Search

While the demand for electrical energy in the world increases daily, a large part of this demand is still provided by fossil fuels. However, the most significant contribution to solving the economic and environmental problems that arise is the spread of renewable energy production systems. Solar power generation systems are one of these renewable energy generation systems. In this study, cell and module parameters are modeled and estimated in different ways to obtain maximum energy from solar cells used in solar power generation systems. Cell and model vendors need to provide complete information to the end user. Therefore, the systems created turn into a nonlinear problem with many unknown parameters. In this study, single-diode model (SDM), double-diode model (DDM), and triple diode model (TDM) for photovoltaic (PV) cells as well as parameter estimations of four different PV modules produced by other vendors were performed for the first time with the dingo optimization algorithm (DOA). The mathematical model of PV module parameters is derived using open-circuit voltage (Voc), short-circuit current (Isc), and maximum power point values (Pmpp). The parameter values obtained by the algorithm aim to get the maximum power point curve for each model and module with minimum error. These values are compared with five traditional and five recent meta-heuristic algorithms, which have extreme positions in the literature. [ABSTRACT FROM AUTHOR]

Introduction. Solar photovoltaic (PV) has recently become very important especially in electrical power applications for countries with high luminosity because it is an effectively unlimited available energy resource. Depending on solar radiation and temperature, the PV generator has a non-linear characteristic with a maximum power point (MPP). The novelty is the efficiency improvement of a PV energy module, it is necessary to track the MPP of the PV array regardless of temperature or irradiation circumstances. Purpose. This paper presents the modeling and the digitally simulation under MATLAB/Simulink of a Fuzzy Logic Controller based on Voltage Perturbation Algorithm (FLC-VPA) applied to PV battery charging system, which consists of PV module, DC-DC boost converter, MPP tracking (MPPT) unit and battery storage. Methods. The DSP1104 is then used to experimentally implement this MPPT algorithm for real-time driving. The obtained results show the high precision of the proposed FLC-VPA MPPT around the optimal point compared to the conventional VPA under stable and changing meteorological conditions. Practical value. The experimental results approve the effectiveness and validity of the proposed total control system in the PV system. References 30, tables 3, figures 17. [ABSTRACT FROM AUTHOR]

Abstract Tracking the Maximum Power Point (MPP) in solar Photovoltaic (PV) systems is a difficult task under changing environmental weather conditions. Furthermore, the tracking method gets more complex under changing irradiance conditions because of the nonlinearity in power voltage characteristics. The Maximum Power Point Tracking Technique (MPPT) under changeable irradiance and temperature-varying conditions is presented in this study. Using a swarm algorithm known as Multi-Verse Optimization (MVO), this technique combines a Fuzzy Logic Controller (FLC). In this work, the MVO algorithm is used to generate the optimized scaling factors of FLC to track the accurate MPP. A MATLAB/Simulink environment is used to model the PV system and the proposed MPPT method. The simulated system was assessed under uniform, abruptly changing, ramp with abruptly changing irradiance and temperature variation circumstances. The results demonstrate that the proposed approach is efficient in tracking the MPP and has a quick response time when compared to FLC-based MPPT and conventional P&O.

Introduction. Solar photovoltaic (PV) has recently become very important especially in electrical power applications for countries with high luminosity because it is an effectively unlimited available energy resource. Depending on solar radiation and temperature, the PV generator has a non-linear characteristic with a maximum power point (MPP). The novelty is the efficiency improvement of a PV energy module, it is necessary to track the MPP of the PV array regardless of temperature or irradiation circumstances. Purpose. This paper presents the modeling and the digitally simulation under MATLAB/Simulink of a Fuzzy Logic Controller based on Voltage Perturbation Algorithm (FLC-VPA) applied to PV battery charging system, which consists of PV module, DC-DC boost converter, MPP tracking (MPPT) unit and battery storage. Methods. The DSP1104 is then used to experimentally implement this MPPT algorithm for real-time driving. The obtained results show the high precision of the proposed FLC-VPA MPPT around the optimal point compared to the conventional VPA under stable and changing meteorological conditions. Practical value. The experimental results approve the effectiveness and validity of the proposed total control system in the PV system. References 30, tables 3, figures 17.

The paper presents an analysis of the performance of a Proton Exchange Membrane (PEM) fuel cell, which is equipped with a flow field design featuring dual inlets and outlets, while operating under conditions of excess stoichiometry. These experiments were conducted using a fuel cell system connected to a station that allowed for the precise adjustment of gas flow rates. During the initial phase of experimentation, various proportions of excess oxygen were systematically applied, while maintaining constant hydrogen flow rates of 80 mL/min and 100 mL/min. Particularly noteworthy, for the case of a 100 mL/min hydrogen gas flow rate and the optimized excess oxygen proportion of 150%, further experiments were undertaken to ascertain the ideal humidification conditions. The outcomes of these experiments revealed that a hydrogen gas flow rate of 100 mL/min consistently outperformed the 80 mL/min flow rate in terms of fuel cell performance. Moreover, it was observed that the introduction of excess oxygen significantly improved performance, up to a 50% oxygen proportion for the 80 mL/min hydrogen flow rate and up to a 150% proportion for the 100 mL/min hydrogen flow rate. One intriguing observation pertained to the influence of humidification. Specifically, it was found that the utilization of a humidification temperature of 100°C, or the absence of humidification altogether, resulted in notably diminished power output. In contrast, intermediate humidification temperatures of 60°C, 70°C, 80°C, and 90°C consistently yielded identical maximum power points (MPP) when combined with a 150% excess oxygen supply and a hydrogen flow rate of 100 mL/min. The twin inlet-twin outlet flow field provides a slight advantage over the conventional serpentine flow field in the overall analysis. [ABSTRACT FROM AUTHOR]

