Fast Tracking Speed Research Articles

Research on Control Strategy of PMSG-PWM Power Generation System with Tidal Energy

Aiming at the characteristics of machine-side output instability and the wide-range variation in the tidal energy PMSG-PWM power generation system, we propose a control scheme with a high anti-interference capability applied to the PWM rectifier. The outer voltage loop adopts sliding mode control (SMC) with the variable exponential convergence law based on expanded state observer (ESO). By compensating the perturbation observation value into the sliding mode controller in advance, it improves the robustness and response speed of the system and suppresses the direct current (DC)-side voltage jitter phenomenon. The current inner loop combines model prediction (MP) with direct power control (DPC). By introducing time-delay compensation, it improves the control accuracy, reduces the machine-side harmonic content, and enables the system to maintain unit power factor operation despite external disturbances. Simulation and experiment results show that, under the wide range of PMSG output, the PMSG-PWM power generation system adopting the “ESO_SMC+MDPDC” scheme has a fast dynamic regulation of DC output voltage, obvious vibration suppression effect, low harmonic content of the current on the PMSG side, and fast current tracking speed, which verify the feasibility of applying this system to the field of tidal energy generation.

Optimized Perturb and Observe Techniques for Sudden Irradiance and Temperature Change

Abstract: Solar Power generation plays vital role in RES due to its natural abundance and environment friendly. The efficiency of the PV System depends on the more optimized MPPT Technique. Several methods for extracting GMPP are discussed that works better on steady state oscillations and fast tracking time. In this paper all recent techniques for MPPT are discussed and more efficient algorithm and simple in approach is revealed. This paper tries to find more efficient algorithm with fast tracking speed and optimized duty cycle.

Target Voltage Iterations Based Global Flexible Power Point Tracking Algorithm Under Partial Shading Photovoltaic Systems

Global flexible power point tracking (GFPPT) is proposed to realize the flexible power point tracking (FPPT) of photovoltaic systems under partial shading conditions (PSC). Because the working process of GFPPT is similar to global power point tracking (GMPPT), the performance of GFPPT can be improved by applying the existing advanced GMPPT algorithm. However, the improvement of GFPPT is limited while applying the existing advanced GMPPT algorithm directly. So, a target-voltage-iterations (TVI) based GFPPT algorithm is proposed in this paper to further increase the performance of GFPPT during PSC. Compared with the advanced GFPPT algorithms, the proposed TVI-based GFPPT algorithm shows the advantages of fast-tracking speed and little energy difference during the tracking by skipping the voltage section without the reference power. Followed by theoretical analysis, the simulation and experimental evaluation validate the claimed advantages of the proposed TVI-based GFPPT algorithm.

Prescribed performance sliding mode control for the bursting oscillation of a fractional-order hydro-turbine governing system based on time-varying tangent barrier Lyapunov function

A dynamic model for a hydro-turbine governing system is established that accounts for the periodic excitation caused by the dynamic transfer coefficient. The dynamic analysis shows that the turbine regulation system produces bursting oscillatory behaviors when its transmission coefficient changes. The bursting oscillation behavior can adversely affect its stable operation. In response to this challenge, a prescribed performance sliding mode controller based on the time-varying tangent barrier Lyapunov function is proposed. Specifically, a time-varying constraint function of the exponential decay type is initially established. This function is then combined with the prescribed performance constraint function and the dynamic sliding surface to construct the time-varying tangent barrier Lyapunov function. In addition, to ensure that the system output exhibits a slight overshoot and fast-tracking speed at the initial time while satisfying the steady-state index, the relevant parameters of the time-varying constraint function are presented. Finally, the stability of the proposed controller is rigorously proven, verifying the effectiveness of the controller under five working conditions. The simulation results demonstrate that the prescribed performance sliding mode control based on the time-varying tangent barrier Lyapunov function can enable the hydro-turbine governing system to quickly break away from the bursting oscillation state. The proposed control strategy achieves faster convergence times for the rotor angle and generator speed (0.3 s) as well as the guide vane opening (0.3 s) than the ordinary sliding mode controller. The proposed strategy is also superior in controlling the steady-state error, which means that the proposed controller has a faster response speed, higher control accuracy, and optimized transient performance of the system than the traditional controller.

