Naval University of Engineering

Similar large signal synchronizing instability that is common in traditional power system also exists in voltage source converter (VSC) dominated power system, which is increasingly reported and investigated. In this paper, the large signal instability of phase locked loop (PLL) synchronized VSC connected to weak ac grid is investigated. First, the influence of high grid impedance on PLL's dynamics is explored and an additional feedback loop is found to be introduced, which deteriorates the performance of PLL. Then an analysis model that is similar as the rotor motion model of synchronous generator (SG) is developed. Based on the developed model, equal-area method is employed to carry out the large-signal stability analysis. The large signal instability is found to be mainly resulted by the following two factors. One is the nonexistence of equilibrium point, which is similar as that there does not exist intersection between the mechanical power input curve and electromagnetic restoring force curve in SG grid connected system. The other is related with the transition process resulted by insufficient decelerating area, which leads to that the system cannot stably go through the transition from initial point to the existing equilibrium point. The proposed analysis not only contributes to revealing the physical mechanism but also provides guidance for the future improvement measures.

As a new kind of artificial material developed in recent decades, metamaterials exhibit novel performance and the promising application potentials in the field of practical engineering compared with the natural materials. Acoustic metamaterials and phononic crystals have some extraordinary physical properties, effective negative parameters, band gaps, negative refraction, etc., extending the acoustic properties of existing materials. The special physical properties have attracted the attention of researchers, and great progress has been made in engineering applications. This article summarizes the research on acoustic metamaterials and phononic crystals in recent decades, briefly introduces some representative studies, including equivalent acoustic parameters and extraordinary characteristics of metamaterials, explains acoustic metamaterial design methods, and summarizes the technical bottlenecks and application prospects.

Wind energy conversion system, aiming to convert mechanical energy of air flow into electrical energy has been widely concerned in recent decades. According to the installation sites, the wind energy conversion system can be divided into land-based wind conversion system and offshore wind energy conversion (OWEC) system. Compared to land-based wind energy technology, although OWEC started later, it has attracted more attentions due to its significant advantages in sufficient wind energy, low wind shear, high power output and low land occupancy rate. In this paper, the principle of wind energy conversion and the development status of offshore wind power in the world are briefly introduced at first. And then, the advantages and disadvantages of several offshore wind energy device (OWED), such as horizontal axis OWED, vertical axis OWED and cross axis OWED are compared. Subsequently, several major constraints, such as complex marine environment, deep-sea power transmission and expensive cost of equipment installation faced by offshore wind conversion technology are presented and comprehensively analysed. Finally, based on the summary and analysis of some emerging technologies and the current situation of offshore wind energy utilization, the development trend of offshore wind power is envisioned. In the future, it is expected to witness multi-energy complementary, key component optimization and intelligent control strategy for smooth energy generation of offshore wind power systems.

This paper derives a novel initial alignment method for the strapdown inertial navigation system (SINS), which transforms the attitude alignment into an attitude estimation problem. The process model of the proposed initial alignment method by attitude estimation is established by decomposition of the attitude matrix. The measurement model is constructed based on a generalized velocity integration formula that can integrate the inertial measurements over certain fixed time intervals. The contributions of the work presented here are twofold. First, the attitude estimation-based structure enables the proposed method to estimate the gyroscope biases other than the attitude quaternion, which is celebrated for the low-cost SINS. The second is the application of the proposed generalized velocity integration formula to attenuate the accumulated errors in vector observations caused by the traditional velocity integration formula. Experimental road tests are performed with a low-cost SINS, which validate the efficacy of the proposed method.

