Maximum Power Point Voltage Research Articles

High-efficiency MPPT strategy for PV Systems: Ripple-free precision with comprehensive simulation and experimental validation

This paper presents a newly developed maximum power point (MPP) tracking algorithm (MPPT) to boost the tracking performance of solar photovoltaic (PV) systems. By functioning PV arrays at their MPP and eliminating the ripple problem in the converter's output, the newly developed strategy can markedly improve the precision of tracking and overcome the issues faced by many existing algorithms. The proposed strategy uses an MPP voltage boundary to control the PV voltage and directly generate the required duty cycle using a mathematic expression, resulting in a high convergence speed, drift problem avoidance, and zero MPP fluctuations. To prove and validate the robustness of tracking of the suggested MPPT strategy, both simulation and experiment validations based on the MATLAB/Simulink and dSPACE DS1104 board platforms, respectively, are carried out under swift variations in solar irradiance. The effectiveness of the proposed approach is evaluated by comparing it to the InC algorithm in terms of tracking accuracy. The results from both simulations and experiments demonstrate that the suggested strategy surpasses the InC approach in multiple aspects. It exhibits a shorter response time, eliminates the ripple problem, and achieves a superior tracking efficiency of 99.60 %.

Effects of the Operating Point of Photovoltaic Strings on Sizing of DC‐ and AC‐Connected Energy Storage Systems for Power Smoothing

Photovoltaic (PV) power generators are constantly exposed to operating condition variations. While the electrical characteristic of a PV generator has exactly one maximum power point (MPP) during homogeneous operating conditions, several MPPs may exist during nonuniform operating conditions. Since highly varying global MPP voltage causes large fluctuations in the inverter reference voltage, it would be beneficial to keep the operating point of the inverter all the time close to the nominal MPP voltage to secure more predictable and straightforward operation of the PV system. This article presents a study of the effects of the operating point on the operation of PV strings equipped with energy storage systems (ESS). A scenario in which the MPP closest to the nominal MPP voltage is used as the operating point is compared to operation in the global MPP. The effects of inverter sizing on the selection of the operating point are also studied. Moreover, DC and AC connections of the ESSs are compared. The study is based on measured current–voltage curves of a 23 module PV string. The results show that, in practice, there are no major differences in sizing of DC‐connected ESSs between the two MPPs.

Simplified control algorithm for stable and efficient standalone PV systems: An assessment based on real climatic conditions

This research addresses the critical challenge of optimizing efficiency and performance in solar photovoltaic (PV) systems. It focuses on duty cycle optimization of DC/DC converters for maximum power point tracking (MPPT). The study introduces an innovative hybrid MPPT solution that integrates a novel temperature-based single-sensor approach with an integral backstepping controller. This method precisely achieves the PV array's maximum power point (MPP) under varying weather conditions while optimizing the converter's duty cycle to best align with the MPP voltage. Comprehensive simulations in MATLAB/Simulink reveal that the hybrid method surpasses conventional MPPT systems in key metrics, achieving faster convergence time (below 0.66 ms), reduced power ripple (<0.3 W), and enhanced tracking efficiency (exceeding 97.67 %). Notably, experimental data validates the practical efficacy of the proposed system under real-world conditions in the context of El Jadida, Morocco. This integrated approach advances the theoretical understanding of MPPT optimization and demonstrates tangible improvements in PV system performance, offering significant potential for enhancing solar energy utilization in variable climate regions.

An enhanced maximum power point tracking and voltage control for proton exchange membrane fuel cell using predictive model control techniques

Proton Exchange Membrane Fuel Cells serve as sustainable devices for converting renewable energy sources into electricity, offering advantages such as rapid startup, high power density, and operation at low temperatures. They are highly efficient and environmentally friendly energy sources, yet their non-linear output characteristics under variable conditions present challenges in maximizing power output. Furthermore, they generates direct current, while many electrical devices rely on alternating current. This research addresses the necessity of enhancing the performance and efficiency through the development of an advanced maximum power point tracking technique and voltage control strategy. A modified finite-control-set model predictive control technique is proposed for both maximum power point tracking and voltage control. Specifically, a finite-control-set model predictive control technique approach is employed to modulate the switching signal for both the DC-DC boost converter and the DC-AC inverter. The DC-DC boost converter step up the fuel cell output voltage to the desired level, ensuring it reaches the maximum power point, and a DC-AC inverter to convert the direct current voltage to a pure sinusoidal alternating waveform. The investigation demonstrates the effectiveness of the proposed method in achieving its objectives. The proposed maximum power point tracking technique accuracy achieved the maximum power with a rate of 97 % with excellent respond time within 7 ms. For alternating current power, only less than 1.5 % of total harmonic distortion is recorded. The study evaluates the control scheme under robust operating conditions, demonstrating its effectiveness in optimizing PEMFC output and providing high-quality AC voltage.

