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This article proposes a new open-circuit (OC) fault diagnosis method for power switches in three-level neutral-point-clamped (NPC) rectifiers. This method is based on fault assumption by introducing a new voltage deviation for diagnosis, which is the difference between the switching-state-based voltage and the fault-assumed-based voltage, where the fault-assumed-based voltage is the estimated pole voltage when all inner switches are assumed to be in the OC state. After average and sum operations, diagnostic variables are developed from three-phase voltage deviations without extra hardware and precise modeling. Since the fault characteristics of each switch reflected by diagnostic variables are not changed by other concurrent OC faults, multiple-switch faults can be diagnosed in the same way as single-switch faults. As a result, the proposed method can detect both single-switch and multiple-switch faults without complex diagnostic rules and procedures. Besides, adaptive thresholds are designed by considering voltage imbalance, current amplitude, and power factor. Owing to the self-adjusting characteristics of thresholds, the proposed method provides reliable diagnostic results in various operating states of NPC rectifiers and has good robustness against transient changes. Experiments are performed to confirm its effectiveness and robustness. [ABSTRACT FROM AUTHOR]

The open-circuit (OC) faults for T-type three-level inverters often share similar distorted output features, which are usually distinguished by adopting invasive methods. The purpose of this article is to locate the OC fault by rebuilding the established phase voltage model according to the hypothesis method. Based on the vector decomposition principle and voltage-second balancing theory, the output phase voltage model is established considering neutral point voltage unbalance and time-offset injection. The hypothetical phase voltage vector and residual for fault reasoning can be obtained by inputting the suspected OC faults positions to the model. The hierarchical fault diagnosis scheme involves two steps. First, the group-level fault can be located according to voltage residual vector amplitude and angle. Considering the impact of parameter error, dead time, and delay time, an adaptive residual vector amplitude threshold is designed. Next, the device-level fault can be located by building two suspected phase voltage models to approximate the real system. Furthermore, the fault location rules under zero-crossing (ZC) and non-ZC conditions are summarized. Especially, the proposed hypothesis method has correctness verification ability to avoid misdiagnosis. Experimental results show the robustness and effectiveness of the proposed low coupling fault diagnosis method. [ABSTRACT FROM AUTHOR]

• The two-stage multilevel thresholding image segmentation is proposed. • The 3DOtsu and the determination of target number are used as the fitness function. • The Clonal selection strategy improve the optimal ability of MOEPO. Power transformer is one of the critical and valuableness apparatus in the secure operation of the power system. The infrared image fault diagnosis can reflect the status of the power transformer. However, how to detect the fault point of the diverse infrared images is a difficult problem. We present a two-stage multilevel threshold image segmentation method to finish the task of power transformers fault diagnosis. The multi-objective emperor penguin optimizer based on the clone selection strategy optimizes the multi-level image segmentation method for discovering the optimal threshold. The 3DOtsu and determination of optimal target number function as the fitness function and we convert them into multi-objective optimization problems. The proposed method is compared with the multi-objective optimization algorithms and novel fault diagnosis methods under the classic images and infrared images of the power transformer. The experiment results show that MOEPO/C has a good performance than others compared algorithms in Feature Similarity Index, Uniformity measure, Peak Signal‑to‑Noise Ratio, and CPU time. Especially, the proposed method obtains high accuracy in power transformer fault diagnosis. [ABSTRACT FROM AUTHOR]

Correct diagnosing analog circuit fault is beneficial to the circuit's health management, and its core challenge is extracting essential features from the circuit's output signals. Wavelet transform is a classical features extraction method whose performance relies on its wavelet basis function deeply. However, there are no satisfying rules to discover an optimal wavelet basis function for wavelet transform. In this paper, an improved wavelet transform with optimal wavelet basis function selection strategy is proposed. In the strategy, the optimal wavelet basis function is selected based on calculating the distance score and mean score of its features, and the features extracted by the optimal wavelet basis function are considered as the best features of signals. Subsequently, the features are split into training data and testing data randomly and evenly. By using the training data, a multiple kernel extreme learning machine (MKELM) based diagnosing model is initialized, and the parameters of MKELM are yielded by using particle swarm optimization algorithm. Finally, the MKELM is used to identify the faults of testing data for the purpose of verifying its performance. Fault diagnosis experiments of three circuits are performed to show the proposed optimal wavelet basis function selection strategy and MKELM's establishing process. Comparison experiments are performed to verify that the optimal wavelet basis function selection strategy is effective and MKELM is better than other classifiers in analog circuit fault diagnosis. [ABSTRACT FROM AUTHOR]

With the expansion of large-scale wind farms, wind power penetration has surged. However, high turbine operations correlate with rising maintenance costs and failures, often due to blade icing. Blade icing in wind turbines often leads to expensive mechanical and operational emergencies. While many detection methods identify icing presence, they fall short in assessing its impact severity. By observing power changes before and after icing, we can gauge its effect. Hence, we suggest an icing risk assessment method incorporating wind power predictions (WPPs). Single prediction methods, whether physical or data-driven, have distinct strengths and limitations. Physical models are clear but may struggle with complex nonlinearities, while data-driven ones handle such complexities but lack interpretability and rely heavily on data quality. A hybrid approach merges the clarity of physical models with the flexibility of data-driven ones. Therefore, a two-stage hybrid forecasting strategy is developed to improve the accuracy of WPPs. Specifically, a four-parameter model based on power curves is constructed to find the preliminary WPPs, and an error correction model based on a multilayer perceptron is constructed to correct the WPP results. Previous research illustrates the benefits that additional feature data (beyond wind power) brings to the identification of icing failures. To fully utilize more feature information and improve icing risk assessment reliability, we propose a hybrid sand cat swarm optimization and improved fuzzy C-means clustering algorithm to determine power deviation and other feature data related to icing detection. Real-world sensor data from supervisory control and data acquisition systems are used to validate the proposed icing risk assessment method (that considers WPPs). The results indicate that the proposed method can achieve effective risk classification and provide a basis for O&M decisions affecting wind turbine equipment. [ABSTRACT FROM AUTHOR]

