A switched-inductor (SL) multi-cell boost converter is analyzed in this paper for a high-voltage gain application, stepping up a dc voltage from 36 V to 380 V in the first stage of a photovoltaic (PV) conversion chain. A fast maximum power point tracker (MPPT), processing the system input voltage, is used to extract the maximum power from the PV generator regardless of atmospheric conditions. A single sliding-mode control (SMC) loop forces the PV generator voltage to follow the maximum power point (MPP) voltage provided by a Perturb and Observe (P&O) algorithm. The sliding-mode analysis uses the equivalent control approach to demonstrate that the linearized ideal sliding dynamics are unconditionally stable. Theoretical predictions are corroborated by simulations and experimental measurements of the system under step-type changes in input irradiance and output load. The MPPT performance is experimentally evaluated against two classical approaches applied to a canonical boost converter: a current-based SMC and a voltage-based PWM. Both approaches track the MPP current and voltage, respectively, as given by the P&O algorithm. The proposed system outperforms the two classical systems, showing a better tracking accuracy.

The emerging vehicle-to-vehicle communication technique enables vehicle platoon control, which greatly increases road throughput and travel efficiency. For cybernetically connected vehicles (CVs) with dynamics heterogeneities subject to actuator uncertainties, a dynamic event-triggered platoon control protocol is proposed in this paper to significantly reduce communication overheads. By considering the platoon problem of CVs as an output tracking consensus problem of heterogeneous multi-agent systems (MASs), a hierarchical control framework composed of an upper-level interactive event-triggered observer layer and a lower-level local tracking controller layer is established. Within the proposed framework, considering a virtual dynamic leader with non-zero inputs and external disturbances, a distributed observer is first designed for each following vehicle to observe the leader in an event-triggered manner. An internal dynamic variable is introduced to construct the dynamic triggering law, which not only relaxes the requirement on continuous state transmission in triggering detection but also benefits the exclusion of the Zeno behavior. Then, the solutions of a set of regulator equations associated with the proposed observer and the observer itself are integrated into the local tracking controller design to guarantee the tracking ability of each follower agent to its own observer. As thus, an event-triggered platoon control mechanism of heterogeneous CVs is proposed. Simulations and experiments on unmanned ground vehicles (UGVs) are conducted, which validate the effectiveness of the proposed platoon control method.

As the complexity of the power system continues to increase, the frequency of the power system anomalies is on the rise. These anomalies have significant and widespread impacts on the stability of the power grid. Therefore, the rapid and accurate classification of these anomalies is crucial in preventing their further propagation and mitigating potential economic losses. This study presents an algorithm based on Phasor Measurement Unit (PMU) data for monitoring the state of power systems and identifying the types of anomalies. First, a dataset for anomaly event classification is created based on PMU data, which is used to train and validate the anomaly event classification model. Subsequently, a robust anomaly event classification model is constructed, consisting of a residual module with one-dimensional Convolutional Neural Networks (CNN) and a cascaded fully connected neural network classifier. This algorithm has undergone rigorous testing in the IEEE New England 39 bus test system, demonstrating exceptional event recognition accuracy.

This study focuses on distributed event-triggered consensus control under the scenario where only inaccurate agent model information is available. By designing a novel triggering error, a distributed dynamic event-triggered consensus (DETC) protocol is proposed for multiagent systems (MASs) with general linear dynamics over digraphs, without accurate a priori information of agent models. To improve the efficiency of the dynamic triggering law, a mixed triggering threshold is designed with a resilient function integrated to further enlarge interevent intervals. Within the proposed DETC protocol, the computational cost is significantly reduced especially for MASs with nonsparse and high-dimensional agent system matrices. In addition, for each individual agent, the states of neighboring agents used for triggering detections or controller updates are required in an on-demand (instead of continuous) way, which preserves communication resources and facilitates practical implementation. The feasibility of the designed DETC protocol is corroborated by rigorous theoretical analysis on consensus convergence and Zeno behavior exclusion. Finally, simulations are shown to demonstrate the effectiveness of the studied theory.

