Proposed Maximum Power Point Tracking Algorithm Research Articles

Energy Management Strategy for Distributed Photovoltaic 5G Base Station DC Microgrid Integrated with the CF-P&O-INC MPPT Algorithm

With its technical advantages of high speed, low latency, and broad connectivity, fifth-generation mobile communication technology has brought about unprecedented development in numerous vertical application scenarios. However, the high energy consumption and expansion difficulties of 5G infrastructure have become the main obstacles restricting its widespread application. Therefore, aiming to optimize the energy utilization efficiency of 5G base stations, a novel distributed photovoltaic 5G base station DC microgrid structure and an energy management strategy based on the Curve Fitting–Perturb and Observe–Incremental Conductance (CF-P&O-INC) Maximum Power Point Tracking (MPPT) algorithm from the perspectives of energy and information flows are proposed. Simulation results show that the proposed MPPT algorithm can increase the efficiency to 99.95% and 99.82% under uniform irradiation and partial shading, respectively. Under the proposed strategy, when the base station load changes drastically, the voltage fluctuation of the DC bus is less than 1.875%, and returns to a steady state within 0.07s, alleviating the high energy consumption of 5G base stations effectively and achieving coordinated optimization management of various types of energy in multi-source power supply systems.

Novel Global MPPT Technique Based on Hybrid Cuckoo Search and Artificial Bee Colony under Partial-Shading Conditions

Under partial-shading conditions (PSCs), the output P-V curve of the photovoltaic array shows a multi-peak shape. This poses a challenge for traditional maximum power point tracking (MPPT) algorithms to accurately track the global maximum power point (GMPP). Single intelligent algorithms such as PSO and ABC have difficulty balancing tracking speed and tracking accuracy. Additionally, there is significant power oscillation during the tracking process. Therefore, this paper proposes a new hybrid method called the Cuckoo Search Algorithm and Artificial Bee Colony algorithm (CSA-ABC) for photovoltaic MPPT. The CSA-ABC algorithm combines the local random walk and the global levy flight mechanism of the cuckoo algorithm, by probability selection, to decide whether to group the population, and introduces adaptive weight factors and gravitational mechanisms between adjacent individuals, incorporating an algorithm restart mechanism to track new MPPs in response to changes in the external environment. The algorithm is implemented in MATLAB/Simulink using a photovoltaic power-generation system model. Simulation verification is performed under different PSC scenarios. The results show that the proposed MPPT algorithm is 6.2–78.6% faster than the PSO, CSA, and ABC algorithms and two other hybrid algorithms, with a smaller power oscillation during the tracking process and zero power oscillation during the steady process.

A new framework for improving MPPT algorithms through search space reduction

In this article, a novel framework for improving maximum power point tracking (MPPT) is presented. A robust initialization of the MPPT algorithm is implemented, and the search space is reduced by rapidly predicting the maximum power point (MPP) voltage. In order to accomplish this, unknown parameters required to establish an empirical irradiance-free relationship between the open circuit voltage and MPP voltage were derived using three most prevalent PV panel models. The P&O MPPT algorithm implements this framework, which establishes a systematic boundary to confine the MPP voltage to a reduced search space by virtue of this relationship. The evaluations performed on three distinct varieties of commercial PV panels demonstrated that the PV panels modeled in this study exhibit a high degree of conformity with the data provided by the manufacturers. Furthermore, the estimation of MPP voltage allows for a reduction of the search space by as much as 2% of its initial area. The efficacy of the proposed MPPT algorithm is found to be 96.64%, higher than that of the P&O and other implemented contemporary algorithms, when evaluated against a common platform and rapidly changing climatic conditions. Real-world climatic experiments are performed in order to assess the feasibility of the established research. The experimental results indicate that the established irradiance-free MPP voltage has a mean error of merely 0.0128V. Furthermore, the combined output power generated by the MPPT proposed algorithm under experimental conditions is comparatively superior in performance.

