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The application of active adhesion control to the traction control system of an electric train holds great appeal for maximizing longitudinal acceleration force. Most of the currently reported works regulate the adhesion between wheel and rail by adjusting the torque reference of a cascade motor drive controller, which suffers from slow speed response and excessive start torque. This article proposes a cascadefree predictive adhesion control strategy for electric trains powered by an interior permanent magnet synchronous motor (IPMSM) to address these issues. The proposed control scheme utilizes an improved perturbation and observation method to predict the time-varying wheel-rail adhesion state and determine the optimal slip speed. The initial setpoint reference command from the driver master is then adjusted to a dynamic reference that continuously adapts to the predicted adhesion conditions. Finally, the predictive speed control method is employed to ensure rapid convergence of the tracking objective. The simulation and hardware-in-the-loop testing results confirm that this approach achieves excellent dynamic performance, particularly during the train start-up phase and in the high-speed weak magnetic area of the IPMSM. [ABSTRACT FROM AUTHOR]

The integration of renewable energies, particularly photovoltaic energy, into green hydrogen production presents a highly promising prospect in the energy sector. Nonetheless, these energy sources face challenges due to their inherent instability and susceptibility to various atmospheric factors such as temperature and illumination. Therefore, it's imperative to tackle these challenges before renewable energy can be widely adopted as a primary source in hydrogen production. To address this, we propose constructing an autonomous photovoltaic system using MATLAB software. This system will employ a DC-DC boost converter to connect the PV array to the load. Furthermore, to enhance the efficiency of photovoltaic power generation, we will implement the perturbation and observation maximum power point tracking (MPPT) approach. The research endeavor extends towards integrating this optimized system with an electrolysers developed a sophisticated electrolyte model utilizing MATLAB Simulink software, paving the way for hydrogen gas production. [ABSTRACT FROM AUTHOR]

Partial shading conditions (PSCs) caused by uneven illumination become one of the most common problems in photovoltaic (PV) systems, which can make the PV power-voltage (P-V) characteristics curve show multipeaks. Traditional maximum power point tracking (MPPT) methods have shortcomings in tracking to the global maximum power point (GMPP), resulting in a dramatic decrease in output power. In order to solve the above problems, intelligent algorithms are used in MPPT. However, the existing intelligent algorithms have some disadvantages, such as slowconvergence speed and large search oscillation. Therefore, an improvedwhale algorithm (IWOA) combined with the P&O (IWOA-P&O) is proposed for the MPPT of PV power generation in this paper. Firstly, IWOA is used to track the range interval of the GMPP, and then P&O is used to accurately find theMPP in that interval. Compared with other algorithms, simulation results show that this method has an average tracking efficiency of 99.79% and an average tracking time of 0.16 s when tracking GMPP. Finally, experimental verification is conducted, and the results show that the proposed algorithmhas better MPPT performance compared to popular particle swarm optimization (PSO) and PSO-P&O algorithms. [ABSTRACT FROM AUTHOR]

A novel approach defined by the artificial neural network (ANN) model trained by the improved Gauss-Newton in conjunction with a simulated annealing technique is used to control a step-up converter. To elucidate the superiority of this innovative method and to show its high precision and speed in achieving the right value of the Maximum Power Point (MPP), a set of three comparative Maximum Power Point Tracker (MPPT) methods (Perturbation and observation, ANN and ANN associated with perturbation and observation) are exanimated judiciously. The behavior of these methods is observed and tested for a fixed temperature and irradiance. As a result, the proposed approach quickly tracks the right MPP = 18.59 W in just 0.04382 s. On the other hand, the outstanding ability of the suggested method is demonstrated by varying the irradiance values (200 W/m2, 300 W/m2, 700 W/m2, 1000 W/m2, 800 W/m2 and 400 W/m2) and by varying the temperature values (15℃, 35℃, 45℃ and 5℃). Therefore, the ANN trained by Gauss-Newton in conjunction with simulated annealing shows a high robustness and achieves the correct value of MPP for each value of irradiance with an efficiency 99.54% and for each value of temperature with an efficiency 99.98%; the three other methods sometimes struggle to achieve the right MPP for certain irradiance values and often remains stuck in its surroundings.