Solar photovoltaic technology is dealing with the challenge of optimal power production from PV cells under all circumstances. This is possible by forcing the PV system to operate at maximum power point (MPP). Therefore, an algorithm is designed to take the operating power point towards MPP using an electronic circuit. The most used algorithm in the market is P&O due to its simple structure, easy implementation, and cheap circuitry. Whereas the weaknesses associated with P&O are steady state oscillations, PV array dependency, and slow tracking speed. We have put efforts into reducing the tracking time and removing the steady-state oscillations. After the detailed analysis, we have made some structural changes to get the required results. The proposed technique is named the optimized P&O (OP&O) algorithm. Both algorithms have been applied to the 90-watt PV system specifically designed for this purpose. The PV system with both algorithms is simulated in MATLAB/Simulink at different loads. The results have shown remarkable achievement in tracking speed, steady state oscillations, and ease of implementation. [ABSTRACT FROM AUTHOR]

The maximum power point tracking (MPPT) algorithms are required to deliver the optimal energy from solar photovoltaic cells/array (PV) under numerous weather conditions. Therefore, MPPT circuits driven by defined rules called algorithms are designed. These algorithms range from simple to complex in design and implementation and are selected based on the scenarios of the surroundings. However, the incremental conductance (InC) MPPT algorithm is one of the market's most simple, easy to implement, and demanding algorithms. The drawback associated with the InC algorithm is its tracking speed. To overcome this weakness various researchers have made multiple improvements. Although the performance became better but was not satisfied. Further, the improvements introduce steady-state oscillations of the operating point around the MPP. So, the user needs to pick and choose based on demand. Keeping the target in focus, we have introduced a couple of modifications in the structure of INC. that remain fruitful. The proposed structure named the augmented InC algorithm has shown marvelous improvement in tracking speed and steady-state oscillations. The results have been compared with the conventional InC algorithm, where the proposed augmented InC algorithm has outperformed the conventional InC algorithm in tracking speed and steady-state oscillations. We have used the MATLAB script to code the conventional InC algorithm and proposed augmented InC algorithms based on their designed flowchart. Both algorithms have been applied to the standalone solar photovoltaic system composed of a solar photovoltaic array, DC/DC boost converter, illumination and temperature inputs, MPPT algorithm, and a DC load. The model is designed in Simulink/MATLAB. [ABSTRACT FROM AUTHOR]

Developers of electronics for the Internet of Things are considering nonstandard substrate materials like recyclable, low-cost, and eco-friendly cardboard paper. From this perspective, this article reviews the design and experimental results of a 2D-rectenna for scavenging radio-frequency energy at 2.45 GHz on various cardboard paper substrates for both the antenna and rectifier. Four types of recycled cardboard material, each with different thicknesses, air gaps, and surface roughness, are selected for characterization. A linearly polarized rectangular microstrip patch antenna with microstrip transmission feeding is adopted for ease of fabrication. At 2.45 GHz, the antenna has a simulated and measured global gain of 2.98 dB and 2.53 dB, respectively, on a 2.2 mm thick cardboard material. The rectifying element consists of a voltage-doubler configuration connected through a T-matching network to the antenna. At low RF input power (−10 dBm), the maximum available DC output power is experimentally evaluated at 1.73 μW, 7.5 μW, and 8.5 μW for HSMS-2860, HSMS-2850, and SMS7306-079L diodes, respectively. The respective rectifiers with diodes SMS7306-079L, HSMS-2850, and HSMS-2860 exhibit optimal load values of 2 kΩ, 2.6 kΩ, and 8 kΩ. The rectifier designed using the SMS7306-079L diode experimentally reaches a maximum power conversion efficiency (PCE) of 14.2% at −5 dBm input power when the optimal load value is 1.5 kΩ. [ABSTRACT FROM AUTHOR]

The power generated by photovoltaic (PV) systems is influenced by environmental factors. This variability hampers the control and utilization of solar cells' peak output. In this study, a single-stage grid-connected PV system is designed to enhance power quality. Our approach employs fuzzy logic in the direct power control (DPC) of a three-phase voltage source inverter (VSI), enabling seamless integration of the PV connected to the grid. Additionally, a fuzzy logic-based maximum power point tracking (MPPT) controller is adopted, which outperforms traditional methods like incremental conductance (INC) in enhancing solar cell efficiency and minimizing the response time. Moreover, the inverter's real-time active and reactive power is directly managed to achieve a unity power factor (UPF). The system's performance is assessed through MATLAB/Simulink implementation, showing marked improvement over conventional methods, particularly in steady-state and varying weather conditions. For solar irradiances of 500 and 1,000 W/m2, the results show that the proposed method reduces the total harmonic distortion (THD) of the injected current to the grid by approximately 46% and 38% compared to conventional methods, respectively. Furthermore, we compare the simulation results with IEEE standards to evaluate the system's grid compatibility. [ABSTRACT FROM AUTHOR]

This paper focuses on simple technique from conventional techniques for the rapid maximum power point (MPP) pursuit with a less oscillations under sudden changes. In investigations, the controllability of the conventional boost converter model shows that the converter can be controlled from the current of its inductance (inductor). To get reference current, a conventional technique known as method of short-circuit current has been transformed to clearly establish with supporting evidence an estimated photocurrent. Different test was done on MATLAB/Simulink under irradiance, temperature and load changes. Facing the P&O and short-circuit method, the proposed method presents a response time of 11.08 ms and power variations at MPP around 0.1 Watts. A test made on another converter shows that this method can be applied on DC-DC converters where the inductor current need to be controlled for proper operation.