State of charge (SOC) represents the amount of electricity stored and is calculated and used by battery management systems (BMSs). However, SOC cannot be observed directly, and SOC estimation is a challenging task due to the battery’s nonlinear characteristics when operating in complex conditions. In this paper, based on the new advanced deep learning techniques, a SOC estimation approach for Lithium-ion batteries using a recurrent neural network with gated recurrent unit (GRU-RNN) is introduced where observable variables such as voltage, current, and temperature are directly mapped to SOC estimation. The proposed technique requires no model or knowledge of the battery’s internal parameters and is able to estimate SOC at various temperatures by using a single set of self-learned network parameters. The proposed method is evaluated on two public datasets of vehicle drive cycles and another high rate pulse discharge condition dataset with mean absolute errors (MAEs) of 0.86%, 1.75%, and 1.05%. Experiment results show that the proposed method is accurate and robust.

Fault diagnosis and prognosis (FDP) tries to recognize and locate the faults from the captured sensory data, and also predict their failures in advance, which can greatly help to take appropriate actions for maintenance and avoid serious consequences in industrial systems. In recent years, deep learning methods are being widely introduced into FDP due to the powerful feature representation ability, and its rapid development is bringing new opportunities to the promotion of FDP. In order to facilitate the related research, we give a summary of recent advances in deep learning techniques for industrial FDP in this paper. Related concepts and formulations of FDP are firstly given. Seven commonly used deep learning architectures, especially the emerging generative adversarial network, transformer, and graph neural network, are reviewed. Finally, we give insights into the challenges in current applications of deep learning-based methods from four different aspects of imbalanced data, compound fault types, multimodal data fusion, and edge device implementation, and provide possible solutions, respectively. This paper tries to give a comprehensive guideline for further research into the problem of intelligent industrial FDP for the community.

In order to reduce the cost pressure on cold-chain logistics brought by the carbon tax policy, this paper investigates optimization of Vehicle Routing Problem (VRP) with time windows for cold-chain logistics based on carbon tax in China. Then, a green and low-carbon cold chain logistics distribution route optimization model with minimum cost is constructed. Taking the lowest cost as the objective function, the total cost of distribution includes the following costs: the fixed costs which generate in distribution process of vehicle, transportation costs, damage costs, refrigeration costs, penalty costs, shortage costs and carbon emission costs. This paper further proposes a Cycle Evolutionary Genetic Algorithm (CEGA) to solve the model. Meanwhile, actual data are used with CEGA to carry out numerical experiments in order to discuss changes of distribution routes with different carbon emissions under different carbon taxes and their influence on the total distribution cost. The critical carbon tax value of carbon emissions and distribution cost is obtained through experimental analysis. The research results of this paper provide effective advice, which is not only for the government on carbon tax decision, but also for the logistics companies on controlling carbon emissions during distribution.

This paper presents an improved analytical method for calculating the magnetic field and cogging torque in the surface-mounted permanent-magnet (PM) machines accounting for any eccentric rotor shape. The magnetic field in the surface-mounted PM machines is predicted according to the surface-current method of the PM, the subdomain model, and the superposition principle of vector potential. The cogging torque is calculated based on Maxwell stress theory and the air-gap magnetic field. The investigation shows that harmonic contents of radial flux density can be reduced a lot by changing eccentric distance of eccentric magnet poles compared with conventional surface-mounted PM machines with concentric magnet poles. Moreover, the analysis has made it clear that the cogging torque of motor with eccentric magnet poles is much smaller than the cogging torque of motor with concentric magnet poles. The finite-element results confirm that the developed analytical method has high accuracy for predicting the magnetic field and cogging torque.

This technical note concerns the deterministic sampling points construction strategy for unscented Kalman filter (UKF) and cubature Kalman filter (CKF). From the numerical-integration viewpoint, a new deterministic sampling points set is derived by orthogonal transformation on the cubature points. By embedding these points into the UKF framework, a modified nonlinear filter named transformed unscented Kalman filter (TUKF) is derived. The TUKF can address the nonlocal sampling problem inherent in CKF while maintaining the virtue of numerical stability for high dimensional problems. Moreover, the methodology proposed in this technical note can be used to construct nonlinear filters with improved accuracy for certain problems. The performance of the proposed algorithm is demonstrated through a nonlinear high dimensional problem.

