Maximum Power Point Tracking Speed Research Articles

An MPPT Control Strategy Based on Current Constraint Relationships for a Photovoltaic System with a Battery or Supercapacitor

When the battery or supercapacitor is connected to the output of a PV system, the conventional voltage equation expressing its mathematical model usually must be replaced by the current relationship to study the maximum power point tracking (MPPT) control theory. However, hitherto, there is a lack of an attempt to disclose the current constraint relationships at the maximum power point (MPP), which leads to the potential risk of MPPT failure. To solve this problem, in this paper, the MPPT constraint conditions on the basis of currents are built and then a new MPPT control strategy is proposed. In this strategy, a linearized model parameter of a PV cell is used as the bridge to find the current relationships. On the basis of them, some expressions involving the duty cycle are built to directly calculate the control signal of the MPPT controller. Meanwhile, an implementation method is designed to match this proposed MPPT strategy. Finally, some simulation experiments are conducted. The simulation results verify that the proposed MPPT constraint expressions are accurate and workable and that the proposed MPPT strategy and its implementation process are feasible and available. In addition, the simulation results also show that the proposed MPPT strategy has a better MPPT speed and the same MPPT accuracy when the P&O method and fuzzy algorithm are compared. By this work, the MPPT constraint conditions based on current relationships are first found, representing a breakthrough in disclosing the inherent relationships between different currents when the PV system is operating around the MPP.

Machine learning based dynamic super twisting sliding mode controller for increase speed and accuracy of MPPT using real-time data under PSCs

Photovoltaic (PV) systems are created according to the series, parallel or series-parallel connection type of PV panels. Solar panels that form the PV system can have different irradiance values at different times of the day. This condition, which is called partial shading, causes the current and voltage values produced by the panels to fluctuate. Maximum power point tracking (MPPT) algorithms are used to ensure that PV systems operate at maximum power under all environmental conditions. MPPT algorithms are used to optimize the duty cycle of DC-DC converters in order to transfer maximum power to the load. In this study, a two-stage structure is used for MPPT. In the first stage, a reference voltage value is generated using the gaussian process regression (GPR) machine learning algorithm. In the second stage, the duty cycle of the PWM signal required for MPPT is optimized using the super twisting sliding mode controller (STSMC). The performance of the proposed method for three different scenarios is compared with the cuckoo search algorithm (CSA) from metaheuristic optimization algorithms and the perturb and observe (P&O) algorithm from classical optimization algorithms using real-time data. The software used to analyze and compare the researched methods is MATLAB/Simulink R2021b. The proposed method has performed better compared to both CSA and P&O for four conducted scenarios. Additionally, it was observed that the proposed method leads to less oscillation at the MPP point compared to P&O and CSA.

An Integral-Backstepping Controller for Interleaved Boost Converter based on Photovoltaic Systems

Photovoltaic systems are one of the most widely used technologies for sustainable energy production due to their various advantages, such as being clean, free, and renewable energy sources. However, the power generated by the PV systems is strongly dependent on the atmospheric conditions. In addition, a suitable DC/DC converter must be used with a robust controller to facilitate system operation point changes associated with the changed climate conditions. In this paper, to deal with this challenge, a photovoltaic system based on four leg interleaved boost converter (FLIBC) has been proposed and studied. For robustness, the presented system is controlled using nonlinear integral backstepping control (IBSC) to track the maximum power point (MPP) and ensure an equal sharing current among the FLIBC legs. Moreover, the particle swarm optimization algorithm is used to find the optimum gains of the proposed IBSC. The entire system is simulated and validated using MATLAB/ Simulink environment. The simulation results demonstrate the efficiency of the proposed system based on the optimized IBSC (OIBSC) controller in terms of MPP tracking speed, overshoot and undershoot reduction, power ripple, and optimization of the performance term integral time square error (ITSE).