Confronted with the task environment full of repetitive textures, the state-of-art description and detection methods for local features greatly suffer from the "pseudo-negatives," bringing inconsistent optimization objectives during training. To address this problem, this article develops a self-supervised graph-based contrastive learning framework to train the model for local features, GCLFeat. The proposed approach learns to alleviate the pseudo-negatives specifically from three aspects: 1) designing a graph neural network (GNN), which focuses on mining the local transformational invariance across different views and global textual knowledge within individual images; 2) generating the dense correspondence annotations from a diverse natural dataset with a self-supervised paradigm; and 3) adopting a keypoints-aware sampling strategy to compute the loss across the whole dataset. The experimental results show that the unsupervised framework outperforms the state-of-the-art supervised baselines on diverse downstream benchmarks including image matching, 3-D reconstruction and visual localization. The code will be made public and available at https://github.com/RealZihaoWang/GCLFeat.

The task of key-points detection and description is to estimate the stable location and discriminative representation of local features, which is a fundamental task in visual applications. However, either the rough hard positive or negative labels generated from one-to-one correspondences among images may bring indistinguishable samples, like false positives or negatives, which acts as inconsistent supervision. Such resultant false samples mixed with hard samples prevent neural networks from learning descriptions for more accurate matching. To tackle this challenge, we propose to learn the transformation-predictive representations with self-supervised contrastive learning. We maximize the similarity between corresponding views of the same 3D point (landmark) by using none of the negative sample pairs and avoiding collapsing solutions. Furthermore, we adopt self-supervised generation learning and curriculum learning to soften the hard positive labels into soft continuous targets. The aggressively updated soft labels contribute to overcoming the training bottleneck (derived from the label noise of false positives) and facilitating the model training under a stronger transformation paradigm. Our self-supervised training pipeline greatly decreases the computation load and memory usage, and outperforms the sota on the standard image matching benchmarks by noticeable margins, demonstrating excellent generalization capability on multiple downstream tasks.

The offshore wind power is an effective replacement for thermal power plants to realize the high renewable penetrated power system and guarantee the zero emissions. AC transmission and grid connection has the advantage among various solutions from the viewpoint of economics in implementation, where AC submarine cable is the medium of power transmission to deliver the offshore wind farm to the onshore grid. However, such offshore wind power system is more susceptible to the risk of high-frequency oscillation due to the highly distributed capacitance of AC submarine cables. To address this problem, this paper builds the impedance model for offshore wind power via ac submarine cable and further conducts the frequency-domain analysis towards the associated oscillation problem. Firstly, the impedance model of the collection network is constructed and validated. Furthermore, the system stability is analyzed based on the Nyquist criteria so as to analyze the effects of cable parameters on the oscillation frequency of the interconnected system. Finally, the simulation results are provided to verify the correctness of the proposed theoretical analysis.

Significant progress has been witnessed for the descriptor and detector of local features, but there still exist several challenging and intractable limitations, such as insufficient localization accuracy and non-discriminative description, especially in repetitive- or blank-texture regions, which haven't be well addressed. The coarse feature representation and limited receptive field are considered as the main issues for these limitations. To address these issues, we propose a novel Soft Point-Wise Transformer for Descriptor and Detector, simultaneously mining long-range intrinsic and cross-scale dependencies of local features. Furthermore, our model leverages the distinct transformers based on the soft point-wise attention, substantially decreasing the memory and computation complexity, especially for high-resolution feature maps. In addition, multi-level decoder is constructed to guarantee the high detection accuracy and discriminative description. Extensive experiments demonstrate that our model outperforms the existing state-of-the-art methods on the image matching and visual localization benchmarks.