Fuel cells are one of the most promising renewable energy sources, offering advantages like reliability, eco-friendliness, and low pollutant emissions, which have spurred rapid advancements in power generation technologies. However, fuel cells face significant challenges, including high initial costs, limited fuel availability, and the difficulty of maintaining operation at the maximum power point, which hinders their use in stand-alone applications. In this paper, a Modified Particle Swarm Optimization (MPSO) method is proposed for maximum power point tracking (MPPT) to optimize the power output of Proton Exchange Membrane Fuel Cells (PEMFCs). The proposed method dynamically adjusts to key operational parameters such as cell temperature, hydrogen partial pressure, and membrane water content, areas that have not been comprehensively addressed in previous research. In this paper, an MPSO algorithm-based MPPT tracking approach without a PID controller is proposed to achieve the maximum power point (MPP) of a PEMFC. Under rapid temperature fluctuations in the fuel cell, the proposed MPSO MPPT method achieved a maximum power of 1223.5 W with an average of 5.66 iterations. In comparison, the meta-heuristic particle swarm optimization (PSO) method and the conventional perturb and observe (P&O) method achieved maximum power outputs of 1218.5 W and 1213.65 W, respectively, with PSO requiring 12.33 iterations. Additionally, the proposed approach showed improvements in power efficiency by 2.47 %, 2.87 %, and 13.58 % for the Jaya algorithm. demonstrating effective MPPT tracking under different operating conditions and perturbations. The MPSO method is implemented in the Simulink/MATLAB environment and is compared with the Perturb & Observe (P&O) and Conventional PSO (CPSO) methods. The results demonstrate that the proposed MPSO approach outperforms these traditional techniques in terms of tracking speed, efficiency, and stability under varying conditions. This successful implementation lays a strong foundation for future integration into real-world PEMFC systems.

In this paper a comparison of different types of maximum power point search methods for the photovoltaic panels is presented. The methods that represent each group of maximum power point techniques will be implemented in the software that allows to test the behaviour of photovoltaic panels in different environment conditions including partial shading. In this paper each implemented method was compared including time of convergence with the maximum power point, tracking error and differences in the energy obtained from photovoltaics during the simulation time. The algorithms were compared under both uniform lighting and partial shade conditions. [ABSTRACT FROM AUTHOR]

In this paper, an improved constant current step based on the grey wolf optimization (CCS-GWO) algorithm for photovoltaic systems is investigated. The development of grey wolf optimization has been widely spread over photovoltaic applications. This method is one of the metaheuristic swarm optimization algorithm groups inspired by an optimum means of chasing prey by grey wolves. The proposed technique applies constant current steps to the pack of wolves (alpha, beta, and omega) by monitoring the average of the internal current step and external current step in order to target the leader alpha wolf position. Moreover, the proposed technique solves the convergence process issues, low convergence speed, and premature local optima problems of the traditional GWO algorithm. This CCS-GWO algorithm accurately tracks the maximum power point from the photovoltaic systems for load charging in different partial shading conditions (PSCs). A number of standard benchmark functions are presented with low average cost functions and their corresponding standard deviation values. The simulation results revealed that the proposed CCS-GWO approach outperforms the existing GWO and GA algorithms in terms of efficiency (98.55%) and tracking time (0.3 s). [ABSTRACT FROM AUTHOR]

The performance of a new vertical-axis wind turbine rotor based on the mathematical equation of the cycloid is analyzed and compared through simulation and experimental testing against a semicircular or S-type rotor, which is widely used. The study examines three cases: equalizing the diameter, chord length and the area under the curve. Computational Fluid Dynamics (CFD) was used to simulate these cases and evaluate moment, angular velocity and power. Experimental validation was carried out in a wind tunnel that was designed and optimized with the support of CFD. The rotors for all three cases were 3D printed in resin to analyze their experimental performance as a function of wind speed. The moment and Maximum Power Point (MPP) were determined in each case. The simulation results indicate that the cycloid-type rotor outperforms the semicircular or S-type rotor by 15%. Additionally, experimental evidence confirms that the cycloid-type rotor performs better in all three cases. In the MPP analysis, the cycloid-type rotor achieved an efficiency of 10.8% which was 38% better than the S-type rotor. [ABSTRACT FROM AUTHOR]

