Fast Tracking Speed Research Articles

A new simple MPPT algorithm to track MPP under partial shading for solar photovoltaic systems

ABSTRACTExtraction of electrical power from solar photovoltaic (PV) systems under nonuniform irradiance such as partial shading (PSC) or fast transient conditions (FTC) is the most challenging task for the solar PV researchers. Inaccurate detection of PSC or FTC affects the efficiency of maximum power point tracking (MPPT) algorithms. As multiple peaks are found in the P–V curve, searching of the global peak among local peaks and stabilizing the output voltage is a typical task for an MPPT algorithm during PSC or FTC. Particularly for small isolated PV systems, the output system voltage stability is of more concern than power efficiency during PSC. Here, a Modified Perturb and Observation (P&O) (MP&O) MPPT algorithm is proposed for accurate detection of PSC, which stabilizes the system output voltage without compromising with power efficiency. For accurate detection of PSC, the MP&O MPPT algorithm uses irradiance (Ir(k)) and generated PV power (P(k)) as an input parameter and regulates the step size (dD) of the boost converter duty cycle (D). A PV system is simulated in MATLAB/Simulink and the performance of the proposed algorithm is also investigated using dSPACE controller, solar PV emulator and a SiC Mosfet switch under PSC. The accuracy to detect PSC or FTC of this algorithm is found satisfactory with fast tracking speed.

Butterfly optimization algorithm based maximum power point tracking of photovoltaic systems under partial shading condition

ABSTRACT Because of dust, trees, high buildings in the surrounding area, partial shading conditions (PSC) occur in photovoltaic (PV) systems. This condition affects the power output of the PV system. Under PSC there is a global maximum power point (GMPP) besides there are a few local maximum power points (LMPP). This condition makes the maximum power point tracking (MPPT) procedure a challenging task. In order to solve this issue, soft computing techniques such as gray wolf optimization (GWO), particle swarm optimization (PSO) and Gravitational Search Algorithm (GSA) are implemented. However, the performance of MPP trackers still needs to be improved. The main contribution of this paper is improving the tracking speed by implementing BOA to the MPPT of the PV system under PSC. Thus, in real-time applications a promising alternative presented to the literature to improve the performance of the PV systems under variable PSC because of its fast tracking speed. PV system consists of PV array, boost converter and load are modeled and simulated in MATLAB/Simulink. BOA algorithm is implemented for three different insolation scenarios on the PV array. The results of the BOA algorithm verified by a comparative analysis with PSO-GSA and GWO algorithms. The results show that BOA can give high accuracy and better tracking speed than these algorithms in recent literature.

A Global Maximum Power Point Tracking Technique of Partially Shaded Photovoltaic Systems for Constant Voltage Applications

The P–V characteristics of photovoltaic (PV) array exhibit several maximum power points (MPP) during non-uniform insolation (i.e., during partial shading) conditions; there exists only one global MPP (GMPP), whereas others are referred to local MPP. This paper presents a technique to track the GMPP for the constant voltage or battery loads during partial shading conditions using a single sensor connected to the battery terminals. The proposed method introduces fast and efficient scanning based method, i.e., scanning I batt– D curve of power electronic interface at selective duty cycles to recognize the kind of the solar shading pattern (i.e., kind of P–V curve) on PV array and to find the GMPP neighborhood. Moreover, the proposed method overcomes the drawbacks of existing methods such as low convergence speed, increased number of sensors, and heavy computational complexity. The proposed GMPPT method is simulated in MATLAB/Simulink and validated through test results on a prototype for various non-uniform insolation conditions. The results have shown that this paper tracks the GMPP with best tracking efficiency and fast tracking speed. Further, the proposed method is compared with two P–V curve scanning based GMPPT methods and one global optimization based artificial bee colony method.

Hybrid Moth-Flame Optimization Algorithm and Incremental Conductance for Tracking Maximum Power of Solar PV/Thermoelectric System under Different Conditions