A novel approach defined by the artificial neural network (ANN) model trained by the improved Gauss-Newton in conjunction with a simulated annealing technique is used to control a step-up converter. To elucidate the superiority of this innovative method and to show its high precision and speed in achieving the right value of the Maximum Power Point (MPP), a set of three comparative Maximum Power Point Tracker (MPPT) methods (Perturbation and observation, ANN and ANN associated with perturbation and observation) are exanimated judiciously. The behavior of these methods is observed and tested for a fixed temperature and irradiance. As a result, the proposed approach quickly tracks the right MPP = 18.59 W in just 0.04382 s. On the other hand, the outstanding ability of the suggested method is demonstrated by varying the irradiance values (200 W/m2, 300 W/m2, 700 W/m2, 1000 W/m2, 800 W/m2 and 400 W/m2) and by varying the temperature values (15℃, 35℃, 45℃ and 5℃). Therefore, the ANN trained by Gauss-Newton in conjunction with simulated annealing shows a high robustness and achieves the correct value of MPP for each value of irradiance with an efficiency 99.54% and for each value of temperature with an efficiency 99.98%; the three other methods sometimes struggle to achieve the right MPP for certain irradiance values and often remains stuck in its surroundings. [ABSTRACT FROM AUTHOR]

Enhancing the performance of photovoltaic (PV) systems has recently become a key concern because of the market demand for green energy. To obtain the most possible power from the solar module, it is imperative to allow the PV system to operate at its maximum power point (MPP) regardless of the climatic conditions. In this study, a comparison of distinctive Maximum Power-Point Tracking (MPPT) techniques is provided, which are Perturb and Observe (P&O) and Modified Variable Step-Size P&O, as well as Incremental Conductance (INC) and Modified Variable Step-Size INC, using a boost converter for two types of solar panels. Using MATLAB software, simulations have been performed to assess the efficiency of the solar module under several environmental conditions, standard test conditions (STCs), and sudden and ramp variations in both solar irradiance and temperature. The output power efficiency, time response, and steady-state power oscillations have all been taken into account in this study. The simulation results of the improved algorithms demonstrate an enhancement in the PV module performance over conventional algorithms in many factors including steady-state conditions, tracking time, and converter efficiency. Furthermore, a boost in the dynamic response in monitoring the MPP is observed in a variety of climatical circumstances. Moreover, the proposed P&O MPPT algorithm is implemented in a hardware system and the experimental results verified the effectiveness, regarding both fast-tracking speed and lower oscillations, of the proposed Variable Step-Size P&O algorithm and its superiority over the conventional P&O technique. [ABSTRACT FROM AUTHOR]

This study proposes a novel maximum power point tracking (MPPT) control strategy for the solar photovoltaic power system (SPPS). The proposed system adopts two solar photovoltaic modules of 430 W, which are connected to a boost converter and an MPPT controller, since the traditional MPPT algorithm (such as perturbation and observation [P&O] algorithm) can hardly reach maximum power point (MPP) under low irradiance level and partial shading conditions (PSC), which leads to the low efficiency of the SPPS. The speed of light optical path difference measurement (SLOPDM) MPPT control strategy has been developed in this study to overcome this problem. The estimation of the optical path angle difference is used as the basis for the proposed control strategy. This is done by determining the relationship between the optical path angle difference, solar photovoltaic power impedance Rspv and load Ro, and then calculating the duty cycle corresponding to the MPP, which then drives the boost converter to capture the MPP. The experimental results verify the proposed system, which shows the efficiency comparison between the SLOPDM MPPT algorithm, solar angle and horizon (SAH) algorithm, and P&O algorithm under PSC and uniform irradiance conditions (UIC) at irradiance levels of 700 W/m2 and 65 W/m2. It is evident from the comparison that the efficiency of the SLOPDM MPPT algorithm is 99% under both conditions, which is higher than the SAH and P&O algorithms. The SLOPDM MPPT algorithm can precisely, rapidly, and stably be operated at MPP. The contribution of this study is that the proposed MPPT control strategy can help achieve the high−performance of SPPS without changing the hardware circuit design and requiring any additional solar power meter. This reduces the cost and the complexity of the system significantly. [ABSTRACT FROM AUTHOR]

