Search

In this work, an Insulated Metal Substrate (IMS) for packaging high-power electronic devices was created using hexagonal Boron Nitride (h-BN) mixture-based Thermally Conductive Adhesive (TCA) and copper plate with a matte side. Mechanical exfoliation of micron-scale BN particles results in a BN mixture containing h-BN nanosheets, micron particles, and nanoparticles. TCA was synthesized by dispersing a 40 % BN mixture modified by γ-aminopropyltriethoxysilane (KH550) into epoxy resin. The TCA has a relatively high thermal conductivity of 4.48 W·m-1·K-1 and a breakdown strength of 15.32 kV·mm-1. As the TCA film was coated on the matte side of the copper plate, the peel strength can reach 1.63 N·mm-1, indicating excellent practicality in the field of electronic packaging.

Abstract 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.

Battery life prediction is of great significance to the safe operation, and reduces the maintenance costs. This paper proposes a hybrid framework considering feature extraction to achieve more accurate and stable life prediction performance of the battery. By feature extraction, eight features are obtained to fed into the life prediction model. The hybrid framework combines variational mode decomposition, the multi-kernel support vector regression model and the improved sparrow search algorithm to solve the problem of data backward, uneven distribution of high-dimensional feature space and the local escape ability, respectively. Better parameters of the estimation model are obtained by introducing the elite chaotic opposition-learning strategy and adaptive weights to optimize the sparrow search algorithm. The algorithm can improve the local escape ability and convergence performance and find the global optimum. The comparison is conducted by dataset from National Aeronautics and Space Administration which shows that the proposed framework has a more accurate and stable prediction performance. Compared with other algorithms, the SOH estimation accuracy of the proposed algorithm is improved by 0.16%–1.67%. With the advance of the start point, the RUL prediction accuracy of the proposed algorithm does not change much.

In intelligent lithium-ion battery management, the state of health (SOH) of battery is essential for the batteries’ running in electric vehicles. Popularly, the battery SOH is estimated by using suitable features and data-driven methods. However, it is difficult to extract appropriate features characterizing battery SOH from the charging and discharging data of batteries owing to various state of charges (SOCs) and working conditions of batteries. In order to effectively estimate the battery SOH, an estimation method based on gradual decreasing current, double correlation analysis and gated recurrent unit (GRU) is proposed in this paper. Firstly, gradual decreasing current in the constant voltage charging phase is measured as the raw data. Then, the double correlation analysis method is proposed to select combined features characterizing the battery SOH from different categories of features. Meanwhile, the number of input features is also ensured by the method. Finally, the GRU algorithm is employed to set up a SOH estimation model whose learning rate is improved by using a sparrow search algorithm (SSA) for the purpose of capturing the hidden relationship between features and SOH. The adaptability of the proposed method is validated by SOH estimation experiments of a single battery and a battery pack. Additionally, contrast experiments are performed to show the advanced estimation performance of the proposed method.

Traffic flow prediction is a key step to realizing the effective guidance and control of intelligent transportation systems. For using the short-term non-stationarity and spatio-temporal correlation presented in traffic flow, a spatio-temporal hybrid prediction model, which called ST-VGBiGRU, based on improved Variational Modal Decomposition (VMD), Graph Attention Network (GAT), and Bidirectional Gated Recurrent Unit (BiGRU) network is proposed. First, the traffic flow sequence is decomposed into a series of relatively stationary modal components using VMD algorithm to reduce its short-term non-stationarity. The high-frequency modal components are noise-reduced using the Fuzzy Entropy (Fuzzy En) method to improve the accuracy of decomposition. After that, the GAT network is used to capture the different attention levels of the prediction node to their neighboring traffic nodes, which obtain more spatial characteristics of traffic flow. Then, each modal component containing spatial features is fed into the BiGRU network separately to capture its temporal correlation. Each model parameter is trained to the optimum using the improved RMSProp algorithm, which improves the model’s prediction accuracy while speeding up the convergence of RMSProp algorithm. In order to illustrate the performance of the ST-VGBiGRU model, the RTMC traffic dataset is used to conduct ablation experiments on the improved VMD module, the GAT module, and the improved BiGRU module. Meanwhile, we combined the PeMS traffic dataset to conduct baseline experiments and multi-step prediction experiments with the other six models. The results show that the prediction performance of our model is better than all the other baseline models.

This paper considers constraints on the cycle number of energy storage systems (ESSs), travel distances of switchable capacitors reactors (SCRs), on load tap changers (OLTCs) and step voltage regulators (SVRs). The characteristics and operational constraints of these equipments are properly formulated with binaries and big-M method, obtaining desirable linear descriptions. The branch flow equations of distribution systems for active and reactive power coordination are constructed. Considering physical constraints, a multi-period mixed-integer deterministic second-order conic programming (SOCP) to minimize network losses is formulated. Then, considering uncertainties of loads and active power of renewable distributed generators (DGs), a two-stage robust model is developed. A directly iteratively solving method between the first and second stage models is proposed. In contrast to the traditional column-and-constraint generation (CCG) method, increases to variables and constraints are not needed to solve the first stage model. To solve the second stage multi-period model, only the model of each single period needs to be solved first. Then their objective function values are accumulated, and the worst scenarios of each period are concatenated. As a result, the solving complexity is greatly reduced. The capabilities of the proposed method are validated by three simulation cases. It is found that the computational rate using the proposed method is significantly enhanced with much less computer storage. Specifically, the simulation results of 4, 33 and 69-bus systems indicate that the computing rate of the proposed method is about 28 times higher than CCG method when 8 iterations are performed. Meanwhile, the precision of optimization results is also improved.

In the existing multi-period robust optimization methods for the optimal power flow in radial distribution systems, the capability of distributed generators (DGs) to regulate the reactive power, the operation costs of the regulation equipment, and the current of the shunt capacitor of the cables are not considered. In this paper, a multi-period two-stage robust scheduling strategy that aims to minimize the total cost of the power supply is developed. This strategy considers the time-of-use price, the capability of the DGs to regulate the active and reactive power, the action costs of the regulation equipment, and the current of the shunt capacitors of the cables in a radial distribution system. Furthermore, the numbers of variables and constraints in the first-stage model remain constant during the iteration to enhance the computation efficiency. To solve the second-stage model, only the model of each period needs to be solved. Then, their objective values are accumulated, revealing that the computation rate using the proposed method is much higher than that of existing methods. The effectiveness of the proposed method is validated by actual 4-bus, IEEE 33-bus, and PG 69-bus distribution systems.

