Photovoltaic Maximum Power Research Articles

Optimizing Step-Size of Perturb & Observe and Incremental Conductance MPPT Techniques Using PSO for Grid-Tied PV System

A maximum power point tracking (MPPT) technique plays an important role to ensure maximum photovoltaic (PV) output power is extracted under stochastic weather conditions. The research to date tends to focus on developing a standalone optimization MPPT algorithm rather than looking into a hybrid MPPT algorithm. This paper introduces particle swarm optimization (PSO) to optimize the maximum PV output power and to determine the best design variable for penalising the step size of the conventional methods namely the perturb and observe (PO) and the incremental conductance (IC). With the help of the hybrid MPPT algorithm (PSO+IC and PSO+PO), the step size is no longer fixed, and it is changing according to the solar irradiance. To evaluate the proposed hybrid algorithm, a single-stage grid connected PV system is designed for several different scenarios with various weather conditions. The performance of the hybrid MPPT algorithm and the conventional methods is compared. The results demonstrate that the hybrid MPPT algorithm is remarkably better than the conventional methods in terms of the output power ripple and the settling time.

Optimal Design of Fuzzy Controller for Photovoltaic Maximum Power Tracking Using Particles Swarm Optimization Algorithm

Solar panels have non-linear current-voltage characteristics and a specified maximum power point, which depends on environmental factors like the solar radiation and ambient temperature. The voltage-power curve of the photovoltaic system has multiple peaks under different atmospheric conditions that reduce the efficiency of the maximum power tracking techniques. This paper proposes an optimal design of a fuzzy controller using particle swarm optimization algorithm to track the maximum power point of a photovoltaic system operating under different conditions to improve its performance. The proposed system optimizes the particle swarm to produce an optimal working coefficient, which varies with photovoltaic parameters to extract maximum power. Results of simulations – performed using the MATLAB software - show the advantages of the proposed method, namely the ability to track the maximum power point in a short time and maintain the output waveform despite the relatively high variations in environmental conditions.

Spectral-splitting concentrator agrivoltaics for higher hybrid solar energy conversion efficiency

By integrating solar cells into agricultural lands, agrivoltaics is a promising route to widely deploy photovoltaics, and it can reduce land competition for food and energy production. Although various agrivoltaics have been proposed, their optical properties and effects on plant growth remain disputed, which largely limits the widespread deployment of agrivoltaics. Here, we present spectral-splitting concentrator agrivoltaics (SCAPV), which can effectively harvest photosynthetically excess light energy for photovoltaic power without compromising crop productivity. This system transmits a selected light spectrum for plant growth while reflecting the remaining spectrum for electricity generation. Field trials show that SCAPV can increase plant biomass by 13% and decrease plant heat dissipation by approximately 50%. Meanwhile, SCAPV has a maximum photovoltaic power conversion efficiency (PCE) of 9.9%. The overall solar energy conversion efficiency is higher than the theoretical limitation of photosynthesis. Economic analysis shows that SCAPV has a levelized cost of energy (LCE) of $0.033/kWh. Our results show that with proper spectral management, only a portion of sunlight is sufficient to support or even enhance plant growth. Compared to other spectral-splitting agrivoltaics, SCAPV is a scalable technology with flexible spectrum and high transparency and can be implemented using low-cost components.

An improved photovoltaic maximum power point tracking technique-based model predictive control for fast atmospheric conditions

In this article, an enhanced maximum power point tracking (MPPT) technique for photovoltaic (PV) systems is presented. The proposed MPPT is designed for fast-changing operating conditions, where the conventional methods suffer from divergence (drift) under such conditions. Mainly, the reason for divergence in the traditional methods is the confusion between power change due to the perturbation of the control parameter and power variation because of the atmospheric conditions. Therefore, and to differentiate between these cases, additional control loops are added to the conventional perturb and observe (P&O) method to enhance its behavior. Furthermore, in our suggested scheme, the finite-set model predictive control (FS-MPC) principle is integrated with the proposed algorithm to enhance its transient performance. However, and to decrease the computational effort, the prediction stage (including cost function calculation) is eliminated. The suggested methodology is compared with the conventional FS-MPC for evaluation using experimental results at different atmospheric conditions (static and dynamic radiation).