Maximum Power Point Extraction in PV Array Under Partial Shading Conditions Using GWO-Assisted Beta Method

This paper presents a hybrid maximum power point tracking (MPPT) algorithm, which combines the grey wolf optimization (GWO) technique and Beta method for extraction of maximum power from a photovoltaic (PV) system operating under partial shading conditions. The proposed algorithm named GWO-Beta-based MPPT uses the GWO technique to achieve the global maximum power point (GMPP), while, simultaneously, the MPPT reference using Beta method is calculated according to the PV voltage and current at GMPP. After that, the MPPT algorithm is switched to operate only with the Beta method that keeps tracked on the MPP reference, guarantying the GMPP in that specific operation point. By means of computational simulation results, the performance of the proposed MPPT algorithm is evaluated taking into account the convergence speed and power oscillations to achieve the GMPP.

An adaptive fuzzy maximum power point tracking for PV systems by a mutant gray wolf optimization algorithm

Abstract An innovative and adaptive maximum power point tracking (MPPT) based on a fuzzy controller is proposed in this article. The proposed controller is optimized by the mutant gray wolf optimization (MGWO) algorithm, and the objective function uses an integral squared time squared error (ISTSE) index. To indicate the efficiency of the proposed MPPT algorithm (MGWO-AF-MPPT), the simulations for several irradiation conditions are modeled in MATLAB. In addition, two fair comparisons are performed between the simulation results of the proposed MPPT method with an adaptive fuzzy controller that optimized with particle swarm optimization (PSO-AF-MPPT), as well as a Perturbation and Observation method (P&O-MPPT). The comparison results illustrate that the amount of generated electrical power from the PV system for all adaptive MPPT systems is almost close to each other, indicating the high accuracy of both fuzzy-based controllers. However, the PV power generation of the proposed MGWO-AF-MPPT is higher, and also its ripple is lower than PSO-AF-MPPT. Note that, the calculated total harmonic distortion (THD) for the P&O-MPPT, PSO-AF-MPPT, and the proposed MGWO-AF-MPPT methods are calculated at 3.4%, 2.3%, and 2.1%, respectively. A lower THD indicates better power quality generated by the PV system employing the MGWO-AF-MPPT method.

Numerical Experimental Validation of a Proposed MPPT Algorithm with Dynamic Hysteresis for PV Systems

In the last decades, the renewable energies have had a highly positive effect on the daily life. This due to the necessity of reducing the common fuel energies that have negatively impacted the environment. Among all the available renewable energies, the solar energy is one of the most common. This energy is obtained through Photovoltaic (PV) systems. Thus, in previous years, new techniques of Maximum Power Point Tracking (MPPT) to raise the quality of the energy provided by PV panels have been proposed. Since the energy provided by the PV cells depends on the external environmental conditions, as temperature or sun irradiance, the MPPT methods should be adequate to deal with these external changes by maintaining the desired power level. Hence, this paper proposes a recent hysteretic dynamic technique to extract the maximum power from a PV panel array by employing a Boost DC/DC converter to supply energy to an inductive load. Here, a comparative study between the results obtained with the well known Perturb and Observer (P&O) algorithm and by using our dynamic hysteretic MPPT method is analyzed, specifically when the PV panel is submitted to fast variations in temperature and irradiance. It will be proved through numerical experiments realized in MatLab/Simulink that our hysteretic MPPT algorithm provides a better achievement of the maximum power of the PV panel in comparison to the conventional Perturb and Observer method.

Design of a battery charging system fed by thermoelectric generator panels using MPPT techniques

Abstract Thermal energy is a renewable energy source to generate electrical energy that is not fully developed. One device that converts thermal energy into electrical power is a thermoelectric generator (TEG). TEGs are available as modules of various sizes and voltage levels. This paper is about the design of a battery charging system powered by a TEG panel. The TEG panel is implemented using 150 TEG modules interconnected in series and parallel. Its power is transferred to a battery using two stages of DC/DC converters. The 1st stage is a Lou converter that is used for maximum power point tracking (MPPT) by a referenced perturb and observe (referenced P&O) algorithm. The 2nd stage is a bidirectional converter based on buck-boost modes of operation. The system is used to charge a 9 V 1.2 Ah battery. The proposed MPPT algorithm’s performance is compared with a traditional P&O algorithm. The TEG panel provided 27.5 W at a ΔT of 30 0C. The designed system is simulated in MATLAB SIMULINK.