Accurate wind power forecasting is crucial for the safe, stable, and economical operation of green power systems. Multi-step forecasting of wind power has received increasing attention in recent years. From the perspective of machine learning, multi-step prediction can be transformed into a multi-task learning problem, i.e., multiple single-step prediction tasks. From a data-driven perspective, it is often difficult to respect the differences and relationships among multi-source heterogeneous data that are typically used in multi-step forecasting research, e.g., wind power and numerical weather prediction (NWP) data. This work proposes a multi-modal multi-task transformer network (M2TNet) model that can achieve ultra-short-term wind power multi-step forecasting based on multi-source heterogeneous data. The M2TNet is a unified framework that integrates multiple feature extractors, including a Transformer, a feature fusion layer-based fully-connected network, and a prediction terminal layer. The developed model applies multi-modal and multi-task learning strategies to effectively fuse multi-modal information and enable knowledge sharing among multiple single-step prediction tasks. In addition, the Transformer’s computing efficiency and ability to mine complex dependent data is exploited for joint learning of multi-source heterogeneous data. Data, including NWP and wind power, from a wind farm in Northeast China were used to validate M2TNet. The correlations between the input variables were analyzed using maximal information coefficient method to control the scale of the model. For prediction accuracy, the experimental results showed that, compared with the existing model, M2TNet reduced the root mean square error by 0.19%, 0.99%, 1.05%, and 1.53% in 4-, 8-, 12-, and 16-step ahead predictions, respectively. Furthermore, for computational efficiency, the training time of the existing model at a wind farm is 1.66 times that of M2TNet. This confirmed that the M2TNet model performs better in terms of prediction accuracy and computational efficiency. Our work illustrates the potential of M2TNet for large-scale wind farm applications.

Abstract This paper presents a novel insulator defect detection scheme based on Deep Convolutional Auto‐Encoder (DCAE) for small negative samples. The proposed DCAE scheme combines the advantages of supervised learning and unsupervised learning. In order to reduce the high cost of training Deep Neural Networks, this paper pre‐trained the Convolutional Neural Networks (CNN) through open labelled datasets. Through transferring learning, the encoder part of the traditional Convolutional Auto‐Encoder was replaced by the first three layers of the CNN, and a small number of defect samples were used to fine‐tune the parameters. A threshold discrimination scheme was designed to evaluate the model detection, realising the self‐explosion detection of insulator by judging the residual result and abnormal score. The experimental results show that compared with the existing insulator self‐explosion detection schemes, the proposed scheme can reduce the model training time by up to 40%, and the recognition accuracy can reach 97%. Moreover, this model does not need a large number of insulator labelled data and is especially suitable for small negative sample application.

The involvement of cardiovascular or respiratory complications in cases of brucellosis are extremely rare. Herein, a case of myocarditis and pneumonia with pericardial effusion, pleural effusion and biliteral pleural thickening with pleural adhesion in a 35-year-old female patient, is described. Using next-generation sequencing, the patient was differentially diagnosed with Brucella -related myocarditis and pneumonitis, and treatment with oral doxycycline, rifampicin, and trimethoprim/sulfamethoxazole, along with intravenous gentamycin, was commenced. Following treatment, the patient was clinically improved. When a patient with brucellosis presents with chest pain, clinicians should be aware of this clinical manifestation. Next-generation sequencing may be used to identify pathogens and provide insights into the disease when appropriate cultures are negative.

In this work, the influences of special environments (hydrogen gas and high temperature, high humidity environments) on the performance of three types of SiC MOSFETs are investigated. The results reveal several noteworthy observations. Firstly, after 500 h in a hydrogen gas environment, all the SiC MOSFETs exhibited a negative drift in threshold voltage, accompanied by an increase in maximum transconductance and drain current (@ VGS/VDS = 13 V/3 V). This phenomenon can be attributed to that the hydrogen atoms can increase the positive fixed charges in the oxide and increase the electron mobility in the channel. In addition, high temperature did not intensify the impact of hydrogen on the devices and electron mobility. Instead, prolonged exposure to high temperatures may induce stress on the SiO2/SiC interface, leading to a decrease in electron mobility, subsequently reducing the transconductance and drain current (@ VGS/VDS = 13 V/3 V). The high temperature, high humidity environment can cause a certain negative drift in the devices’ threshold voltage. With the increasing duration of the experiment, the maximum transconductance and drain current (@ VGS/VDS = 18V (20 V)/3 V) gradually decreased. This may be because the presence of moisture can lead to corrosion of the devices’ metal contacts and interconnects, which can increase the devices’ resistance and lead to a decrease in the devices’ maximum transconductance and drain current.

Abstract In the present research, a novel mathematical model for the motion of cilia using non-linear rheological fluid in a symmetric channel is developed. The strength of analytical perturbation technique is employed for the solution of proposed physical process using mectachoronal rhythm based on Cilia induced flow for pseudo plastic nano fluid model by considering the low Reynolds number and long wave length approximation phenomena. The role of ciliary motion for the fluid transport in various animals is explained. Analytical expressions are gathered for stream function, concentration, temperature profiles, axial velocity, and pressure gradient. Whereas, transverse velocity, pressure rise per wave length, and frictional force on the wall of the tubule are investigated with aid of numerical computations and their outcomes are demonstrated graphically. A comprehensive analysis for comparison of Perturb and numerical solution is done. This analysis validates the analytical solution.

Ni-xCu/Sn soldering joints were aged at 200 °C, and the microstructure evolution and mechanical properties during the solid-state reaction were studied under shear loading. Results showed that the intermetallic compounds (IMCs) exhibited a Cu content-dependent transformation from the (Ni,Cu)3Sn4 phase to the (Cu,Ni)6Sn5 phase at the Ni-xCu/Sn interface. Furthermore, a Cu3Sn layer was observed exclusively at the Cu/Sn interface. The shear strength of the soldering joints after thermal aging exhibited an initial decrease followed by an increase, except for a significant enhancement at the Cu content of 60 wt.%. In addition, the evolution law of mechanical properties and failure mechanism of the thermal aging joints were elucidated based on the fracture microstructure and the fracture curve of the joints.