A New MPPT-Based Extended Grey Wolf Optimizer for Stand-Alone PV System: A Performance Evaluation versus Four Smart MPPT Techniques in Diverse Scenarios

Photovoltaic (PV) systems play a crucial role in clean energy systems. Effective maximum power point tracking (MPPT) techniques are essential to optimize their performance. However, conventional MPPT methods exhibit limitations and challenges in real-world scenarios characterized by rapidly changing environmental factors and various operating conditions. To address these challenges, this paper presents a performance evaluation of a novel extended grey wolf optimizer (EGWO). The EGWO has been meticulously designed in order to improve the efficiency of PV systems by rapidly tracking and maintaining the maximum power point (MPP). In this study, a comparison is made between the EGWO and other prominent MPPT techniques, including the grey wolf optimizer (GWO), equilibrium optimization algorithm (EOA), particle swarm optimization (PSO) and sin cos algorithm (SCA) techniques. To evaluate these MPPT methods, a model of a PV module integrated with a DC/DC boost converter is employed, and simulations are conducted using Simulink-MATLAB software under standard test conditions (STC) and various environmental conditions. In particular, the results demonstrate that the novel EGWO outperforms the GWO, EOA, PSO and SCA techniques and shows fast tracking speed, superior dynamic response, high robustness and minimal power fluctuations across both STC and variable conditions. Thus, a power fluctuation of 0.09 W could be achieved by using the proposed EGWO technique. Finally, according to these results, the proposed approach can offer an improvement in energy consumption. These findings underscore the potential benefits of employing the novel MPPT EGWO to enhance the efficiency and performance of MPPT in PV systems. Further exploration of this intelligent technique could lead to significant advancements in optimizing PV system performance, making it a promising option for real-world applications.

MPPT study of PV cells in complex environments

In operation, the solar power generating system’s output power from the photovoltaic array is sensitive to external conditions including light intensity and temperature, and displays typical non-linear characteristics. Hence, monitoring the maximum output power of PV cells in real time is necessary to guarantee that the maximum power output of PV cells is constantly maintained, enhancing the efficiency with which PV cells are used. Therefore, this paper is based on the immune algorithm, simulates that the biological immune system will be affected by the antigen infestation and produce resistance to carry out antigen recognition, memory, and other immune response reactions through MATLAB / Simulink simulation, and adopts the traditional perturbation observation method. The results of the simulation show that the Composite algorithm has the benefits of high tracking accuracy, fast tracking speed, stable tracking, and low false positives.

Robot joint module based on universal joint configuration driven by SMA wires

Modular robots can adapt to different production needs and working conditions. However, the complexity of structures and the choice of driving modes limit the development of modularization. In this paper, a robot joint module with a universal joint configuration driven by shape memory alloy (SMA) wires is proposed, which has 2-DOFs. For lightweight, the main body of the module is made of polylactic acid materials. SMA wires are used as the actuator due to the advantages of large output displacement, high mass-to-energy ratio, and low activation voltage. Adopting antagonistically arranged SMA wires and bias springs combination method increases the recovery speed and driving angles. A locking device is creatively proposed to avoid the influence of self-biasing spring compression. The detailed kinematic model of the module is established. A proportional integral differential controller is used to precisely control module movements, and the attitude angle sensor is used as the monitoring device. Through the maximum rotation angles and trajectory tracking movement experiments, the module is tested comprehensively, and the results show that the module has good movement characteristics and control precision. In addition, analyzing the tracking performance of the module in the time domain shows that the module has a relatively fast tracking speed. This module is beneficial to the research of configurations, actuators, and controls of modular robots.