A maximum power point tracking of a photovoltaic system connected to a three-phase grid using a variable step size perturb and observe algorithm

Purpose. The production of electricity from solar energy is necessary because of the global consumption of this energy. This article’s study is based on increased energy extraction by improving maximum power point tracking (MPPT). From different MPPT techniques proposed, the perturb and observe (P&O) technique is developed because of its low implementation cost and ease of implementation. Methods. A modified variable step-size P&O MPPT algorithm is investigated which uses fuzzy logic to automatically adjust step-size to better track maximum power point, compared with the conventional fixed step-size method. The variable step P&O improves the speed and the tracking accuracy. This controller is implemented on a boost DC-DC power converter to track the maximum power point. The suggested controlled solar energy system includes a boost converter, a voltage-source inverter, and a grid filter. The control scheme of a three-phase current-controlled pulse-width modulation inverter in rotating synchronous coordinate d-q with the proposed MPPT algorithm and feed-forward compensation is studied. Results. The photovoltaic grid-connected system controller employs multi-loop control with the filter inductor current of the inverter in the inner loop to achieve a fast dynamic response and the outer loop to control bus voltage for MPPT, the modeling, and control of three phase grid connected to photovoltaic generator is implemented in the MATLAB/Simulink environment and validated by simulation results.

Maximum Power Point Tracking Algorithm of Photo-Voltaic Array through Determination of Boost Converter Conduction Mode

In this paper, we present a new maximum power point tracking (MPPT) algorithm that can identify whether a boost converter is operating in continuous conduction mode (CCM) or discontinuous conduction mode (DCM). The conventional MPPT algorithm assumes that the converter is always in CCM mode, even though this is not always the case. The converter can enter DCM mode due to factors such as the inductor size, irradiance and temperature conditions, voltage step size of the algorithm, and operating point of the PV array. In the proposed work, the conduction mode of a boost converter is evaluated under different conditions. The region of the I–V curve where the converter is likely to operate in DCM mode is identified and a mathematical expression developed in this work is then used to detect the conduction mode of the converter. The proposed algorithm incorporates this expression into a modified perturb and observe (P&O) algorithm. In each iteration, the algorithm first detects the conduction mode of the converter. If the converter is in DCM mode, the algorithm takes a large voltage step to force the converter back into CCM mode, i.e., into the constant current region. The proposed MPPT algorithm was tested using simulation experiments, and the results show that the proposed algorithm can significantly improve the efficiency of the MPPT process.

Adaptive Maximum Power Point Tracking Control Algorithm for Wind Energy Conversion Systems

For small-scale wind energy conversion systems (WECSs), an adaptive maximum power point tracking (MPPT) algorithm is presented in this paper to increase wind energy harvesting. For its adaptability across a wide range of WECSs and quick tracking of maximum power point, the proposed algorithm combines the computational behavior of hill climb search, tip speed ratio, and power signal feedback control algorithms. Using buck–boost-featured single-ended primary inductor converters, the proposed MPPT algorithm is implemented in this paper to extract maximum power from the entire wind velocity profile. A laboratory-scaled dc motor drive-based WECS emulator is used to test the proposed algorithm. The in-lab experimental setup's control schemes are carried out with the help of a 32-bit floating point digital signal controller called TMS320F28335. The experimental results demonstrate that the proposed algorithm's tracking capability is effective and efficient in both sudden and gradual wind conditions.