Abstract 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 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 This paper presents an open‐switch fault detection, and identification method for a neutral‐point clamped rectifier system. A new calculation method for phase‐to‐phase pole voltage deviations is proposed for reducing calculation errors. In this way, the more accurate diagnosis can be achieved. Both single‐switch faults, and multiple‐switch faults can be identified effectively by using fewer diagnostic variables, which makes the diagnostic method simpler, and more reliable. And all the variables can be simply calculated by signals which are available to the control system of the rectifier, avoiding the use of additional hardware. Moreover, different voltage thresholds are designed for different faulty feature sections to further reduce the diagnostic time. Additionally, a diagnostic result checking method is proposed to avoid the misdiagnosis when considering the effect of outer‐switch faults. Finally, experiments are carried out, and the results show the robustness, and effectiveness of the proposed method.

Parameter estimation of Direction of Arrival (DOA) using deterministic and stochastic computing paradigms is an enabling development for underwater acoustic signal processing beside its applications in the field of seismology, astronomy, earthquake and bio-medicine. In this work, the comparative study between state of the art deterministic and heuristics algorithms is presented for viable DOA estimation for different underwater dynamic objects. A Uniform Linear Array (ULA) of eight hydrophones is used for impinging acoustic waves from far-field targets. In order to evaluate the performance, the viability of innovative statistical indices is utilized to explain. Performance analysis of Genetic Algorithm(GA) and Particle Swarm Optimization(PSO) is conducted with standard counterparts including MVDR, MUSIC, ESPRIT and UESPRIT for different number of targets in terms of estimation accuracy, robustness against the number of elements and noise, cumulative distribution function of Root Mean Sqaure Error(RMSE), frequency distribution of the RMSE over the monte carlo trials, the resolution ability and computational complexity in the presence of white Gaussian measurement noise. Crammer Rao Bound (CRB) based analysis is also performed for the validation assessments and results on Monte Carlo simulations depict that the Genetic Algorithm(GA) showed the outperform counterparts on precision, convergence and complexity indices.

The detection of partial discharge (PD) is a crucial method to evaluate the insulation status of transformers. The main difficulties of the current localization algorithms are the complexity of the solution and sensitivity to time delay errors. This article proposes a PD localization method in transformers based on linear conversion and density peak clustering (DPC). First, to reduce the complexity of solving the localization equations, the nonlinear localization equations are transformed into linear localization equations by eliminating the second-order terms. Then, to reduce the influence of time delay errors on localization accuracy, the initial localization values are located by multiple acoustic emission (AE) sensors. Finally, the optimal PD coordinates are determined by clustering the initial location values using density peaks clustering algorithm with automatic finding centers (AFC-DPC). The experimental results show that the proposed method can improve PD localization accuracy in transformers, and the average localization error is 5.30 cm.

With the development of urban and rural infrastructure, the power system is enforced to operate nearly at its full capacity which result in heavily stressed power grid operation, greater loss, peak generation, security threat and instability of the electrical network. The performance of the legacy electric network can be enhanced by solving optimal reactive power dispatch (ORPD) problems such as, tuning of the grid voltages, transformers tap setting, capacitor bank rating, allocation and sizing of flexible AC transmission system (FACTS) through computational intelligence tools. In last few decades, several optimization strategies are developed to solve ORPD problems but still research is in progress to leverage the performance of power system. In this line of thought, this paper provides a comprehensive literature of major optimization tools designed for the solution of ORPD problems with aim of improving the power system performance. The purpose of this study is to document up to date information on the optimal reactive power dispatch (ORPD), ORPD incorporating FACTS, mathematical model of ORPD problems with corresponding constraints, mathematical model of FACTS in ORPD, and applications of ORPD. At the end, a new computational tool based on fractional calculus has also been introduced for performance improvement of traditional swarming techniques to support researchers in field of energy/power sector and carry out further research.

Novel application of integrated swarming intelligence computing paradigm is exploited for reliable treatment of nonlinear active noise control (ANC) systems using global search capacity of grasshopper optimization algorithm (GOA) combined with local search efficacy of sequential quadratic programming (SQP), i.e., GOA-SQP. The designed optimization mechanism GOA-SQP is applied to minimize the cost function of ANC controller incorporating the nonlinear Volterra filtering having linear/nonlinear primary/secondary paths in case of different noise interferences of sinusoidal, random, and complex random type signals. The comparison of the results through statistical observations in terms of accuracy, convergence and complexity indices reveals that the GOA-SQP based ANC controllers are operative, resilient and viable.

The open-switch fault of three-phase voltage source rectifiers (VSR) may lead to ac current distortion. In the past decade, some current-based fault diagnosis methods have been proposed, which directly used the phenomenon of current distortion but lacked a detailed analysis of the cause of it. In this paper, the current distortion of VSR with unity power factor operation is analyzed from two aspects: the effect of the three-phase switching state on the ac current and the response of the VSR control system after the fault. Besides, the impact of the VSR system and control parameters on the current distortion is also discussed. The analysis verifies the feasibility of the distortion current for fault diagnosis, providing a theoretical basis for current-based fault diagnosis methods of VSRs. To substantiate the analysis of the distorted current, we designed a simplified fault diagnosis method, which determined the faulty phase by the difference of the normalized three-phase distorted current, and located the faulty switch by the grid voltage angle. The experiments are conducted to verify the analysis of current distortion and the reliability of the proposed fault diagnosis method.

Analog circuit is one of the most commonly used components in industrial equipment, and circuit failure may lead to significant causalities and even enormous financial losses. To address this problem, a novel scheme based on the wavelet spectrum features, feature selection, and Naive Bayes classifier is presented for the fault location of an analog system in this paper. The scheme mainly consists of three stages. First, the cross-wavelet transform (XWT) method is utilized to obtain the time-frequency representations of the raw signals of analog circuits. Second, the local optimal-oriented pattern is applied to all the XWT spectrum images to generate the original high-dimensional feature set. Then, an integration feature selection approach via joint Hilbert-Schmidt independence criterion and kernel Fisher linear discriminant analysis is proposed and utilized to obtain low-dimensional fault features, which are uncorrelated and distinctive. Finally, the training samples set is imported into the Naive Bayes classifier, and the fault diagnosis results can be drawn through inputting the testing samples set into the trained Naive Bayes classifier. The simulation results on two typical circuits have demonstrated that the proposed method is a promising means to detect and classify most analog circuit faults, achieving a better diagnosis accuracy than that of the other published works.