For an efficient energy harvesting by the PV/thermoelectric system, the maximum power point tracking (MPPT) principle is targeted, aiming to operate the system close to peak power point. Under a uniform distribution of the solar irradiance, there is only one maximum power point (MPP), which easily can be efficiently determined by any traditional MPPT method, such as the incremental conductance (INC). A different situation will occur for the non-uniform distribution of solar irradiance, where more than one MPP will exist on the power versus voltage plot of the PV/thermoelectric system. The determination of the global MPP cannot be achieved by conventional methods. To deal with this issue the application of soft computing techniques based on optimization algorithms is used. However, MPPT based on optimization algorithms is very tedious and time consuming, especially under normal conditions. To solve this dilemma, this research examines a hybrid MPPT method, consisting of an incremental conductance (INC) approach and a moth-flame optimizer (MFO), referred to as (INC-MFO) procedure, to reach high adaptability at different environmental conditions. In this way, the combination of the two different algorithms facilitates the utilization of the advantages of the two methods, thereby resulting in a faster speed tracking with uniform radiation distribution and a high accuracy in the case of a non-uniform distribution. It is very important to mention that the INC method is used to track the maximum power point under normal conditions, whereas the MFO optimizer is most relevant for the global search under partial shading. The obtained results revealed that the proposed strategy performed best in both of the dynamic and the steady-state conditions at uniform and non-uniform radiation.

Advanced MPPT Algorithm for Distributed Photovoltaic Systems

The basic and adaptive maximum power point tracking algorithms have been studied for distributed photovoltaic systems to maximize the energy production of a photovoltaic (PV) module. However, the basic maximum power point tracking algorithms using a fixed step size, such as perturb and observe and incremental conductance, suffer from a trade-off between tracking accuracy and tracking speed. Although the adaptive maximum power point tracking algorithms using a variable step size improve the maximum power point tracking efficiency and dynamic response of the basic algorithms, these algorithms still have the oscillations at the maximum power point, because the variable step size is sensitive to external factors. Therefore, this paper proposes an enhanced maximum power point tracking algorithm that can have fast dynamic response, low oscillations, and high maximum power point tracking efficiency. To achieve these advantages, the proposed maximum power point tracking algorithm uses two methods that can apply the optimal step size to each operating range. In the operating range near the maximum power point, a small fixed step size is used to minimize the oscillations at the maximum power point. In contrast, in the operating range far from the maximum power point, a variable step size proportional to the slope of the power-voltage curve of PV module is used to achieve fast tracking speed under dynamic weather conditions. As a result, the proposed algorithm can achieve higher maximum power point tracking efficiency, faster dynamic response, and lower oscillations than the basic and adaptive algorithms. The theoretical analysis and performance of the proposed algorithm were verified by experimental results. In addition, the comparative experimental results of the proposed algorithm with the other maximum power point tracking algorithms show the superiority of the proposed algorithm.

Spatio-Temporal Context Tracking Algorithm Based on Correlation Filtering

The Correlation Filter tracking algorithm is different from the traditional method based on target feature, which has high accuracy and fast tracking speed. In this paper, the Spatio-Temporal Context(STC) target tracking algorithm is combined with Correlation Filtering, the position of the target is predicted by using Correlation Filtering and the prediction result is corrected by using the STC target tracking algorithm. The target tracking algorithm combined with Correlation Filtering and STC can realize effective tracking when the target is occluded or there is an interference target. The experimental results show that the proposed algorithm can not only be applied to the tracking of visual targets in complex backgrounds such as illumination changes, target rotation and background region interference, but also have better robustness.

A Remote Free-Head Pupillometry Based on Deep Learning and Binocular System

Objective: Pupillometer plays a key role in a variety of research areas, including disease diagnosis, human-machine interaction, and education. Here, we set out to leverage the deep learning theory to develop a remote binocular vision system for pupil diameter estimation. Approach: the system consists of three parts: eye detection, eye tracking, and pupil diameter estimation. We first train a convolutional neural network based on YOLO V2 to perform eye detection, leading to high accuracy and robustness under ambient light interference. By exploring the similarity of binocular camera images, we then propose a master–slave structure for eye tracking, surpassing the traditional parallel structure in tracking speed while keeping considerable accuracy. Furthermore, we develop a pupil diameter estimation algorithm based on binocular vision, avoiding the personal calibration procedure and reducing the measurement distortion error. Main results: Experimental results on real datasets reveal that our system exhibits the state-of-the-art performance with high eye detection accuracy (90.6%), fast eye tracking speed (<11 ms per frame), low pupil diameter estimation error [(0.022±0.017) mm mean absolute error, and (0.6 ± 0.7)% percentage of the mean absolute error] and excellent flexibility. Significance: in contrast with previous pupillometers, which lead to pupil diameter measurement distortion error through a 2-D projection image on a single camera, our system measures pupil diameter in 3-D space without distortion influence, thus improving its robustness to head angle variation and making it more practical for real applications.