A Comprehensive Review of Recent Maximum Power Point Tracking Techniques for Photovoltaic Systems under Partial Shading

To operate photovoltaic (PV) systems efficiently, the maximum available power should always be extracted. However, due to rapidly varying environmental conditions such as irradiation, temperature, and shading, determining the maximum available power is a time-varying problem. To extract the maximum available power and track the optimal power point under these varying environmental conditions, maximum power point tracking (MPPT) techniques are proposed. The application of MPPT for extracting maximum power plays a crucial role in developing efficient PV systems. These MPPT techniques face several issues and limitations, particularly during partial shading conditions caused by non-uniform environmental conditions. Researchers have been focusing more on mitigating the partial shading condition in PV systems for the last few years due to the need to improve power output and efficiency. This paper provides an overview of MPPTs proposed in the literature for uniform and non-uniform environmental conditions broadly categorized as MPPT-based and circuit-based methods. The MPPT-based methods are classified as conventional, soft computing, and hybrid techniques. A critical analysis of each approach regarding tracking speed, algorithm complexity, and dynamic tracking under partial shading is discussed. The literature shows hybrid strategies provide fast-tracking speed and are efficient with a tracking efficiency of around 99% compared to conventional methods; however, their design and practical implementation are complex. This comprehensive review of MPPT methods aims to provide power utilities and researchers with a reference and guideline to select the best MPPT method for normal operation and partially shaded PV systems based on their effectiveness and economic feasibility.

A Concise Control Method Based on Spatial-Domain $dp/dv$ Calculation for MPPT / Power Reserved of PV Systems

The idea of perturbation and observation has been widely applied in PV power regulation strategies. However, the conventional Perturb and Observe (P&O) algorithm has an optimal trade-off in choosing efficient step size to balance steady-state oscillations and dynamic response. Modified P&O algorithms improve the performances but increase the computational complexity. This paper develops a concise PV control method taking <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$dp/dv$</tex-math></inline-formula> as a control variable calculated in the spatial-domain. The spatial-domain <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$dp/dv$</tex-math></inline-formula> calculation is based on the current moment data and the spatially adjacent data to avoids zero denominator problem. This control method achieves oscillations-free in steady-state and has a fast dynamic tracking speed with high-frequency control signal updating. Moreover, it unifies both MPPT ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$dp/dv = 0$</tex-math></inline-formula> ) and power reserved modes ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$dp/dv &lt; 0$</tex-math></inline-formula> ). The simulation test system with different scenarios is established to highlight its benefits. Also, experiments on a micro-boost converter combined with a PV panel in the practical set-up are carried out to verify its effectiveness. Performance of an spatial-domain <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$dp/dv$</tex-math></inline-formula> calculation based control method is compared with the popular and recent state-of-the-art methods. The satisfactory dynamic and steady-state performances show the superiority over state-of-the-art methods.

A novel constraint‐based improved equilibrium optimization for global peak tracking of photovoltaic system under complex shading conditions

SummaryThe P–V characteristics of photovoltaic (PV) array exhibit multiple peaks during partial shading conditions (PSCs). Traditional maximum power point tracking (MPPT) approaches are unable to locate the global power point and frequently converge to the local power point. In recent years, evolutionary techniques have given a satisfactory result by tracking global maximum power point (GMPP) but at the cost of ample memory storage and computational burden. Therefore, this paper proposes an improved equilibrium optimization (IEO) technique to track maximum power from the solar photovoltaic system under PSC. The reduced search space exploration by using the skipping method is implemented to improve the convergence speed. The proposed IEO algorithm is capable of differentiating between uniform shading, different PSCs, and solar intensity variation with fast tracking speed. Similarly, the proposed algorithm has been compared with deterministic Jaya (DM‐Jaya), crow search algorithm (CSA), and equilibrium optimization (EO) algorithms. The effectiveness of the proposed IEO technique‐based MPPT has been validated through simulations and experiments. The results demonstrate the capability of IEO in tracking GMPP with an average efficiency of 99.72% and average tracking time of 0.708 s. Further, the proposed algorithm has been implemented for grid integration under PSC. The results reveal that the proposed method has high PV tracking efficiency, negligible steady‐state error, a low MPPT period, and a quick convergence velocity.

Modeling and Simulation of Modified MPPT Techniques under Varying Operating Climatic Conditions

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&amp;O) and Modified Variable Step-Size P&amp;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&amp;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&amp;O algorithm and its superiority over the conventional P&amp;O technique.