Pilot contamination resulting from pilot reuse seriously restricts the communication performance of the cell-free massive multiple-input multiple-output (MIMO) networks. To deal with this problem, an efficient pilot assignment scheme using a weighted graphic framework is proposed in this correspondence paper. Specifically, a novel metric is first introduced for capturing the severity of potential mutual pilot contamination in cell-free topology. On this basis, the weighted pilot contamination graph is constructed to depict the dynamic interference relationship corresponding to the network. Then, the pilot assignment optimization is mapped to the Max k-Cut problem, and a heuristic algorithm is exploited to realize pilot decontamination. Numerical simulation results reveal the superior performance of the proposed scheme, which is capable of achieving a significant improvement in throughput with low complexity. [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]

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]

The time difference of arrival (TDOA) localization method has been widely applied in partial discharge (PD) localization in substations based on an ultra-high-frequency (UHF) antenna array. However, TDOA errors obtained from different UHF antennas can result in poor localization accuracy. In this paper, a novel nonlinear transform localization algorithm based on regularization for PD in substations is proposed. First, to avoid the problem of difficult solutions and heavy calculations caused by solving nonlinear localization equations, the equations are transformed from nonlinear to linear by eliminating the second-order terms. Then, for the ill-conditioned problem of the localization equations caused by the antenna array positions and time difference errors, the equations are optimized by centralization and row balance to reduce their ill-conditioned degree. Finally, the Tikhonov regularization method is used to solve the localization equations with an ill-conditioned problem, and then, the PD position is determined. The experimental results show that the proposed localization algorithm can improve the accuracy of PD localization in substations and that the localization errors are within 2.23 m. [ABSTRACT FROM AUTHOR]

The open-switch fault of voltage source converters (VSC) may change the switching state and distort the current, which is the basis for model-based fault diagnosis methods. Based on the analysis of the switching state, this letter presents a post-fault current model to estimate the current of each phase after one open-switch fault occurs. Combined with the current model-based method, the proposed post-fault model can be used not only to check the accuracy of the diagnosis result but also to detect multiple open-switch faults. The current, voltage, and driving signals used in this model are all from the VSC control system, therefore, this model can be easily implemented without additional sensors or hardware. Finally, the effectiveness of the proposed model is verified by the experimental results. [ABSTRACT FROM AUTHOR]

Condition monitoring for wind turbine (WT) planetary gearbox is of great significance to the reliable operation of WT. A novel condition monitoring method for WT planetary gearbox is proposed in this article. This method can identify and predict the fault in the initial stage. Raw vibration signals are gathered from WT planetary gearbox through an enhanced piezoelectric self-powered wireless sensor. First, after the data processing back-end receives the original samples transmitted by the wireless sensor, the high-dimensional raw vibration signals are compressed and collected by adopting a random Bernoulli matrix to obtain the compressed samples containing the characteristics of the raw signals by compressed sensing (CS). Moreover, the operation of reducing the signal dimension not only eliminates noise pollution but also greatly reduces the overall calculation. Second, the training part of compressed samples is first exploited to optimize the deep belief network (DBN) by the chaotic quantum particle swarm optimization (CQPSO) algorithm. In the optimization process, the CQPSO algorithm can avoid local optimal problem better compared with traditional quantum particle swarm optimization (QPSO) algorithm, which ensures that the optimized DBN architecture can extract distinguishing and deep features from compressed samples. Furthermore, the choice of compression ratio (CR) is realized in this process with the least-squares support vector machine (LSSVM) classifier. Third, testing samples are input into the DBN structure. A regression layer added on the last hidden layer, which stores extracted features, achieves the prediction of fault. In addition, LSSVM is exploited to distinguish fault types of features. The experimental results show that the modified wireless sensor can collect and transmit signals stably and achieve better performance in transmission interval and energy management. More importantly, the experimental results of prediction and diagnosis show that the proposed approach achieves excellent performance in terms of condition monitoring for the WT planetary gearbox. [ABSTRACT FROM AUTHOR]

