Nonlinear Output Characteristics Research Articles

Research on photovoltaic MPPT under complex conditions based on an advanced great wall construction algorithm method.

To mitigate the challenges posed by the non-linear multi-peak power-voltage output characteristics of photovoltaic (PV) systems operating under partial shading conditions, which often lead to suboptimal performance of conventional Maximum Power Point Tracking (MPPT) algorithms, a novel approach was introduced. We introduce the LGWGCA-P&O method, which synergistically combines an modified Great Wall Construction Algorithm (LGWGCA) with the Perturbation and Observation (P&O) technique. The LGWGCA is refined with a positional update mechanism inspired by the Grey Wolf Optimization (GWO) algorithm, optimizing the distribution of solution agents, while a Levy flight strategy is employed to reduce excessive randomness during agent replacement and recombination, thereby accelerating the tracking process. As the algorithm approaches the maximum power point, it seamlessly transitions to the P&O method, leveraging its rapid convergence to ensure precise and efficient power point identification. Experimental results demonstrate that the LGWGCA-P&O method achieves a PV conversion efficiency exceeding 99.96%, significantly minimizing power losses during MPPT. Furthermore, the method enhances convergence speed by approximately 40% compared to traditional GWO-P&O approaches, and it consistently outperforms Particle Swarm Optimization and Cuckoo Search Optimization algorithms under extreme conditions, demonstrating superior computational efficiency and robustness.

An enhanced maximum power point tracking and voltage control for proton exchange membrane fuel cell using predictive model control techniques

Proton Exchange Membrane Fuel Cells serve as sustainable devices for converting renewable energy sources into electricity, offering advantages such as rapid startup, high power density, and operation at low temperatures. They are highly efficient and environmentally friendly energy sources, yet their non-linear output characteristics under variable conditions present challenges in maximizing power output. Furthermore, they generates direct current, while many electrical devices rely on alternating current. This research addresses the necessity of enhancing the performance and efficiency through the development of an advanced maximum power point tracking technique and voltage control strategy. A modified finite-control-set model predictive control technique is proposed for both maximum power point tracking and voltage control. Specifically, a finite-control-set model predictive control technique approach is employed to modulate the switching signal for both the DC-DC boost converter and the DC-AC inverter. The DC-DC boost converter step up the fuel cell output voltage to the desired level, ensuring it reaches the maximum power point, and a DC-AC inverter to convert the direct current voltage to a pure sinusoidal alternating waveform. The investigation demonstrates the effectiveness of the proposed method in achieving its objectives. The proposed maximum power point tracking technique accuracy achieved the maximum power with a rate of 97 % with excellent respond time within 7 ms. For alternating current power, only less than 1.5 % of total harmonic distortion is recorded. The study evaluates the control scheme under robust operating conditions, demonstrating its effectiveness in optimizing PEMFC output and providing high-quality AC voltage.

A multilayer integrative approach for diagnosis, classification and severity detection of electrical faults in photovoltaic arrays

Early detection of faults in photovoltaic (PV) systems is crucial for improving their lifespan and reliability. Conventional protection devices may not be able to detect electrical faults under critical conditions. This usually occurs due to (i) the current-limiting nature and non-linear output characteristics of PV arrays, (ii) the changing environmental conditions (such as irradiance and temperature variations), and (iii) the influence of the maximum power point tracker (MPPT), particularly in the presence of critical fault impedance values or mismatch levels. Therefore, modern data-driven methods are required for fault detection and classification in PV arrays. However, careful investigation in previous studies reveals the existing gaps and limitations, such as poor accuracy, and incomprehensive models which have not considered various electrical faults, low mismatch levels, and critical fault impedance values. To this end, in this study, a comprehensive multilayer model is proposed which consists of six layers for diagnosis, classification and severity identification of electrical faults in PV arrays. Each layer adopts a weighted ensemble learning (WEL) algorithm consisting of three classifiers namely Support Vector Machine (SVM), Naïve Bayes (NB) and Logistic Regression (LR). Besides, a genetic algorithm (GA) is employed in each layer to find the optimal weights for each classifier (i.e., SVM, NB and LR). The initial dataset is acquired through an investigation into the PV array current–voltage (I-V) characteristic curve and extraction of several features under various faulty and normal conditions. To reduce the dataset dimensionality thus computationally simplifying the training process, the sequential floating forward selection (SFFS) algorithm is utilized in each layer as a powerful feature selection technique. The results show a highly accurate performance in fault diagnosis, classification, and severity assessment, with an average simulation and experimental accuracies of 98.9% and 98.37%, respectively.