Modified control approach for MPP tracking and DC bus voltage regulation in a hybrid standalone microgrid

This paper proposes modified MPPT control technique for hybrid standalone microgrid. Standalone PV based microgrid has emerged as potential solutions to electricity challenges in the region where grid is not available. The battery energy storage system (BESS) is integrated into the system to facilitate synchronized load control and power flow. Variation in solar irradiation, load demand and fluctuation in battery SOC results in DC voltage fluctuation and maximum power point tracking challenges. Hybrid PV-Battery systems therefore must require control algorithms that can both flexibly adjust power flows as well as stabilize bus voltages. Proposed control technique is successful in regulating DC bus voltage and balancing the power flow in system under various operating conditions. A bidirectional converter is employed to maintain the nominal DC voltage conditions by charging/discharging the battery due to intermittent PV generation. It regulates the voltage of the DC bus to the maximum power point of the PV array. The main goal of this research article is to implement modified MPPT technique which includes Incremental conductance algorithm with double closed loop controller to track maximum power and to regulate the DC bus voltage of the system under different type of dynamic and atmospheric conditions.

An efficient implementation of three-level boost converter with capacitor voltage balancing for an advanced MPPT approach in PV Systems

Ensuring optimal extraction of power from photovoltaic (PV) systems under diverse climatic circumstances is a significant challenge, which requires an efficient Maximum Power Point (MPP) Tracking (MPPT) algorithm and an adequate conversion stage. Therefore, this paper proposes an advanced MPPT strategy implemented by a Three-Level Boost converter (TLB), which serves as the interface between the PV generator and the DC load, offering advantages such as reduced output voltage distortion, minimized inductor current ripple, and decreased switching losses. To achieve more accurate tracking, a robust voltage balance control across the TLB converter capacitors is also suggested. The proposed system is meticulously implemented and evaluated using MATLAB/Simulink, demonstrating its robust control capabilities in MPP tracking and voltage balancing across the TLB converter capacitors under diverse climatic scenarios. Furthermore, comparative analysis under various test circumstances including static and dynamic insolation conditions and sudden load variations, shows that the suggested MPPT algorithm offers noteworthy improvements over the InC algorithm. Additionally, a thorough comparison with existing research methods proves its effectiveness over these methods. The advanced MPPT strategy achieves a steady-state error of 0 W, an average tracking efficiency between 99.13 % and 99.44%, and fast-tracking velocity. Further, a comprehensive real-time evaluation using the cutting-edge RT-LAB platform confirms the consistency of the results with the simulation outputs, emphasizing the robustness and low implementation complexity of the overall proposed control strategy.

Efficient maximum power point extraction using hybrid optimisation based parameter estimation model of solar double diode model

ABSTRACT PV systems operate most efficiently when they are operated at their Maximum Power Point (MPP). Despite the wide range of MPPT approaches available, there is potential to improve the efficiency of MPP Tracking (MPPT). One method to achieve this improvement is to use model-based MPPT techniques, which can speed up the MPP search process by estimating the MPP with a high degree of accuracy. The main objective of the research, which is to create an efficient double-diode model-based MPPT technique with a focus on hybrid Optimisation, is informed by this justification. The suggested method for predicting the MPP voltage makes use of an accurate double-diode model for PV modules that is dependent on temperature and irradiance. This research employs a parameter estimation system based on the proposed Optimisation algorithm known as Memory Integrated Sand Cat Swarm Optimisation Algorithm (MI-SCS) to overcome the limitations of standard parameter estimate methods, which are related to the availability of experimental p-V curves or datasheets with precise temperature and irradiance values. The method that is being given is based on the integration of the Crow Search Optimisation (CSO) Algorithm with the Sand Cat Swarm Optimisation (SCSO) algorithm.

Explicit representation of S-shaped and standard V–I curve of illuminated solar cell

A generalized explicit model is proposed in this study to model the S-shaped and standard voltage-current characteristics (V–I) of illuminated solar cells. Even though the voltage-current characteristics of a typical solar cell follow a single exponential model (single diode model), there is evidence of S-shaped voltage-current characteristics that cannot be captured by the existing standard analytical models. The proposed explicit analytic representation is comparatively simple and produces satisfactory results in depicting the voltage-current curve for a wide variety of solar cells, including those with S-shaped characteristics. The methodology for determining the empirical parameters of the proposed model is also described, including an easy estimation of maximum power point voltage using model parameters.