Electric vehicles have become a trend as a replacement to gasoline-powered vehicles, and been promoted by worldwide policy makers as a solution to combat environmental problems and stimulate economy, whereas the lack of extreme fast charging infrastructure has become one main obstacle to broad adoption of electric vehicles. To promote the commercial success of electric vehicles, millions of extreme fast charging stations are expected to be established in the next decade in the U.S. However, the widespread of electric vehicles and extreme fast charging infrastructure would impact transportation systems, such as mobility, congestion, equity, toll revenue, and infrastructure maintenance needs. As transportation infrastructure can last for a long time, effective planning must be able to predict the impact of projects and policies decades into the future. Given the nature of transportation systems that is of multiple interacting systems, this study develops mathematical models to quantitatively analyze this complicated and important problem. In particular, we utilize game theory to understand the dependency between the choices made by travelers, and the congestion and delay in the system with respect to traffic assignment. Our research results provide insights and guidance to strategic planning of extreme fast charging infrastructure.

Modern power systems utilize information, communication, and computation technologies for control and coordination of the various subsystems and enhancement of the system's operating performance. In this paper, we develop a model to simulate cascading failure in a cyber-coupled power system considering the system-level control provided by the cyber network. In the events of failure of components or subsystems, the states of the power network will be monitored, collected, and transmitted in real time to the control center, where control algorithms are performed to generate commands for controlling the power at each node. We develop an optimization algorithm for the control center that maximizes power generation and avoids power imbalance between generators and loads and overloading on each component. The vulnerability of the cyber-coupled power system to initial component failures is assessed based on the model and cyber faults that damage the state-monitoring and controlling function of the cyber layer are considered. Simulation results demonstrate that a power system coupled with a cyber control system can effectively reduce the cascading failure risk. However, the control dysfunction of the cyber layer leads to catastrophic cascading failure and intensifies the extent of power outage, making the system more vulnerable to cascading failure.

This study employs the Caputo-Fabrizio fractional derivative to determine the model of fractional-order RC circuits with arbitrary voltage input which can be widely used in a variety of electrical systems. Analog circuit implementation of fractional-order RC circuits defined by Caputo-Fabrizio fractional derivative is presented and verified by comparing with the model proposed in this work. For the purpose of judging whether the fractional-order model defined by the Caputo-Fabrizio derivative is practical, the comparison experiments are carried out. By using Laplace transform, the analytical solutions of fractional-order RC circuits based on the Caputo-Fabrizio derivatives with constant and periodic voltage sources are deduced. Fractional-order model of RC circuits with arbitrary input are also calculated using the convolution formula. The correctness of the derivation of the model using the Caputo-Fabrizio derivative is verified. Through discussing the impedance model of capacitor in frequency domain, the analog realization of fractional capacitor based on the Caputo-Fabrizio derivative is derived. The fractional-orders of the RC circuits models defined by the Caputo and Caputo-Fabrizio fractional derivatives are fitted respectively through repeated charging and discharging experiment data. The fractional-order models based on the Caputo and Caputo-Fabrizio derivatives, and the integer-order model are all compared with the experiment data.

As a significant part of the next-generation smart grid, electric vehicles (EVs) are essential for most countries to achieve energy independence, secure energy supply, and alleviate the pressure on environmental protection and energy security. Although EVs have grown rapidly, the slow recharge time is still the biggest obstacle to a wider application. While gasoline vehicles can pump enough gasoline in less than ten minutes, which can carry themselves a few hundred miles. However, most of today’s fast-charging techniques take half an hour only to provide very limited miles of electric driving range.

The unmanned aerial vehicles (UAVs) formation needs the frequent data-exchanging of individual's state between units for monitoring and instruction uploading from the ground station, which inevitably occupies huge communication bandwidth. This paper presents the extended Kalman filter (EKF) design based on the event-triggered strategy to get UAVs greatly relieved of the communication burden with guaranteed accuracy. The event-triggered strategy firstly selects only the state measurements containing innovational information for the purpose of filtering. Due to the nonlinearity of UAV system, the EKF is further applied to make full use of the information from the prior event-trigger strategy so as to enhance the performance of estimation. The proposed algorithm is verified on the physical UAVs regarding the estimation quality and communication rate, demonstrating the robust dynamic performance with effectively reduced communication rate.