Multiresolution Models of DC Traction Power Supply Systems With Reversible Substations

Reversible substations (RSs) permitting bidirectional power flows can recover the regenerative braking energy of trains in DC traction power supply systems (TPSSs), increasing the energy efficiency of railway systems. To predict their effects on system dynamics and energy savings, the paper develops multiresolution models (MRMs) to simulate the RS roles with different fidelities. A high‐resolution model for the transient simulation replicates a particular topology where a three‐level voltage source inverter is connected to the secondary winding of an existing 12‐pulse rectifier transformer and regulated to keep a constant DC voltage in the inverting mode. Furthermore, it can model the transient effects of pantograph‐to‐line arcing by inserting arc voltage profiles at the train’s input stage. To increase the computation speed in the long‐term energy flow simulation, a low‐resolution model simplifies the rectifiers into a series connection of a diode and a controlled voltage source depicting their nonlinear output characteristics and then places a DC voltage source in parallel to form a reverse path for braking power recovery. In addition, nonlinear conversion efficiencies are introduced to calculate energy flows across substations. The MRMs are tested based on a 1.5 kV DC TPSS and discussed alongside system dynamics under normal operation or pantograph arcing and the consistencies between different models. The RS using bidirectional voltage source converters only is additionally modelled to compare the technical performance of the two topologies in terms of system dynamics and energy efficiencies.

MPPT Techniques Exploration under Uniform and Non-Uniform Solar Irradiance Condition–A Survey

Solar PV has become increasingly popular in the past decade, both for standalone and grid-connected systems. Solar PV systems exhibit non-linear output characteristics due to weather fluctuations, which can substantially impact the overall PV system’s output. Consequently, selecting the optimal MPPT technique for a given PV system is a complex task, as each method has different strengths and weaknesses. Therefore, conducting a comprehensive examination of these techniques is crucial. A survey of academic publications on ‘Google Scholar’ spanning the past seven years (2015-2022) revealed a total of seventy-one review articles addressing various MPPT techniques. Among the studies, seven have directed their attention toward non-uniform solar irradiance, while four have dedicated their focus to uniform conditions. Surprisingly, none have ventured into the realm of hybrid optimization MPPT techniques. Moreover, several of these reviews have covered a restricted range of MPPT methods, and none have delivered an all-encompassing assessment that encompasses both online and offline strategies for uniform conditions, alongside an evaluation of hybrid MPPT techniques in the context of non-uniform solar irradiance conditions. Unfortunately, there have been minimal efforts in this direction. Consequently, the need of the hour is a comprehensive review paper that encompasses nearly all known MPPT techniques within a single publication. This paper places its primary emphasis on categorizing MPPT algorithms into offline, hybrid, and online optimization techniques, addressing both non-uniform and uniform irradiance scenarios. It serves as a comprehensive resource by consolidating diverse MPPT techniques, presenting their mathematical formulations, operational principles, and visual representations through block diagrams and flowcharts. The objective of this research endeavor is to provide a valuable reference for strategists, researchers, and energy engineers, offering them a roadmap to steer future research endeavors and practical implementations in the realm of optimization of MPPT.