Photovoltaic MPPT algorithm based on adaptive particle swarm optimization neural-fuzzy control

Since the BP neural network has poor performance and unstable learning rate in the maximum power point tracking (MPPT) algorithm of photovoltaic (PV) system, an adaptive particle swarm optimization BP neural network-fuzzy control PV MPPT algorithm (APSO-BP-FLC) is proposed in this paper. First, the inertia weight, learning factor and acceleration factor of particle swarm optimization (PSO) are self-updating, and the mutation operator is adopted to initialize the position of each particle. Second, the APSO algorithm is used to update the optimal weight threshold of BP neural network, where the input layer is irradiation and temperature, and the output layer is the maximum power point (MPP) voltage. Third, the fuzzy logical control (FLC) is employed to adjust the duty cycle of Boost converter. The inputs of FLC are voltage difference and duty ratio D(n-1) at the previous time, and the output is duty ratio D(n). Moreover, D(n-1) is optimized by |dP/dU| to improve the search range of FLC. The irradiation, temperature and MPP voltage of PV cell are adopted as the datasets for simulation in a city in Shaanxi province, China. Simulation results show that the proposed MPPT algorithm is superior to the APSO-BP, FLC and perturbation and observation (P&O) algorithm with tracking performance, steady state oscillation rate and efficiency. In addition, the efficiency of proposed MPPT algorithm is improved by 0.37%, 6.2%, and 6.8% as compared to APSO-BP, FLC and P&O algorithm.

A Novel Approach to Achieve MPPT for Photovoltaic System Based SCADA

In this study, an improved artificial intelligence algorithms augmented Internet of Things (IoT)-based maximum power point tracking (MPPT) for photovoltaic (PV) system has been proposed. This will facilitate preventive maintenance, fault detection, and historical analysis of the plant in addition to real-time monitoring. Further, the simulation results validate the improved performance of the suggested method. To demonstrate the superiority of the proposed MPPT algorithm over current methods, such as cuckoo search algorithms and the incremental conductance approach, a performance comparison is offered. The outcomes demonstrate the suggested algorithm’s capability to track the Global Maximum Power Point (GMPP) with quicker convergence and less power oscillations than before. The results clearly show that the artificial intelligence algorithm-based MPPT is capable of tracking the GMPP with an average efficiency of 88%, and an average tracking time of 0.029 s, proving both its viability and effectiveness.

A Novel MPPT Algorithm for Photovoltaic Systems Based on Improved Sliding Mode Control

Due to the poor tracking performance and significant chattering of traditional sliding mode control in the maximum power point tracking (MPPT) algorithm, a novel MPPT algorithm based on sliding mode control for photovoltaic systems is proposed in this paper. The sliding mode control structure and new sliding mode surface of the multi-power reaching law are designed with the boost converter as the carrier of the photovoltaic system, and the sigmoid function is proposed to replace the symbolic function and saturation function in the power reaching law to improve the reaching rate and control quality of the traditional sliding mode control. Furthermore, the Liapunov function is employed to analyze the accessibility, existence and stability of the improved sliding mode control. Simulation results under dynamic and partial shading conditions show that compared with exponential sliding mode and constant speed sliding mode, the improved sliding mode control strategy can quickly track the maximum power point of photovoltaic systems under various atmospheric conditions. The proposed MPPT algorithm has stronger robustness and universality. Additionally, the efficiency of the proposed algorithm is improved by 2.3% and 5.6% as compared to the exponential sliding model control algorithm and constant velocity sliding model control algorithm. In addition, the experimental platform is constructed to further validate the feasibility and effectiveness of the proposed algorithm.