An improved three phase cascaded multilevel inverter for maximum power point tracking application

A Three Phase Cascaded Multilevel (21 level) Inverter (3PMLI) is a superior alternative to medium-voltage inverter with intrinsic component redundancy. The 3PMLI achieves high quality input current and output voltage through a series connection of single-phase inverters. In case of applications like elevators, downhill conveyors and energy plants, regenerative issue is considered maximal due to bidirectional power flow. Hence, a 3PMLI fails in providing the required regenerative operation. In this paper, the Grey Wolf Optimisation - Fuzzy Logic Controller (GWO-FLC) with PI controller is used to control the Maximum Power Point (MPP) tracking of the MPPT. This method uses a GWO-FLC is designed to tune the membership function of FLC. The GWO-FLC with PI controller produces reference waveform for the level shifting (LS) method, which generates commutation signals for the boost converter. LS used in the study is Multicarrier Level Shifting - Pulse Width Modulation (MCLS-PWM) that generates the commutation signals for boost converter. A 21 levelled 3PMLI is used with a regenerative topology that increases the applicability of MLI. The regenerative 3PMLI is used to drive the Photo-Voltaic(PV)Maximum Power Point Tracking (MPPT) application. The mathematical modelling of PV MPPT application is carried out with Simulink. The coding is developed in very high-speed description language (VHDL) and implemented on Xilinx SPARTAN-III 3AN-XC3S400-FPGA processor. The proposed strategy is compared with existing fuzzy based Genetic Algorithm and GWO to prove its effectiveness. An extensive experimental analysis of the proposed technique takes place on benchmark datasets and the resultant outcomes are investigated with respect to distinct measures. The simulation results highlighted the enhanced performance of the proposed technique compared to existing techniques.

A combination of novel hybrid deep learning model and quantile regression for short‐term deterministic and probabilistic PV maximum power forecasting

Recently, the increasing penetration of renewable energy resources in power system, especially photovoltaic (PV) systems, has caused severe technical issues due to its randomness and dependency on primary sources. Therefore, precise output power forecast is vital for both system operators and PV system owners to improve grid stability and generation quality, respectively. This article proposes a novel hybrid deep learning model named EDSACL comprising four components, namely, convolutional neural network (CNN), long short-term memory (LSTM), self-attention mechanism (SAm), and residual learning (RL) strategy for short-term PV maximum power prediction with forecast horizon of 1, 2, and 4 h. This proposed model initially extracts spatial-temporal features of input data based on CNN and LSTM before using SAm to achieve hidden features of LSTM layers. RL strategy is employed to maintain the information flow entering the network. Besides, interval prediction is also implemented based on a combination of the EDSACL model and quantile regression with different probabilities. The forecast accuracy of the proposed model is validated based on two real-world data sets. The predicted results show the superiority of the proposed model over numerous predictive models; in particular, mean absolute percentage error values of the proposed hybrid model are under 6% in all case studies.

Grid-Interfaced Photovoltaic–Battery Energy Storage System With Slime Mold Optimized Adaptive Seamless Control

This article presents a photovoltaic (PV)–battery energy storage (BES) system functioning in both grid-tied and standalone modes while performing multifunctional operations, including reactive power compensation, power balancing, and power quality enhancement. The PV–BES system ensures uninterrupted power supply to the critical loads even during seamless transitions from grid-tied mode to islanded mode, and vice versa. The incremental conductance technique controls the boost converter and confirms maximum PV power extraction. The bidirectional converter connects BES to the dc bus. The dc bus voltage <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$({{V}_{dc}})$</tex-math></inline-formula> is regulated by a slime mold optimized proportional-integral controller to limit the dynamic state variation and improve stability. The voltage-source converter (VSC) is controlled by a minimum kernel-risk-sensitive mean <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> -power loss algorithm during the grid-synchronization mode. In contrast, the voltage control algorithm executes islanded control of VSC. The VSC control transition is achieved seamlessly without any significant disturbance at the point of common coupling. The hardware test results demonstrate that the present system remains stable during dynamic conditions and performs adequately as per the IEEE 519-2014 standard.

