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Summary: Most of the existing adaptive filter algorithms pay more attention to improving performance while ignoring the computational complexity and the impact of the impulsive environment. When encountering the impulsive noise environments, the performance of traditional nonlinear adaptive filter may be significantly reduced and usually needs high computational cost. Therefore, this article proposes a hyperbolic tangent Leclerc robust nonlinear adaptive filter based on the low complexity decomposable Volterra model (HTLNAF‐DVM). The filter is implemented by imposing a rank‐one structure on the full Volterra model to get a product of linear filters, and employs a hyperbolic tangent Leclerc function as a robust norm to effectively improve the robustness against the impulsive noise. In addition, we give the theoretical analyses of the steady‐state mean‐square performance of the proposed HTLNAF‐DVM. Finally, the simulation results prove that the proposed HTLNAF‐DVM algorithm has better performance than the existing algorithms and fit well with the theory. [ABSTRACT FROM AUTHOR]

Summary: The time delay unit‐based PLLs (TD‐PLLs) are widely used in grid synchronization in single‐phase power systems. However, the TD‐PLL is vulnerable to frequency variations and harmonic disturbances, leading to grid synchronization failures. Analysis shows that the performance degradation is due to the inability to obtain the variation of the input signal frequency. This paper proposes a new time delay‐based phase‐locked loop called immune to double frequency component TD‐PLL (IDFC‐TD‐PLL) based on a feedback structure to accurately capture the variation of input signal frequency, which can improve the dynamic performance and the performance against harmonic interference. Simulations and experiments have evaluated the performance of the proposed phase‐locked loop, demonstrating its superior accuracy and dynamic performance compared to the phase‐locked loops referred in the paper. [ABSTRACT FROM AUTHOR]

With the development of smart meters and other intelligent electronic devices, more and more data‐driven fault location methods based on wide area measurement are emerging. However, these diagnostic methods for dealing with the whole tested system often appear complex. This paper presents an innovative subsystems‐based fault location strategy in distribution grid by the sparsity promoted Bayesian learning algorithm. To avoid taking measurement for the whole distribution system, the fault‐included subsystem is selected according to the distribution characteristics of negative sequence voltage. Then the data for fault location is measured by allocating meters in subsystem, which can reduce the number of required meters. For accurately estimating the fault location, a sparse prior is proposed for the Bayesian learning, which could improve the accuracy of the fault location algorithm by about 4%. The performance is tested on a 12.66‐kV, 69‐bus distribution system in response to various fault scenarios. The results show that the accuracy of the proposed method for the fault section location can reach 90%. It also verifies the robustness and accuracy for fault line location, faced different fault types, fault resistance, noise, etc. [ABSTRACT FROM AUTHOR]

In multi‐cell massive multiple‐input multiple‐output (MIMO) cellular networks, pilot contamination (PC) caused by ineluctable pilot reuse severely restricts the system performance in the effectiveness and reliability of transmission. To deal with this problem, an efficient pilot assignment scheme based on fractional pilot reuse and max k‐cut (FPR‐MKC) is therefore proposed in this study. Specifically, first, the measure of susceptibility to interference for each user is designed, and based on this, an innovative boundary is determined for properly selecting the edge users who could employ the unique pilot sequences. Subsequently, the process of allocating reused pilots is innovatively treated as the partition of the vertices of a graph. Building on this idea, an edge‐weighted undirected graph is constructed to present the potential interference intensity among users, and eventually, the max k‐cut (MKC) strategy is executed, which aims to partition centre users into a desired number of mutually exclusive subsets with maximizing the total weight of the edges between the disjoint subsets. Compared with some existing pilot assignment schemes, the proposed FPR‐MKC scheme can adequately mitigate the PC with significantly enhanced quality of service for edge users, which is verified by theoretical analysis and numerical simulation results. [ABSTRACT FROM AUTHOR]