The paper presents an approach to enhancing the dynamic response of a cascaded DC/DC converter system, where virtual impedance control schemes are used for stabilization. A cascaded DC/DC converter system may not be stable when the source and load converters are not designed appropriately. Such system can be stabilized by reshaping the input impedance of the load converter with the virtual impedance control method. However, the dynamic performance suffers due to the added virtual-impedance control loop. In this paper, the dynamic performance of the cascaded DC/DC converter system is enhanced with an additional auxiliary circuit that is connected to the output port of the load converter and functions to buffer fast load transients. The simulation result shows that the voltage transient deviation on the load converter is significantly reduced with the proposed method.

Due to the advantages of large unit capacity, high efficiency and high reliability, direct-drive permanent magnet synchronous generators (PMSGs) wind turbines (WTs) have been widely used in the offshore wind power generation. The power loop controller aims to maintain the DC-Bus voltage of each PMSG-WT is equal to the others, guaranteeing that multiple WTs can be connected in DC serial parallel collection. In this paper, the DC-Port sequence impedance model of the PMSG-WT is established based on the harmonic linearization method, which takes the outer power loop into consideration. The impedance model is further verified by point-by-point scanning in MATLAB/Simulink. The proposed DC-Port impedance model of the PMSG-WT facilitates the establishment of offshore direct-drive wind farms, and is of great significance for analyzing and improving the system stability.

The wide area measurement systems (WAMS) plays a critical part in power system reliability. Among all the WAMS applications, the dynamic state estimation (DSE) gathers all the data from the phasor measurement units (PMU) through the communication link so that the real-time wide area monitoring is facilitated at the application server to capture the dynamics of every generators. However, the DSE inevitably suffers from the communication link congestion and latency due to the explosive increase of grid size. To deal with this problem, this paper designs the event-trigger cubature Kalman filter (ETCKF) as an effective solution to get the communication burden relieved, by transmitting only the PMU measurements containing innovation. In order to provide the design-oriented strategy, various kinds of event-trigger strategies are evaluated in view of the estimation performance under the same communication rate. Finally, the standard IEEE 39-bus system is under test to verify the feasibility of ETCKF and the performance of various strategies.

The tensor product model transformation (TPMT) is an emerging numerical framework of the Takagi-Sugeno (T-S) fuzzy (or polytopic) system modeling for a linear matrix inequality based system control design. A nested TPMT (NTPMT) is proposed in this paper, which merges the dimensions of the tensors and performs the TPMT iteratively. The resultant fuzzy model is in a multilevel nested tensor product (TP) structure. The vertex tensor obtained by NTPMT has fewer dimension results than the original TPMT so the number of vertices or fuzzy rules, which have been the main bottleneck for further application of the TPMT in higher dimensional systems, is expected to decrease significantly. It is also proven that the NTPMT contains the hierarchical fuzzy logic, meaning that the NTPMT is capable of conducting hierarchical fuzzy modeling and reduction. Furthermore, because the inclusion of multiple TPMTs is prone to augment the conservativeness of the resultant fuzzy model, a suboptimal convex hull rectification algorithm for the TPMT is developed based on a newly defined tightness measure, and then extended to render the NTPMT as less conservative as possible. Finally, numerical simulations on two real physical systems (two- and four-parameter dimension) are verified to demonstrate the performance of the methods.