Channel prediction is a vital technique that can be used to support adaptive transmissions and mitigate the feedback delay of the channel state information (CSI) in the wireless communications, especially in the frequency division duplex (FDD) system. In this paper, we focus on the channel prediction issue in multiple input-multiple output orthogonal frequency division multiplexing (MIMO-OFDM) systems. First, we introduce the general channel prediction framework based on the temporal and spatial correlations in MIMO-OFDM systems. Then, we combine the broad learning idea and recurrent computation, and further introduce the broad echo state network (BESN) for channel prediction in MIMO-OFDM systems. Third, to estimate the output weight matrix, we offer two versions of the BESN, i.e., the basic BESN (B-BESN) and the group forward variable selection (GFVS)-based BESN (GFVS-BESN). In the latter, we develop the GFVS strategy to further extract useful information from those collected features in the BESN and make the BESN more able to process the CSI samples. Fourth, inspired by deep learning, we import the conjugate gradient descent backpropagation (CGDBP) technique to fine-tune the random weights and biases in the BESN and give the related derivations. Then, we prove the echo state property in the BESN and analyze the computational complexity. In the simulation section, we comprehensively evaluate the prediction performances for the standard Extended Vehicular A model (EVA) and Extended Typical Urban model (ETU) under different signal-to-noise ratios (SNRs), different antenna configurations, different spatial correlations, different maximum Doppler shifts and different channel prediction methods. The simulation results indicate that the BESN has excellent prediction performance in MIMO-OFDM systems. [ABSTRACT FROM AUTHOR]

Analog circuits are one of the most commonly used components in industrial equipment. Circuit failure may lead to significant causalities and even enormous financial losses. To cope with this problem, a novel deep learning strategy based on the cross-wavelet transform (XWT) and generative adversarial networks (GANs) is proposed for analog-circuit fault diagnosis. Different from the traditional methods that simply apply the amplitude characteristic on the time–frequency plane, XWT is applied to obtain the spectral images of the fault signals that can be used to capture the comprehensive features with the involvement of the amplitude, phase, and coherence characteristics. Furthermore, a data-regularization trick is employed on these spectral images to construct three-channel feature images data. The input data can reflect the complex relationship effectively between the measured signals and the feature space, with abundant information of multiple domains on the time–frequency plane. Finally, the different distributions for various fault conditions can be learned by the proposed GAN. The adversarial training manner of the generative network and the discriminative network of the GAN can not only mine the complex nonlinear relationship between the root cause of the failure and the signal signature but also alleviate the problem of overfitting that is caused by the limited availability of the training samples. Furthermore, we expand the GAN to a supervised classifier with the coordinative usage of two auxiliary classifiers, and two convolution neural networks are adopted to establish the basic structure of the proposed model. The comparisons of classification performance with other methods indicate that the proposed approach has good potential for analog-circuit fault diagnosis. [ABSTRACT FROM AUTHOR]

Numerous studies show that power semiconductors are significant contributors to the overall failure rate of pulsewidth modulation (PWM) voltage source rectifier (VSR) systems. Aimed at this problem, this paper presents an improved diagnosis system for open-circuit faults of a three-phase PWM VSR based on ac current distortion characteristics, which consists of a diagnosis module, a register module, and a decision module. The diagnostic module employs a hysteresis comparator to judge the change trend of the currents, and thereby diagnoses the single open-circuit (OC) faults. The register module is employed to achieve multi-switch OC fault diagnosis, and the decision module is used to output the final result. The proposed method extends the previous work to multi-switch OC fault conditions as well as improves the diagnosis accuracy. The experimental results show that the proposed method can correctly achieve fault diagnosis, including 6 kinds of single-switch faults and 15 kinds of different double-switch faults. Additionally, the proposed method also shows superior anti-interference performance and high robustness under abrupt load transient conditions, different power factor conditions, unbalanced and/or distorted grid voltage conditions, and different ac filter structure conditions. Furthermore, the average diagnostic time of this method is only 3.68 ms. [ABSTRACT FROM AUTHOR]

In real-time movement controlling systems, it is essential to deploy a suitable sensor to measure tilt angle on-line. When the tilt angle is bigger than the preset threshold, the remedial operation should be made timely. This paper proposes an on-line dynamic tilt angle measurement method by compensating gyroscope drift error. First, gyroscope is selected as a measuring unit because it can overcome the interference of external forces to measure dynamic angular speed. Second, an improved least squares support vector machine (LSSVM) is proposed for gyroscope drift-error compensation. The vector base learning method is used to reduce those less important support vectors, and the sparsity of LSSVM can be changed by adjusting the value of $\theta $. Third, the quantum-behaved particle swarm optimization algorithm is introduced as the optimization method to optimize regularization parameter C and kernel parameter $\gamma $ of LSSVM model because it has good optimization result and fast convergence speed. Fourth, the experiment platform and implementation process are illustrated in detail. Finally, the corresponding experimental results demonstrate that the proposed methodology could measure the dynamic tilt angle accurately. [ABSTRACT FROM AUTHOR]

Wind power is becoming a clean and effective energy source for electric power generation. However, the abnormity, multi-modal, and uncertainty represented in wind power data are commonly undesired. Thus, accurate wind power forecasting is a significant method for keeping the power system operations steady. To solve these issues, a multi-modal multi-step wind power forecasting model is presented. To obtain this, the density-based spatial clustering of applications with noise (DBSCAN) is improved by the k-dimensional tree (kd-tree) for detecting abnormal data. Then, the low-rank matrix fusion method fuses the wind speed, wind direction, and air density modalities for obtaining a unified representation. To further increase model accuracy, we propose a stacking deep learning model (SDLM) for overcoming the uncertainty phenomenon, which contains the bidirectional gated recurrent unit (BGRU) and leaky echo state network (LESN). The final forecasting results are acquired by a meta-learning operator. To validate the accuracy and stability of the presented approach, the inland and offshore wind farm datasets are used for forecasting. The contrastive results demonstrate that the presented model outperforms satisfactory performance in multi-step wind power prediction. [ABSTRACT FROM AUTHOR]