Suns-Vmp method for health monitoring of 110 PV modules

One can monitor the health of the PV modules by analyzing their current-voltage (I–V) curves, which rarely are measured in commercial settings. Fortunately, nearly all PV installations measure the maximum power point current (Imp) and voltage (Vmp), along with the ambient weather conditions, which the proposed Suns-Vmp method uses to generate pseudo I–V curves. In this work, the proposed methodology was rigorously tested on 110 PV modules of various technologies installed under harsh desert conditions, for approximately 7 years and 9 months. For these modules, the fitting accuracy between the actual field measured and pseudo I–V curves, did not exceed an RMSE value of 0.35 A. The median power degradation of the mono-crystalline, poly-crystalline, and thin-film technologies were estimated to be around 0.21 %/year, 0.49 %/year, and 1.51 %/year. From the root-cause analysis using the fitted PV parameters, it was found that the dominant degradation contribution in all the modules came from series resistance, followed by shunt resistance. For mono-crystalline, poly-crystalline, and thin-film technologies, the mean yearly rate of change for series resistance was 1.47 %, 4.37 % and 13.37 %, while for shunt resistance these values were 0.68 %, 1.26 % and 3.95 % respectively. As field measured I–V curves are seldom available, techniques like the Suns-Vmp method can offer valuable insights for effective solar farm management and maintenance, without any interruption in the operation.

Efficient and Highly Stable Lateral Perovskite Single‐Crystal Solar Cells through Crystal Engineering and Weak Ion Migration

AbstractThe lateral device structure for perovskite solar cells (PSCs) has garnered significant attention, primarily due to its elimination of the need for expensive transparent electrodes. However, the performance of lateral devices, which are more sensitive to crystal quality and charge carrier transport bottlenecks, has lagged far behind the predominant vertical PSCs. Herein, by modulating the crystal nucleation and growth processes of thin FA0.75MA0.25PbI3 (FA = formamidinium; and MA = methylammonium) single crystals, crystal quality and carrier transport are improved, resulting in a power conversion efficiency (PCE) of 12.64%, a record for lateral PSCs. Investigation of the device's stability reveals that iodide ion migration is suppressed due to a reduction in the iodide vacancy concentration combined with weak interface iodide ion migration. It is shown that the latter effect is a result of the perpendicular direction of the ion migration and the electric field in the lateral PSCs. Consequently, these lateral single‐crystal PSCs display remarkable operational stability, retaining 100% of their initial PCE after 1200 h of steady‐state output at the maximum power point voltage (Vmpp) under 1 sun illumination. This work highlights the advantages of lateral single‐crystal devices and their potential to address key ion migration issues of PSCs.

Global Maximum Power Point Tracking of Photovoltaic Module Arrays Based on an Improved Intelligent Bat Algorithm

In this paper, a method based on an improved intelligent bat algorithm (IIBA) in cooperation with a voltage and current sensor was applied in maximum power point tracking (MPPT) for a photovoltaic module array (PVMA), where the power generation performance of a PVMA was enhanced. Due to the partial shading of the PVMA from climate changes or the surrounding environment, multiple peak values were generated on the power–voltage (P-V) curve, where the conventional MPPT technology could only track the local maximum power point (LMPP), hence the reduction in output power of PVMAs. Therefore, the IIBA-based MPPT was proposed in this paper to solve such issues and to ensure the capability of a PVMA in tracking the global maximum power point (GMPP) and utilization for enhancing the output power of a PVMA. Firstly, the Matlab/Simulink software was used to establish a boost converter model that simulated the actual 4-series–3-parallel PVMA under different shaded conditions, where the P-V curve with 1-peak, 2-peak, 3-peak and 4-peak values were generated. Subsequently, the tracking paces of the conventional bat algorithm (BA) were adjusted according to the gradient of the P-V curve for a PVMA. At the same time, 0.8 times the maximum power point (MPP) voltage Vmp under standard test conditions (STCs) for a PVMA was set as the initial tracking voltage. Lastly, the simulation results proved that under different environmental impacts, the proposed IIBA led to better performances in tracking both dynamic and steady responses.

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.