Modelling of Novel Architecture of PV Generator Based on a-Si: H/c-Si Materials and Using Solar Tracker for Partial Shading

The purpose of this study is to investigate the shading effect on a novel architecture of photovoltaic generator (PVG) proposed in this paper. This architecture consists of three photovoltaic (PV) modules in series connected. Two of them consist of amorphous silicon cells in series connected. The third module consists of monocrystalline silicon cells in series connected. This architecture is conceived as a PV concentrator, where the two amorphous PV modules are located in the lower position, and the third one is located in the upper position precisely in the focus. The role of the upper module is to absorb the solar rays, which are reflected by the two other modules to gain the maximum of solar energy. This novel architecture is aimed at solving the problems existing with the architecture of tandem solar cells proposed in literature. Those problems are the mismatch between cells and the tunnel junction costs and fabrication. In this work, we use MATLAB/Simulink for modelling this architecture and studying its characteristics (I–V and P–V) in case of partial shading. Through this study, it was found that the maximum PV power is affected by the partial shading. To solve this problem, we have implemented in this work a solar tracker.

Hybrid photovoltaic maximum power point tracking of Seagull optimizer and modified perturb and observe for complex partial shading

&lt;span&gt;Due to natural randomness, partial shading conditions (PSCs) to photovoltaic (PV) power generation significantly drop the power generation. Metaheuristic based maximum power point tracking (MPPT) can handle PSCs by searching PV panels’ global maximum power point (GMPP). However, trapped at local maxima, sluggishness, continuous power oscillations around GMPP and inaccuracy are the main disadvantages of metaheuristic algorithm. Therefore, the development of algorithm under complex PSCs has been continuously attracting many researchers to yield more satisfying results. In this paper, several algorithms including conventional and metaheuristic are selected for candidate, such as perturb and observe (P&amp;amp;O), firefly (FF), differential evolution (DE), grey wolf optimizer (GWO) and Seagull optimizer (SO). From the preliminary study, SO has shown best performance among other candidates. Then, SO is improved for rapid global optimizer. Modified variable step sizes perturb and observe (MVSPO) is applied to enhance the accuracy tracking of SO. To evaluate the performances, high complexity multipeak partial shading is used to test the algorithms. Statistical results are also provided to analyze the trend of performances. The proposed method performances are shown better fast-tracking time and settling time, high accuracy, higher energy harvesting and low steady-state oscillations than other candidates.&lt;/span&gt;

Optimization of fuzzy photovoltaic maximum power point tracking controller using chimp algorithm

&lt;span&gt;In this paper, a photovoltaic (PV) fuzzy maximum power point tracking (MPPT) method optimized by the chimp algorithm is presented. The fuzzy logic controller (FLC) of seven triangular membership functions (MFs) is used. The optimization fitness function is composed of transient and steady-state indices under different irradiation and temperature operating conditions. By using MATLAB package, the performance of optimized method is examined and compared with asymmetrical FLC and well-known perturb and observe (P&amp;amp;O) tracking methods at different operating conditions in terms of: transient rising time (tr) and energy yield during 30 s. Moreover, the tracking methods are also compared in terms of the fitness &lt;span&gt;function value. From the comparison of simulation results, a more energy can be&lt;/span&gt; harvested by using the proposed optimized tracking method compared to the &lt;span&gt;other methods. Consequently, at the various operating conditions, the proposed&lt;/span&gt; method can be used as a more reliable tracking method for PV systems.&lt;/span&gt;

Solar photovoltaic Maximum Power Point Tracking controller optimization using Grey Wolf Optimizer: A performance comparison between bio-inspired and traditional algorithms