GCEVT: Learning Global Context Embedding for Vehicle Tracking in Unmanned Aerial Vehicle Videos

Vehicle tracking in the unmanned aerial vehicle (UAV) videos is a fundamental but vital computer vision task. It mainly consists of two key components, that is, detection and reidentification (ReID). Recently, one-shot trackers, which integrate detection and ReID in a unified network, have received significant attention for their fast-tracking speed. However, existing one-shot trackers typically utilize local information to distinguish the detected targets. Due to the lack of global relations, which are key cues for tracking, these methods struggle to identify targets in UAV videos accurately. To alleviate the above issue, we deliberately design an ReID head that combines nonlocal blocks and the transformer layer to capture the global semantic relation in this letter. First, we propose a novel pyramid fusion network (PFN) to obtain the pixel-wise relations of features at multiple levels and aggregate them into features with richer semantic information. Then, we present a channel-wise transformer enhancer (CTE) to model the dependencies among the channels of the feature map and predict fine-grained identity embeddings. Extensive experiments on VisDrone2021 and UAVDT benchmarks demonstrate that our tracker, namely global context embedding for vehicle tracking (GCEVT), achieves state-of-the-art tracking performance.

Adaptive Spatial Regularization Target Tracking Algorithm Based on Multifeature Fusion

The accuracy and robustness of object-tracking algorithms are challenging tasks in the field of artificial intelligence. The discriminative correlation filter has fast tracking speed and target discrimination ability in visual target tracking, but it will be affected by unnecessary boundary effects. Although spatially regularized discriminative correlation filters (SRDCF) effectively solve this problem, it faces a slow tracking speed and cannot meet the real-time requirement. Moreover, the constraints added by this algorithm are fixed during the tracking process, which needs to reflect better the characteristics and appearance changes of a particular object. Therefore, we propose an adaptive spatial regularization target tracking model based on multifeature fusion and learn the spatial regularization weights by adaptive spatial regularization terms, so that the filter can adapt to the changes of the target, which can highlight the target area and suppress the background area more accurately. At the same time, in order to cope with the problem of target loss when the target is obscured by a large area or rotated to a large extent, texture features are introduced to improve the deficiencies of HOG features and CN features, so as to achieve the complementary advantages among different features. In addition, the benchmark algorithm SRDCF uses Gaussian–Seidel iterative method to solve the filter, which makes the tracking speed very slow and cannot be tracked in real time. Therefore, this paper proposes an ADMM (alternating direction method of multiplier) algorithm to optimize the solution of the filter and achieve the tracking real-time requirement. The experimental results on the OTB-2015 dataset show that the accuracy and success rate of the improved algorithm reach 86% and 81.9%, respectively. At the same time, in the OTB-2013 dataset, it has increased by 3.4% and 6.3%, respectively, on the basis of the benchmark algorithm. It is verified that the improved model not only improves the robustness of the algorithm but also can better adapt to the changes of the target, while effectively reducing the computational overhead and meeting the tracking real-time requirements.

Modified Levy-based Particle Swarm Optimization (MLPSO) with Boost Converter for Local and Global Point Tracking

This paper presents a modified Levy particle swarm optimization (MLPSO) to improve the capability of maximum power point tracking (MPPT) under various partial shading conditions. This method is aimed primarily at resolving the tendency to trap at the local optimum particularly during shading conditions. By applying a Levy search to the particle swarm optimization (PSO), the randomness of the step size is not limited to a specific value, allowing for full exploration throughout the power-voltage (P-V) curve. Therefore, the problem such as immature convergence or being trapped at a local maximum power point can be avoided. The proposed method comes with great advantages in terms of consistent solutions over various environmental changes with a small number of particles. To verify the effectiveness of the proposed idea, the algorithm was tested on a boost converter of a photovoltaic (PV) energy system. Both simulation and experimental results showed that the proposed algorithm has a high efficiency and fast-tracking speed compared to the conventional HC and PSO algorithm under various shading conditions. Based on the results, it was found that the proposed algorithm successfully converges most rapidly to the global maximum power point (GMPP) and that the tracking of GMPP under complex partial shading is guaranteed. Furthermore, the average efficiency for all test conditions was 99% with a tracking speed of 1.5 s to 3.0 s and an average output steady-state oscillation of 0.89%.