• The adjacent double-disc is affected to make the simulation results more accurate. • The ratio of excitation-response bolsters the variation to detect the minor fault. • The polar plot considers the phase-frequency feature, containing more signatures. • The distribution of the frequency sampling points classified the fault states. • The Minkowski distance quantifies the extent of the failure and the location. Power transformer is a significant transmission and distribution equipment. Irreversible minor mechanical failures frequently occur in the transformer winding, which is difficult to detect in the early latent stage. Frequency response analysis (FRA) is widely used to detect transformer winding faults. However, the current interpretations of Frequency response analysis pay more attention to amplitude-frequency characteristics without phase-frequency characteristics, which is inaccurate in identifying minor flaws, detecting fault states, and quantifying fault severity. To fill these issues, this paper proposes a transformer winding fault diagnosis method based on the polar plot. The ratio of excitation-response is put forward for calculating the relative factor to detect the minor fault. And then, the fault states are diagnosed by the distribution of the polar plot, which combines amplitude-frequency and phase-frequency characteristics. Finally, the Minkowski distance of the frequency sampling points quantifies the extent of the failure. The experiments are implemented in a 10KV power transformer and COMSOL platform simulation. The fault status contains axial displacement, axial deformation, radial displacement, and radial deformation. The experimental results show that different states greatly influence the distribution of sampling points in polar plot. The proposed method effectively detects the minor flaw of transformer winding and performs well in judging the degree of fault severity and location. [ABSTRACT FROM AUTHOR]

The realization of the Internet of Things can not be separated from the wireless sensor network which was the extension of communication and perception. The shortage of spectrum resources will become one of the bottlenecks that hinder the rapid development of Internet of Things. A large number of sensor communication makes the interference problem of wireless channel more and more serious. Therefore, the research of wireless channel characteristics and model was important for development of Internet of Things. In this paper, the time domain multipath propagation characteristics of direct, reflection, diffraction and transmission were introduced, which provided a more accurate basis for the design of indoor short distance Ultra-wideband (UWB) communication model. Considering the practicability of simulation, the multipath propagation characteristics of UWB signals under indoor short distance environment with wooden furniture were studied. Moreover, Aluminium door partitions, glass partitions, and plastic furnitures were also considered during our simulation. Based on the time domain ray tracing method, the UWB signal propagation characteristics such as received power distribution, root mean square delay spread, multipath arrive delay distribution parameters were also discussed under line-of-sight and non-line-of-sight propagation. The analysis results provided deployment basis of UWB signal under the indoor environment, which benefit to research and develop technology for indoor Internet of Things. [ABSTRACT FROM AUTHOR]

This paper presents a multiple heterogeneous kernel relevance vector machine (MHKRVM) approach for analog circuit fault prognostic. Compared to other kernel learning methods, multiple kernel learning method produces the optimal kernel function because many effective kernels' combination always generates better generalization performance. By using the multiple heterogeneous kernel learning method, the proposed MHKRVM method's kernel function holds its diversification. Meanwhile, the sparse weights of consisted basic kernels in the MHKRVM are helpful in prediction accuracy, and they are yielded through particle swarm optimization algorithm. Six fault prognostic cases are conducted to demonstrate the whole prognostic procedure, and prove that the presented MHKRVM can predict the trend of falling circuit elements' health degree trajectories closely and estimate the remaining useful performance of failing circuit elements accurately. [ABSTRACT FROM AUTHOR]

Health management for transformer in service is of great significance to the reliable operation of power system. This paper presents a cost-efficient transformer condition prognosis methodology. First, a power management circuit is designed to guarantee the stable performance of self-powered RFID sensor. Moreover, the sensor data storage mechanism is modified by embedding the sensor data into the ID information of RFID sensor to realize less response time and lower power consumption compared with the traditional approach. Second, a denoising method based on band restricted empirical mode decomposition and a fuzzy threshold is introduced to extract noise-free vibration signal from the measured raw signal. Third, the multiple kernel relevance vector machine (MKRVM) is imported to predict the future condition of transformer. The kernels of MKRVM keep diversity for higher prediction accuracy by using the heterogeneous kernel learning approach. Moreover, the double chain quantum genetic algorithm is employed as optimization tool to find the optimal sparse weights of basic kernel functions in MKRVM. The experimental results indicate that the modified RFID sensor can stably operate even when the load of the transformer is abruptly decreased to zero. The response time and power consumption of RFID sensor are markedly decreased by using the improved sensor data storage mechanism. Furthermore, the prediction experiment results demonstrate that the proposed approach is effective in term of health monitoring for transformer. [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]

• A wind turbine anomaly detection model using only normal samples was developed. • Generalized multiscale Poincare plots was proposed to construct anomaly detection indicator. • Two case studies on real datasets demonstrated the validity of the proposed method. Anomaly detection is critical for the reliability and safety of wind turbine. Toward this objective, this paper proposes an anomaly detection scheme for wind turbine using only normal samples. Specifically, a novel nonlinear tool, generalized multiscale Poincare plots (GMPOP), is firstly developed to capture the behavior changes of vibration signals through scales. Subsequently, support vector data description (SVDD) is established to learn the acceptance region defined for anomaly detection from the GMPOP information under normal conditions. The feasibility of GMPOP is initially studied on simulated signals. Further, two datasets, from an experimental bearing and a 2 MW wind turbine, are analyzed to illustrate the evolution process. Comparing to such the state-of-the-art indicators as root mean square, permutation entropy and dispersion entropy, the GMPOP-based method can successfully detect the state anomalies of before the occurrence of the outer race fault and inner race fault, and provide anomaly alarms in advance. The study suggests that the proposed GMPOP is an effective way to assess the dynamical alteration of wind turbine. Moreover, compared with other baselines: auto-encoder, principal components analysis, minimum covariance determinant and exponentially weighted moving average control chart, the proposed detection model GMPOP-SVDD produces favorable performance regarding multiple aspects. [ABSTRACT FROM AUTHOR]