On the basis of introducing the origin and development of finite time thermodynamics (FTT), this paper reviews the progress in FTT optimization for internal combustion engine (ICE) cycles from the following four aspects: the studies on the optimum performances of air standard endoreversible (with only the irreversibility of heat resistance) and irreversible ICE cycles, including Otto, Diesel, Atkinson, Brayton, Dual, Miller, Porous Medium and Universal cycles with constant specific heats, variable specific heats, and variable specific ratio of the conventional and quantum working fluids (WFs); the studies on the optimum piston motion (OPM) trajectories of ICE cycles, including Otto and Diesel cycles with Newtonian and other heat transfer laws; the studies on the performance limits of ICE cycles with non-uniform WF with Newtonian and other heat transfer laws; as well as the studies on the performance simulation of ICE cycles. In the studies, the optimization objectives include work, power, power density, efficiency, entropy generation rate, ecological function, and so on. The further direction for the studies is explored.

Cell membranes have been continuously imitated and used for the modification of nanoparticles (NPs) to improve NP biological properties. Cell membrane-coated NPs, where core NPs are wrapped with plasma membrane vesicles, show high biocompatibility, targeting specificity and low side effects. Compared with conventional strategies, this novel approach directly leverages intact and natural functions of cell membranes, instead of replicating these features via synthetic techniques. This top-down technique bestows NPs with enhanced biointerfacing capabilities with potential in the diagnosis and treatment of cancer, infection and other diseases. Herein, we report on the advances in cell membrane-coated NPs, including the preparation process, source cell membranes for wrapping and potential applications of these cell membrane-coated NPs.

This paper presents a study of a quasi-zero-stiffness (QZS) isolator. A unique relationship between the geometry configuration and the stiffness of the spring elements is obtained in order to design the property of high-static-low-dynamic stiffness. Analytical solutions of the nonlinear QZS system are derived with the harmonic balance method for the characteristic analysis of the force transmissibility and critical conditions for occurring jump-down and jump-up phenomena. The effects of damping and excitation force on the system behaviors are discussed. A series of experimental tests demonstrate that the QZS system greatly outperforms a corresponding linear isolation system. The former enables vibration to be attenuated at 0.5 Hz, while the latter can only execute attenuation after 4.2 Hz. The QZS system is especially effective for vibration isolation in the low-frequency range.

This study concerns the unscented Kalman filter (UKF) for the non-linear dynamic systems with error statistics following non-Gaussian probability distributions. A novel robust unscented Kalman filter (NRUKF) is proposed. In the NRUKF the measurement information (measurements or measurements noise) is reformulated using Huber cost function, then the standard unscented transformation (UT) is applied to exact non-linear measurement equation. Compared with the conventional Huber-based unscented Kalman filter (HUKF) which is derived by applying the Huber technique to modify the measurement update equations of the standard UKF, the NRUKF, without linear (statistical linear) approximation, has much-improved performance and versatility with maintaining the robustness. Then the NRUKF is applied to the target tracking problem. The validity of the algorithm is demonstrated through numerical simulation study.

The integration of the inertial navigation system and the global positioning system (INS/GPS) is a widely used procedure for position and attitude determination applications. The Kalman type filter (KTF) is the primary mechanism to perform the integration. In the KTF, the process noise is always assumed to be Gaussian distribution, which may be violated by the vehicle's severe maneuver, resulting in a much degraded performance. In this paper, the Huber's M-estimation methodology is investigated to suppress the process uncertainty, founded on the cascaded form of the M-estimation-based Kalman filter. An iterated algorithm is designed to construct the weighted matrix to rescale the prior state estimate covariance. The proposed process uncertainty robust algorithm is embedded into the newly derived modified unscented quaternion estimator to perform the standard inertial navigation equations-based INS/GPS integration. The car-mounted experiments are carried out to validate the proposed method against the process uncertainty.