Intersection Point Determination Method: A novel MPPT approach for sudden and fast changing environmental conditions

It is challenging to obtain maximum power output from a Solar Photovoltaic System (SPVS) under uncertain weather conditions due to rapid change in temperature and irradiance. It is achievable through an application of an appropriate Maximum Power Point Tracking (MPPT) technique. The precise tracking of Maximum Power Point (MPP) is crucial for randomly varying irradiance and temperature due to sudden occurrence of clouds and rain during uncertain variable weather conditions. In this paper, an approach based on Intersection Point Determination Method (IPDM) is proposed to effectively track the MPP under such uncertain climatic conditions. It is an iterative method which starts with formation of a rectangle by drawing straight lines parallel to voltage and current axes of I–V curve, subsequently identifies the point of intersection of its diagonals. This identified intersection point is used as the initial point for the next iteration until the convergence is achieved i.e. the new intersection point superimposes the previous point in two successive iterations. The computer simulations have been carried out on a stand-alone (MSX-60) solar PVS of 60W along with a boost converter designed in MATLAB/Simulink. The performance of proposed IPDM technique is tested and compared with other well established MPPT techniques in four distinct theoretical test cases comprises all possible variable weather conditions to ensure its vast applicability. Further, an experimental setup is established in laboratory environment to also validate its performance for the real-life input variables. In which, the maximum and average power outputs are achieved to 50.5598 W and 30.6338 W respectively, for the unfavourable real-life weather conditions i.e. mostly a cloudy day with rainfall and sunshine for short durations. All these simulation results as obtained by all five MPPT methods are statistically validated through t-Tests with 5% of confidence of interval. The statistical analysis proves the superiority of the proposed IPDM technique as compared to the other four competing MPPT techniques. The proposed method is promising in terms of better convergence, fast tracking speed, negligible oscillations, and maximum power output in all possible weather conditions.

Hybrid Global Maximum Power Tracking Method With Partial Shading Detection Technique for PV Systems

The conventional maximum power point tracking (MPPT) controllers are unable to track the global maximum power point (GMPP) during partial shading conditions (PSCs). As a result, many optimization strategies have been used to track the GMPP. Optimization approaches, on the other hand, are iterative methods that cannot distinguish uniform shading conditions (USCs) from PSCs. In this article, a new partial shading detection approach has been hybridized with the new modified rat swarm optimization (MRSO) algorithm. It successfully distinguishes USCs from PSCs in two samples and triggers the MRSO only when the PSC is detected. Consequently, it avoids the excessive global peak search during the USCs, which improves the tracking time for USCs without an unnecessary scan of the entire <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$V$ </tex-math></inline-formula> curve. Furthermore, the proposed MRSO is simple to implement with simple tuning parameters and fast-tracking speed for PSCs. The proposed method has been experimentally validated on a boost converter with a sampling time of 0.05 s. The experimental results showed the successful implementation of the proposed method with an average tracking time for uniform and PSCs of 0.42 s with an average steady-state efficiency of 99.8%. Subsequently, a comparison with state-of-the-art methods in this domain has been provided to assess the efficacy of the proposed method.

A 25 MHz Fast Transient Adaptive-On/Off-Time Controlled Three-Level Buck Converter

This work presents a 25 MHz adaptive on/off-time-controlled three-level buck converter with a fast reference tracking speed and high light load efficiency. The self-balancing scheme of the flying capacitor voltage is revised and extended for a broad conversion ratio range from 0.17 to 0.833. To reduce the error of the self-balancing scheme when applied to a high frequency three-level buck converter with an on-chip flying capacitor, a real-time calibration loop on the flying capacitor voltage is introduced, effectively reducing the balancing error from 340 mV to 20 mV. A turning-point output voltage prediction for the three-level buck converter is presented to achieve near-optimal reference tracking with a fast tracking speed and negligible output voltage undershoot or overshoot. The up- and down-tracking takes 81 ns and 72 ns, respectively, for a 1-V step with an inductor of 47 nH and an output capacitor of 47 nF. The three-level converter achieves a precise discontinuous conduction mode (DCM) operation with an adaptive switching frequency at light load by means of a feedback loop-based method. An approximately 28.2% increment in efficiency is achieved with a load of 10 mA. The three-level buck converter has a peak efficiency of 92.8% and an over 85% efficiency in most of the operation regions.