A novel global maximum power point tracking algorithm for photovoltaic system with variable perturbation frequency and zero oscillation

By combing the advantages of two widely-used methods together, namely the particle swarm optimization (PSO) and the perturb and observe (PO), this paper develops and discusses a novel GMPPT algorithm with fast tracking speed for different partial shading patterns and zero steady-state oscillation. Three key elements that form the foundation of the proposed algorithm have been discussed: (1) variable perturbation frequency (VPF) to speed up the dynamic tracking speed; (2) PO tracking is used after convergence of PSO to tackle the reinitialization problem of the PSO since PSO is often unable to detect the small change in the PV system; (3) fixed duty cycle is implemented for the steady state with the PO to realize the zero oscillation. Different partial shading patterns and varying irradiance scenarios are used to evaluate the transient as well as steady state operation of the algorithm. Both simulation and experimental results show that the VPF can greatly reduce the duration of the searching interval of the PSO tracking stage while the following PO tracking stage ensure the correct detection of the proposed algorithm for small changes in the PV system and zero oscillation for the steady state operation.

Fast Object Tracking on a Many-Core Neural Network Chip.

Fast object tracking on embedded devices is of great importance for applications such as autonomous driving, unmanned aerial vehicle, and intelligent monitoring. Whereas, most of previous general solutions failed to reach this goal due to the facts that (i) high computational complexity and heterogeneous operation steps in the tracking models and (ii) parallelism-limited and bloated hardware platforms (e.g., CPU/GPU). Although previously proposed devices leverage neural dynamics and near-data processing for efficient tracking, their flexibility is limited due to the tight integration with vision sensor and the effectiveness on various video datasets is yet to be fully demonstrated. On the other side, recently the many-core architecture with massive parallelism and optimized memory locality is being widely applied to improve the performance for flexibly executing neural networks. This motivates us to adapt and map an object tracking model based on attractor neural networks with continuous and smooth attractor dynamics onto neural network chips for fast tracking. In order to make the model hardware friendly, we add local-connection restriction. We analyze the tracking accuracy and observe that the model achieves comparable results on typical video datasets. Then, we design a many-core neural network architecture with several computation and transformation operations to support the model. Moreover, by discretizing the continuous dynamics to the corresponding discrete counterpart, designing a slicing scheme for efficient topology mapping, and introducing a constant-restricted scaling chain rule for data quantization, we build a complete mapping framework to implement the tracking model on the many-core architecture. We fabricate a many-core neural network chip to evaluate the real execution performance. Results show that a single chip is able to accommodate the whole tracking model, and a fast tracking speed of nearly 800 FPS (frames per second) can be achieved. This work enables high-speed object tracking on embedded devices which normally have limited resources and energy.

Adaptive Speed Control of Induction Motor Drive With Inaccurate Model

An adaptive controller for the speed control of induction motor (IM) drives with inaccurate models is designed in this paper. Specifically, we assume that every equation in the state-space model of the drive is subject to slowly varying error. The proposed controller is composed of an adaptive feedforward control term, which compensates for the nonlinear and uncertain factors, and a feedback control term, which guarantees the system stability. The proposed scheme is not only simple and easy to implement, but also it guarantees a precise and fast speed tracking. Stability of the proposed speed controller is confirmed using the Lyapunov theorem and a related lemma. The designed control algorithm is compared to a controller based on nonadaptive feedback linearization control (FLC), conventional field oriented control (FOC), and adaptive backstepping sliding mode control (ABSMC). Experiments on a developed IM drive prototype of rated power of 4 kW confirm good control performance [better robustness, smaller mean square, and maximum absolute errors (MAEs)] compared to the competitors, especially in the case of severe parameter mismatch between the real drive and model used for controller design.

An Improved Perturb and Observe Algorithm for Photovoltaic Motion Carriers

An improved perturbation and observation algorithm for photovoltaic motion carriers is proposed in this paper. The model of the proposed algorithm is given by using Lambert W function and tangent error method. Moreover, by using matlab and experiment of photovoltaic system, the tracking performance of the proposed algorithm is tested. And the results demonstrate that the improved algorithm has fast tracking speed and high efficiency. Furthermore, the energy conversion efficiency by the improved method has increased by nearly 8.2%.