This paper proposes a chipless Radio-Frequency Identification (RFID) sensor tag for low-cost and long-term humidity monitoring. The slotted scatterer structure are fabricated on FR4 substrate by grooving etching. The encoding unit stores the ID information by the combination of the different length slots. Si nanowires are deposited on the surface of the slots to form the sensing unit. Based on the principle of radar electromagnetic backscattering, the test system measures the transmission coefficient curve to obtain the ID and humidity information of the tag. The test results show that the proposed chipless sensor tag achieves good hysteresis and consistence performances and the maximum error is not exceed 10%. [ABSTRACT FROM AUTHOR]

Analog circuits are one of the most commonly used components in industrial equipment and, therefore, circuit failure may lead to significant causalities and even huge financial losses. To address this problem, this work presents a fault diagnosis method based on spectrum images for analog circuits. Unlike traditional analysis methods in a one-dimensional space, this study employs a computing method in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed approach mainly involves the following steps. First, the sampling signals are converted into spectrum images by utilizing cross-wavelet transform, which can be further processed by the following image-based feature extraction method. Then, Krawtchouk moment is applied to extract both the global and local features of the spectrum images and finally form the feature vector. Feature weighted kernel Fisher discriminant analysis is then introduced for locating faults. Two typical analog circuits, video amplifier circuit and opamp high-pass filter circuit, are chosen to demonstrate the effectiveness of the proposed method. Simulation results show that the proposed approach based on spectrum images achieves a high accuracy, thus providing a highly effective means to fault diagnosis for analog circuits. [ABSTRACT FROM AUTHOR]

Considering on the current difficulties of predicting the compressive strength of recycled aggregate concrete, this paper proposes a prediction model based on deep learning theory. First, the deep features of water-cement ratio, recycled coarse aggregate replacement ratio, recycled fine aggregate replacement ratio, fly ash replacement ratio as well as their combinations are learned through a convolutional neural networks. Then, the prediction model is developed using the softmax regression. 74 sets of concrete block masonry with different mix ratios are used in the experiments and the results show that the prediction model based on deep learning exhibits the advantages including higher precision, higher efficiency and higher generalization ability compared with the traditional neural network model, and could be considered as a new method for calculating the strength of recycled concrete. [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]

Portable sensor system was constructed for detection of acetone vapor based on the cataluminescence (CTL) theory. Excellent microspheres were prepared successfully which increased the contact probability with vapors. The instrument architecture, optimal experiment conditions and measurement characteristics were discussed in detail. The results showed the CTL sensor instrument exhibited excellent CTL properties including visible intensity, high signal/noise (S/N) values, short response time (within 2 s) and recovery time (within 3 s). The sensor instrument covered a linear test range from 5 ppm to 2500 ppm, while the sensor showed non-linearity when concentration ranged from 2500 ppm to 8000 ppm. Furthermore, the sensor system showed outstanding selectivity to acetone compared with other ten kinds of common vapors. Moreover, the CTL sensor also showed an excellent response for H 2 S, the CTL performance of H 2 S under different wavelength and temperature was also discussed. The excellent sensing and online detecting properties proved this sensor instrument to be an excellent candidate for real-time gas contaminant detection. Finally, the sensing control processes and monitoring software interface was also presented. [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]

On-line tilt angle detection and prognosis are the important prerequisites of real-time movement controlling. This paper develops a time-effective tilt angle prognosis methodology and its evaluation prototype. First, a complementary filter is imported to extend both the superiorities sufficiently of accelerometer and gyroscope, accurate tilt angle detection then can be achieved in a wide range of frequency. Second, an improved sparse least-squares support vector regression is proposed for tilt angle prognosis, its configurable singularity spectrum technology keeps away from the high-order matrix operation through eliminating nonprincipal components among raw vectors. Third, novel composite judgment criteria and its derived dynamic sliding window mechanism are proposed to pursue scientific balance of the prediction precision and computational cost. Fourth, double chains quantum genetic algorithm is introduced as the optimization tool to search optimal parameters of the above-mentioned models. Finally, the prototype setup and the corresponding experimental results are demonstrated and discussed in detail, which promise that our proposed methodology could be potentially applied to the actual motion control systems. [ABSTRACT FROM AUTHOR]

Kernel entropy component analysis (KECA) is a new method to extract features and perform dimension reduction by preserving most of Renyi entropy. However,it appears to have deficiencies. The entropy estimation in KECA may be not the most accurate and it is sensitive to a single kernel radius. This paper proposes a multiplicative bias correction method of the entropy estimation. According to the analysis of the relation of statistics in the kernel feature space,we introduces two kernel radii to make KECA less sensitive to the kernel radius.A method for fault diagnosis of analog circuits based on the combination of improved KECA and extreme learning machine (ELM) is presented.Through wavelet decomposition of sampled signals,features are extracted, following improved KECA for feature dimension reduction.Next the fault patterns are classified by ELM.Case studies on two analog circuits demonstrating our diagnostics method are presented. [ABSTRACT FROM AUTHOR]