This paper first proposes nonlinear spline adaptive filter based on the robust Geman-McClure estimator (SAF-RGM). The proposed algorithm is obtained by minimizing the cost function relied on the Geman-McClure estimator. Since the Geman-McClure estimator can remove outliers with large amplitude from dataset, the proposed algorithm can obtain the excellent performance in the impulsive noise. Moreover, the mean and mean square behaviors of the SAF-RGM algorithm are analyzed. Simulations are conducted to confirm that the proposed SAF-RGM algorithm achieves better performance than the existing spline nonlinear adaptive filtering algorithms. Besides, simulation results validate the theoretical conclusions.

In modern monitoring systems, it is essential to deploy sensor nodes and deliver related data to the information center. Wireless sensor networks (WSNs) usually work in harsh environments with vibration, temperature variations, noise, humidity, and so on. The batteries of sensor nodes are always not replaceable because of difficult access. Most of existing literature tries to prolong network lifetime by improving sleep scheduling strategies and deployment methods, independently or jointly. However, the congenital defects of mesh network can't be avoided completely. To overcome the technology challenges, this paper develops a LoRaWAN-based WSN and investigates its energy efficient scheduling method. Firstly, the basics and the limits of LoRaWAN are introduced and the feasibility and the considerations of LoRaWAN-based star wireless sensor network are discussed. Secondly, an improved compressed sensing algorithm named ISL0 (improved SL0) is proposed for network data reconstruction and compressed sensing algorithm can reduce the number of LoRa nodes transmitting data packets to avoid collision and latency. Thirdly, a sleep schedule method is proposed to reliably monitor environment data and device operating status. By using the proposed method, not only the abnormal information can be detected on time, but also the overall network data can be recorded termly. Simulation and measurement results verify all nodes have same power level at different times, and the network lifetime is maximized.

As a promising wireless communication technology, the IEEE802.11ah standard is designed to connect various sensors in the Internet of Things (IoT) in future. It is important to investigate adaptive transmission in the IEEE802.11ah standard. However, exact channel state information (CSI) is required. Channel prediction is an available approach. Therefore, an adaptive elastic echo state network (AEESN) for channel prediction in the IEEE802.11ah standard-based orthogonal frequency division multiplexing (OFDM) system is introduced in this paper. The AEESN includes two key components, a basic echo state network and an adaptive elastic network. The latter is imported to overcome collinearity problems due to vast neurons in the former and to avoid ill-conditioned solutions when estimating output weights in the former. Moreover, the latter can produce sparse output weights, which reduces memory storage requirements. To evaluate system performances, 1MHz and 2MHz bandwidth cases with specified parameters are tested. One-step prediction, multi-step prediction and robustness are evaluated for various signal to noise ratios (SNRs). The results indicate that the AEESN not only offers satisfactory prediction performance, but also effectively avoids ill-conditioned solutions and produces sparse output weights. Therefore, it can assure adaptive IoT communication.

State of health (SOH) estimation and remaining useful life (RUL) prediction can ensure reliable and safe system operation and reduce unnecessary maintenance costs. In this paper, to improve the accuracy and reliability of SOH estimation and RUL prediction, a novel method based on second-order central difference particle filter (SCDPF) is proposed. By optimizing the importance probability density function, the particle degeneracy phenomenon of particle filter (PF) can be solved. Experiments from the National Aeronautics and Space Administration (NASA) and the Center for Advanced Life Cycle Engineering (CALCE) of the University of Maryland are conducted to demonstrate the effectiveness and satisfactory performance of the proposed SCDPF approach. The maximum error and the root mean square error (RMSE) of the SCDPF fitting approach are quite small, the minimum values of those are 0.006102 Ah and 0.001599, which are lower than those of the unscented particle filter (UPF) and particle filter (PF). The average RUL errors and average PDF width of SCDPF method are also smaller, which validates the accuracy and stability of the proposed method.

As the evolution of traditional electroencephalogram (EEG) monitoring unit for epilepsy diagnosis, wearable ambulatory EEG (WAEEG) system transmits EEG data wirelessly, and can be made miniaturized, discrete and social acceptable. To prolong the battery lifetime, analog wavelet filter is used for epileptic event detection in WAEEG system to achieve on-line data reduction. For mapping continuous wavelet transform to analog filter implementation with low-power consumption and high approximation accuracy, this paper proposes a novel approximation method to construct the wavelet base in analog domain, in which the approximation process in frequency domain is considered as an optimization problem by building a mathematical model with only one term in the numerator. The hybrid genetic algorithm consisting of genetic algorithm and quasi-Newton method is employed to find the globally optimum solution, taking required stability into account. Experiment results show that the proposed method can give a stable analog wavelet base with simple structure and higher approximation accuracy compared with existing method, leading to a better spike detection accuracy. The fourth-order Marr wavelet filter is designed as an example using Gm-C filter structure based on LC ladder simulation, whose power consumption is only 33.4 pW at 2.1Hz. Simulation results show that the design method can be used to facilitate low power and small volume implementation of on-line epileptic event detector.

Multiple-input multiple-output (MIMO) technology can potentially help to achieve high data rates for multiuser communication. To achieve better performance, the channel state information (CSI) is estimated by the pilot. However, the estimated CSI cannot be used in downlinks when the mobile speed is very high, since it becomes outdated due to the rapid channel variation. In a massive MIMO system, the issue of outdated CSI is serious when using traditional techniques. Therefore, in order to obtain accurate CSI, the prediction of future CSI is required. In this paper, a low complexity online extreme learning machine (ELM) is proposed for the online prediction of the fast fading channel. First, we introduce the structure of the online sequential extreme learning machine (OS-ELM) and combine the training process of the OS-ELM with a forgetting mechanism (FM) to predict fast changing channels. Second, we use the truncated polynomial expansion (TPE) to reduce the computational complexity of the OS-ELM with the FM (FOS-ELM). In addition, the performance of the proposed algorithm is verified through simulation results, and we apply channel prediction in the precoding process. It is shown that the communication quality is improved by our channel prediction algorithm.