Effective fault diagnosis for cascaded H‐bridge multilevel inverter (CHMLI) can reduce failure rate and prevent the unscheduled shutdown. Nevertheless, traditional signal‐based feature extraction and feature selection methods show poor distinguishability for insufficient fault features in a one‐dimensional space. The shallow learning models are prone to fall into local extremum, slow convergence speed and overfitting. To cope with these problems, a novel image‐oriented fault diagnosis strategy based on sparse representation (SR) and deep convolutional neural network (DCNN) is proposed for CHMLI. Initially, Hilbert–Huang transform (HHT) is applied to obtain the HHT spectral images of original monitoring signals, where these images comprehensively represent the features with detailed information of multiple domains on the time‐frequency plane. Furthermore, an image fusion method based on the SR algorithm is employed on these spectral images of the same fault category to construct fused feature images, which effectively reflects the complicated relationships between the measured signals and fault features. Ultimately, the DCNN models can not only mine the relationship between the various fault categories and the different fused feature images but also can alleviate the problem of overfitting that is caused by the limited availability of training samples. [ABSTRACT FROM AUTHOR]

The Cauer thermal network has made a realistic physical representation of thermal behaviour of a multi‐chip module (MCM) theoretically possible. However, the traditional method assumes the heat conduction is critical, with an approximate one‐dimensional heat conduction path for calculation and fixed heat spreading angle to make up for the compensation brought by the approximation. It will limit the accuracy and cause a significant error in estimating the junction temperature of MCM. In this study, the heat spreading angle in the area of two adjacent chips is studied by using a cross‐section to change the problem to a two‐dimensional heat transfer question. Based on the heat flow got from the results of calculation by using Fourier's law, an expression for heat spreading angle as a function of boundary conditions is given, which is capable of estimating the junction temperature by adjusting effective heat conduction area of an MCM whose thermal behaviour is seriously coupled among neighbour chips. The accuracy of the proposed method and expression is validated by both the finite‐element method and experimental results and they both show a good agreement with the calculation. [ABSTRACT FROM AUTHOR]

The power transformer directly affects whether the power system can operate safely and reliably, and the prediction of the dissolved gas content in the transformer oil is an effective means to discover transformer defects and latent faults, which helps to better carry out state maintenance work. This study extracts the time series data in transformer oil and analyses the interactions and effects between various gas components in the oil. Then, combined with the changing trend of the content of each component, the original sequence was processed using an accumulated method to extract the intrinsic characteristics of the sequence, thus establishing the MGM(1,n)(grey multivariate model). As the grey prediction model can reduce the prediction accuracy of the sequence with longer time span, this study combines the adaptive regression and Markov correction models to optimise the grey prediction model from the perspective of residual correction. Finally, the accuracy and effectiveness of the model are verified by comparing with the original prediction method. [ABSTRACT FROM AUTHOR]

In this paper, a novel fault localization method combining frequency response analysis (FRA) and evidential reasoning process is proposed. FRA test is conducted at first so that we can obtain corresponding responses of several monitoring nodes on the winding. Traditional FRA interpretation methods simply compares those response curves, while in our work, evidential reasoning method is adopted to present beliefs about the fault locations. Extracted features from monitoring sensors are mapped into deviation matrix by which possibility assignments of each fault location is computed. So we finally obtain fault distribution probabilities. According to test calculation results, proposed method can give fault probabilities of each section of transformer winding, which correctly indicate the fault position. [ABSTRACT FROM AUTHOR]

Analogue circuits are one of the most commonly used components in industrial equipment, but circuit failure may lead to significant causalities and even enormous financial losses. To address this issue, in this work the authors propose a new feature extraction scheme based on cross‐wavelet transform (XWT) and variational Bayesian matrix factorisation (VBMF). Primarily, fault signals acquired from defect circuits are collected and processed by using XWT to obtain the joint time‐frequency representation. VBMF is utilised to fetch the time‐frequency information of the fault signal. A nine‐dimensional feature vector is then constructed. Finally, a support vector machine optimised by a flower pollination algorithm is introduced to locate faults. Results show that the proposed approach can effectively locate the different kinds of defection while achieving a higher accuracy. [ABSTRACT FROM AUTHOR]