Wind turbines are one of the leading renewable sources of electricity. They are located in areas where the energy potential of the wind is high and more or less permanent. Their efficient operation depends on certain technical characteristics of the entire wind system (wind turbines, generator, automation system, regulation, and control). This paper analyses experimental data from wind turbines, WT, in the Dobrogea area: wind speed, v, velocity, n, and power, PEG, at the electric generator, EG. These are used to identify the local maxima of wind turbine power at different wind speeds. Based on this, a mathematical model for wind turbines and the power losses caused by inefficient control are determined. The mathematical model is used to visualize the maximum energy zones. At the end of this study, an algorithm is given that allows for the visualization of the optimal energy zone. The algorithm resulting from the analyzed case studies can be implemented by the control system in order to maximize the produced electricity with any variation in wind speed. [ABSTRACT FROM AUTHOR]

In this study, a frequency regulation is proposed that adjusts the release of rotational kinetic energy to ensure that a wind turbine remains near the maximum power point in the event of persistent under-frequency. The proposed scheme calculates wind turbine power by multiplying the frequency deviation with the variable control gain and then adding the resulting value to the torque controller power. Considering the operational characteristics of the wind turbine, separate control gains are applied in both the over-frequency section (OFS) and the under-frequency section (UFS). When the rotor speed approaches the maximum power point (MPP), the UFS control gain decreases, causing the wind turbine to gradually transition to maximum power point tracking (MPPT). This overcomes the limitations of conventional frequency regulation, enhancing both the power output of the wind turbine and the average grid frequency. To validate the proposed scheme, an IEEE 14 bus system, including a wind power plant and a photovoltaic plant, was simulated using the EMTP-RV simulator. The simulation results demonstrate that the proposed scheme effectively performs frequency regulation in the power grid regardless of the wind penetration. [ABSTRACT FROM AUTHOR]

The paper presents an analysis of the performance of a Proton Exchange Membrane (PEM) fuel cell, which is equipped with a flow field design featuring dual inlets and outlets, while operating under conditions of excess stoichiometry. These experiments were conducted using a fuel cell system connected to a station that allowed for the precise adjustment of gas flow rates. During the initial phase of experimentation, various proportions of excess oxygen were systematically applied, while maintaining constant hydrogen flow rates of 80 mL/min and 100 mL/min. Particularly noteworthy, for the case of a 100 mL/min hydrogen gas flow rate and the optimized excess oxygen proportion of 150%, further experiments were undertaken to ascertain the ideal humidification conditions. The outcomes of these experiments revealed that a hydrogen gas flow rate of 100 mL/min consistently outperformed the 80 mL/min flow rate in terms of fuel cell performance. Moreover, it was observed that the introduction of excess oxygen significantly improved performance, up to a 50% oxygen proportion for the 80 mL/min hydrogen flow rate and up to a 150% proportion for the 100 mL/min hydrogen flow rate. One intriguing observation pertained to the influence of humidification. Specifically, it was found that the utilization of a humidification temperature of 100°C, or the absence of humidification altogether, resulted in notably diminished power output. In contrast, intermediate humidification temperatures of 60°C, 70°C, 80°C, and 90°C consistently yielded identical maximum power points (MPP) when combined with a 150% excess oxygen supply and a hydrogen flow rate of 100 mL/min. The twin inlet-twin outlet flow field provides a slight advantage over the conventional serpentine flow field in the overall analysis.

Due to the intermittent and uncertain nature of photovoltaic systems, their incorporation within a DC microgrid presents a challenge to primary control, which directly interfaces with the generation converter. By utilizing a weighted mixed sensitivity control, this study aims to amplify the robustness of a noninverting buck-boost converter integrated photovoltaic system in addressing the uncertainties and disturbances arising from simultaneous fluctuations in irradiance, temperature, and load. The robust control algorithm was formulated by employing frequency-weighting functions and imposing a requirement for the minimum norm of the transformation matrix to achieve robust performance and robust stability. Furthermore, the involvement of reference models in a robust control synthesis offered additional advantages in enhancing the damping of the system. Consequently, the entire design configuration could effectively establish the converter robustness against both generation and load intermittencies occurring simultaneously. The simulation and experiment results are demonstrated to illustrate the efficiency of the designed algorithm.

With partial shading conditions, it is essential to acquire Maximum Power Point at which the Photovoltaic systems (PV) operate effectively despite the variation in the cell temperature and incident angle of sunlight rays on the panels. This study explores the use of a Smell Agent Optimization (SAO) algorithm for Maximum Power Point Tracking (MPPT) in partial shaded PV systems. The proposed MPPT system is composed of a PV model, a DC-DC converter model and a control part. The Smell Agent Algorithm (SAA) was adopted in the control part of the MPPT system to implement the optimization algorithm using four different shading patterns (SPs) and to calculate the optimal switching duty cycle of the DC-DC converter. The effectiveness of the proposed system was verified using simulations in the MATLAB/Simulink environment. The SAO respectively track maximum values for Power, Voltage and Current as 845.8476 W, 211.7308 V, 3.99492 A while the maximum values for Power, Voltage and Current for Perturb and Observe (P and O) are 845.0465 W, 211.6305 V, 3.993028 A respectively during SP1. The results showed that the SAO algorithm has excellent tracking results in terms of convergence speed, accuracy, power extracted stability, and dynamic response in reaching the optimum point.