Precise and coordinated gearshift control for AMT gearshift system equipped with two linear actuators

A type of automated manual transmission gearshift system that uses two linear actuators to perform gearshift events is presented. The shifting mechanism can be made simpler by using linear actuator to drive the shift fork directly. The mathematical model of the actuator is built and analyzed. Coordinated control of the engagement and disengagement processes is created based on a detailed analysis of the gearshift process in order to shorten the shift time. The force characteristics test reveals the nonlinear output characteristics. The precise displacement control need for coordinated control and stable control requirement for the shifting operation are satisfied by a linear extended state observer based controller. To verify the novel gearshift system and the control method, a test bench and control system are constructed. The results of the simulations indicate that the displacement control's accuracy is enhanced. As the speed difference reaches 500 r/min, the testing results demonstrate a noticeably reduced of shift force fluctuation and jerk generated during the gearshift process, from 20 m/s3 to 3.1 m/s3. With the improved gearshift system and control method, the gearshift coordinated control can also save 25 ms of shift time.

Graphene-Based Optoelectronic Mixer Device for Time-of-Flight Distance Measurements for Enhanced 3D Imaging Applications.

A large and growing number of applications benefit from innovative and powerful 3D image sensors. Graphene photodetectors can achieve 3D sensing functionalities by intrinsic optoelectronic frequency mixing due to the nonlinear output characteristics of the sensor. In first proof of principle distance measurement demonstrations, we achieve modulation frequencies of 3.1 MHz, signal-to-noise ratios of ∼40 dB, distance detection up to at least 1 m, and a mean accuracy of 25.6 mm. The scalable More than Moore detector approach enables geometrical fill factors close to 100% and can easily complement powerful functionalities by simple back-end integration on top of CMOS electronics.

Investigation on the non-linear behaviour of silicon nanowires and assessment of the biosensing potential

We perform a rigorous study on the conduction characteristics of long, low doped and relatively thick p-type silicon nanowire (SiNW) with different bias polarities/strengths to find out the origin of the non-linear electrical characteristics and to find out the applicable bias window for ensuring the gate responsive operation as biosensor. Depending on the polarity and strength of the drain bias (VDS)/liquid gate voltage (VGS), conduction is found to be confined in the bulk, in the surface accumulation region or in a path that comprises both accumulation region and the bulk thereby results in the diode or transistor like non-linear output characteristics. Although the SiNW is long without any junctions in the source/drain, the transfer characteristics exhibits a distinct shift with the increase of VDS when VDS is positive. The non-linear characteristics of SiNW is analysed to extract the gate responsive biasing window and it is found that the positive VDS application provides the flexibility of choosing diverse range of VDS and VGS voltages. As such, the shift in the transfer characteristic which is not appreciated for logic operation appears to be beneficiary for nanowire biosensors. This study sheds light on the variable electrical characteristics of SiNW reported in the literature and shows the way to choose appropriate bias conditions for biosensing applications.

An Accurate Analytical Model of SiC MOSFETs for Switching Speed and Switching Loss Calculation in High-Voltage Pulsed Power Supplies

Nanosecond output pulse and high efficiency are achieved in high-voltage pulsed power supplies (HVPPSs) by applying silicon carbide (SiC) metal-oxide-semiconductor field-effect transistors ( <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mosfet</small> s), whose switching speed and switching loss are two vital characteristic parameters. However, the existing research on switching characteristics of SiC <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mosfet</small> s is mainly based on the double pulse test circuits with inductive loads, which is not suitable for assessing the devices in HVPPSs with resistive loads. Besides, some simplified analytical methods in HVPPSs lead to poor precision. To accurately predict the switching behavior of SiC <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mosfet</small> s in HVPPSs for guiding the design of gate driving circuits and power loops, this article proposes an accurate analytical model considering parasitic inductances, nonlinear parasitic capacitances, transfer characteristic, and output characteristics of SiC <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mosfet</small> s. High-precision fitting of transfer characteristic is realized by using the Gaussian function. Besides, the dynamic parasitic gate-drain capacitance is measured by experiment, and three-dimensional curve fitting is performed on the output characteristics to exactly represent <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</small> -resistance. Furthermore, switching speed and switching loss can be directly calculated according to the solved state variables. Finally, the analytical model is verified by experiment, and the effects of gate driving circuits and power loops on switching characteristics are researched in detail.

Experimental Study on Magnetic Coupling Piezoelectric-Electromagnetic Composite Galloping Energy Harvester.