Jellyfish search algorithm based optimal thermoelectric generation array reconfiguration under non-uniform temperature distribution condition

Thermoelectric generators (TEGs) can convert heat to electricity based on the temperature difference between their hot and cold sides. However, during practical operations, low thermoelectric conversion efficiency is usually caused by non-uniform temperature difference (NTD) conditions; thus, reliable and effective maximum power point tracking (MPPT) under NTD conditions is imperative for TEG arrays. Therefore, in this study, a jellyfish search (JS) algorithm-based TEG array reconfiguration strategy is proposed to realise MPPT under NTD conditions, upon which the overall power loss is effectively decreased, the energy conversion efficiency is efficiently enhanced, and the MPPT speed is improved. In particular, the simulation results demonstrate that the JS algorithm-based TEG array reconfiguration using a random pattern can enhance the maximum output power by 23.32% for 9 × 9 symmetric TEG arrays and 15.78% for 10 × 15 asymmetric TEG arrays. Finally, a hardware-in-the-loop (HIL) test was conducted on the RTLAB platform to verify its feasibility for hardware implementation.

Improvement of Voltage Collapse of MPPT Single-stage Photovoltaic Grid-connected based on Conductance Increment Method

In order to make the supply of resources meet the rapidly developing world situation, it is of great practical significance to further develop and apply renewable resources. PV power system has many advantages, such as cleanness, environmental protection, high efficiency, etc., and shows great application potential in the field of new energy. At present, solar cells are easily affected by external factors such as light intensity, temperature, etc., and can't work at the maximum power point continuously and stably, resulting in low power generation efficiency. Therefore, the introduction of Maximum power point tracking (MPPT) technology is particularly important. In this paper, a variable step conductance increment method is proposed, which can identify the current distance from the maximum power point by observing algorithm, change the tracking step and quickly identify the maximum power point, so as to solve the voltage collapse problem of single-stage photovoltaic grid-connection. Simulation results show that the new algorithm can optimize both dynamic response and steady-state oscillation, and improve the accuracy and speed of MPPT.

The Performance of a New Heuristic Approach for Tracking Maximum Power of PV Systems

This paper presents a new heuristic method for maximum power point tracking (MPPT) in PV systems under normal and shadowing situations. The proposed method is a modification of the original queen honey bee migration (QHBM) to shorten the computation time for the maximum power point (MPP) in PV systems. QHBM initially uses random target locations to search for targets, in this case, MPP. So, we adjusted it to be able to do MPP point quests quickly. We accelerated the mQHBM learning process from the original randomly. We had fairly compared the mQHBM with several heuristics. Simulations were carried out with 2 scenarios to test the mQHBM. Based on the simulation results, it was found that mQHBM was able to exceed the capabilities of other methods such as original QHBM, particle swarm optimization (PSO) and perturb and observe (P&O), ANN, gray wolf (GWO), and cuckoo search (CS) in terms of MPPT speed and overshoot. However, the accuracy of mQHBM cannot exceed QHBM, ANN, and GWO. But still, mQHBM is better than PSO and P&O by about 15% and 18%, respectively. This experiment resulted in a gap of about 2% faster in speed, 0.34 seconds better in convergence time, and 0.2 fewer accuracies.

Performance Optimization of a Ten Check MPPT Algorithm for an Off-Grid Solar Photovoltaic System

In order to operate a solar photovoltaic (PV) system at its maximum power point (MPP) under numerous weather conditions, it is necessary to achieve uninterrupted optimal power production and to minimize energy losses, energy generation cost, and payback time. Under partial shading conditions (PSC), the formation of multiple peaks in the power voltage characteristic curve of a PV cell puzzles conventional MPP tracking (MPPT) algorithms trying to identify the global MPP (GMPP). Meanwhile, soft-computing MPPT algorithms can identify the GMPP even under PSC. Drawbacks such as structural complexity, computational complexity, huge memory requirements, and difficult implementation all affect the viability of soft-computing algorithms. However, those drawbacks have been successfully overcome with a novel ten check algorithm (TCA). To improve the performance of the TCA in terms of MPPT speed and efficiency, a novel concept of data arrangement is introduced in this paper. The proposed structure is referred to as Optimized TCA (OTCA). A comparison of the proposed OTCA and classic TCA algorithms was conducted for standard benchmarks. The results proved the superiority of the OTCA algorithm compared to both TCA and flower pollination (FPA) algorithms. The major advantage of OTCA in MPPT stems from its speed as compared to TCA and FPA, with almost 86% and 90% improvement, respectively.