The I–V curve serves as an effective representation of the inherent nonlinear characteristics describing typical photovoltaic (PV) panels, which are essential for achieving sustainable energy systems. Over the years, several PV models have been proposed in the literature to achieve the simplified and accurate reconstruction of PV characteristic curves as specified in the manufacturer's datasheets. Based on their derivation, PV models can be classified into three distinct categories: circuit-based, analytical-based, and empirical-based models. However, an extensive analysis of the accuracy of the reconstructed curves for different PV models at the maximum power point (MPP) has not been conducted at the time of writing this paper. The IEC EN 50530 standard stipulates that the absolute errors within the vicinity of MPP should always be less than or equal to 1%. Therefore, this review paper conducts an in-depth analysis of the accuracy of PV models in reconstructing characteristic curves for different PV panels. The limitations of existing PV models were identified based on simulation results obtained using MATLAB and performance indices. Additionally, this paper also provides suggestions for future research directions. [ABSTRACT FROM AUTHOR]

In the traditional technique of power conversions used for electric vehicles battery recharging from solar photovoltaic, a boost converter is utilized in the initial stage, continued by a full-bridge converter with a distinct switch design. However, the circuit's parasites contribute significantly to power losses, which detract from performance. This paper proposes a highly efficient power converter for charging an electric vehicle battery from solar photovoltaic. A high-gain improved quasi-Y-source DC–DC step-up converter operating at a low-duty ratio is implemented at the first stage to offset power losses at various conversion stages. In the next stage, the phase-shifted full-bridge converter incorporates the zero-voltage transition technique to consider circuit parasites and isolate the source and load with an isolation transformer. The extracted system voltage from SPV is 50-V DC, which is boosted to 150 V for charging a battery bank of 60 V, 16 Ah, by a specified power range of 1 kW. An experimental design of 0.5 kW is developed, and its performance is evaluated to illustrate the technical value of the proposed converter. Simulation results show an efficiency of 92.6% with 74 watts loss, while hardware results show an efficiency of 91.8% with 41 watts loss. [ABSTRACT FROM AUTHOR]

Abstract The integration of artificial intelligence (AI) models in renewable energy resources management, particularly in the utilization of maximum power point tracking (MPPT) optimizers, has gained significant attention. This study focuses on investigating the tradeoff between accuracy, response time, and system complexity by varying the number of neurons in artificial neural network (ANN) models for MPPT in wind energy conversion systems (WECSs). Traditionally, MPPT algorithms in WECSs are implemented using direct or indirect methods. However, these methods lack an accumulative learning curve and rely on instantaneous inputs. In contrast, ANN models trained on pre-existing datasets offer the potential for improved maximum point capturing processes. Nevertheless, the incorporation of ANN models may introduce additional complexity to the system. Two ANN models, direct and indirect, are examined in comparison to a reference model using the perturb and observe conventional MPPT algorithm. The results show that the ANN direct model exhibits better time response in the face of high variations in wind speed profiles. On the other hand, the ANN indirect model demonstrates a 4% increase in accuracy with minimal ripples.

A novel optimization algorithm, namely Harbor Seal Whiskers Optimization Algorithm (HSWOA) is proposed in this work. Harbor seals use their whiskers to find underwater disturbances which are in the form of oscillating spheres and track prey even though they lack lateral-line systems. HSWOA mimics the high-level sensing that seal whiskers possess. As such, HSWOA has an excellent exploration capability for the search space and a high exploitation capacity for exploiting the all-optimum solutions to reach the most optimum solution. To validate these abilities, the proposed HSWOA utilizes two sets of test functions: 33 benchmark functions and five IEEE Congress on Evolutionary Computation (CEC2019) benchmark functions. The results of HSWOA are compared with ten well-established optimization algorithms. The comparison results show that HSWOA offers superior performance indices to reach an optimum solution while requiring less control variables. The results also show that HSWOA is more efficient regarding computational demand and resolution accuracy. Finally, the HSWOA is employed to track Maximum Power Point (MPP) of Photovoltaic (PV) array with partial shading conditions (PSCs) for two case studies. The results show that HSWOA extracts maximum power in minimum tracking time and high average power capturing capability compared to other optimization techniques.

The expansion of the use of solar energy in most cases depends on the energy capabilities of each specific country. In Tajikistan, where 93% of the territory is mountains, it is necessary to build solar power plants high in the mountains. At mountain heights, the performance of panels increases in winter, in addition, the snow cover that reflects the sun's rays also contributes to an increase in the efficiency of solar panels. Experts note that solar panels receive less radiation in valleys, as clouds and fogs often prevent the passage of sunlight. At the heights of the mountains, there is no air pollution and, as a result, a longer operating time of the solar power plant. In the conditions of Tajikistan, one more drawback of solar stations in the valleys should be added - this is the rapid pollution of the panels due to dust storms in the summer-autumn period. The Chinese company Sun Tech Power Holdings will build a 10 MW power plant in Tibet at an altitude of 4000 meters above sea level.

Due to the fact that thermoelectric generation (TEG) system commonly operates in environment where temperature distribution is non-uniform, consequently the output characteristic curve P–V for centralized TEG systems gives rise to several local maximum power points (LMPP). The paper suggests a COOT algorithm-based maximum power point tracking (MPPT) method for centralized TEG system, by imitating the collective behavior of Coot water birds, which involves their leadership structures and chain movements, to enhance its capability of jumping out the local optima. Furthermore, it integrates a dynamic reverse learning strategy and a dynamic termination condition, which shortens the iteration times and convergence time of the algorithm. To evaluate its performance, this paper conducted distinct case studies that included engine speed at 1000, 2500, and 4000 rpm, step changes and randomly changes. The effectiveness of the COOT algorithm is evaluated by comparing its performance with that of the traditional Perturbation and Observation (P&O) algorithm as well as the meta-heuristic algorithms Gray Wolf Optimization (GWO) and Particle Swarm Optimization (PSO). It is shown that the COOT algorithm can achieve optimum power output and effectively reduce fluctuations in output power, while converging to the optimal solution more quickly.