Solar photovoltaic systems are widely used; however, their performance is bound to weather conditions, depending on irradiation, temperature, and the effect of shadows. Maximum Power Point Tracking techniques have been developed to solve this issue. Standard methods use mainly-two algorithms: Perturb and Observe and Incremental Conductance. However, such algorithms perform differently when the Solar photovoltaic system works under sudden solar irradiation changes, temperature, and load changes. This research proposes an optimized Maximum Power Point Tracking controller based on the Grey Wolf Optimization algorithm using the MATLAB/Simulink software as an alternative to the traditional techniques. Global efficiency and Root Mean Square Error evaluate the controller's performance. The response time is analyzed using the Grey Wolf Optimizer algorithm, Wolf Optimizer Algorithm, Simulated Annealing, and Particle Swarm Optimization. These four metaheuristic algorithms are compared to the Perturb and Observe, and Incremental Conductance algorithms. The models are analyzed for the transient state and full-day operation scenarios for constant and variable irradiations, temperatures, and loads. The comparative results show that the Maximum Power Point Tracking controller optimized by the Grey Wolf Optimizer algorithm has superior performance, giving an average 6% output power higher than the other controllers under the test scenarios evaluated. The efficiency of the proposed model was, on average, 3% higher than the Incremental Conductance and Perturb & Observe controllers. For the MPPT controller tunning stage, the Grey Wolf Optimizer Algorithm had the best performance with an RMSE of 255.3549 with a compute time of 27.3 min; the worst performing was the Particle Swarm Optimization with an RMSE of 332.4075 and 27.8 min computation time. The proposed GWO optimized MPPT controller had the faster settling time for each irradiation level compared, with an average of 0.175 s. Also, results showed an improvement of the system response throughout the Maximum Power Point Tracking controller optimized by the Grey Wolf Optimizer algorithm since a lower curling effect is obtained at power converter outputs.

An Efficient Control Scheme Based on PLL Integrated PI Controller for Grid-connected Solar Photovoltaic Systems

The market for solar energy is growing these days, thanks to recent developments in Photovoltaic (PV) systems. The integration of solar energy to the grid is required for its optimum utilization. A novel control of a single stage 3 phase grid connected solar inverter is presented in this paper. A PV array, Voltage Source Inverter (VSI) for converting dc to ac, and a filter that connects it to the grid are the major components of the proposed system. The inverter output is regulated with respect to the grid using Synchronous Reference Frame (SRF) control. A 3-phase Phase Locked Loop (PLL) is used to lock the grid frequency and phase in relation to inverter output voltage. The Perturb and Observe (P&amp;O) algorithm is used to obtain the reference voltage corresponding to the maximum PV power. To remove the high frequency harmonics, the inverter output is combined with an LCL filter. The dynamic performance of the proposed system under the number of stochastic environmental conditions are validated using MATLAB Simulink platform.&#x0D; HIGHLIGHTS&#x0D; &#x0D; The proposed PLL integrated PI controller provided high robustness under stochastic environmental conditions&#x0D; Enhanced system dynamic performance and accurate MPP tracking&#x0D; The inverter output is free from the high frequency harmonics&#x0D; Improved control performance&#x0D; &#x0D; GRAPHICAL ABSTRACT

Optimum sizing of components for photovoltaic maximum power point tracking buck converter

A photovoltaic (PV) maximum power point tracking (MPPT) converter consists of MPPT algorithm and converter. Although there has been a lot of improvement for MPPT algorithm, there is still much to improve for the converter. This paper proposed optimum sizing for the ideal PV MPPT buck converter with resistive and battery loads. The optimum sizing ensures the MPPT converter operates in the continuous current mode (CCM) operation and within the defined output voltage ripple for the specified range of irradiance. These conditions are achieved by deriving the equations of the inductances and capacitances required for the MPPT buck converter. The CCM operation is designed to be closed to the DCM to ensure a small inductance value. The specified output voltage ripple ensures the quality of the output of the MPPT buck converter. The verification of the derivation is done using MATLAB/Simulink circuit simulation. The conventional single diode PV model and perturb and observe (PnO) MPPT is used in the system. The result shows that the range of output resistance is highly affecting the duty cycle range, which leads to MPPT failure if the output resistance is not calculated properly. The converters for both resistive and battery loads can operate in the CCM and close to the DCM operation based on the proposed design. These converters also maintain output voltage ripple factor at 1% or lower, which the proposed derivation follows the specification. In conclusion, the proposed derivation of the MPPT buck converter is accurate.