The use of electromagnetic launch (EML) technology in the future launching mode is an inevitable trend. Based on the analysis of the common characters of EML technology, this paper presents the status of three technological branches such as EM aircraft launch, EM rail gun, EM thrust launch, and discusses some concrete problems and related research achievements in detail. Furthermore, clues and important points proposed in this paper will provide valuable reference for developing EML technology.

It is well known that very high dv/dt and di/dt during the switching instant is the major high-frequency electromagnetic interference (EMI) source. This paper proposes an improved and simplified EMI-modeling method considering the insulated gate bipolar transistor switching-behavior model. The device turn-on and turn-off dynamics are investigated by dividing the nonlinear transition by several stages. The real device switching voltage and current are approximated by piecewise linear lines and expressed using multiple dv/dt and di/dt superposition. The derived EMI spectra suggest that the high-frequency noise is modeled with an acceptable accuracy. The proposed methodology is verified by experimental results using a dc-dc buck converter

This paper proposes a modified unscented Kalman filter (UKF) with both adaptivity and robustness. In the proposed filter, the adaptivity is achieved by estimating the time-varying measurement noise covariance based on variational Bayesian (VB) approximation. The robustness is achieved by modifying the filter update based on Huber's M-estimation and Gaussian-Newton iterated method. In Gaussian assumptions, the proposed filter has a comparable filtering accuracy with the original UKF and better filtering consistency. When the measurement noise covariance is time-varying and there are outliers in the measurements, the proposed filter can outperform UKF and other adaptive or robust filters (such as VB-based UKF and Huber-based UKF) in terms of both filter accuracy and consistency. The efficacy of the proposed filter is demonstrated through the numerical simulation test and integrated navigation shipborne test.

A frequency selective surface with absorptive/ transmissive property is represented. It allows waves at high frequency around 10 GHz to transmit with very low insertion loss by using the resonance between a parallel microstrip LC structure. It also possesses a wide absorption over lower band by inserting lumped resistors into elements. The absorption band is over 3–9 GHz. A prototype is fabricated and its absorptive/ transmissive performance is measured.

In this paper, a load torque observer and two moment of inertia identification methods are developed for permanent magnet synchronous motor drive systems. First, the load torque identification method is proposed based on sliding mode observer, in which the mismatches of moment of inertia, electromagnetic torque, and viscous friction are all considered. It is analyzed that the chattering phenomenon will be well weakened if appropriate observer parameters are chosen. Second, based on the property that the mismatch of the moment of inertia will cause the load torque observation error, two types of moment of inertia identification methods, called direct calculation (DC) method and proportional integral (PI) regulator method, are presented to identify the moment of inertia. In addition, the identification errors of the DC and PI regulator methods are also analyzed theoretically. Simulation and experimental results show that compared with the conventional identification method of the moment of inertia, the proposed DC and PI methods have improved estimation accuracy. Moreover, the presented load torque observer also has high observation precision and fast convergence speed.

Land use and land cover (LULC) mapping in urban areas is one of the core applications in remote sensing, and it plays an important role in modern urban planning and management. Deep learning is springing up in the field of machine learning recently. By mimicking the hierarchical structure of the human brain, deep learning can gradually extract features from lower level to higher level. The Deep Belief Networks (DBN) model is a widely investigated and deployed deep learning architecture. It combines the advantages of unsupervised and supervised learning and can archive good classification performance. This study proposes a classification approach based on the DBN model for detailed urban mapping using polarimetric synthetic aperture radar (PolSAR) data. Through the DBN model, effective contextual mapping features can be automatically extracted from the PolSAR data to improve the classification performance. Two-date high-resolution RADARSAT-2 PolSAR data over the Great Toronto Area were used for evaluation. Comparisons with the support vector machine (SVM), conventional neural networks (NN), and stochastic Expectation-Maximization (SEM) were conducted to assess the potential of the DBN-based classification approach. Experimental results show that the DBN-based method outperforms three other approaches and produces homogenous mapping results with preserved shape details.