Reasonable determination of the installation inclination and array spacing of PV power plant modules is essential to improve the power generation efficiency of PV power plants. This paper firstly derives the formula for calculating the north-south spacing of PV arrays with arbitrary slope inclination and visualizes the north-south spacing of complex mountain PV arrays using ArcGIS. Secondly, a mountain PV array system is proposed to ensure that the system can still operate at the maximum power point in real-time when the solar radiation intensity changes drastically due to unpredictable environmental variables. Finally, to verify the feasibility of the active PV array system in real-life production, an experimental platform is built, and an operational test experiment of the active PV array system under partial shading conditions is conducted, as well as a long-term power boost comparison test. The experimental results show that the mountain PV array system has a 95.7% matching degree in the operation test experiment, which can be perfectly adapted to most PV plants; in the power boost comparison test, the power generation of the traditional PV system is 254Wh, and the power generation of the mountain PV array system is 483Wh, which is about 1.9 times higher than the performance of the traditional PV system. The mountain PV array system has good adaptability to various harsh and unexpected conditions and solves the problem of improving the power output of PV systems in the shadow-shaded environment of mountainous areas, which improves the general usability of PV.

The integration of artificial intelligence (AI) models in renewable energy resources management, particularly in the utilization of maximum power point tracking (MPPT) optimizers, has gained significant attention. This study focuses on investigating the tradeoff between accuracy, response time, and system complexity by varying the number of neurons in artificial neural network (ANN) models for MPPT in wind energy conversion systems (WECSs). Traditionally, MPPT algorithms in WECSs are implemented using direct or indirect methods. However, these methods lack an accumulative learning curve and rely on instantaneous inputs. In contrast, ANN models trained on pre-existing datasets offer the potential for improved maximum point capturing processes. Nevertheless, the incorporation of ANN models may introduce additional complexity to the system. Two ANN models, direct and indirect, are examined in comparison to a reference model using the perturb and observe conventional MPPT algorithm. The results show that the ANN direct model exhibits better time response in the face of high variations in wind speed profiles. On the other hand, the ANN indirect model demonstrates a 4% increase in accuracy with minimal ripples. [ABSTRACT FROM AUTHOR]

When photovoltaic (PV) arrays are subjected to partial shading conditions, the energy generated by the entire system diminishes with the appearance of multiple peaks in the P–V characteristics. A novel configuration for PV modules (PVMs) in an array has been put forth in this paper that aims to increase the output power generated by the array under partial shading conditions. This configuration involves the physical arrangement of PVMs as per the novel irregular SuDoKu (IRS) puzzle pattern connected to each other in a total cross-tied (TCT) arrangement. The electrical connections between the PVMs remain undisturbed in accomplishing this arrangement. The shading effect on PVMs within any single row is significantly reduced by this arrangement of PVMs which, in turn, increases the power generated by the PV array (PVA). Modeling and validations of these PVA configurations (PVACs) are done using MATLAB/SIMULINK. The considered PVACs are evaluated by taking numerous performance factors (i.e., mismatch power losses, efficiency, global maximum power point (GMPP) and the fill factor (FF)). The results of the proposed IRS arrangement are compared with TCT and recently proposed configurations such as SuDoKu, optimalA SuDoKu, Futoshiki SuDoKu, optimalB SuDoKu, improved SuDoKu, modified SuDoKu, hyper SuDoKu and multi-diagonal SuDoKu. The GMPP, FF and efficiency are the highest for the IRS method, with the values being 5894.4 W, 71.88% and 14.08%, respectively. Hence, the IRS is the best method in comparison with the other listed reconfiguration techniques. [ABSTRACT FROM AUTHOR]

In the wind energy system, an appropriate and effective voltage regulation of the converter is essential to guarantee the maximum power point operation. A technique based on direct pulse width modulation controller is presented in the suggested paper for the boost converter, whose working principle is based on single gain (k). Apart from developing the controller, the main contribution of this work is to tune the gain k on run time in accordance with the wind's velocity. For this purpose, initially, a thorough investigation has been executed with regard to k values and wind parameters, which will ensure the robust response of the controller. Thereafter, the direct values of duty cycle (D) of the converter are computed through the fundamental relation of the proposed controller. A comparative study for different wind conditions has been conducted to validate the performance of the proposed controller over previous controllers. The simulation results highlight a superior steady response, where the proposed controller shows a steady state error of 0.35% as compared to P&O and FOIC, where their errors are 3.5% and 1.5%, respectively. While the proposed controller's convergence response time is 110 ms, the response time of FOIC is 135 ms, and P&O is maximum while achieving its maximum power point. [ABSTRACT FROM AUTHOR]

