed to ensure a smooth switching from an operating mode to another. Moreover, an improved Incremental Conductance IC-MPPT algorithm is adopted to extract the maximum power from the PV array. Finally, MATLAB/SIMULINK simulations are carried out and the results obtained demonstrate the good performance of the proposed control strategy under different atmospheric conditions, both in power balance control and MPPT control, notably in terms of efficiency (99.79%), steady-state power oscillations (0.03 W), and convergence time (0.03 s).

Desain Perbandingan MPPT P&amp;O dan IC pada Solar Sel 1500 WP Menggunakan Konverter SEPIC

Electricity is a very important requirement for life. The need for electrical energy has been dependent on fossil energy, while the availability of fossil energy is decreasing along with the increasing demand for electricity in the community, especially in Siman Village, Lamongan. To overcome this problem, many sources of energy substitutes for fossil energy have been developed and researched in the form of renewable energy sources, one of which is the source of solar energy. One of the applications of solar energy utilization is a solar cell. The proposed solar cell uses Photovoltaic (PV) 1500 WP as energy conversion. PV produces power that is volatile or erratic. So it requires Maximum Power Point Tracking (MPPT) so that PV works at its optimum point. MPPT can find the greatest power from PV using the Perturb and Observe (P&amp;O) algorithm. The converter used is a SEPIC converter which functions to increase or decrease the voltage without changing the polarity of the resulting voltage. Furthermore, the MPPT P&amp;O is compared the results with the MPPT Incremental Conductance (IC) as a comparison of the results of the average MPPT power.

Experimental Measurements of a Low Power CMOS Analog MPPT Power Conditioning Circuit for Energy Harvesting Applications

This paper presents the complete measured performance and characterization of a fabricated power conditioning integrated circuit for energy harvesters with on-chip maximum power point tracking (MPPT) and external energy storage. This ultra-low power circuit employs an AC/DC-to-DC converter compatible with both AC and DC voltage energy harvesters. The MPPT design follows the perturbation and observation algorithm. This MPPT is capable of tracking the maximum power point of types of energy harvesters. The circuit is implemented using the AMS CMOS 0:35 μm high voltage technology and all the circuit blocks use analog electronic techniques, with the transistors operating in the sub-threshold region, in order to obtain a minimum power consumption. This power conditioning circuit consumes less than 2 μW while featuring an input voltage range of -0:5V to -50V and a power range from 10 μW to 200mW.

Testing Maximum Power Point Tracking Controller for PV System

Recently, the demand for renewable energy sources, in particular solar, has been increasing. This is due to declining demand for other fuels due to the poor reputation, waste of nuclear energy and limited fossil resources such as coal. The growing popularity of silicon significantly reduces the cost and increases the availability of solar energy. The main technique of increasing efficiency of the converter is to find maximum power point tracking (MPPT) that addressing problem of efficiency of power transfer from the solar cell depends on both the amount of sunlight falling on the solar panels and the electrical characteristics of the load. As the amount of sunlight varies, the load characteristic that gives the highest power transfer efficiency changes, so that the efficiency of the system is optimized when the load characteristic changes to keep the power transfer at highest efficiency. This load characteristic is called the maximum power point and MPPT is the process of finding this point and keeping the load characteristic there. Electrical circuits can be designed to present arbitrary loads to the photovoltaic cells and then convert the voltage, current, or frequency to suit other devices or systems, and MPPT solves the problem of choosing the best load to be presented to the cells in order to get the most usable power out.This article considers the construction of MPPT controller of solar panel, which must operate at up to 60W power of solar panel and up to 30V of output battery voltage. Given the structure of the system and the necessary operating parameters, for its construction it is necessary to take into account various circuit solutions for the power unit, as well as methods for finding the point of maximum power for the control circuit.The design and construction of the MPPT controller of the photovoltaic system and testing for different irradiation values is considered. Various circuit design solutions and algorithms for the operation of the MPPT controller were analyzed and a two-switch buck-boost circuit and a perturb and observe algorithm were selected. After making the system task is at general checking of possible operating in a given range of current and voltage, as well as the ability to respond to changes in current and voltage values and some external conditions that directly affect the work of a solar panel. The algorithm of work, formulas for emulation and an example of system work were given. The algorithm of work, formulas for emulation and an example of system work were given. The time diagrams of the control signals of the transistors at the time of switching from buck mode to boost mode are shown. A step-by-step description of the changes that occur in the system during the change of parameters and how the feedback stabilizes the operation of the device to maintain the maximum power of the system.After the developed experiment, the workability of the layout was checked and various variants of operation of the device for different lighting conditions were simulated. A conclusion was drawn on the potential for further development of the proposed solution. The circuit used operates at a given power and the proposed idea has the potential for further development and experimenting.

A novel MPPT technique based on Henry gas solubility optimization

Solar energy is the most promising renewable energy resource. In order to harvest maximum solar energy in PV systems, maximum power point tracking (MPPT) techniques have been investigated. In this paper, a novel MPPT technique based on the henry gas solubility optimization (HGO) is proposed. The HGO-based MPPT technique can successfully tackle two major issues of PV systems, including random oscillation caused by traditional MPPT techniques and power loss caused by partial shading (PS). The proposed HGO-based MPPT technique is compared with some state-of-the-art MPPT techniques, including perturb and observe (P&O), particle swarm optimization (PSO), dragonfly optimization algorithm (DFOA), cuckoo search algorithm (CSA), and grasshopper optimization (GHO). The comparative results confirm that the HGO-based MPPT technique can the aforementioned two major MPPT issues and outperform those existing MPPT techniques. Moreover, a study using field atmospheric data is done to estimate the impact on energy harvest during different seasons of a year. The HGO-based MPPT technique is robust and effective in improving power efficiency by 1–4% against some SI (swarm intelligence)-based techniques. Compared with a few traditional gradient-based techniques, the HGO-based MPPT technique reduces the tracking time by 15–30%, the settling time by 20%, and the oscillation by 80%.

Nonlinear Maximum Power Point Tracking Control Method for Wind Turbines Considering Dynamics

A combined strategy of torque error feed-forward control and blade-pitch angle servo control is proposed to improve the dynamic power capture for wind turbine maximum power point tracking (MPPT). Aerodynamic torque is estimated using the unscented Kalman filter (UKF). Wind speed and tip speed ratio (TSR) are estimated using the Newton–Raphson method. The error between the estimated aerodynamic torque and the steady optimal torque is used as the feed-forward signal to control the generator torque. The gain parameters in the feed-forward path are nonlinearly regulated by the estimated generator speed. The estimated TSR is used as the reference signal for the optimal blade-pitch angle regulation at non-optimal TSR working points, which can improve the wind power capture for a wider non-optimal TSR range. The Fatigue, Aerodynamics, Structures, and Turbulence (FAST) code is used to simulate the aerodynamics and mechanical aspects of wind turbines while MATLAB/SIMULINK is used to simulate the doubly-fed induction generator (DFIG) system. The example of a 5 MW wind turbine model reveals that the new method is able to improve the dynamic response of wind turbine MPPT and wind power capture.