Degradation of circuit components are typically accompanied by a deviation in component parameters from their normal values, which can ultimately influence the stable operation of complex analog circuit. To address this concern, remaining useful performance (RUP), regarded as the useful performance from the current time to the end of performance, is an effective way to ensure system safety by providing early warning of failure and enabling forecast maintenance. In this paper, a novel RUP estimation method based on the two-stage maximal information coefficient (TSMIC) and bidirectional gate recurrent unit (Bi-GRU) network is proposed. Initially, the run to failure data of the circuit in real-time is obtained by RT-LAB hardware-in-the-loop. Additionally, to obtain suitable features reflecting degradation trend over cycles, a TSMIC method is proposed to eliminate features hardly changing with degradation cycle in the first stage, mine mutual information between features in the second stage. Furthermore, the linear regression model is used as a performance evaluation to retain the original pattern in the selected features. Through the fusion of the selected multi-features, health indicators of different circuit components are constructed. Ultimately, the deep Bi-GRU unit network, which can extract representative time-series information and explore subtle differences of the degradation cycles, is used to generate prediction results. The proposed framework is verified through a case study on the complex analog circuit, and comparisons with other state-of-the-art methods are presented. The experimental results of the case study show the effectiveness and superiority of the proposed approach.

Optimal reactive power dispatch (ORPD) problems in power system have been solved by using several variants of traditional nature inspired particle swam optimization (PSO) with aim to achieve a promising solution for a given objective such as line loss, voltage deviation and overall cost minimization. Several schemes have been designed to improve the performance of the optimization technique in tunning the operational variables and analyzed by evaluating the final results. In this article, a different method is designed to solve ORPD problems, by introducing Shannon entropy based diversity in the fractional order PSO dynamics, i.e., FOPSO-EE. The results show that synergy of both, the Shannon entropy and the fractional calculus can be used as the useful tools for enhancing the optimization strength of algorithm while solving the ORPD problems in standard IEEE 30 and 57 bus power systems. The performance of the design FOPSO-EE is further validated through results of statistical interpretations in terms of histogram analysis, box plot illustration, quantile-quantile probability plot and empirical probability distribution function.

Most of the power transformer fault diagnostic researches so far focuses on its fault type diagnosis, but there are less related researches on fault positioning, and the diagnostic methods of which are still less intelligent. This paper proposes a two-dimensional Hilbert ID considering multi-window feature extraction for deep vision fault positioning of the transformer winding. Firstly, sweep frequency response data containing complex fault characteristics is obtained based on pspice simulation. Next, a multi-window feature extraction method with logarithmic constraints is introduced to process the original data to obtain feature sequences. Then the proposed Hilbert visualization is used to further highlight the graphic feature of the feature sequences, and obtain Hilbert ID (MAPE) dataset. Finally, it is used to conduct transfer learning on the convolutional neural network. Different intelligent positioning methods are compared, and the proposed deep vision fault positioning method is 6.51% higher than other methods on average. What's more, the positioning effects based on different data processing methods are also compared. The accuracy of the proposed Hibert ID (MAPE) dataset is 10.35% higher than the other data processing methods on average. Finally, the positioning accuracy of Hilbert ID (MAPE+CC) combining two feature sequences can reach 96.09%, having an increase of 2.50%.

Due to its advantages of small size and light weight, multi-pulse rectifier has a broad application prospect in the field of aviation. The 18-pulse rectifier is more popular in practical engineering application because of its better rectifier characteristics than 12-pulse, its simpler structure process and easier to realize than 24-pulse. The theoretical research and engineering application of the 18-pulse rectifier have achieved some success. However, whether the reliability of the system can be satisfied has always been one of the key problems to be solved in practical engineering. The aim of this work is to study the reliability modeling and prediction of the 18-pulse rectifier power supply. Its reliability block diagram and mathematical model are established. According to US Military handbook MIL-HDBK-217F, the most authoritative and widely used in the world, the two most commonly used methods for reliability prediction are parts count method and part stress analysis method. The reliability data are estimated by these two methods. It is concluded that the value of mean time between failures in airborne environment is 23326 h, which meets the requirement of 1.5 times of 15000 h. According to the reliability data, the failure mode and effect analysis of its weak links are carried out, and the improvement measures are proposed. This will promote and guide the reliability growth and engineering popularization of the 18-pulse rectifier power supply. At the same time, the reliability of the actual project can be extended to other related engineering applications to ensure the reliable operation of the system.

The traditional method of searching transmission section generally has the problem of omission and time-consuming. A topology converging algorithm of power grid adjacency matrix is proposed in this paper. It aggregates neighboring buses based on a simple matrix transformation rule, and identify key transmission sections based on matrix operations and power flow distribution factors. This method has the advantage of no presetting the sub-zone, which can avoid the simplification of the grid topology in the sub-zone and improve the calculation accuracy. The complex logical operations of the traversal algorithm adopted by most of traditional methods can be avoided, and the calculation speed can be effectively improved. Besides, the algorithm also has a memory characteristic which can be applied to the online search of key transmission sections. The results of several IEEE test systems show the effectiveness of the proposed algorithm.

The problems of sub-synchronous oscillation and high-frequency oscillation related to inter-harmonics need to be solved in intermittent energy generation systems. The paper proposes a new Phase-Shifted Rotating Pulse Width Modulation (PSR-PWM) technique for specific inter-harmonics control, which can eliminate the specific inter-harmonic that may cause related inter-harmonic oscillations in power system. Based on the time-shift and phase-shift characteristic of signals, the initial phase angle of a characteristic harmonic is linearly modulated to realize the frequency control of moving the spectrum to the desired inter-harmonic spectrum. Therefore, the inverter controlled by the proposed PWM technique behaves as a controlled inter-harmonic voltage source, so as to suppress the specific inter-harmonics in the grid. The analysis of typical simulation cases shows that the maximum frequency offset range for any modulated output voltage characteristic harmonic of the inverter is from -50Hz to 50Hz theoretically. The amplitude of the inter-harmonic voltage combined with its phase angle and frequency can be continuously controlled over a wide frequency range simultaneously, and the suppression effect of other parasitic inter-harmonics is obviously better. Finally, the feasibility and correctness of proposed PSR-PWM techniques are verified by MATLAB simulations and experiments. This paper can be helpful for the study of new energy grid-connected inverters to damp sub-synchronous oscillations and high-frequency oscillation.