A novel optimization algorithm, namely Harbor Seal Whiskers Optimization Algorithm (HSWOA) is proposed in this work. Harbor seals use their whiskers to find underwater disturbances which are in the form of oscillating spheres and track prey even though they lack lateral-line systems. HSWOA mimics the high-level sensing that seal whiskers possess. As such, HSWOA has an excellent exploration capability for the search space and a high exploitation capacity for exploiting the all-optimum solutions to reach the most optimum solution. To validate these abilities, the proposed HSWOA utilizes two sets of test functions: 33 benchmark functions and five IEEE Congress on Evolutionary Computation (CEC2019) benchmark functions. The results of HSWOA are compared with ten well-established optimization algorithms. The comparison results show that HSWOA offers superior performance indices to reach an optimum solution while requiring less control variables. The results also show that HSWOA is more efficient regarding computational demand and resolution accuracy. Finally, the HSWOA is employed to track Maximum Power Point (MPP) of Photovoltaic (PV) array with partial shading conditions (PSCs) for two case studies. The results show that HSWOA extracts maximum power in minimum tracking time and high average power capturing capability compared to other optimization techniques. [ABSTRACT FROM AUTHOR]

This paper proposes a concept of battery-assisted battery charger with maximum power point tracking for DC energy transducer such as thermoelectric generator and photo voltaic generator, and shows experimental results to prove the concept. The DC energy transducer is connected in series with a battery to increase the voltage. The plus terminal for the DC energy transducer is connected with the input terminal of a DC-DC buck converter, whereas the battery is connected with the output terminal of the converter. Thus, the current is boosted from the input to the output. When the net current to the battery is positive, the system works as a battery charger. To extract the as much power from the DC energy transducer as possible for high charging efficiency, maximum power point tracking is introduced. The converter was designed in 180 nm 3V CMOS with a silicon area of 1.05 mm2. The concept was experimentally proven by varying the reference voltages to control the input voltage. An all-solid-state battery was charged up from 2.2 V to 2.3 V in two hours by the converter with a flexible thermoelectric generator which had an open-circuit voltage of 0.6 V. [ABSTRACT FROM AUTHOR]

The growing interest in solar energy has led to large photovoltaic (PV) energy parks in today's world. Though the commonly used total‐cross‐tied (TCT) interconnection scheme reduces mismatch losses (MLs), the occurrence of partial shading on massive PV arrays (PVAs) reduces the output power. It invokes numerous local maximum power points (LMPPs) on the power–voltage (P–V) profiles. To counteract the undesired LMPPs, reconfiguration of PVAs is done either through electrical or physical reconfiguration methods. In this respect, physical relocation scheme is more economical for better distribution of partial shade. A novel greater‐than‐SuDoKu (GTS) topology is presented in this paper for achieving physical rearrangement of PV modules amalgamated with TCT interconnection scheme. For analysis, five shading cases are considered and obtained results are compared with conventional TCT and 10 newly established methods. In general, the proposed GTS strategy has shown its eminence in receiving a smoother P–V characteristic, enhanced efficiency, better fill factor, and reduced MLs, which ranges up to 1343 W. Moving forward, herein, the efficacy of the proposed reconfiguration scheme is validated, under the influence of partial shading conditions, through an experimental lab prototype for a 3 × 3 PVA. [ABSTRACT FROM AUTHOR]

The performance of a new vertical-axis wind turbine rotor based on the mathematical equation of the cycloid is analyzed and compared through simulation and experimental testing against a semicircular or S-type rotor, which is widely used. The study examines three cases: equalizing the diameter, chord length and the area under the curve. Computational Fluid Dynamics (CFD) was used to simulate these cases and evaluate moment, angular velocity and power. Experimental validation was carried out in a wind tunnel that was designed and optimized with the support of CFD. The rotors for all three cases were 3D printed in resin to analyze their experimental performance as a function of wind speed. The moment and Maximum Power Point (MPP) were determined in each case. The simulation results indicate that the cycloid-type rotor outperforms the semicircular or S-type rotor by 15%. Additionally, experimental evidence confirms that the cycloid-type rotor performs better in all three cases. In the MPP analysis, the cycloid-type rotor achieved an efficiency of 10.8% which was 38% better than the S-type rotor.

In this study, a photovoltaic system is simulated for maximum power point tracking using an artificial neural network. The interest in photovoltaic systems is increasing with inadequacy of fossil resources and rise in demand for energy. The variation of photovoltaic system performance depending on environmental conditions reduces efficiency. It is possible to prevent this by reaching maximum power point. Many techniques have been developed to operate system at maximum power point. With the spread of artificial intelligence, smart systems have started to be used in determining maximum power point. Artificial neural networks are intelligent systems that have the ability to learn, generalize and make decisions. In this study, maximum power point is determined with an artificial neural network whose inputs are temperature and radiation. The system was simulated in MATLAB/Simulink environment. A total of 1000 data were used in training of network in which Levenberg-Marquardt algorithm was used, 70% in training, 15% in validation and 15% in testing stages. It was observed that power of photovoltaic panel decreased from 225.1W to 46.9W, and power of boost converter from 220.9W to 45.75W when radiation was reduced from 1000W/m² to 200W/m² at regular intervals. When temperature was increased from 25°C to 45°C at regular intervals, it was determined that power of photovoltaic panel decreases from 225.1W to 194.6W, and power of boost converter from 220.9W to 190.7W. At constant radiation (1000W/m²) and temperature (25°C), system has the highest efficiency value of 98%. At constant temperature and different radiation conditions, it was seen that efficiency decreased as radiation value decreased. Similarly, under constant radiation and different temperature conditions, temperature increase caused a decrease in efficiency. The results show that photovoltaic system with artificial neural network based maximum power point tracking reaches maximum power point under changing environmental conditions. [ABSTRACT FROM AUTHOR]