• A methodology is developed to mine the dynamics of vibrations signals for wind turbine drivetrain system fault diagnosis. • An effective visualization technique namely distribution recurrence plots is originally designed to reveal intrinsic dynamic changes of vibration signal. • A new quantifier pattern entropy is proposed for feature extraction. • The proposed method can be extended to identify various wind turbine drivetrain system faults. Extracting sensitive information from vibration signal has become a frequently adopted way in fault diagnosis. However, most previous methods fragmented the relationship between quantification and visualization analysis, which affects the interpretability, accuracy and comprehensiveness of the extracted information. To this end, this paper proposes distribution recurrence plots (DRP) and measures (DRM) to realize the unity of visualization and quantification analysis of the signals. Specifically, DRP is a novel feature graphical representation method following the thought of symbolic dynamics. Derived from DRP, DRM is developed containing four quantifiers for extracting comprehensive fault features that allows a multiclass support vector machine (SVM) to identify the fault types of wind turbine drivetrain system (WTDS). Specially in DRM, pattern entropy is a newly designed quantifier by considering pattern distribution to obtained more accurate quantitative representation of the signals. Using simulated data, DRP and DRM are validated to reveal the intrinsic structural changes for different dynamic systems and robustness to noise. Applications on wind turbine gearbox illustrate that the proposed method has favorable diagnosis performance and stability compared with other competitors. This approach is easy to interpret, is robust to noise, and has a low computational burden, becoming viable for WTDS fault diagnosis. [ABSTRACT FROM AUTHOR]

• The Leaky-ESN generates the related data to expand the dataset to solve the small sample data problem of the power transformer. • The SE network makes DRSN focus on the relationships between channels and reduce the architecture of the network. • The proposed method extracts different features from the generated data to obtain the health assessment of the power transformer. • The proposed method automatically learns the importance of the characteristics of different channels and filter diverse noisy data. The health evaluation is an effective method to detect the health condition of power tran.3sformer. However, the redundancy, complexity, and small sample of dataset influence the performance of health evaluation method. To solve this issue, this paper presents a deep learning neural network (DLNN) to detect the HE of the power transformer. First, the echo state network (ESN) generates the data associated with the original data for solving the small sample problem. And then, the significant features are extracted by DLNN which contains improved Deep Residual Shrinkage Networks (IDRSN) and the one-dimension convolution neural network (1DCNN). Finally, the health status of the power transformer is obtained by Concat layer and Softmax layer. The DLNN is verified by datasets of dissolved gas collected on a real power transformer. The experiment results demonstrate that the proposed method obtains a better performance than the latest neural networks and health assessment method of power transformer. [ABSTRACT FROM AUTHOR]

• A multi-model information analysis is proposed for transformer fault diagnosis. • The MIA consists of a SKnet, a BGRU, and a cross attention mechanism. • The cross-attention mechanism learns mutual effects among diverse modalities. • The algorithm has good performance in fault diagnosis of the power transformer. Fault diagnosis is important to the timely repair of the power transformer. However, machine learning has not been exploited effectively for fault diagnosis due to the limitation of multi-modal heterogeneity of data and the ratio of missing samples. To solve this problem, a novel multi-modal information analysis method is presented to effective and speedy evaluate power transformer fault with time sequences and multi-modal data. The proposed method consists of a Selective Kernel Network, a bidirectional gated recurrent unit, and a cross attention mechanism. The proposed approach is verified by datasets of dissolved gas and infrared image modes which come from real power transformers and the historical data. The results show the advantage and efficiency of the proposed method for its higher diagnostic accuracy and shorter diagnostic time than those of the comparison approaches. [ABSTRACT FROM AUTHOR]

Gaseous sensor system was developed for the detection of sulfur hexafluoride vapor based on its cataluminescence (CTL) emission by using coral-like Zn-doped SnO 2 composite. The test architecture, experiment characteristics and optimal conditions were studied in detail under optimized experiment conditions. Results showed that the gas sensor system covered a linear detection range from 8 mg/L to 5600 mg/L (R = 0.9963, n = 7) with a detection limit of 6.2 mg/L (S/N = 3), which was below the standard permitted concentration. The sensor showed a rapid response of 2 s and a recovery time of 30 s, respectively. Moreover, pattern recognition method was used to test the recognizable performance of this sensor, which has found that analyte can be distinguished clearly. As sensing materials for a CTL gas sensor, this coral-like Zn-doped SnO 2 demonstrates excellent CTL behavior (that is, high sensitivity, superior selectivity to sulfur hexafluoride compared with other eleven kinds of common volatile organic compounds (VOCs) as well as a fast response and recovery). The excellent sensing properties including improved sensitivity, selectivity and stability proved this sensor to be a promising candidate for detecting gas contaminants. [ABSTRACT FROM AUTHOR]

This paper presents a novel analog circuit fault diagnosis approach using generalized multiple kernel learning-support vector machine (GMKL-SVM) method and particle swarm optimization (PSO) algorithm. First, the wavelet coefficients' energies of impulse responses are generated as features. Then, a diagnosis model is constructed by using GMKL-SVM method based on features. Meanwhile, the PSO algorithm yields parameters for the GMKL-SVM method. Sallen-Key bandpass filter and two-stage four-op-amp biquad lowpass filter fault diagnosis simulations are given to demonstrate the proposed diagnose procedure, and the comparison simulations reveal that the proposed approach has higher diagnosis precision than the referenced methods. [ABSTRACT FROM AUTHOR]