In order to solve the demand for low-power microcomputers and micro-electro-mechanical system components for continuous energy supply, a magnetic coupling piezoelectric-electromagnetic composite galloping energy harvester (MPEGEH) is proposed. It is composed of a piezoelectric energy harvester (PEH) and an electromagnetic energy harvester (EEH) coupled by magnetic force. The bistable nonlinear magnetic coupling structure improves the output power of the MPEGEH. The advantages and output performance of the MPEGEH are analyzed. The prototype of the energy harvester is made, and the nonlinear output characteristics under different load resistances are analyzed. Through the experiment on the key parameters of the composite energy harvester, it is found that the higher the coupling degree of the two parts of the MPEGEH, the stronger the nonlinear characteristics and the better the output characteristics. The results show that the onset wind velocity and output power of the MPEGEH are better than the classic galloping piezoelectric energy harvester (CGPEH). At the same wind speed, with the increase in the distance d0 between magnets A and B, the output power of both the PEH and the EEH decreases. When d0 is 37 mm, the output power of the EEH is the largest. The distance s0 between magnets B and C has little influence on the output power of the PEH but has a great influence on the EEH. When s0 is 23 mm, the EEH has the best output characteristics. Compared with the CGPEH, the onset wind velocity is reduced by 28%, and the output power is increased by 136% when the wind speed is 11 m/s.

Application of Artificial Intelligence in PV Fault Detection

The rapid revolution in the solar industry over the last several years has increased the significance of photovoltaic (PV) systems. Power photovoltaic generation systems work in various outdoor climate conditions; therefore, faults may occur within the PV arrays in the power system. Fault detection is a fundamental task needed to improve the reliability, efficiency, and safety of PV systems, and, if not detected, the cost associated with the loss of power generated from PV modules will be quite high. Moreover, maintenance staff will take more time and effort to fix undetermined faults. Due to the current-limiting nature and nonlinear output characteristics of PV arrays, fault detection is not that easy and the application of artificial intelligence is proposed for the sake of fault detection in PV systems. The idea behind this approach is to compare the faulty PV module with its accurate model (factory fingerprint) by checking every PV array’s I–V and P–V curves using the Artificial Neural Network (ANN) logarithm as a subsection of the Artificial Intelligence’s (AI) techniques. This proposed approach achieves a high performance of fault detection and gives the advantage of determining what type of fault has occurred. The results confirm that the proposed logarithm performance becomes better as the number of distinguishing points extend, providing great value to the Solar PV industry.

A multi-scale dual-stage model for PV array fault detection, classification, and monitoring technique

The output generated by photovoltaic arrays is influenced mainly by the irradiance, which has non-uniform distribution in a day. This has resulted in the current-limiting nature and nonlinear output characteristics, and conventional protection devices cannot detect and clean faults appropriately. This paper proposes a low-cost model for a multi-scale dual-stage photovoltaic fault detection, classification, and monitoring technique developed through MATLAB/Simulink. The main contribution of this paper is that it can detect multiple common faults, be applied on multi-scale photovoltaic arrays regardless of environmental conditions, and be beneficial for photovoltaic system maintenance work. The experimental results show that the developed algorithm using supervised learning algorithms mutual with k-fold cross-validation has produced good performances in identifying six common faults of photovoltaic arrays, achieved 100% accuracy in fault detection, and achieved good accuracy in fault classification. Challenges and suggestions for future research direction are also suggested in this paper. Overall, this study shall provide researchers and policymakers with a valuable reference for developing photovoltaic system fault detection and monitoring techniques for better feasibility, safety, and energy sustainability.