This research aims to design a modified P&O algorithm for the efficient tracking of maximum power point (MPPT) for standalone and grid-connected systems. The proposed research work modifies the P&O algorithm for the dc-dc converter where the fixed step size P&O algorithm is translated into variable step size with the help of ant colony optimization (ACO) to generate optimal parameters for the PID controller to generate a variable step size in the P&O algorithm. This variable step size is dependent upon the error that is the difference between the generated power and desired power. By doing this it improves the efficiency of the P&O algorithm and its limitations are overcome. Furthermore, the PV is extended to connect with a grid where the inverter is controlled by a fuzzy logic controller (FLC) so that the combined structure of variable P&O and fuzzy helps to achieve MPP efficiently. The robustness of the proposed work is compared with other state-of-the-art controllers to justify the effectiveness of the proposed work. Finally, a stability test of the system is carried out to verify the overall stability of the power system. [ABSTRACT FROM AUTHOR]

Although the fixed-step Perturbation and Observation Maximum Power Point Tracking (P&O MPPT) is a commonly applied algorithm for wave power generation, its tracking performance is weakened in irregular waves. To overcome the disadvantage, this paper proposes a novel 2-D lookup table based (2DLTB) MPPT scheme. It utilizes a 2-D optimal-duty-cycle table and real-time wave data to tune the duty cycle of pulse-width-modulation (PWM) signal. Two tuning methods are introduced. The first one is called single-wave-period regulation (SWPR), which adjusts the duty cycle wave by wave. The second one named multiple-wave-period regulation (MWPR) tunes the duty cycle at intervals of multiple wave periods. Both 2DLTB MPPT and the fixed-step P&O MPPT are simulated and compared in different irregular waves generated from the real sea states. Results show that 2DLTB MPPT is insensitive to the number of waves. However, the fixed-step P&O MPPT is sensitive to both update duration and step size. Meanwhile, 2DLTB MPPT has the stronger power tracking performance than the fixed-step P&O MPPT. In addition, SWPR outperforms MWPR in the short time window, while MWPR performs better in the long time window. In general, as a simple, reliable and excellent MPPT algorithm, 2DLTB MPPT is worthy of promotion. [ABSTRACT FROM AUTHOR]

The output power of Photovoltaic (PV) arrays presents a nonlinear behavior. Its maximum power point varies with the cell's temperature and solar radiation. It is due to this situation that Maximum Power Point Tracking (MPPT) methods have been proposed and used in order to maximize the PV array output power. This paper presents an artificial neural network (ANN) combined with the classic Perturbation and Observation (P&O) algorithm to accelerate the search of such Maximum Power Point. Simulations generated using Matlab/Simulink show the improvement compared to the P&O alone and the hardware implementation, using a 16-bit microcontroller corroborates these findings.

Photovoltaic power generation system is easy to be affected by the environment and load changes, which will generate frequency and power fluctuations in the process of grid connection. The traditional PI control method is slow in response and poor in stability. To solve this problem, a new robust droop control design method is proposed in this study. An integral sliding mode controller is designed in the double closed - loop current inner loop control structure of bi - directional interconnected converter. The designed controller can not only suppress the voltage and frequency fluctuation of photovoltaic power generation system in the process of grid connection, but also ensure the stability of power system in different environmental conditions. The simulation results in MATLAB/Simulink show that the new robust droop control method is faster than the traditional PI control, and the fluctuation amplitude is reduced by nearly half, which ensures the continuous and stable operation of the grid after the photovoltaic power generation system is connected to the grid. [ABSTRACT FROM AUTHOR]

Solar energy is abundant and sustainable energy that photovoltaic (PV) cells can transform directly into electricity. Several conditions such as temperature and solar irradiance, influence the power production of PV systems. Maximum power point tracking (MPPT) methods have been developed and used in PV systems to improve productivity due to changing weather conditions. This paper addresses the modeling and simulation of MPPT of a grid-connected PV system with the aid of a fuzzy logic controller (FLC). FLC results compared with Perturb & Observe (P&O) algorithm and incremental conductance (IC). A simulation toolbox MATLAB Simulink was used to validate the findings experimentally. The performance of the suggested MPPT FLC indicates that the FLC reaches the optimum power point more effectively than traditional approaches. [ABSTRACT FROM AUTHOR]