The IGBT chip temperature is an important index affecting device reliability. Due to the existence of packaging, the temperature data of the IGBT chip is difficult to obtain directly. Because the large IGBT switching frequency, the temperature changes quickly. The chip junction temperature fluctuation is too difficult to measure. Based on the Cauer electrothermal coupling model, this paper deduces the calculation formula of the IGBT chip temperature fluctuation. Through the on-stage voltage, collector current, thermal resistance, thermal capacitance, on time and off time that can be directly monitored, the chip temperature fluctuation inside the package can be calculated. Then the accuracy of the calculation formula of temperature fluctuation is verified by experimental method, and the experimental results confirm the feasibility of this method. The chip temperature fluctuation monitored by this method is the premise for the lifetime prediction and reliability research of the IGBT, which plays a great role in the engineering field. [ABSTRACT FROM AUTHOR]

• Multi-modal Multi-task Spatiotemporal Attention Network (M2STAN) model for wind power prediction. • Implementation of M2STAN on 23 wind farms in China for ultra-short-term power prediction. • Accuracy and computational efficiency were used to evaluate the performance of M2STAN. • Comparisons with reported models and ablation strategies support the design of individual modules in M2STAN. • M2STAN considering spatiotemporal correlations proves potential for large-scale wind farm applications. In recent years, wind power has continued to emerge as a key source of renewable energy. When large-scale wind farm clusters are connected to the grid for power generation, accurate multi-location ultra-short-term wind power predictions carry significant value in terms of ensuring the safety, stability, and economical operation of the power system. However, there are complex temporal and spatial correlations among multiple wind farms in multiple locations, which makes wind power predictions involving wind farm clusters very challenging. The development of artificial intelligence technology, especially graph machine learning, provides new approaches for modeling such spatiotemporal correlations. In addition, compared with single-step forecasting, multi-step forecasting can better reflect the general situation, and thus, it is more widely applicable in reality. To optimize multi-step wind power predictions in multiple locations, this report proposes a Multi-Modal Multi-Task Spatiotemporal Attention Network (M2STAN) model. The developed model employs a graph attention network and a bidirectional gated recurrent unit (Bi-GRU) to model the spatial and temporal dependence, respectively. In addition, the introduction of multi-modal and multi-task learning strategies improves the accuracy and computational efficiency of this predictive model. The results indicate that the proposed method is superior to existing methods, including support vector regression, Bi-GRU, multi-modal multi-task graph spatiotemporal networks, and graph convolutional deep learning architectures in terms of prediction performance. [ABSTRACT FROM AUTHOR]

• This paper uses the adaptive synthetic oversampling (ADASYN) algorithm to expand the original samples to solve the problem of small and unbalanced samples. • This paper reconstructs the characteristic gas to increase the power transformer features and build the relationship between the fault type and various gases. • The improved CDCN fuses the previously extracted feature information in the depth direction and sends it to the subsequent convolutional layer together, effectively preventing overfitting and reducing the loss of features when the depth of the network increases. • The input data of the CDCN is multi-scale, where the dimension of the input data is differential. The normal operation of the power transformer guarantees the safety and reliability of the power system. However, the data of gas in oil exists the phenomenon of insufficient and unbalanced fault samples and few features. This paper proposes a fault diagnosis model, which contains the adaptive synthetic oversampling (ADASYN), the reconstructed data method, and an improved deep coupled dense convolutional neural network (CDCN). Firstly, the ADASYN expanded the normalized data set. Then, the characteristic gas in the data set is reconstructed to increase the number of features. Finally, the improved CDCN extracts the features from the generated data and obtains the fault status of the power transformer. The IEC TC 10 data and the collected data are used as the test data. The experiment results show the proposed method obtains better performance than the compared algorithms. Primarily, the accuracy of the proposed method acquires 94.05%. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries are widely used in many electronic systems. Therefore, it is significantly important to estimate the lithium-ion battery’s remaining useful life (RUL), yet very difficult. One important reason is that the measured battery capacity data are often subject to the different levels of noise pollution. In this paper, a novel battery capacity prognostics approach is presented to estimate the RUL of lithium-ion batteries. Wavelet denoising is performed with different thresholds in order to weaken the strong noise and remove the weak noise. Relevance vector machine (RVM) improved by differential evolution (DE) algorithm is utilized to estimate the battery RUL based on the denoised data. An experiment including battery 5 capacity prognostics case and battery 18 capacity prognostics case is conducted and validated that the proposed approach can predict the trend of battery capacity trajectory closely and estimate the battery RUL accurately. [ABSTRACT FROM AUTHOR]