Prognostics and health management (PHM) for electronic devices is intricate yet crucial in an era of electricity. The impending future of electric vehicles and clean energy requires more in-depth scrutiny and monitoring of their storage elements, lithium-ion batteries. State-of-health (SOH), as one of the most evident indicators for battery degradation quantifications, is a matter of vital importance that worths more attention. In this regard, this article proposes a novel SOH prognostics framework for lithium-ion batteries considering the limitations in the recorded measurements through the use of linear statistical k-nearest neighbors (LSKNN) data interpolation, maximal information entropy search (MIES), and collective sparse variational Gaussian process regression (CSVGPR). First, the incomplete charging measurements are processed by LSKNN to infer the missing data points and suppress the unanticipated noises in the extracted temporal features, which indicate the trend of degradation. Then, the MIES scheme is proposed to filter the features that are extraneous to the SOHs of the corresponding batteries and that greatly correlate to the other features in the feature set. Finally, the CSVGPR model, considering the uncertainties within each of the sparse variational Gaussian processes, is utilized to implement SOH prognosis. The proposed framework is verified by a subset of the repository from NASA. In the test, multiple prognostics comparisons of inner-battery tests, cross-battery tests, and tests with other statistical learning methods are presented. The experiment results lend support to the superiority and effectiveness of the work.

Wireless channel scene recognition plays a key role in cognitive radio (CR) mobile communication systems. This paper proposes a wireless channel scene identification framework based on the autocorrelation function and deep learning. First, a feature extraction (FE) method is developed to perform a channel scene date analysis based on the autocorrelation function (AF). The AF is used to realize the FE method because it can be combined with Fourier transform (FT) to accurately extract the characteristics accurately from a time-varying channels scene. Second, a deep belief network (DBN) with a robust learning approach is introduced to perform wireless channel scene recognition. A novel learning architecture is employed, which combines the feature parameter and classification techniques to achieve a high classification correct recognition rate. Third, the k-step contrastive divergence (CD-k) algorithm is introduced as the preliminary training method to optimize the traditional DBN network. This method can effectively calculate the logarithmic gradient of the Boltzmann machine. Moreover, the up-down optimization algorithm is applied to optimize the network parameters. Finally, the theoretical implementation is described in detail, and the method is verified by constructing an experiment platform for an engineering application. The experimental results indicate that the proposed classifier is an excellent approach to realize channel scene recognition through advanced methods. The classification accuracy of the proposed approach is higher than that of several existing techniques.

This paper aims to present a robust passivity-based control (PBC) strategy to solve the instability problem caused by the constant power loads (CPLs) in dc microgrid systems. This strategy is designed to stabilize and regulate the dc-bus voltage of the dc microgrid and to eliminate the dc-bus voltage deviations caused by the system disturbances such as load and input voltage variations. To this end, the control robustness of the PBC strategy is improved by adding the nonlinear disturbance observer (NDO). Whereas, the PBC is applied to damp the system oscillation caused by the CPLs and to ensure that each parallel subsystem in dc microgrid is passive (stable). Based on estimation technique, the NDO works in parallel with the PBC strategy to compensate the system disturbances through a feed-forward compensation channels. Furthermore, the PBC strategy provides self-(I-V) droop characteristics, which able to eliminate the voltage mismatch between the parallel converters and obtain equal current sharing between them. This control strategy ensures large-signal stability, globally asymptotically stabilization and reacts extremely fast against system disturbances as compared with other PBC strategies. The MATLAB simulation and hardware-in-loop (HIL) experimental results are presented to verify the control robustness of the proposed controller.

Recently, the applications of artificial intelligence through soft computing and machine learning algorithms have become the focal point of researcher's consideration on account of their convenience for accurate modelling, ease in simulation and effective assessment. This article endorses soft computing based backpropagated neural networks (BNNs) with Levenberg Marquardt technique (LMT), i.e., BNN-LMT, over a novel mathematical model based on biconvection, second grade combine convection nanofluid (BSCCN) flow associated with Cattaneo-Christove (CC) heat flux model for thermal transportation and viscous dissipation, Darct-Forhheimer (DF) law for permeable medium and Hall (H) current for high intensity electric conductive on flow motion model, i.e., BSCCN-CCDFH flow model. Self-similar transformations are used to reduce the multivariable function model to mathematical system of a single variable. The assessment of thermal buoyancy parameter, Hall parameter, porosity parameter, thermophoresis factor, Lewis number and Peclet number over the flow rate dynamics, energy, nanofluid concentration and microorganism concentration profiles is made through dataset based on Adam numerical solver for different physical quantity based scenarios. The results of exhaustive numerical simulation studies show that the proposed technique BNN-LMT is an efficient, reliable, accurate and rapid convergent stochastic numerical solver exploited viably for the BSCCN-CCDFH flow model having number of physical variations.

Image encryption is an effective way to protect image data. However, existing image encryption algorithms are still unable to strike a good balance between security and efficiency. To overcome the shortcomings of these algorithms, an image encryption algorithm based on plane-level image filtering and discrete logarithmic transformation (IEA-IF-DLT) is proposed. By utilizing the hash value more rationally, our proposed IEA-IF-DLT avoids the overhead caused by repeated generations of chaotic sequences and further improves the encryption efficiency through plane-level and three-dimensional (3D) encryption operations. Aiming at the problem that common modular addition and XOR operations are subject to differential attacks, IEA-IF-DLT additionally includes discrete logarithmic transformation to boost security. In IEA-IF-DLT, the plain image is first transformed into a 3D image, and then three rounds of plane-level permutation, plane-level pixel filtering, and 3D chaotic image superposition are performed. Next, after a discrete logarithmic transformation, a random pixel swapping is conducted to obtain the cipher image. To demonstrate the superiority of IEA-IF-DLT, we compared it with some state-of-the-art algorithms. The test and analysis results show that IEA-IF-DLT not only has better security performance, but also exhibits significant efficiency advantages.