Summary: Based on an existing 4‐dimensional Lorenz hyperchaotic system, a novel 5‐dimensional hyperchaotic system is proposed by an extra linear control coefficient in this paper. Some related characters are analysed comprehensively, including the stability of equilibrium, dissipativity, rotation symmetry, bifurcations, Lyapunov exponent, and Poincare surface of a section of the hyperchaotic system. The 5‐dimensional hyperchaotic system is then implemented and simulated in the circuit simulation software NI Multisim. The results indicate that the proposed hyperchaotic system exhibits hyperchaos, chaos, and quasi‐periodic and periodic dynamic behaviours, which can facilitate and enhance the security of chaos‐based communication. [ABSTRACT FROM AUTHOR]

Several varieties of cycloaddition as well as nucleophilic and free radical addition to the highly reactive central bond of the title fluorocarbon (1) are described. [2 + 2] Cycloaddition to 1 has made available four of the five known representatives of the [2.2.2]propellane ring system, which are rare because of their extreme strain (in the absence of fluorine substitution) and high reactivity. Two of these, a propellanone and a propellene, are first reported here. Quantum mechanical calculations have shed light on many of these transformations. [ABSTRACT FROM AUTHOR]

The three‐phase pulse‐width modulating (PWM) voltage source rectifier (VSR) system is widely used because of its advantages in performance. Numerous studies show that power semiconductors are significant contributors to the overall failure rate of power converters. This study presents an online diagnosis method for a single open‐switch fault of a three‐phase PWM VSR, because existing research mainly focuses on fault diagnosis of inverters. The proposed method employs current distortion to diagnose faults in a convenient and simple way. It requires no extra hardware but only AC current signals, which are available in the control system and can be embedded into the existing driving software as a subroutine without excessive computational effort. The proposed method offers high diagnostic effectiveness and reliability. Experimental results show that the proposed method can diagnose the faulty switch at low currents as well as under abrupt load transient conditions without false alarms. Furthermore, the average diagnostic time of this method is only 1.8 ms. [ABSTRACT FROM AUTHOR]

To enhance the convergent rate and robustness of buck-type DC-DC converter system, a new finite-time voltage regulation control algorithm is proposed in this paper. First, an average state space-based model is analyzed, which considers both the parameters uncertainties and the variations of load and input voltage. By using saturation finite-time control theory, at the first step, in the absence of disturbance, a new fast voltage regulation control algorithm is designed, which can guarantee that the output voltage converges to the reference voltage in a finite time. Because the saturation constraint is considered during the controller design, the duty ratio function of the converter satisfies the constraint between 0 and 1. Second, in the presence of disturbance, a finite-time convergent disturbance observer is designed to estimate the unknown disturbances in a finite time. Finally, a disturbance observer-based finite-time voltage regulation control algorithm is developed. Compared with PI (Proportional-Integral) control algorithm, circuit simulations show that the proposed algorithm has a faster regulation performance and stronger robustness performance on disturbance rejection. [ABSTRACT FROM AUTHOR]

The second-order consensus problem of nonlinear leader-following multi-agent systems is investigated in this paper. To solve the case that the velocities of all agents cannot be measured and the nonlinearity is unknown, an observer-based dynamic output feedback controller is proposed based on a non-separation principle method. Using the feedback domination technique, it is shown that the systems output can reach consensus by choosing appropriate gains. Two examples are given to verify the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

This study investigates the problem of designing a sampled‐data output feedback controller to globally stabilise a class of upper‐triangular systems with delay in the input. By using a linear observer, a linear dynamic sampled‐data output feedback control law is explicitly constructed. To dominate the unknown non‐linear perturbing terms and handle the case with a larger input delay, a tunable scaling gain is introduced to the controller by using a coordinates change. With the help of an appropriate Lyapunov–Krasoveskii functional and the technique of sampled‐data output‐feedback domination, it is shown that the considered upper‐triangular non‐linear system with any bounded input delay can be globally stabilised by the proposed controller with appropriate gains. Finally, an example is given to verify the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

A novel constant time‐delay control technique for switching dc–dc converters is presented. The controller with simple implementation only consists of a comparator and a delay module. Compared output voltage with a reference voltage and then delayed for a preset time interval, a control pulse for driving main power switch is finally generated. Taking constant time‐delay controlled buck converter as an example, theoretical analyses and PSIM circuit simulations are performed, which indicates that the proposed control technique has the advantages including well regulation, fast transient response and robust stability. [ABSTRACT FROM AUTHOR]