Accurate wind power predictions (WPPs) are highly significant to the safety, stability, and economic operation of power systems. The reported encoder-–decoder architectures have demonstrated clear advantages over traditional methods in multi-step WPP tasks. However, the reported frameworks still have defects involving insufficient information mining abilities and low computing efficiencies. To address these shortcomings, this study proposed three improved encoder–decoder architectures, sequence-to-sequence bidirectional gated recurrent unit (SBIGRU), attention-based sequence-to-sequence Bi-GRU (ASBIGRU) and Transformer, in natural language processing for multi-step WPP. Data, including numerical weather predictions and wind powers, from 12 wind farms located in 12 different regions of China were used to validate our proposed models. The correlations between the datasets from multiple wind farms were analyzed using Pearson's correlation coefficient method to demonstrate the feasibility of our proposed models even without considering the spatial correlations. We adopted an effective strategy combining manual experience and machine grid searches to define the hyper-parameters needed to optimize the performance of our proposed models. The prediction accuracies and computational efficiencies of the reported and proposed models were compared experimentally. For prediction accuracy, the experimental results showed that, compared with existing models, Transformer, ASBIGRU and SBIGRU reduced the root mean square error by 3.21%, 1.06% and 0.88% in 16-step-ahead predictions, respectively. Furthermore, for computational efficiency, the training time of the existing model at a wind farm is 3.57 times that of Transformer. This confirmed that the Transformer model performs better in terms of prediction accuracy and computational efficiency. Our work illustrates the potential of Transformer for large-scale wind farm applications. • Novel encoder-decoder architecture of NLP are used for multi-step wind power forecasting. • Validating the modeling results with historical wind power and NWP data. • Hyper-parameters are determined by combining manual experience and machine grid searches. • Transformer has obvious advantages in prediction accuracy and computational efficiency. [ABSTRACT FROM AUTHOR]

Dynamics of fractional-order memristor circuit system and its control are investigated in this paper. With the help of stability theory of fractional-order systems, stability of its equilibrium points is analysed. Then, the chaotic behaviours are validated using phase portraits, the Lyapunov exponents and bifurcation diagrams with varying parameters. Furthermore, some conditions ensuring Hopf bifurcation with varying fractional orders and parameters are determined, respectively. By using a stabilization theoremproposed newly for a class of nonlinear systems, linear feedback controller is designed to stabilize the fractional-order system and the corresponding stabilization criterion is presented. Numerical simulations are given to illustrate and verify the effectiveness of our analysis results. [ABSTRACT FROM AUTHOR]

A new three-dimensional autonomous chaotic system is proposed in this paper. It analyzes dynamic behaviors of this new system, including the stability of equilibria, the system dissipativity, poincare maps, Lyapunov exponent, bifurcation, wave forms, and spectrum. Numeral simulation is performed in Matlab. A chaotic circuit is designed, and the corresponding circuit simulation is conducted. The results show that the chaotic attractor exists in the new system. The synchronization problem of the chaotic system is solved with a synchronization method, and its synchronization circuit is designed. Numeral simulation and circuit simulation show that the synchronization problem of this chaotic system proposed is solved. [ABSTRACT FROM AUTHOR]

Correct incipient identification of an analog circuit fault is conducive to the health of the analog circuit, yet very difficult. In this paper, a novel approach to analog circuit incipient fault identification is presented. Time responses are acquired by sampling outputs of the circuits under test, and then the responses are decomposed by the wavelet transform in order to generate energy features. Afterwards, lower-dimensional features are produced through the kernel entropy component analysis as samples for training and testing a one-against-one least squares support vector machine. Simulations of the incipient fault diagnosis for a Sallen-Key band-pass filter and a two-stage four-op-amp bi-quad low-pass filter demonstrate the diagnosing procedure of the proposed approach, and also reveal that the proposed approach has higher diagnosis accuracy than the referenced methods. [ABSTRACT FROM AUTHOR]

A systematic design method for 3-D grid multiscroll chaotic attractors by using staircase nonlinear functions (SNFs) is proposed in this paper. Equations of parameters for 3-D grid multiscroll chaotic attractors are derived in the consideration of the dynamic range for the active device and the number of multiscroll chaotic attractors. 5 × 4 and 4 × 3 of 3-D grid multiscroll chaotic attractors are generated by this method. A chaotic circuit is designed only using current feedback operational amplifiers (CFOAs). The circuit consists of three integrators and two nonlinear circuits. The parameters of the circuit are calculated by the parameters of 3-D grid multiscroll chaotic attractors. The circuit can generate 3-D grid multiscroll chaotic attractors. The central frequency of the circuit is higher with fewer active devices and simpler circuit construction for the good frequency characteristic and port characteristic of the CFOA. Numerical simulation results and the circuit simulation results show that the method is feasible and the designed circuit is correct. [ABSTRACT FROM AUTHOR]

In this paper, we present a new recursive method to compute higher order sensitivities of node voltages, as well as those of circuit performances (gain, input and output impedances, or reflection coefficients) with respect to all circuit parameters. Using the sensitivity coefficients we formulate multivariate polynomial equations. Fault identification is obtained by solving these equations with respect to element deviations. This task can be accomplished by using a multivariable Newton-Raphson procedure (mNR) for solving nonlinear multivariable equations. [ABSTRACT FROM AUTHOR]

This paper proposes a method of analog circuit fault diagnosis by using high-order cumulants and information fusion. We extract the original voltage and current signals from output terminal of the circuit under test, and determine corresponding kurtosis and skewness as fault eigenvectors, which are then used to improve Error Back Propagation (BP) neural network for fault diagnosis. With respect to fault eigenvectors consider more about the information which are sometimes ignored by principal component analysis (PCA) using second order statistics. By employing information fusion to integrate voltage with current as fault eigenvectors, eigenvectors can be used to express fault information better. Diagnosis examples are used to illustrate that our fault eigenvectors own higher recognition rate and diagnosis accuracy. [ABSTRACT FROM AUTHOR]