The low-harmonic and high-power density multi-pulse autotransformer has many advantages, such as simple structure, high reliability, high efficiency, strong overload ability, and so on. Therefore, it has a broad application prospect in aviation field. The multi-pulse rectifier power system is more and more widely used in aerospace. However, there are many practical engineering problems of the aircraft power supply characteristics that are difficult to predict. In this paper, the aircraft power supply characteristics of the rectified power system with 18-pulse autotransformer rectifier are simulated and investigated. According to the handbook MIL-HDBK-704-3, the performance of the power supply in the normal, abnormal, and fault state is verified and tested in detail. The research and discussion are carried out from six aspects: the load measurement, the steady-state limits, the voltage phase difference, the voltage transient, the frequency transient, and the power failure. The system can meet the requirements of the U.S. military standard MIL-STD-704A for the aircraft power supply characteristics. It has been applied to the radar power supply system of a large transporter, and the conformance verification test of the aircraft power supply characteristics has been completed, which provides the practical application basis for the engineering realization of the multi-pulse rectifier power supply in the radar power supply system. It also provides a reference for the development of the power supply system for other aircraft.

In recent years, many scholars have been committed to improving the security and practicability of chaotic image encryption. In addition, their proposed schemes mainly focus on two aspects, new or improved chaotic systems and new or improved encryption processes. However, according to cryptanalysis works of scholars, encryption processes of chaotic image encryption schemes deserve more attentions. In this paper, a recently reported chaotic image encryption scheme named the 2D logistic-adjusted-sine-map-based image encryption scheme is comprehensively investigated, and some security, practicability, and rationality problems are found. Therefore, we first point out these problems existing in the reported encryption scheme and make some improvements in practicability and rationality. Next, under the conditions of chosen plaintext attack, we cryptanalyze it and propose a corresponding attack algorithm. For our attack algorithm, simulation test results show that it can completely recover plain images without knowing any secret key related information. Finally, we also present some possible improvements for the security problems of chaotic image encryption scheme under study.

Industrial 4.0 placed higher demands on the field of intelligent equipment monitoring. The transformer is one of the critical power devices, its intelligent monitoring and fault positioning require in-depth studies. In this study, an efficient fault localization method for transformer internal thermal faults was proposed by introducing different deep convolutional neural networks (CNNs) and image segmentation. First, the transformer monitoring images of temperature and velocity fields in fault conditions were simulated using the lattice Boltzmann method (LBM), and the images were also used to highlight features information. In practice, transformer degradation does not frequently occur, so that the fault samples for deep learning are insufficient. To solve this problem, a transfer learning method was employed. Subsequently, fault locations were defined as classification labels, and different CNN's were used to classify the labels to achieve the fault localization results. Next, image segmentation was performed to extract the features of fault areas and simplify the data volumes. Likewise, the CNN's were employed to perform the fault localization again. Afterward, since the monitoring sensors were not located everywhere in a transformer in practical applications, information of partial monitoring areas where the monitoring sensors located was trained following a similar procedure. After image segmentation, the average fault localization accuracy using the information obtained by sensors decreased from 97.95% to 94.42%, while the data volume was reduced to nearly 1% of the original one. Besides, the average calculation time per iteration decreased by 8.816%, while the loss value was reduced by 37.68%. Finally, the Friedman hypothesis test and Nemenyi post hoc test were performed to compare the evaluation indicators of different networks, and the performance of GoogLeNet in this case was considered the best.

This paper presents a transmission tower tilt angle prognosis method based on solar-powered LoRa sensor node and sliding XGBoost predictor. The proposed LoRa sensor node, which consists of solar panel, LoRa radio frequency chip, super-capacitors, MCU, accelerometer, and gyroscope, can measure the initial tilt angle of the transmission tower and the angular rate of the transmission tower. Then, the measuring signals of transmission tower were wirelessly transmitted to the LoRa gateway and were processed online. First, the noise of the raw angular rate is reduced by using PCA (principal components analysis) method and the tilt angle of the transmission tower can be calculated by integrating the angular rate. Second, a sliding XGBoost predictor is proposed for tilt angle prognosis, which collects the training data and trains the regression model dynamically. Third, a novel parameter optimization algorithm named DCCPSO (double chain chaos particle swarm optimization) and its execution strategy are proposed to determine the values of hyper-parameters. Finally, the experimental system and the corresponding experimental results are demonstrated and discussed in detail, which shows that the proposed method is effective in transmission tower to tilt angel on-line prognosis.

Compressed sensing (CS) based channel estimation methods can effectively acquire channel state information for Massive MIMO wireless powered communication networks. In order to solve the problem that the existing sparsity-based adaptive matching pursuit (SAMP) channel estimation algorithm is unstable under low signal to noise ratio (SNR), an optimized adaptive matching pursuit (OAMP) algorithm is proposed in this paper. First, the channel is pre-estimated. Next, the energy entropy-based order determination is raised to optimize the reconstruction performance of the algorithm. Then, a staged adaptive variable step size method is put forward to further promote the accuracy of channel estimation. Finally, theoretical analysis and simulation results demonstrate that the proposed OAMP algorithm improves the accuracy at the expense of a small amount of time complexity, does not require a priori knowledge of sparsity and its comprehensive performance is superior to other existing channel estimation algorithms.

With the development of machine learning technology, numerous studies have been proposed to diagnose the open circuit (OC) faults in the pulse width modulation (PWM) voltage source rectifier (VSR) systems. However, most methods require system signals of more than one current period, which show poor real-time performance. Aiming at this problem, this paper presents an improved diagnosis system based on deep belief networks (DBN) and least square support vector machine (LSSVM). First, the double chain quantum genetic algorithm (DCQGA) is employed to obtain the proper length of measured signals and DBN structure parameters. Then, the fault features are extracted from the signals through DBN. Finally, these features are used to train the LSSVM fault classifier to construct the diagnosis model. The experimental results show that the proposed method can achieve the fault diagnosis including six kinds of single switch faults and 15 kinds of different double switches faults correctly. Besides, the proposed method also shows the superior anti-interference performance and high robustness on abrupt load transient conditions, unbalanced, and/or distorted grid voltage conditions, as well as, different power factor conditions. Furthermore, the average diagnostic time of this method is only 2.57 ms.