Energy Storage System Based on Low Power Photovoltaic Power Generation

The IU and PU characteristic curves of PCs output in photovoltaic generation (PVG) were analyzed according to the working principle of photovoltaic cells (PCs) in the study, so as to provide a theoretical basis for the further improvement of the maximum power point tracking (MPPT) algorithm. The three-stage charging method was used to charge the battery to improve the energy conversion efficiency in the charging process of the system. The variable step incremental conductance algorithm (ICA) was proposed, which can effectively increase the speed of MPPT in the system and enhance its stability. By setting up a test platform, verification experiments of the tracking algorithm were performed on sunny (higher light intensity), cloudy (medium light intensity), and rainy (lower light intensity) days. The results showed that the adoption of MPPT algorithm can effectively increase the output power (OP) of PCs, the OP in sunny days was higher than in cloudy days and OP in cloudy days was higher than in rainy days compared with the system without MPPT algorithm. Compared with the traditional incremental conductance algorithm, the output power of the PCs using the MPPT algorithm improved by the incremental conductance algorithm had increased by 5.86%. The feasibility and effectiveness of the energy storage system in the study were verified.

A variable-weather-parameter MPPT method based on a defined characteristic resistance of photovoltaic cell

A characteristic resistance of photovoltaic (PV) cell is defined in this paper to find the mathematical equation of the control signal at the maximum power point (MPP) and to obtain the maximum power point tracking (MPPT) constraint conditions. Based on it, a variable-weather-parameter (VWP) MPPT method is proposed. The major purpose is to greatly improve the MPPT speed and make the design of PV system more convenient. By this method, the real-time value of the control signal at the MPP can be directly calculated. Meanwhile, the topology of PV system can be conveniently selected by the proposed MPPT constraint conditions. Finally, some simulations whose models are built by MATLAB software are done. Results show that the proposed MPPT constraint conditions is accurate regardless of the solar irradiance or temperature, verify that the proposed method is feasible, available and accurate to track the MPP successfully, and illustrate that there is a better MPPT performance than the perturbation and observation (P&O) method under fast changing weather conditions.

Recent Nature Inspired Techniques for Solar Power Point Tracking Application

Photovoltaic (PV) array generates non-linear I-V and P-V characteristics. As a result, it is difficult to transfer the maximum power from source to load. The Maximum Power Point Tracking (MPPT) is used to track the Maximum Power Point (MPP) and extract the maximum power of solar PV. The disadvantages of conventional MPPT techniques are less MPP tracking speed, high MPP settling time and less accuracy. In order to overcome the disadvantages of conventional techniques, in this article different types of recent nature inspired optimization techniques are reviewed to track the MPP. In addition, the application of optimization technique based Maximum Power Point Tracking (MPPT) technique for standalone and grid connected application is explained in detail. The advantages of optimization techniques are highly reliable, better accuracy, less time to convergence and fast response.

Real-Time MPPT Optimization of PV Systems by Means of DCD-RLS Based Identification

Maximum power point tracking (MPPT) algorithms continuously change duty cycle of a power converter to extract maximum power from photovoltaic panels. In all of MPPT methods, two parameters, i.e., perturbation period $(T_{p})$ and amplitude ${(\Delta \text{D)}}$ , have a great effect on speed and accuracy of MPPT. Optimum value of the perturbation period is equal to the system settling time, which is the system model-dependent parameter. Since the system model varies according to the change of irradiance level and temperature, the value of $T_{p}$ has to be determined online. In this paper, the parametric identification method is adopted to identify the online value of $T_{p}$ . The proposed method is based on the dichotomous coordinate descent-recursive least squares algorithm and uses an infinite impulse response adaptive filter as the system model. Computation of this algorithm is based on an efficient, fixed-point, and iterative approach with no explicit division operations; these features are highly suitable for online applications. As a result, the proposed method compared to previous works leads to more accurate and faster identification of the system settling time. To test and validate the proposed method, it has been simulated and implemented to be further validated with experimental data.