A modified firefly algorithm (MFO)-based adaptive neuro-fuzzy inference system (ANFIS) combined with the perturbation and observation (P&O) is used in this paper to track the maximum power point (MPP) in photovoltaic systems (PVs). The proposed method identifies and tracks the MPP in two stages. First, according to the irradiance on the solar panels, the ANFIS approximately identifies the MPP. In the second stage, the P&O method starts to act in the tracking cycle and initiates an accurate searching process from that point. The suggested hybrid method covers the problems of commonly-used methods, such as inability in detecting the global MPP under partial shading conditions (PSCs) and trapping in the local optima. Furthermore, the method provides significantly higher speed for the MPP tracking under various irradiance patterns. To prove the above-mentioned claims, the given approach is compared with the P&O method as a common method in the MPPT and particle swarm optimisation (PSO) which operates based on swarm intelligence. Simulation results obtained from MATLAB/Simulink environment show that the proposed method identifies and tracks the MPP under uniform irradiance and PSCs in a very short time of roughly 0.2 s. [ABSTRACT FROM AUTHOR]

This paper presents an intelligent maximum power point tracker which combines the conventional perturbation and observation method and an artificial neural network. The proposed controller overcomes the drawback of the conventional perturbation and observation under partial shading conditions. A MATLAB model for a photovoltaic system is developed so as to simulate system's performance and characteristic under partial shading condition. This model includes models for PV array, DC–DC boost converter as well as the proposed algorithm. Results show that the proposed algorithm is able to track the global maxima accurately regardless the number of surrounding local maxima points with an efficiency of 95%. [ABSTRACT FROM AUTHOR]

Highlights • Considering renewable energy resources. • Proposing fuzzy logic controllers (FLC) to improve the MPPT in PV system. • Implementing fuzzy logic controller to improve the dynamic performance. • Reporting optimized using Firefly Algorithm (FA) to generate the proper duty cycle. Abstract P-V characteristic of the solar cells are nonlinear and depends on the environmental conditions such as irradiations, sunlight incident angle, cell temperature, and load conditions. Therefore it is crucial to operate the photovoltaic module at its maximum power point (MPP) all the time. Hence, a Maximum power point tracking (MPPT) methods are used to maximize the PV module output power by tracking continuously the MPP. In this paper, fuzzy logic controllers (FLC) are designed for maximum power point tracking (MPPT) in a photovoltaic system and then fuzzy membership functions of the fuzzy controller are optimized using Firefly Algorithm (FA) to generate the proper duty cycle. FA which is inspired by natural species to optimize nonlinear functions is one of the most successful and low-cost algorithms in this field. Finally, PV system with FLC-FA is compared with other methods like perturbation and observation (P&O) and fuzzy controller- Particle swarm optimization (PSO). According to the simulation results, asymmetric fuzzy membership functions based on FA increase tracking speed of MPPT and improve tracking accuracy compared to P&O, symmetric fuzzy membership functions and asymmetric fuzzy membership functions based on PSO. [ABSTRACT FROM AUTHOR]