The reliable operation of doubly-fed induction generator (DFIG) wind turbine (WT) systems rely on the accurate information of states. However, due to the unavailability of some states from phasor measurement units (PMUs), dynamic state estimation (DSE) for DFIG-WT connecting to the power system becomes essential. Although various DSEs have been applied, the variable stochastic wind speed was excluded into consideration, leading to the inaccurate estimation results. This paper develops the DSE using centralized Kalman filter (CKF) for DFIG-WT under the stochastic wind speed. The wind speed is modeled by stochastic differential equations (SDE), which can generate the trajectories with statistical properties similar to the wind speed historical data available for a particular location, so that the variable wind speed can be applied to the filtering process. Finally, the system involving a DFIG connected to a standard IEEE 14-bus system is utilized to verify the feasibility of the proposed method with the occurrence of electric faults.

The lifetime prediction of LED lighting system is important to guide the designers to fulfill the design specifications and to benchmark the cost-competitiveness of different lighting technologies. Currently, the lifetime of LED system is usually predicted from the source part and the driver part separately, and then the thermal design is also optimized independently. In practice, the LED source and driver are usually compacted in a single fixture. The heat dissipated from LED source and driver will be coupled together and affect the heat transfer performance, which may degrade the whole system and accelerate the failure. In this paper, a new thermal model concerning the thermal coupling is proposed with Finite Element Method (FEM) simulation for parameter acquirement. The proposed model has a better estimation of the thermal stresses of key components in the LED lamps and therefore an improved lifetime prediction of the LED systems. Moreover, with a given lifetime requirements, this method can be used to guide the thermal design of the LED system. A case study of an outdoor lighting application is then demonstrated by both FEM simulations and experimental verifications in this digest.

Distributed generation (DG) is connected through three-phase inverter to the microgrid. The virtual power based droop control can guarantee the pure decoupling between active and reactive power irrespective of low-voltage or high-voltage microgrid application if the line-impedance information is known in advance. However, unbalances are commonly existent in practice so that this conventional control fails in application due to the significant coupling between active and reactive power. To overcome this problem and further extend virtual power based method into unbalanced conditions, this paper proposes an improved virtual power decoupling technique using full-dq transformation, which is utilized to identify arbitrary unbalanced components under unbalanced conditions. The identified unbalances are further compensated as an virtual impedance so as to ensure the pure decoupling of active and reactive power in the virtual power frame. Finally, simulation results are provided to verify the effectiveness of the proposed method under unbalanced conditions.

Three-phase inverter is a very important interface in microgrid. Droop control and virtual impedance are widely used to improve the power sharing capability and stability of such system, which however becomes ineffective under unbalanced conditions and may even cause potential stability problems. This paper proposes a fully self-adjusted virtual impedance design to guarantee the decoupling of active and reactive power. The arbitrary unbalanced information is acquired through the pseudo-inverse impedance matrix modeled by full-dq technique. Based on the unbalanced impedance identified, the adaptive droop control is achieved for not only accurate power sharing but also reliable voltage support with autonomous unbalanced voltage compensation. Simulations results verify the accurate identification of unbalanced impedance and effectiveness of the proposed method on the unbalanced compensation.

This paper explores the dynamic behavior of dual flux coupled memristor circuits in order to further ascertain fundamental theory of memristor circuits. Different cases of flux coupling are mathematically modelled where two memristors are connected in both series and parallel, with consideration given to the polarity of each device. The dynamic behavior is characterized based on the constitutive relations, with a variation of memductance represented in terms of flux, charge, voltage and current. The agreement between theoretical and simulation analyses affirm the memristor closure theorem with coupled memristor circuits behaving as a different type of memristor with higher complexity.

Plenty of distributed generation (DG) units directly export DC power such as the PV arrays. As a commercial interface, three-phase voltage-source inverters (VSIs) are commonly equipped for the power conversion from the DC power sources to the AC utility. In a practical DG system, the grid-connected loads are full of dynamics at the point of common connection (PCC). Thus, grid-connected loads inevitably have mutual interaction with VSI at PCC, which may have an impact on the stability and deteriorate the regulation capability of VSI. In this paper, the oscillatory behavior of single three-phase VSI is studied in the presence of resistive loads. The corresponding oscillation boundaries of the system are numerically collected for various circuit conditions with respect to the emergence of a low-frequency oscillation as a Hopf-type phenomenon.