Photovoltaic Fuzzy Logical Control MPPT Based on Adaptive Genetic Simulated Annealing Algorithm-Optimized BP Neural Network

The P–U characteristic curve of the photovoltaic (PV) cell is a single peak curve with only one maximum power point (MPP). However, the fluctuation of the irradiance level and ambient temperature will cause the drift of MPP. In the maximum power point tracking (MPPT) algorithm of PV systems, BP neural network (BPNN) has an unstable learning rate and poor performance, while the genetic algorithm (GA) tends to fall into local optimum. Therefore, a novel PV fuzzy MPPT algorithm based on an adaptive genetic simulated annealing-optimized BP neural network (AGSA-BPNN-FLC) is proposed in this paper. First, the adaptive GA is adopted to generate the corresponding population and increase the population diversity. Second, the simulated annealing (SA) algorithm is applied to the parent and offspring with a higher fitness value to improve the convergence rate of GA, and the optimal weight threshold of BPNN are updated by GA and SA algorithm. Third, the optimized BPNN is employed to predict the MPP voltage of PV cells. Finally, the fuzzy logical control (FLC) is used to eliminate local power oscillation and improve the robustness of the PV system. The proposed algorithm is applied and compared with GA-BPNN, simulated annealing-genetic (SA-GA), particle swarm optimization (PSO), grey wolf optimization (GWO) and FLC algorithm under the condition that both the irradiance and temperature change. Simulation results indicate that the proposed MPPT algorithm is superior to the above-mentioned algorithms with efficiency, steady-state oscillation rate, tracking time and stability accuracy, and they have a good universality and robustness.

A system identification-based MPPT algorithm for solar photovoltaic pumping system under partial shading conditions

ABSTRACT This paper presents a Maximum Power Point Tracking (MPPT) algorithm using system identification (SI) for solar photovoltaic (PV) pumping system under partial shading (PS) conditions. The measurement of the PV panel current was not required for the proposed MPPT algorithm. The MPPT algorithm was developed for an electric motor with a nonlinear pump load. In this study, a high-efficiency synchronous reluctance motor (SynRM) was designed as a 4-inch submersible pump motor. The system aimed to the small-scale solar photovoltaic water pumping systems. The SynRM was designed and optimized to use at low voltage levels such as solar PV panels without requiring a boost converter. Thus, the motor could be connected directly with any low voltage leveled power supply. The motor was produced 0.55 kW power with an efficiency of 86.5%. Eight PS patterns were generated to obtain four different scenarios for analyzing of the proposed algorithm. The Maximum Power Point (MPP) was determined with high accuracy for global maximum for all PS conditions using the proposed algorithm. Thus, the motor was operated at the MPP in all conditions thanks to the developed algorithm. The overall system efficiency was obtained between 72% and 82% under these conditions.

Implementation of Modified Incremental Conductance MPPT A lgorithm in Grid Connected PV System Under Dynamic Climatic Conditions

Background: The main goal of the grid-connected Photo Voltaic (PV) system is to extract maximum power from the array with reduced loss by using an appropriate MPPT method. Methods: A boost converter topology with linear INCMPPT algorithm is used to provide maximum power of 100 kW from the solar PV array to the grid. The integral regulator in the modified algorithm minimizes the error and guarantees exact control of the duty cycle of the DC-DC converter to produce constant DC voltage 500V with a minimum error of 2V. This proposed methodology perfectly matches with the optimal design of the DC-AC converter (inverter) for grid integration. A three-level IGBT-based voltage source inverter and filters are designed to supply pure AC voltage to the existing 100 kW grid. Detailed simulation work is completed using MATLAB/ Simulink software to prove the effectiveness of the proposed algorithm under various temperature (25-50◦C) and irradiance (200-1000W/m2) levels. Findings: The proposed MPPT algorithm is simple and effective in reducing the error required for the duty cycle correction from 0.45 to 0.518 and eliminating the oscillations at MPP. By using this methodology, the output voltage of the boost converter is adjusted to match the reference value. The simulation results reveal the better performance of the grid-connected PV system with the modified incremental conductance MPPT method. This modified INC MPPT algorithm exactly controls the duty cycle of the boost converter to provide better settling time and low ripples in the grid parameters. Novelty: This article provides a simple and effective improved incremental conductance (INC) MPPT algorithm to minimize the tracking error (1.5%). No additional tuning and selection of parameters, computational burden and memory are required for the implementation of the proposed control algorithm. Keywords: Gridconnected photovoltaic (PV) system; Maximum power point tracking (MPPT); Voltage Source Inverter (VSI); Boost converter; Incremental conductance (INC)