A novel MPPT circuit with 99.1% tracking accuracy for energy harvesting

A novel maximum power point tracking (MPPT) circuit based on Buck–Boost converter is presented for micro-power energy harvesting, which efficiently improves the power efficiency and robustness of system. The proposed MPPT uses the low-power analog multiplier and multi-outputs self-powered common-gate comparator to track the input power, and simplifies data calculation and structure greatly. The fast dynamic switching circuit and digital control circuit are introduced to enhance the adaptability and flexibility of system. The performance of whole converter was validated by the simulation results in a 65-nm CMOS process. The minimum starting voltage is 0.15 V. The peak output power is 40.5 µW, with a power loss of 14.1 µW. The peak power efficiency and peak tracking efficiency are 92.1 and 99.1%, respectively. The proposed MPPT has the advantages such as low power, high efficiency, fast tracking speed, simple structure.

A novel tangent error maximum power point tracking algorithm for photovoltaic system under fast multi-changing solar irradiances

In this paper, a novel tangent error maximum power point tracking algorithm based on perturb and observe method is presented to achieve the high efficiency for photovoltaic system under fast multi-changing solar irradiances. In order to get a proper initial value, the mathematical model of optimal voltage is established. By using tangent error method, a dynamic perturbation step is calculated. Meanwhile, the characteristics for tracking direction is analyzed in detail to distinguish oscillating power from changes in irradiances and perturbation step. Moreover, the tracking performance of the proposed algorithm is investigated by modeling, simulation and experiment of PV system. Furthermore, errors and statistical analyses are carried out to illustrate the accuracy of the tangent error algorithm. Compared with previous methods in other works, the results demonstrate that the proposed method not only has the fast tracking speed but also the high efficiency. In addition, it is found that the overall energy conversion efficiency of the photovoltaic module has increased by approximately 3% during the whole time. Besides, due to its ease implementation, high efficiency and reliability under fast varying irradiances, the proposed method can be applied to provide energy for moving carriers, such as UAVs, cars and trains.

Autonomous Landing of a Fixed Wing UAV with a Ground-based Visual Guidance System

Fixed wing UAV autonomous landing guidance system needs high precision, fast tracking speed. This paper designs and built a set of Ground-based visual guidance system, which first introduces the visual landing guidance system principle, composition, analysis of the mathematical model and error analysis of the system; The Adaboost algorithm is applied to the recognition of UAV, where UAV samples and non-UAV are chosen for training cascade classifie. After the recognition of UAV, the UAV tracking system based on vision is designed. Finally, the reliability, tracking speed and precision of the system are verified by field experiments. The experimental results show that the designed landing guidance system could meet the requirements of the precise autonomous landing of a fixed wing UAV.

An Improved Beta Method With Autoscaling Factor for Photovoltaic System

Maximum power point tracking (MPPT) is essential to improve the energy yield of solar energy systems. However, conventional MPPT algorithms show obvious problems such as the conflict of the steady-state oscillations and dynamic speed, and the clash of high computational burden and accuracy. Originated from the beta method, which shows the advantages of fast tracking speed in the transient stage, small oscillations in the steady-state, and medium complexity of implementation, this paper proposed an improved beta method to further improve the overall performance, especially for practical applications. Instead of manually tuning key parameters such as the range of $\beta$ parameter and scaling factor $N$ for different operating conditions, an autoscaling factor is used, which make the method easier in practical implementation and suitable for wider conditions. The meteorological data of two distinct locations are used to verify that the $\beta$ parameters derived from photovoltaic (PV) modules are valid for one whole year under different environmental conditions. A PV system with the proposed MPPT method was built in MATLAB/Simulink, and different indices such as the rise time, the setting time, and the tracking energy loss are used to evaluate the performance of various MPPT algorithms. Finally, two experimental tests were carried out, including the indoor test with solar array emulator and the outdoor test with an actual PV module, respectively, to show the effectiveness of the proposed MPPT algorithm.

A novel maximum power point tracker for thermoelectric generation system

In this paper, a novel hybrid maximum power point tracking (MPPT) method is proposed and investigated. The proposed MPPT technique combines the simplicity of perturb and observe (P&O) method and the fast tracking ability of open circuit voltage (OCV) method. The advantages of the proposed MPPT approach include fast tracking speed, no additional circuit required and no temporary power loss. To validate the feasibility of the proposed MPPT technique, an 1.2 kW thermoelectric generation system for industrial waste heat recovery is also constructed, experimental results show that comparing with conventional P&O technique, the proposed method can improve the tracking speed for 42.9% and 86.2% when temperature differences are ΔT = 60 °C and ΔT = 180 °C, respectively. Moreover, the energy loss can be improved by 24.0% and 87.0% when temperature differences are ΔT = 60 °C and ΔT = 180 °C, respectively.