Analysis of a Grid-Tie Solar Power System for Efficient Transition between Grid-Connected Modes

Abstract: Solar Photo Voltaic (PV)-battery energy storage based micro-grid with a multifunctional Voltage Source Converter (VSC) is presented in this research paper. Whenever the grid fails, this system operates in SA mode automatically, thereby without causing any interruption in supplying the load. Similarly, it automatically shifts to the GC mode, when the grid is restored. The VSC functions in current control for GC mode, and it operates in voltage control for SA mode of operation. This system is capable of extracting the maximum power from the solar PV array irrespective it is operating in the GC mode or SA mode. It regulates the dc-link voltage to the maximum power point voltage of the PV array. If the absence of the battery is detected, then the control is automatically shifted to VSC for performing the extraction of the maximum power of the PV array

Incorporating Incremental Conductance MPPT Techniques into Solar Power Extraction

Research into alternative, green energy sources such as solar power has been driven by concerns about environmental sustainability, escalating petroleum costs, and surging energy demand. Solar energy can power the entire world sustainably, since it is abundant and easy to access. Solar radiation, cell temperature, and load impedance all play a part in improving the efficiency of solar energy utilization. In order to maximize solar energy utilization, Maximum Power Point Tracking (MPPT) techniques are used. In order to address factors such as solar effectiveness, dynamic response, convergence speed, complexity, cost, and sensor requirements, different MPPT techniques have been developed. Using Incremental Conductance (INC) as an example, this paper provides a comprehensive overview of MPPT techniques. P&amp;O’s drawback of oscillations around the Maximum Power Point (MPP) is overcome by INC, which minimizes them. The MPP voltage is maintained until the incremental conductance equals zero by comparing the instantaneous conductance of the panel with the incremental conductance. In addition to being easy to implement, INC-based methods offer rapid tracking and efficiency gains. Results from simulations demonstrate INC MPPT’s effectiveness in maximizing power extraction from photovoltaic systems, especially when environmental conditions change rapidly.

A parallel input parallel output current fed front end DC‐DC converter‐based solid state transformer utilised in grid connected mode PV applications

AbstractThe presented work operated a PIPO Current fed front end converter‐based solid‐state transformer (SST) for maximum power point tracking (MPPT) and voltage boost operation in medium power range PV applications. The proposed system employed the perturb and observe (P&amp;O)‐based MPPT technique and upheld the ZVS and ZCS operations of all primary side switches and secondary side rectifier diodes, respectively. The proposed system introduced a new fundamental extractor titled ‘Pre‐filter double fundamental signal extractor (DFSE) based multi‐layered DFSE (p‐DFSE‐MDFSE)’ and utilised it in the grid controller for synchronisation and harmonics mitigation operation. The primary goal was to achieve a compact size, reduced weight and cheaper magnetic component‐based front‐end converter of SST on the PV array side and a fast, secure and trustworthy mitigation control of current harmonics on the utility grid side. The proposed system was inspected on the OPAL‐RT (RT‐LABv2021.3.2.307) platform for various grid anomalies, and its conduct was found to be well within the IEEE 519 norms.

A new real-time maximum power point tracking scheme for PV-BASED microgrid STABILITY using online DEEP ridge extreme learning machine algorithm

A new deep representation-based Maximum Power Point Tracking (MPPT) controller is proposed in this paper for accurate Control references calculation in Photovoltaic (PV) based Micro Grid (MG) operation. The deep representation is obtained by two-step estimation: Data dimension reduction and MPPT Tracker towards optimal computation. The considered deep learning architecture is targeted for N number (large scale) of PV-based DGs, connected locally in the distribution system (DC link extended to AC utility). The collected data of solar irradiation (in W/m2) and PV panel temperature (in oC) profiles of local DGs are subjected to data dimensions using Extreme Learning Machine (ELM) based on Moore-Penrose inverse technique. The compressed represented PV-DG data is further communicated to the Tertiary Control side MPPT Tracker, where Ridge Regression-based ELM is presented for estimating Maximum Power Point Power (PMPP) and Voltage (VMPP) values for kth instant. The initial randomness present in the proposed Deep Representation based Ridge Regression Extreme Learning Machine (DR-RRELM) is further minimized by adopting Huber's characteristic distribution-based likelihood estimator. The proposed MPPT scheme is effectively implemented for accurate control reference in DC-DC and DC-AC converters in the MG. The proposed controller is also suitable for stability improvement at point of common coupling (PCC). Three different case studies such as past data verification, stability analysis under various operating conditions, irradiant change and source power variation. The efficacy of the proposed deep representation-based MPPT scheme is evidenced in MATLAB-based simulation. The proposed technique provides better tracking ability, faster learning and effective reference generation. The case study with irradiation change is validated in TMS320C6713 (32-bit) based Hardware-in-Loop (HIL) validation.