The DFIG-wind turbine, can create the potential instability to the existent power grid, equivalently presenting itself as a random voltage source with its dynamic impedance, which has the implicit and varying electrical characteristics with the changeable rotor speed. Thus, it inevitably leads to the mutual interaction between DFIG-wind turbines themselves and dynamic custom loads. This paper studies the closed-form impedance analysis of a practical DFIG-wind turbine system. A small-signal model of the DFIG system in the dq-frame is developed with respect to discrete rotor speeds using the impedance-based approach. Moreover, a practical testing method is adopted to facilitate the verification of the impedance modeling.

DFIG wind turbine systems are attractive renewable resources in distributed generation units and micro-grids. Due to the direct-connection of its stator windings to the single point of common coupling, DFIG is vulnerable to the unequally-distributed loads at low voltage and arises various hazardous complex dynamic behavior in reliability and harmonics. This paper studies the dynamic behavior of this system and reveals the period-doubling bifurcation phenomena. Under certain loading conditions when the system is undergoing negative sequences from unbalanced loads, the period-doubling and its stability boundaries has been observed and investigated with full-circuit MatLab simulations, illustrating the potential instability problems from loads and control parameters.

The d-q transformation is widely used in the three-phase system analysis, targeting for symmetrical and balanced systems where the time-varying line-frequency sinusoidal terms of the system state variables can be fully canceled out. With the introduction of the distributed generations, more and more unbalanced infrastructures can be connected to the grid, creating unbalanced systems. It will be desirable to have effective analytical methods and modeling techniques for unbalanced systems. This paper proposes a new approach to analyzing unbalanced systems by demonstrating a decoupled analytical technique for resolving the unbalanced system into the balanced positive-, negative- and zero-sequence system components in the circuit level. The proposed method decomposes the unbalanced admittance of the system into independent admittance bases (IAB) each of which belongs to one of the positive-, negative-or zero-sequence system component to ease subsequent analyses.

This paper proposes a new anti-windup dual-loop control of doubly-fed induction generator (DFIG) under unbalanced grid voltage conditions. System behavior and controller operation of windup under unbalanced conditions are illustrated. To avoid the windup problem, a new extraction method based on Discrete Fourier Series (DFS) is proposed to decouple the sequence components indifferent to sub- or super- synchronous operation of the DFIG system. A dual loop control is developed for the decoupled positive and negative sequences. The positive sequence control loop is responsible for the conventional torque control and the additional negative sequence control loop is responsible for the minimization of 2ndharmonic in torque pulsation and dc link voltage fluctuation. Simulation results using Matlab/SimPowerSystems library are presented for the built-in single loop and modified dual-loop 9 MW DFIG wind generation systems under unbalanced grid voltage condition to demonstrate the system operation and validate the proposed dual-loop control scheme.

Wind energy generation systems are major components of distributed generation units or micro-grids. Application of the doubly fed induction generator (DFIG) in wind energy generation systems allows variable speed operation by using partially rated back-to-back quadruple active and reactive power PWM converters. The control of the system is very complex. Many new control schemes reported in the literature are designed to solve some specific control problems and have not thoroughly been tested for general stability with local loading which is common in micro-grids. In this paper, the oscillation boundaries of the system in the presence of an unbalanced three-phase reactive load are studied. Specifically, we report the system stability in terms of the amplitude modulated oscillatory magnitude of the three-phase voltage and current of the generator under a local reactive three phase load. The oscillation can also be observed as a linear modulated oscillation of the voltage link capacitor inside the wind energy generator. Oscillation boundaries of the system are collected using various damping component values of the three phase load, illustrating the potential instability problems that may be caused by imbalanced reactive loads.