Artificial Neural Network Based Mppt Algorithm for Modern Household with Electric Vehicle

This paper deals with implementation of artificial neural network in the maximum power point tracking (MPPT) controller algorithm for modern household where electric vehicle (EV) was purchased. The proposed MPPT algorithm was designed to achieve the best possible efficiency of the MPP (maximum power point) tracking and the best possible energy harvesting to charge the EV’s battery. The artificial neural networks have strong advantage in fast input to output response of signals and the finding of MPP is faster than in commonly used algorithms. In this article, the optimised simulation model based on artificial neural network will be introduced. The proposed artificial neural network algorithm was designed for non-shielded photovoltaic panels.

A Novel Photovoltaic Module Quick Regulate MPPT Algorithm for Uniform Irradiation and Partial Shading Conditions

This study proposed a new photovoltaic module quick regulate (PVM-QR) maximum power point tracking (MPPT) algorithm, which can eliminate the disturbance problem of the hill-climbing (HC) algorithm, especially under low irradiance level and partial shading conditions (PSC). This proposed algorithm has the advantage that it only uses the detection photovoltaic module (PVM) impedance and the open-circuit voltage to simply and quickly calculate the PVM temperature, the irradiance level, and then the key factor parameter u. To achieve the MPPT quickly and accurately, this proposed algorithm is developed for the prediction of the best MPPT duty cycle based on the irradiance level, parameter u, and load. This study verified the proposed MPPT by the measurement results, where the HC algorithm MPPT has 95% efficiency at 0.55 kW/m2 but diverges at 0.2 kW/m2 under uniform irradiation conditions (UIC), and the proposed MPPT algorithm has an improved efficiency (99%) under the same conditions. Moreover, the proposed MPPT algorithm has 99% efficiency under PSC, while the HC algorithm MPPT’s efficiency is 66%. This study implemented a simple-design circuit with the proposed MPPT algorithm for UIC and PSC, where the actual experiment results can also verify that the proposed algorithm performs better than the HC algorithm.

Design of a Boost Converter with MPPT Algorithm for a PV Generator Under Extreme Operating Conditions

Photovoltaic generators (PVGs) are one of the most popular renewable energy sources (RESs), which achieve 47% of RES in microgrids. The aim of this work is to design and simulate a PVG system with a rated power of about 1,621 kW at the standard test conditions (STC), i.e., 1,000 W/m2 and 25ºC. The main components of the proposed PVG are 12 PV panels connected in series (the peak power of a PV panel at STC is about 135 W). A DC-DC boost converter is proposed for implementing the maximum power point tracking (MPPT) algorithm. The proposed MPPT algorithm is tested under extreme conditions; a wide range of change in temperature, irradiance, and load variations. The boost converter is designed to verify stable power flow from the PVG to the load. The calculated and the simulation results using MATLAB/Simulink are in good agreements and the maximum efficiency of the implemented MPPT algorithm is about 99%.

Internet of things augmented a novel PSO‐employed modified zeta converter‐based photovoltaic maximum power tracking system: hardware realisation

In this study, a particle swarm optimisation (PSO) augmented internet of things (IOT)-based maximum power point tracking (MPPT) algorithm for solar photovoltaic (PV) system has been proposed. A modified DC–DC ZETA converter is used as an interface between solar PV and DC load. The duty cycle of the converter is continuously modulated for harvesting maximum power using PSO-IOT algorithm employing Arduino and Bluetooth system. IOT-based control system provides monitoring and compiling of PV reference voltage for MPPT controller of the PV system. Further, the experimental results validate the improved performance of the proposed algorithm. A performance comparison is provided in order to prove the merit of proposed MPPT algorithm over existing techniques such as perturb and observe, PSO, ant colony optimisation, artificial bee colony.