A Fault Diagnosis Method for PV Arrays Based on New Feature Extraction and Improved the Fuzzy C-Mean Clustering

Photovoltaic (PV) array fault diagnosis is vital for the safe and stable operation of PV systems. Up to now, there are many methods to diagnose and classify PV array faults successfully. However, the difficulty of fault diagnosis is increased because the PV arrays have nonlinear output characteristics and complex working environments. In practice, the difference in characteristic parameters of different faults is not apparent, and it is not easy to effectively obtain labels of many samples. In order to address the above problems, this article proposes a new fault detection method for PV arrays based on the output current–voltage ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</i> – <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</i> ) characteristic, an improved fuzzy C-mean clustering (FCM) algorithm to identify four common PV array faults. The measured <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</i> – <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</i> characteristic curves are used to extract the initial feature parameters and then calculate the initial parameters to obtain characteristic parameters. In addition, it used characteristic parameters as feature variables of the FCM fault diagnosis model. Finally, classification results are verified by inner cluster maximum mean discrepancy and reclassification pseudoparameter based on the relationship between characteristic parameters. This method defines new characteristic parameters and achieves the purpose of fault detection by reclassification; in addition, we verify the high accuracy and simplicity of the method through simulation and experiment.

A piezoelectric–electromagnetic hybrid flutter-based wind energy harvester: Modeling and nonlinear analysis

This paper presents a piezoelectric–electromagnetic hybrid flutter-based energy harvester (HFEH), where the piezoelectric part and electromagnetic part are coupled with each other by magnetic forces. The working principle is explained in detail and the corresponding theoretical model of the HFEH is established based on Lagrange’s equations, Newton’s second law, Kirchhoff’s law, a semi-empirical nonlinear aerodynamic model, and a magnetic dipole model. The advantages of the HFEH, output performance, and nonlinear output characteristics under different magnet distances and load resistances are analyzed. Results show that the cut-in wind speed and the output power of the HFEH are, respectively, lower and higher than those of the typical flutter-based piezoelectric energy harvester (FPEH). When the distance between magnets A and B and that between magnets C and D is small, the amplitude jump phenomenon occurs, and the electromagnetic part has a satisfactory output power near the jump points. The output power of the piezoelectric part and the electromagnetic part of the HFEH, respectively, reaches 1.35 mW and 36.63 mW at a wind speed of 6.70 m/s. Overall, this study provides a theoretical framework for the design of high-efficiency wind energy harvesters.

On the origin of the turn-on voltage drop of GaN-based current aperture vertical electron transistors

Non-linear output characteristics and the related turn-on voltages of GaN-based current aperture vertical electron transistors (CAVETs) are investigated experimentally. The resistive components are systematically analyzed in dependence of the device layout to determine the dominant resistances in the devices. Current–voltage (IV) and capacitance–voltage-characteristics (CV) are compared to a proposed planar-doped barrier diode (PDBD) model, and the influence of the bound sheet charge density and drift layer carrier concentration is discussed. The observed CV characteristics are in contrast to the PDBD model as a clearly voltage-dependent capacitance was observed and dopant-diffusion forming a p-type aperture was ruled out. Thermionic emission was verified by temperature-dependent IV characteristics indicating interface states causing a potential barrier. Transient drain current measurements revealed a single dominating trap level with an activation energy of EA = 1.086 ± 0.015 eV. This activation energy was attributed to carbon-related acceptor states present at the regrowth interface and the drift layer. Additional test structures revealed that the interface potential barrier and the space charge in the drift layer limit the initial charge transport causing a turn-on voltage in the devices. The results point out the significance of a precise control of the regrowth interface properties and the effective carrier density in the drift layer to enable efficient, high-power devices based on the CAVET technology.

An improved method for PV output prediction using artificial neural network with overlap training range

Efficient and accurate photovoltaic (PV) modeling is significant for ensuring the stable operation of the power grid. The nonlinear output characteristics of PV modules are usually described by current–voltage (I–V) curves or power–voltage (P–V) curves. Traditional models normally include a diode model and an analytical model. The process of model parameter's extraction is complex and inconvenient because the equations of the diode model and analytical model are non-linear. In traditional extraction methods using a neural network, all irradiance training data are input into the neural network, which suffer from long training time and insufficient accuracy. In this paper, two neural networks with different training ranges are proposed to replace the whole neural network for predicting I–V curves, P–V curves, and maximum power of the analytical model. Each neural network consists of three layers. In detail, the input is the value of solar irradiance and module temperature, and the output is the shape parameters of the analytical model. The effectiveness of the proposed method is verified by massive experimental data of different PV modules. The output performance estimation of the proposed method is better than that of other methods.