A MPPT speed optimization strategy for photovolatic system using VWP interval based on weather forecast

For most of existing maximum power point tracking (MPPT) methods, it is very difficult to find a common optimization strategy to improve their tracking speed. To solve this question, in this text, a variable-weather-parameter (VWP) interval based on weather forecast (WF-VWP interval), whose lower and upper bounds can change with varying irradiance, temperature and load in real time, is proposed. By the use of it, the irradiance and temperature sensors can be omitted to ensure the low hardware cost. Based on WF-VWP interval, a MPPT optimization strategy, which can implement the complementary advantages between optimized MPPT method and VWP technique, is proposed. Here, the control signal of photovoltaic (PV) system can be always compressed within the WF-VWP interval to guarantee the MPPT rapidity. Finally, some simulation experiments verify that the proposed WF-VWP interval is feasible, available and reasonable regardless of varying irradiance or temperature, illustrate that there is a reasonable width of the WF-VWP interval regardless of varying irradiance, temperature or error band, and show the MPPT rapidity of the conventional P&O method can be greatly improved by the proposed optimization strategy.

Design of an optimal fuzzy controller to obtain maximum power in solar power generation system

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.

Simulation and Analysis of Perturbation and Observation-Based Self-Adaptable Step Size Maximum Power Point Tracking Strategy with Low Power Loss for Photovoltaics

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.

MPPT Method for PV Systems Under Partially Shaded Conditions by Approximating I–V Curve

Partially shaded (PS) photovoltaic (PV) arrays have multiple peaks at their P–V characteristic. Although conventional maximum power point tracking (MPPT) algorithms are successful when PV arrays are under uniform irradiance conditions (UICs), their tracking speeds are low and may fail to track global maximum power point (GMPP) for PS arrays. Several MPPT algorithms have been proposed for PS arrays. Most of them require numerous samplings which decreases MPPT speed and increases energy loss. The proposed method in this paper gets the GMPP deterministically and very fast. It intelligently takes some samples from the array's P–V curve and divides the search voltage range into small subregions. Then, it approximates the I–V curve of each subregion with a simple curve, and accordingly estimates an upper limit for the array power in that subregion. Next, by comparing the measured real power values with the estimated upper limits, the search region of GMPP is limited, and based on some defined criteria, the vicinity of GMPP is determined. Simulation and experimental results and comparisons are presented to highlight the performance and superiority of the proposed approach.

A Hybrid MPPT method for grid connected photovoltaic systems under rapidly changing atmospheric conditions

The modest changes in operating current and voltage of photovoltaic (PV) panel due to the temperature and radiation fluctuation constitute visible variations in the output power. In this paper, a hybrid method to optimize the performance of the maximum power point tracking (MPPT) controller for mitigating these variations and forcing the system to operate on maximum power point (MPP) is developed. The presented Hybrid MPPT method consists of two loops: (i) artificial neural network (ANN) based reference point setting loop and (ii) perturbation and observation (P&O) based fine tuning loop. To assess robustness of the proposed method, a comparison is performed using the conventional P&O, incremental conductance (INC) and ANN based MPPT methods under both rapidly changing radiation and partially shaded conditions by using PSCAD/EMTDC program. The results obtained from the test cases explicitly demonstrate that the presented MPPT method not only achieves an increase in speed of MPP tracking, but also reduces the steady state oscillations and prevents the possibility of the algorithm from confusing its perturbation direction. The system efficiency more than 98.26%, 120ms improvement in convergence speed and 1.16V decrease in the rate of overshoot are obtained with proposed Hybrid MPPT method under the rapidly changing environmental conditions.

MPPT Control Strategy of PV Based on Improved Shuffled Frog Leaping Algorithm under Complex Environments

This work presents a maximum power point tracking (MPPT) based on the particle swarm optimization (PSO) improved shuffled frog leaping algorithm (PSFLA). The swarm intelligence algorithm (SIA) has vast computing ability. The MPPT control strategies of PV array based on SIA are attracting considerable interests. Firstly, the PSFLA was proposed by adding the inertia weight factor w of PSO in standard SFLA to overcome the defect of falling into the partial optimal solutions and slow convergence speed. The proposed PSFLA algorithm increased calculation speed and excellent global search capability of MPPT. Then, the PSFLA was applied to MPPT to solve the multiple extreme point problems of nonlinear optimization. Secondly, for the problems of MPPT under complex environments, a new MPPT strategy of the PSFLA combined with recursive least square filtering was proposed to overcome the measurement noise effects on MPPT accuracy. Finally, the simulation comparisons between PSFLA and SFLA algorithm were developed. The experiment and comparison between PSLFA and PSO algorithm under complex environment were executed. The simulation and experiment results indicate that the proposed MPPT control strategy based on PSFLA can suppress the measurement noise effects effectively and improve the PV array efficiency.