Research on deep correlation filter tracking based on channel importance

Correlation filter tracking requires little prior knowledge of the tracking target (e.g., the shape, and the posture) but has a fast-tracking speed. The deep features extracted by the deep convolutional neural network have strong representation ability, so the tracking method based on the combination of correlation filter and deep convolutional neural network, named as deep correlation filter tracking, is a hot issue in the field of target tracking at present. However, the deep convolutional neural network largely restricts the real-time performance of the deep correlation filter tracking because of its complex network structure and heavy computation burden. To balance the contradiction between tracking speed and tracking accuracy, a new channel importance is defined and the channel importance based method of how to select the important channels is given in this paper. And then, a deep correlation filter tracking method based on channel importance is proposed to lighten the feature network, reduce the computation load and improve the tracking speed under the premise of ensuring the tracking accuracy. In the process of tracking, the structural similarity index measurement (SSIM) of the predicted tracking target in two consecutive frames is calculated in real-time. Based on the SSIM, determine whether the feature network needs to be updated, and decide whether the tracking fails. If the feature network needs to be updated, the feature network will be updated online while the tracking is on. If the tracking fails, the target will be searched again, and the tracking is recovered from the failure. The tracking algorithm proposed in this paper is tested on the OTB2013 data set, and the experiment shows that the tracking algorithm designed in this paper can improve the real-time performance while meeting the requirement of tracking accuracy. The online update of the feature network can make the network adapt to the complex background and target changes to improve tracking accuracy; In the case of tracking failure, the re-tracking module can search for the target again and resume tracking given that the target is always present.

Implementation of adjustable variable step based backstepping control for the PV power plant

The most recent research challenge in sustainable photovoltaic (PV) energy is to maintain a high efficiency of PV arrays. Our contribution in this issue is reducing the power loss caused by environmental condition change and parametric variations. A non linear recursive Backstepping maximum power point tracking (MPPT) controller based on an adjusted variable step Perturb and Observe (P&O) algorithm (VS-Backstepping) is proposed to gather the maximum power with fast converge time from the PV system. The trade off between the high accuracy and the fast tracking speed cannot be realized through single MPPT techniques. Thus this work combines two individual MPPT controllers. However, the accuracy for finding the maximum power is highly related to the part of MPP tracking control using the Backstepping approach as well as the rapidity to reach the MPP is managed with the adjusted variable step P&O algorithm. To further evaluate the effectiveness of the proposed method (VS-Backstepping), simulation and experimental results on a commercial PV panel are presented and compared to an individual fixed step P&O algorithm and a variable step P&O (VS-P&O) controller.

A Novel Approach Based Incremental Conductance Method for MPPT Strategy of PV Systems Considering Partial Shading Conditions

The main advantages in terms of a simple algorithm, easy to use, fast maximum power point tracking (MPPT) speed of the incremental conductance (InC) and perturbation and observation (P&O) algorithms have been adopted in the literature. Besides, the oscillation phenomenon around the maximum power point (MPP) is a major drawback of these typical algorithms. More specifically, these traditional algorithms failed to find the global maximum power peak when the PV system was operating under partial shading conditions where multiple peaks appeared on the PV curve characteristic. Therefore, a new approach based on the conventional InC algorithm is proposed in this paper. By evenly dividing the search area at the early stage, this proposed method avoids the regional traps encountered by the traditional InC algorithm. Then select the regions where there is a high probability of finding the global MPP to implement the conventional InC approach. After finishing this process, the location with the best power is selected and the MPP search is further performed using the various step-size InC algorithm for the final stage. With this improvement, the steady-state oscillation amplitude of the proposed method is significantly reduced. The implementation process and simulation results show that the proposed method has key advantages in terms of simplicity, fast convergence speed, and reduced steady-state oscillation amplitude. Simulation and experimental results are also compared with the conventional P&O and PSO methods to demonstrate the effectiveness of the proposed method in both uniform illuminance and partial shading conditions.