Implementing a Novel Hybrid Maximum Power Point Tracking Technique in DSP via Simulink/MATLAB under Partially Shaded Conditions

This paper presents a hybrid maximum power point tracking (MPPT) method to detect the global maximum power point (GMPP) under partially shaded conditions (PSCs), which have more complex characteristics with multiple peak power points. The hybrid method can track the GMPP when a partial shadow occurs either before or after acquiring the MPP under uniform conditions. When PS occurs after obtaining the MPP during uniform conditions, the new operating point should be specified by the modified linear function, which reduces the searching zone of the GMPP and has a significant effect on reducing the reaching time of the GMPP. Simultaneously, the possible MPPs are scanned and stored when shifting the operating point to a new reference voltage. Finally, after determining the possible location of the GMPP, the GMPP is obtained using the modified P&amp;O. Conversely, when PS occurs before obtaining the MPP, the referenced MPP should be specified. Thus, after recognizing the possible location of the GMPP, the modified P&amp;O can be used to obtain the GMPP. The simulation and experimental implementations for the proposed algorithm are performed with different scenarios of shadowing under different irradiations, which clearly indicate that the proposed method is robust and has a fast tracking speed. Moreover, this work presents the load sizing method for PSCs to avoid controller failure when detecting the GMPP. Additionally, in this paper, the user-friendly method for programming the digital signal processing (DSP) via Simulink/MATLAB is presented in detail.

Photovoltaic Modified β-Parameter-based MPPT Method with Fast Tracking

Maximum power point tracking (MPPT) is necessary for photovoltaic (PV) power system application to extract the maximum possible power under changing irradiation and temperature conditions. The β-parameter-based method has many advantages over conventional MPPT methods; such advantages include fast tracking speed in the transient stage, small oscillations in the steady state, and moderate implementation complexity. However, a problem in the implementation of the conventional beta method is the choice of an appropriate scaling factor N, which greatly affects both the steady-state and transient performance. Therefore, this paper proposes a modified β-parameter-based method, and the determination of the N is discussed in detail. The study shows that the choice of the scaling factor N is determined by the changes of the value of β during changes in irradiation or temperature. The proposed method can respond accurately and quickly during changes in irradiation or temperature. To verify the proposed method, a photovoltaic power system with MPPT function was built in Matlab/Simulink, and an experimental prototype was constructed with a solar array emulator and dSPACE. Simulation and experimental results are illustrated to show the advantages of the improved β-parameter-based method with the optimized scaling factor.

High-Accuracy and Fast-Speed MPPT Methods for PV String Under Partially Shaded Conditions

The photovoltaic (PV) string under partially shaded conditions exhibits complex output characteristics, i.e., the current–voltage $(I\mbox{--}V)$ curve presents multiple current stairs, whereas the power–voltage $(P\mbox{--}V)$ curve shows multiple power peaks. Thus, the conventional maximum power point tracking (MPPT) method is not acceptable either on tracking accuracy or on tracking speed. In this paper, two global MPPT methods, namely, the search–skip–judge global MPPT (SSJ-GMPPT) and rapid global MPPT (R-GMPPT) methods are proposed in terms of reducing the searching voltage range based on comprehensive study of $I\mbox{--}V$ and $P\mbox{--}V$ characteristics of PV string. The SSJ-GMPPT method can track the real maximum power point under any shading conditions and achieve high accuracy and fast tracking speed without additional circuits and sensors. The R-GMPPT method aims to enhance the tracking speed of long string with vast PV modules and reduces more than 90% of the tracking time that is consumed by the conventional global searching method. The improved performance of the two proposed methods has been validated by experimental results on a PV string. The comparison with other methods highlights the two proposed methods more powerful.

Dithering Digital Ripple Correlation Control for Photovoltaic Maximum Power Point Tracking

This study demonstrates a new method for rapid and precise maximum power point tracking in photovoltaic (PV) applications using dithered PWM control. Constraints imposed by efficiency, cost, and component size limit the available PWM resolution of a power converter, and may in turn limit the MPP tracking efficiency of the PV system. In these scenarios, PWM dithering can be used to improve average PWM resolution. In this study, we present a control technique that uses ripple correlation control (RCC) on the dithering ripple, thereby achieving simultaneous fast tracking speed and high tracking accuracy. Moreover, the proposed method solves some of the practical challenges that have to date limited the effectiveness of RCC in solar PV applications. We present a theoretical derivation of the principles behind dithering digital ripple correlation control, as well as experimental results that show excellent tracking speed and accuracy with basic hardware requirements.