Geometric maximum power point tracking and sliding mode control of a bidirectional grid connected single phase two‐stage photovoltaic system with DC loads

This study proposes a new geometric maximum power point tracking (MPPT) method for photovoltaic (PV) power system. The reference point can be calculated and updated in real time directly with the proposed method. The reference point will converge to the real maximum power point (MPP) when approaching MPP. This can improve the MPPT speed comparing with the commonly used step-by-step optimisation. The MPPT method is applied to a DC bus coupled grid-connected PV system with the presence of DC loads, and an adaptive sliding mode controller is designed for the whole system. The proposed control scheme performs the MPPT and the power allocation between the PV array and the grid adaptively. With adaptive method, the converter can control the power flow bi-directionally to balance the power supply and consumption under a unity power factor. Simulation and experimental results are reported to confirm the proposed approach.

A variable-weather-parameter optimization strategy to optimize the maximum power point tracking speed of photovoltaic system

To improve the tracking speed of maximum power point (MPP) of photovoltaic (PV) system, some maximum power point tracking (MPPT) algorithms or their improved algorithms have been studied in depth. However, these algorithms are usually the improvement for a certain MPPT method, now there still exists no common method for improving the rapidity of the MPP tracking. To solve this question, in this paper, the conceptions of load-variable-weather-parameter (RVWP) optimization interval and variable-weather-parameter (VWP) optimization interval are proposed. Meanwhile, on the basis of them, a VWP optimization strategy, which can optimize the tracking speed of most MPPT algorithms, is proposed. In this strategy, the control signal of PV system has been quickly contracted within RVWP interval or VWP interval before a MPPT method which will implement the MPP tracking is operated. Therefore, the essence of this strategy is that the MPP tracking speed is increased by contracting the optimizing range—in other words, the global optimization is converted into local optimization. Finally, in this paper, the conventional perturb and observe (P&O) method is selected as the optimized object to study the MPP tracking speed under constant load, changing load, constant weather and changing weather conditions. The results of simulated experiments show that the MPP tracking speed can be greatly optimized by VWP optimization strategy with RVWP interval in any case and can be optimized by VWP optimization strategy with VWP interval in most cases, and that the output power stability can be also optimized by proposed optimization strategy for PV system with output power oscillating.

Output Feedback control of wind energy conversion system with hybrid excitation synchronous generator

This paper deals with the problem of wind energy conversion systems control. The considered aero-generator involves a hybrid excitation synchronous generator (HESG). To extract the maximum power from wind energy system, it is recommended to operate at variable speed. However, this control requires the measure of wind speed, rotor speed and aerodynamics torque, which are both difficult in practice and not very reliable. This paper presents a design of sensorless maximum power point tracking (MPPT) speed controller for achievement of the above energetic goal without resorting to mechanicals sensors (wind velocity, HESG rotor speed and aerodynamic torque). To this end, the control observation strategy is developed, based on the nonlinear model of the whole controlled system and only using electrical variables measurements. The control strategy involves: (i) online reference-speed optimizer designed using the turbine power characteristic to meet the maximum power point tracking requirement. (ii) an interconnected state observer providing online estimates of stator resistance, rotor permanent flux, rotor speed, aerodynamic torque and wind speed. (iii) a nonlinear MPPT speed regulator designed (using the backstepping technique) for the case where the wind speed remains lower than a maximum level (denoted wb). (iv) an optimization of the electromagnetic torque provided (by controlling the machine timing angle ()) to improvement the HESG efficiency. The performances of the proposed regulator are analyzed using tools from the Lyapunov stability. These theoretical results are validated by a series of simulations to show the contribution of hybrid excitation with a wide variation of the wind speed.