• The working characteristics of compressors at different altitudes are established. • A real-time power optimization strategy based on the P&O algorithm was designed. • PSO feedforward-based P&O method is developed to adjust the air compressor voltage. • Results verify the advantages of the proposed strategy under variable altitude conditions. The control technology of the air supply system is key to the performance and reliability of the fuel cell system (FCS). However, due to the low oxygen pressure in high altitude environment, the power consumption of the air compressor is large, and the output power of FCS is seriously degraded. To effectively increase the output power of the FCS under changeable altitude conditions, this article introduces a real-time power optimization strategy based on the particle swarm optimization (PSO) feedforward and perturbation and observation (P&O) algorithm. Considering the change of total entropy and loss of centrifugal air compressor with altitude, the working characteristics of centrifugal compressors at different altitudes are studied. Aiming at the long optimization time of the traditional P&O method and the misjudgment problem when the external conditions change drastically, the PSO feedforward-based P&O method is designed to adjust the air compressor voltage. The results show that the proposed strategy has real-time power optimization capability and can adapt to altitude change. The average setting time is 1.8 s when the operating condition changes and the power decreases to 9.1% at 3000 m altitude under a higher load (300 A). [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) techniques are widely used in daily life. In addition to the material characteristics and environmental conditions, maximum power point tracking (MPPT) techniques are an efficient means to maximize the output power and improve the utilization of solar power. However, the conventional fixed step size perturbation and observation (P&O) algorithm results in perturbations and power loss around the maximum power point in steady-state operation. To reduce the power loss in steady-state operation and improve the response speed of MPPT, this study proposes a self-adaptable step size P&O-based MPPT algorithm with infinitesimal perturbations. This algorithm combines four techniques to upgrade the response speed and reduce the power loss: (1) system operation state determination, (2) perturbation direction decision, (3) adaptable step size, and 4) natural oscillation control. The simulation results validate the proposed algorithm and illustrate its performances in operational procedures. [ABSTRACT FROM AUTHOR]

This paper elaborates a comprehensive overview of a photovoltaic (PV) system model, and compares the attributes of various conventional and improved incremental conductance algorithms, perturbation and observation techniques, and other maximum power point tracking (MPPT) algorithms in normal and partial shading conditions. Performance evaluation techniques are discussed on the basis of the dynamic parameters of the PV system. Following a discussion of the MPPT algorithms in each category, a table is drawn to summarize their key specifications. In the performance evaluation section, the appropriate PV module technologies, atmospheric effects on PV panels, design complexity, and number of sensors and internal parameters of the PV system are outlined. In the last phase, a comparative table presents performance-evaluating parameters of MPPT design criterion. This paper is organized in such a way that future researchers and engineers can select an appropriate MPPT scheme without complication. [ABSTRACT FROM AUTHOR]

The maximum power tracking of Proton Exchange Membrane Fuel Cells (PEMFC) is important for the optimization of fuel cell system design. It is necessary to operate a fuel cell at maximum power to ensure full efficiency. This study presents a novel Fractional Order Incremental Conductance Algorithm (FOINC) with variable step size control which can be used for maximum power point tracking in the design of fuel cells. The method has high maximum power point tracking speed and good steady-state response, and does not require extra sensing elements for different fuel cell equipment. When compared to the traditional Incremental Conductance (INC) and Perturbation and Observation (P&O) methods, the system simulation results show the method to be feasible and effective. [ABSTRACT FROM AUTHOR]

In this paper a new maximum power point tracking method based on fuzzy sliding mode control is proposed, and employed in a PV water pumping system based on a DC-DC boost converter, to produce maximum power from the solar panel hence more speed in the DC motor and more water quantity. This method combines two different tracking techniques sliding mode control and fuzzy logic; our controller is based on sliding mode control, then to give better stability and enhance the power production a fuzzy logic technique was added. System modeling, sliding method definition and the new control method presentation are represented in this paper. The results of the simulation that are compared to both sliding mode controller and perturbation and observation method demonstrate effectiveness and robustness of the proposed controller. [ABSTRACT FROM AUTHOR]

The output power of photovoltaic generation (PVG) varies with solar irradiance and temperature nonlinearly. Therefore, the maximum power point tracking (MPPT) is very important to operate PVG to harvest the maximum solar energy in real time. The perturbation and observation (P&O) method is one of the most commonly used MPPT methods. However, P&O MPPT suffers a tradeoff in selecting the perturbation step sizes of the control variables. This paper proposes a parameter-estimation-based MPPT for PVG. Based on the measured voltages and currents and the characteristic output function of PVG, the proposed MPPT uses parameter estimation to calculate the solar irradiance and temperature directly. The operating voltage of maximum power point can then be found out immediately. The proposed MPPT can effectively reduce the maximum power tracking time of conventional MPPT methods and increase the harvested energy during MPPT. Simulation and experimental results comparing the proposed MPPT to a P&O MPPT demonstrate the performance achieved by the proposed MPPT. [ABSTRACT FROM PUBLISHER]