Nowadays, due to the increasing interest in renewable energy, modeling and practical research on Photovoltaic (PV) have been given much attention. Currently, the challenge of using PV systems is to reach the maximum power point (MPP) in order to increase their efficiency. Hence, extensive research has been done all over the world to develop the productivity of the PV system by properly tracking the MPP. In this regard, in this article, a new maximum power point tracking (MPPT) technique based on Modified Sparrow Search Algorithm and adaptive neuro-fuzzy inference system (ANFIS) is suggested. To implement the suggested MPPT method, two important steps must be taken into account. Firstly, in different conditions of radiation and temperature, the optimal voltage is obtained by mSSA algorithm. Then, the optimal voltage is achieved by the proposed ANFIS method based on different radiation conditions to find the MPP. In order to evaluate the proposed MPPT technique, simulations are done in MATLAB in different weather conditions and scenarios. The results indicate the suitable performance of the recommended mSSA-ANFIS-based MPPT controller in tracking the MPP and achieving the global optimum in different weather conditions by attaining 99.3% efficiency. [ABSTRACT FROM AUTHOR]

Copyright of Large Electric Machine & Hydraulic Turbine is the property of Large Electric Machine & Hydraulic Turbine Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Petroleum Refinery Engineering is the property of Petroleum Refinery Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Floating photovoltaic power generation has many advantages, it does not occupy arable land, has low temperature and prevents excessive evaporation of water, etc. As a result, it becomes more and more popular among people. However, due to the mechanical vibration of the photovoltaic panel caused by the fluctuation of the water surface, low-frequency oscillation of its output voltage and current will occur and the performance of power generation will deteriorate sharply. A micro-power photovoltaic panel was selected and the dynamic and static output characteristics under typical weathers were studied. Then, a Simulink simulation model of photovoltaic power generation was built based on this to get the power-voltage(P-U) and current-voltage(I-U) curves under dynamic conditions. The change situation of output power extreme points under the mechanical oscillation angle and frequency of photovoltaic panels was studied. Results show that there will be multiple local extreme points in the P-U curve of photovoltaic panels under dynamic conditions and the panel oscillation angle has a greater impact on the power extreme points. It provides more visual data support for the research on maximum power point tracking algorithms of subsequent floating photovoltaic power generation. [ABSTRACT FROM AUTHOR]

A novel nonlinear backstepping controller based on direct current (DC) link voltage control is proposed in three-phase grid-connected solar photovoltaic (PV) systems to control the active and reactive power flow between the PV system and the grid with improved power quality in terms of pure sinusoidal current injection with lower total harmonic distortion (THD), as well as to ensure unity power factor, or to compensate for reactive power required by the load, i.e., the electrical grid. The output power of the PV array is supplied to the grid through a boost converter with maximum power point tracking (MPPT) control and an inverter. Simulation results of the proposed controller show good robustness under nominal conditions, parameter variations, and load disturbances, which presents the main advantage of this controller as compared to an existing controller. The performance of this work was evaluated using a MATLAB/Simulink environment. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) is considered one of the most important sources of energy in the world, owing to its inherent advantages of being cheap, environmental friendliness, and energy-efficient. To increase the output power of the solar PV system, it is most important to enforce it to operate at the highest possible maximum power point tracking (MPPT) algorithm lead an important role in optimization the performance of a photovoltaic (PV) generation systems. This paper compares two widely used MPPT algorithms, namely the Perturb and Observe (P&O) method and the Incremental Conductance (IC) method, known for their low cost and eases of realization. The algorithms were evaluated based on important parameters such as voltage, current, and power output for different combinations, with and without shading. The performance of the photovoltaic array was evaluated using the MATLAB Simulink toolbox. The simulation results demonstrate that the IC method can rapidly detect the maximum power point (MPP) when irradiance changes suddenly, with faster convergence and higher accuracy than the P&O algorithm. Simulations are performed to test the controller's capability oftracking the MPP when sudden variations in weather condition. [ABSTRACT FROM AUTHOR]

A symmetric multilevel inverter is designed and developed by implementing the modulation techniques for generating the higher output voltage amplitude with fifteen level output. Among these modulation techniques, the proposed SFI (Solar Fed Inverter) controlled with Sinusoidal-Pulse width modulation in experimental result and simulation of Digital-PWM results is verified under the lowest THD level. There are three intelligent techniques proposed in SFI, among these intelligent controllers the MPP (Maximum Power Point) based controller gives better results. An open loop, close loop control system is implemented for the different operating load conditions (R and RL load). In the proposed system the Solar-PV array using SPR305W is maintained constant power by implementing an MPP approach to the (DC–DC) Double-lift Converter. The DC–DC converters are fed with SFI inverter circuit. The proposed structure for analysis and implementation for simulated with MATLAB/Simulink (R2020a) software. Experimental setup is carried out with Field Programmable Gate Array based processor to generate the switching sequences based on the proposed methodologies. The performance of the SFI operating with load connected system is analysis with output voltage/current, reactive power, and minimized harmonics level. [ABSTRACT FROM AUTHOR]