Temperature profiles of field-aged photovoltaic modules affected by optical degradation

Moisture ingress into PV module in the presence of ultraviolet radiation, high temperature, and other environmental stressors can affect the optical integrity of the PV module. Optical degradation can take the form of delamination, discolouration of encapsulant, metal grids corrosion, and trapped moisture or chemical species. This can influence the photon absorption and current transport properties in the PV module bulk, which can affect the module operating temperature. In the present work, the relationship between optical degradation and temperature sensitivity of 20-year-old multicrystalline silicon field-aged PV modules have been investigated. The selected PV modules were characterized using visual inspection, current-voltage (I–V) characterization, temperature coefficients profiling, current resistivity profiling, infrared (IR) thermal, ultraviolet fluorescence (UV–F), and electroluminescence (EL) imaging. PV modules affected by optical degradation show weak fluorescence and luminescence signal intensities. The average difference in cell temperature (ΔT) between the warmest and coldest cell for the PV modules investigated was found to be around 10 ± 2 °C and the average power degradation rate was approximately 0.8% per year. The underlying factor for the observed degradation is attributed to the degradation in the temperature coefficients of open circuit voltage (βVoc) and maximum power point voltage (βVmpp). The average temperature coefficient of efficiency (βηm) of the modules was found to be around −0.5%/°C. Finally, a temperature dependent resistivity method for extracting temperature coefficients from IR thermal data of PV modules has been proposed.

Intelligent maximum power point tracking for coastal photovoltaic system concerning the corrosion and aging of modules

Due to the corrosion and aging caused by the special oceanic environment, the characteristic of coastal photovoltaic (PV) system significantly drift after years of operation. In this study, the maximum power point tracking (MPPT) problem for coastal PV system is addressed and a novel MPPT methodology based on deep neural network (DNN) integrated with the corrosion evaluation index (CE-index) and dynamic training-sample (DTS) mechanism is developed. To be specific, the detailed effect of corrosion and aging for the PV modules installed in coastal areas is comprehensively analysed, and a composite indicator for evaluating the PV parameter drift, namely CE-index, is proposed. Then, a novel DNN-based offline MPPT methodology for the large-scale coastal PV system is developed, in which the DTS mechanism is also introduced for overcoming the effect caused by PV module corrosion and aging phenomenon. Finally, the optimal length of DTS for different degrees of CE-index is comprehensively verified by case studies. Experimental result shows that the developed DNN-based MPPT methodology can accurately forecast the maximum power point (MPP) voltage for large-scale coastal PV-system with robust performance, and cooperation of the developed DTS-mechanism and CE-index corrosion evaluation strategy can also effectively overcome the disturbance caused by the harsh oceanic environment.

Revealing capacitive and inductive effects in modern industrial c-Si photovoltaic cells through impedance spectroscopy

To achieve a high performance in sub-module power conditioning circuits, it is important that power converters are designed in accordance with the photovoltaic (PV) cell impedance at the input. Taking this one step further, exploiting the impedance of cell strings could even support novel power conditioning approaches in PV modules. In this work, we characterize the impedance of eight single-cell laminates based on different industrial c-Si PV cell architectures. This characterization is carried out by impedance spectroscopy in dark conditions at room temperature, and the capacitive and inductive effects are evaluated through equivalent model fitting. By comparing the results for the different laminates, it is revealed how the cell design affects its impedance. Our experiments show that the PN junction capacitance at maximum power point varies for the different cells between 0.30 and 45.6 μF/cm2. The two main factors contributing to a high PV cell capacitance at maximum power point are (i) a low wafer dopant concentration and (ii) a high maximum power point voltage. In high-efficiency c-Si PV cells that will be fabricated in the coming years, increasing capacitances are expected for operation near the maximum power point. Furthermore, the single-cell laminates exhibit inductances between 63 and 130 nH, and our results indicate that the inductance is mostly affected by the number of busbars and the geometry of the metal contacts.

Interleaved boost converter voltage regulation using hybrid ANFIS-PID controller for off-grid microgrid

The utilization of a microgrid with a photovoltaic (PV) and wind generation system presents a challenge due to their voltage and power output variations. This problem is majorly addressed within the converter section of the microgrid using maximum power point tracking (MPPT) algorithms and voltage regulation strategies. This paper presents an interleaved boost converter (IBC) modeling and voltage control using a hybrid adaptive neuro-fuzzy inference system-proportional plus integral plus derivative (ANFIS-PID) controller for an off-grid microgrid. The modeling used the interleaving technique to obtain the microgrid’s transfer function (TF) and case study simulation models within MATLAB and Simulink environments. The performance of the ANFIS-PID controller, which regulates voltage in the microgrid, was compared to that of the traditional proportional integral (PI) controller. Results indicated that the hybrid ANFIS-PID controller performed better than the PI controller in terms of reduced settling time, overshoot, rise time, and the ability to address the nonlinear dynamics of the microgrid.