Combined cooling of photovoltaic module integrated with thermoelectric generators, by using earthenware water tank and ultrasonic humidifier: An experimental study

The purpose of the present research is to reduce the temperature of photovoltaic (PV) module using a combined cooling method in order to improve electrical output, having better performance and simultaneous integration of thermoelectric generators (TEG) with it and to generate surplus electricity from the existing temperature difference in the proposed system. An L-shaped earthenware water tank as the passive cooling method and an ultrasonic humidifier inside the tank as the active cooling method formed the combined cooling sections of the proposed system. The ultrasonic humidifier converts the cooled water inside the earthenware tank to cold mist in the enclosed space backside of the PV module by a piezoelectric actuator based on the cavitation phenomenon (reduction of PV module temperature occurs by heat transfer and conductivity). Temperature difference between the surface of the PV module and cold mist on backside of the PV module converts to surplus electricity by TEG. In this study, three cold mist creation capacities of 250, 400 and 550 mL/h were investigated and the greatest improvement in the utilization of solar energy compared to the control PV module was for the cold mist creation capacity of 550 mL/h. Temperature reduction of PV section of about 5.2 °C, PV maximum power improvement of 5.1% and total efficiency improvement of 4.96% were obtained in the proposed system with cold mist creation capacity of 550 mL/h compared to control PV module. It was concluded that unlike cooled PV module, the share of TEG array is very negligible in the total efficiency improvement.

Performance assessment and validation of inverter control current controllers in reduced sensor maximum power point tracking based photovoltaic‐grid tied system

A relative assessment on conventional and adaptive current controllers used in reduced sensor-maximum power point tracking (MPPT) based photovoltaic (PV)-grid tied inverter systems for the improvement of system power quality is suggested. The steady-state and transients errors produced in the conventional PI and proportional resonant controllers, which are used to generate the references, can be fixed by using an intelligent ADALINE-LMS adaptive controller; moreover, it helps in reducing the %THD (total harmonic distortion) level measured at different power zones. Also, to track the maximum PV power, which is further integrated to DC-bus, a reduced sensor-based technology is added into the circuit that sidesteps the problem of tracking local MPP instead of global MPP and the drawbacks of using current sensors. The use of a reduced sensor-based MPPT controller confirms extraction of maximum PV power and it guarantees a constant DC-link voltage under all the possible test conditions. The overall control architectures and system performances, which are tested under different system dynamics, are validated through MATLAB/Simulink as well as experimental findings obtained using the dSPACE RTI 1202 interfacing kit. These experimental results confirm that the adaptive control technique used in reduced sensor-MPPT based PV-grid tied inverter systems performs unbeatably with balanced load and grid voltages, less harmonics, quick response time etc. under the operation of linear, non-linear and transient loads, whereas, conventional controllers are best only for the linear loads.

Self-Consumption Optimization in an Energy Community

In recent years, a fast penetration of renewable sources in power electrical systems is taking place, providing many benefits; indeed, thanks to the Energy Community development, there is the possibility that renewable energy generated by a user can be shared by the other users of the community too. However, to make this happen, power electronics converters are the main characters of a proper management of shared energy. In this paper authors propose a Simulink scheme of an Energy Community, in which converters and related control systems have been modelled. Through the Maximum Power Point Tracking control of the photovoltaic converter, the maximum photovoltaic power is extracted, and through the double-loop control of the grid-connected inverter, only active power is fed into the grid, generating a current in phase with the grid voltage. The major innovation introduced in this paper is the modelling of a power sharing supervisor system that coordinates all the bidirectional converters which interface batteries to the community, according to their states of charge, in order to set power exchanges with the grid to zero. Results obtained from simulations demonstrate that, by varying load, photovoltaic production and batteries states of charge, the self-consumption optimization aim is pursued.

Voltage control in low voltage grids with independent operation of on-load tap changer and distributed photovoltaic inverters

This paper aims to find the optimal setups of voltage control devices in different configurations of Low Voltage (LV) grids with strong PhotoVoltaic (PV) diffusion by performing dedicated simulations. Distributed PV inverters and On-Load Tap Changer (OLTC) are simulated without considering their coordination, to avoid large investments in new communication infrastructures. Thus, each device independently works to decrease voltage deviations in the respective grid connection point. PV generation and consumption profiles are measured and used in two simulated LV grids, connected to the Medium Voltage (MV) grid by a MV/LV transformer with rated powers of 400 and 250 kVA, respectively. The calculation of the optimal devices setups is addressed as a multi-objective problem, considering objectives of voltage quality, grid losses, and OLTC lifespan increase. Multiple simulations are performed by varying the setup of the voltage controls, and considering different positioning and sizes of the generators. In the hardest case, the ratio between the maximum PV power generation and the maximum load in the whole grid is ≈70%. Pareto analysis is carried out to find the non-dominated solutions and TOPSIS is applied to rank the solutions. Finally, a sensitivity analysis is performed by changing the weights attributed to each objective function.