An incipient fault diagnosis method devised for insulated gate bipolar transistor (IGBT) drive circuit based on improved stack auto-encoder (SAE) is recommended. First, the Monte Carlo method is applied to extracting the time domain response signal of the circuit under test as sample data. Then, with SAE used to extract essential features of data, the SAE is employed to extract features of sample data. Meanwhile, multi-classification relevant vector machine (RVM) is involved for fault diagnosis of the acquired features. As the structure of the hidden layer in SAE and the learning rate could exert a significant effect on the feature extraction performance, in this paper, the quantum particle swarm optimization (QPSO) algorithm is used to optimize the above parameters. As revealed by the experimental results, the improved SAE method is effective in the extraction of the essential characteristics of the incipient faults for the IGBT drive circuit. Further with this, the incipient fault multi-classification RVM of the IGBT drive circuit is capable of achieving 100% diagnostic accuracy.

This paper introduces a novel hybrid fault diagnosis method for power transformer. This method employs solar-powered radio-frequency identification (RFID) sensor for transformer vibration signal acquisition and deep belief network (DBN) for feature extraction. The customized RFID sensor employs solar panel as a power source, and a supercapacitor is adopted to be the stand-by power when the solar panel cannot work. A charging circuit is exploited to guarantee constant DC output voltage. The collected hybrid faults signal is characterized as nonlinear and nonstationary; moreover, it contains abundant noises and harmonic components, which makes it difficult to acquire succinct and robust features from the raw signals. Hence, the DBN is adopted to extract features from the collected vibration signal. In order to obtain optimum feature extraction performance, the quantum particle swarm optimization algorithm (QPSO) is employed to determine the hidden layer structure and learning rate of the DBN model. The experiments indicate that the proposed RFID sensor is able to realize reliable data acquisition and transmission. Besides, the optimized DBN achieves remarkable results in feature extraction for the hybrid fault signal and achieves high diagnosis accuracy.

This paper proposes a wind turbine planetary gearbox (PGB) fault diagnosis method based on a self-powered wireless sensor. The proposed wireless sensor, which consists of a piezoelectric energy harvester, a power management circuit, a microcontroller unit (MCU), a radio-frequency (RF) module, and an accelerometer, can acquire the vibration signals of wind turbine PGB by the accelerometer. The piezoelectric energy harvester utilizing vibration environment is optimized as a power supply for the proposed wireless sensor, including the MCU, RF module, and accelerometer. An ac-dc converter combined with a low-dropout voltage regulator is developed to provide stable dc voltage for the proposed wireless sensor. Stacked denoising autoencoder (SDAE) shows excellent performance in learning robust features from the noised signal. Thus, in this paper, the SDAE method is adopted to learn robust and distinguishable features from measured signals. Then, the least squares support vector machine (LSSVM) is employed to classify features extracted by the SDAE. Both the SDAE and LSSVM are optimized by quantum particle swarm optimization (QPSO). The experimental results show that the presented power supply can generate 3.3-V dc voltage, which ensures regular operation of the rest of the wireless sensor. The proposed wireless sensor can achieve a reliable communication distance of 40.8 m in the test environment. Furthermore, the SDAE approach and LSSVM show excellent performance in feature extraction and fault diagnosis, respectively. The experimental results indicate that the proposed method is effective in terms of fault diagnosis for the wind turbine PGB.

In this paper, a novel and more effective fault diagnosis approach for wind turbine planetary gearbox (PGB) is proposed. In order to better detect the faults in the early stage of the faults of the wind turbine PGB, the corresponding maintenance measures can be carried out to prevent the faults from becoming more serious, so as to seriously affect the normal operation of the fan gearbox. The gear with lighter fault degree is used to simulate the early fault signal. Compared with the fault which has seriously affected the normal working condition, the fault characteristics of the early fault signal are more difficult to detect. So in this design, deep belief network (DBN) optimized by quantum particle swarm optimization (QPSO) algorithm is used to extract deeper and more identifiable features of slight fault signal. After optimization by QPSO algorithm, DBN can get a most suitable structure according to the actual working signal of fan gearbox. Then these extracted features are input into the least squares support vector machine (LSSVM) optimized by QPSO for fault diagnosis test. At the same time, the wireless sensor nodes using self-energy in vibration state are optimized. By using microcontroller unit (MCU) MSP430F149 and nRF24L01 radio frequency (RF) chip with lower energy consumption, the normal dormant state can be maintained, the power requirement of transmission mode can be met, the stability of the whole node can be improved, and the phenomenon of energy shortage caused by short-term fluctuation can be prevented. The comparative experiments in this paper show that this method has good effect on the fault diagnosis of wind turbine PGB.

Asset management was a common RFID-based Internet-of-Things (IoT) application scene. RFID tags in the equipment warehouse were usually large, and the communication between the reader and the tag was prone to data collision problems, which affected the recognition efficiency of the device. In practical applications, due to the structural characteristics of the micro-strip UHF RFID tag antenna, the traditional inter-coupling impedance expression had large errors and insufficient accuracy in predicting the mutual coupling effect, such as system frequency shift. In this paper, the 3D initialization model of the tag was used to indirectly extract the electrical parameter values by the ANSYS HFSS software. At the same time, the dual-tag was taken as an example to derive the transimpedance expression between the dense tags to extract the corresponding coupling parameters. Finally, various tag-intensive scenarios in the actual environment were tested and the derivation formula was verified, and the dual-tag UHF RFID near-field frequency shift affected by the environmental factors, such as relative position, attachment, and the stacking method, was discussed. The mutual coupling effect on the minimum transmit power of the reader antenna was also studied. The experimental results showed that the average error of the formula calculated by this method was significantly smaller than that of the traditional formula. When the tag spacing was less than 30 mm, the derived mutual impedance expression was applied to the frequency shift calculation error range (1.6-7.3 MHz). For dense tag systems, the error was less than 9.8% when the number of tags was greater than 7, and the prediction accuracy was higher than the superposition method. The research results provided a theoretical and practical basis for the rapid identification and location of power assets during the dense RFID tag environment.