On the Operation Modes of Dual-Gate Reconfigurable Nanowire Transistors

We investigate the operation of dual-gate reconfigurable field-effect transistor (RFET) in the programgate at drain (PGAD) and program-gate at source (PGAS) configurations. To this end, dual-gate silicon nanowire (SiNW) FETs are fabricated based on anisotropic wet chemical silicon etching and nickel silicidation yielding silicide-SiNW Schottky junctions at source and drain. Whereas in PGAD-configuration ambipolar operation is suppressed, switching is deteriorated due to the injection through a Schottky-barrier. Operating the RFET in PGAS configuration yields a switching behavior close to a conventional MOSFET. This, howewer, needs to be traded off against strongly non-linear output characteristics for small bias.

A software-hardware unit for studying the output characteristic of MEMS pressure sensors and its linearization

The paper presents the development of a linearization algorithm of the output parameters of the sensors based on the circuits that determine the static characteristics of the correction transducers, ensuring the linearity of the general conversion function with a given accuracy. Simulation modeling of correcting the output parameters of MEMS pressure sensors has been carried out. The qualitative characteristics of the measuring system are determined via providing the sensors with a means for determining the degree of reliability of their output data using a virtual exemplary tool. A technique for calibrating and linearizing the output characteristic of an analog MEMS pressure sensor has been developed. An algorithm for polynomial approximation of the nonlinear output characteristic of the sensor and a method for its implementation in Lab View software environment (by National Instruments) are presented. Virtual instruments developed in Lab View software environment have been the basis of software-hardware means for monitoring and processing of measuring data. The developed methodology and means of automation of measuring processes with the use of virtual devices made it possible to make the process of correcting the output parameters of sensors autonomous, independent of stationary measuring devices and debugging tools.

Steep slope carbon nanotube tunneling field-effect transistor

Tunneling field-effect transistors (TFETs) have emerged as a potential candidate to outperform conventional metal-oxide-semiconductor FETs at low voltages, since their operation mechanism can overcome the fundamental subthreshold swing (SS) limit of 60 mV/decade at room temperature. We report carbon nanotube (CNT) based TFETs with abrupt p-i-n tunneling junctions controlled by electrostatic doping. Minimum SS (SSmin) of ∼41 mV/dec is observed with nearly no temperature dependence, as clear evidence of the TFET operation. We further investigate devices using CNTs with smaller bandgaps, reporting a record high band-to-band tunneling (BTBT) current of ∼100 nA for a single CNT. Non-linear output characteristics are observed as expected for devices operating outside of the quantum capacitance limit (QCL). Overall, electrostatically doped CNT TFETs shine a promising path for low-power electronic applications.

Online Fault Diagnosis for Photovoltaic Arrays Based on Fisher Discrimination Dictionary Learning for Sparse Representation

The nonlinear output characteristics of PV arrays and maximum power point tracking (MPPT) techniques bring more difficulties to fault diagnosis. The fault diagnosis model based on electrical transient time-domain analysis is an effective method for solving the above problems. However, existing studies using transient processes usually train their models by extensive labeled datasets, and some approaches apply normalization methods with environmental condition sensors or reference PV panels. Therefore, Fisher discrimination dictionary learning (FDDL) for sparse representation is explored for diagnosing PV array faults, including line-to-line faults (LLF), open-circuit faults (OCF), and partial shading faults (PSF), with a small labeled dataset, and a dynamic normalization method without additional sensors is proposed to process transient data. Moreover, LLF and PSF that have similar characteristics under low mismatch should be further distinguished. The proposed model is designed with two stages. In the first stage, a multiple classifier trained using small labeled datasets with all fault types is applied to diagnose all kinds of studied PV array faults. Then, a dictionary only for PSF and LLF is learned in the second stage to further identify LLF and PSF. Finally, a 1.8 kW rooftop grid-connected PV system with $6\times3$ PV arrays is applied to validate the performance of the proposed model. The comparison result shows the superiority of the proposed model.