Achieving extensive lossless coupling of photovoltaic and thermoelectric devices through parallel connection

Photovoltaic-thermoelectric hybrid systems have received broad attention in recent years owing to the capability of achieving efficient utilization of full-spectrum solar energy. This paper investigates the lossless coupling method of photovoltaic and thermoelectric devices to address the issue of large electrical coupling losses. The properties of the photovoltaic-thermoelectric hybrid systems with three connection modes, which are electrically separated, series, and parallel, are experimentally compared under different incident power densities. The electrical-thermal interaction characteristics of the photovoltaic cell and the thermoelectric device under different connection modes are further investigated with the theoretical method. The mechanism of achieving extensive lossless coupling of photovoltaic and thermoelectric devices under parallel connection is illustrated. The results demonstrate that when the incident power density is less than 15Wcm−2, the photovoltaic and thermoelectric devices can be losslessly coupled by the parallel connection. Both electrically separated and parallel coupling systems achieve the output power of 0.85 W, while the series coupling system power is only 0.41 W under the same incident power density of 15Wcm−2. Compared with the series mode, the parallel lossless coupling method can be achieved over a broader range of operating conditions and is more stable because of the small variation of the photovoltaic maximum power voltage under the practical varying irradiation and the effect of the weakened Peltier effect.

Grey wolf optimization-recurrent neural network based maximum power point tracking for photovoltaic application

To increase the photovoltaic (PV) power-generation conversion, MPPT is the primary concern. This works explains about the grey wolf optimization (GWO - RNN)-based hybrid maximum power point tracking (MPPT) method to get quick and maximum photovoltaic (PV) power with zero oscillation tracking. The GWO – RNN based MPPT method doesn’t need additional sensor for measuring irradiance and temperature variables. The NLT is used for the multi-level inverter (MLI) control strategy to achieve less harmonics distraction and less switching losses with better voltage and current profile. This employed methodology brings remarkable aspects in the PV boosting potential extraction. A GWO – RNN controlled LUO converter is a zero output harmonic agreement impedance matching interface that is MPPT is performed by placing the PV modules between the load regulator power circuit and the load regulator power circuit. To actualize the proposed hybrid GWO – RNN model for the PV system, perturb and observe, RNN, ant colony optimization, and artificial bee colony MPPT techniques are employed. The MATLAB interfaced dSPACE interface is used to finish the hands-on validation of the intended grid-integrated PV system. The obtained results eloquently support the appropriate design of higher-performance control algorithms.

A novel combined Fuzzy-M5P model tree control applied to grid-tied PV system with power quality consideration

ABSTRACT A novel control strategy based on an effective combination of the fuzzy logic controller and the M5P decision tree algorithm (Fuzzy-M5P) for a grid-interfaced solar energy system is proposed in this paper. The aim is to benefit from fuzzy logic advantages using its resulting datasets along with the fast model tree functionality. This mixed control has the ability to cope effectively with the large computation time and complexity of fuzzy logic controllers especially when embedded systems or real-time implementation are considered. Using a photovoltaic (PV) inverter with active filtering capability, the primary objective is to provide a grid-solar topology with a maximum PV energy extraction and power quality enhancement. For the inverter control, the M5P model was tested in the indirect current technique. This harmonic generation method is very efficient and its performances are entirely dependent on the chosen dc-link controller. Moreover, a novel maximum power point tracking technique (MPPT) based on the M5P model tree is performed in which data inputs are collected from fuzzy logic-based MPPT. WEKA and MATLAB software are used to translate the model by formulating a simple hierarchical program structure. To validate the effectiveness of the proposed control, simulation, and experimental tests are done under steady and dynamic conditions. The final developed algorithm based on simple IF-ELSE rules shows promising results, it can reduce harmonic distortions to 2.77%, perfectly compensates reactive power with almost unity power factor, and enhances dynamic performances with fast transient response.