Due to the nonlinear P-V characteristic of fuel cells, it is very important to implement a maximum power point tracking (MPPT) control for practical applications, because it maximizes the power output from a fuel cell system for a given set of conditions, and therefore maximizes their out power efficiency. This paper presents a fuzzy logic control (FLC) method for the maximum power point tracking (MPPT) of a fuel cell system under variable temperature and water content conditions. At first, the model of fuel cell, the perturbation and observation (P&O) technique is briefly introduced. A fuzzy logic control based MPPT is then proposed and is compared to the P&O MPPT based approach. The simulation results demonstrate the stability of the proposed control system and its robustness to operational temperature, water content variation. [ABSTRACT FROM AUTHOR]

Recently, the importance of exploring the plausibility of renewable energy has been progressively increased, not only because of concerns over the shortage of current fossil fuels but also the consideration of sustainable development and the negative environmental impact caused by large scale use of fossil fuels. Among renewable sources, solar energy seems to be one of the promising energy sources for widespread application. Due to its inherent intermittency and fluctuation, one of the important research interests is to harness the maximum power possible from the solar energy falling on a panel. To this end, an efficient maximum power point tracker to harvest as much energy as possible is a key to improving the system’s efficiency and performance. This paper presents a novel hybrid maximum power point tracking mechanism with adaptive perturbation size. The proposed method is implemented, analyzed, and evaluated in MATLAB/Simulink. Both numerical and experimental evaluation results prove that by using the proposed method, better tracking performance can be achieved and the power delivered at steady state can be increased by a factor of 7.31% compared with conventional methods. [ABSTRACT FROM PUBLISHER]

Abstract: Solar panels exhibit non-linear current–voltage characteristics producing maximum power at only one particular operating point. The maximum power point changes with temperature and light intensity variations. Different methods have been introduced for tracking the maximum power point based on offline and online methods. In this paper a new method is presented to improve the performance of maximum power point tracking in solar panels. The proposed algorithm is a combination of two loops, set point calculation and fine tuning loops. First the set point loop approximates the maximum power using offline calculation of the open circuit voltage. The exact amount of the maximum power will, then, be tracked by the fine tuning loop which is based on perturbation and observation (P&O) method. The proposed method is simulated in Matlab/Simulink environment and experimentally verified using a laboratory prototype. In maximum power point tracking, the effects of frequency variation and disturbance amplitude on dynamic response and steady state performance are examined. Simulation and experimental results are compared with other methods and the effectiveness of the proposed method is evaluated. [Copyright &y& Elsevier]

Abstract: Perturbation and observation (P&O) maximum power point tracking (MPPT) algorithms are common in photovoltaic (PV) systems due to its ease of implementation. However, operation with fixed size perturbations results in a trade-off between speed of response and maximum power yield in the steady-state. This paper discusses the use of Fuzzy logic and non-switching zone schemes for implementing variable size perturbations for improved transient and steady-state responses. The potential performance of four different P&O algorithms is investigated by means of simulations. Experimental results are then used to verify how the computational burden of each algorithm and the processing speed of a digital signal processor (DSP) affect the performance of each method in a prototype. The best performance is achieved with a new strategy called non-switching zones in the V PV × I PV plane. The power electronics converter operates with duty cycle (D) equal to 0 or 1, depending on which non-MPP region the system operates, pushing the operating point the fastest way possible towards the MPP region, for improved transient response. At the MPP region, a reduced Fuzzy P&O MPPT algorithm optimized for small variations around the MPP is used for reduced oscillations and increased power yield in the steady-state. [Copyright &y& Elsevier]

Maximum power point tracking (MPPT) techniques are usually used for solar power applications. This paper discusses: (1) various connection methods between solar arrays and loads and (2) various maximum power point control methods and MPPT algorithms. In this paper, the solar array is treated as a current source instead of a voltage source. Analytical models are built for the solar array and converter on the basis of the data sheet of the manufacturer and the principle of energy conservation. An improved MPPT algorithm was proposed to reduce the power loss in the tracking process. A digital signal processor (DSP) based controller was constructed to implement the proposed MPPT control, and the experimental results are presented. [Copyright &y& Elsevier]