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For conventional grid-following inverters, the overcurrent protection can be fulfilled easily by using the current reference limiting method. However, when this method is used for grid-forming (GFM) inverters, it may jeopardize the stability, because the power synchronization control law is destroyed when the current limit is triggered. To address this problem, this paper proposes a power angle limiting method used for the virtual-admittance-based GFM inverters, which can properly limit the output power and current of the inverters. Since limiting the power angle is more reasonable than directly limiting the current, the stability can be improved by using the proposed method. Detailed implementation of the proposed method is illustrated in this paper. Besides, the effectiveness of the proposed method is verified by the time-domain simulation.

This paper proposes an active-clamped inverter (ACI) with duty cycle modulation for achieving simultaneous wireless power and data transmission (WPDT). The proposed method adjusts harmonic amplitudes by changing the duty cycle of the inverter, as a means to encode information alongside power using a shared wireless channel. This method generates a variable unsymmetrical current waveform corresponding with different duty cycles. By controlling the duty cycle, simultaneous power and data transfer can be achieved by using the fundamental and harmonic components respectively. The ACI is designed to operate in continuous current mode to reduce the variation of harmonic components, even under no load conditions. A simple ACI containing two switches is proposed to generate unsymmetrical current waveforms with a high efficiency and high circuit quality factor Q. Two communication methods are developed to balance the power transfer capability and signal-noise-ratio (SNR). Various separation distances and loads are tested, with experimental results showing the proposed system can achieve simultaneous power and data transfer across a shared channel in an inductive wireless power transfer.

Different from grid-following inverters, grid-forming (GFM) inverters tend to be unstable under stronger grid conditions. However, the instability mechanism is still absent in existing research. To fill this gap, the full-order multiple-input multiple-output (MIMO) small-signal model of the GFM inverter is simplified as a single-input single-output (SISO) model on quadrature axis, so that the destabilizing factor can be clearly observed. Based on this SISO model, it is revealed that the power synchronization loop introducing an integral term into the voltage control loop is the main reason for instability. Decreasing the grid strength or the power droop gain can reduce the gain of the integral term and enhance stability. Finally, simulation and experimental results verify the effectiveness of theoretical analysis.

For the type-4 wind generation system, there is a dc link in the back-to-back converter. Whether the dc-link voltage should be controlled by the grid-side converter (GSC) or machine-side converter (MSC) is still an open question, although it is usually controlled by the GSC. Considering the stability of the system mainly depends on the GSC under weak grid conditions, two typical control methods on the GSC (i.e. power control and dc voltage control) are compared in this paper. It is found that the small-signal stability of these two control schemes are basically the same when choosing the same control loop bandwidths. Even so, the power control scheme is better than the dc voltage control scheme, because a wider range is available for tuning the power loop bandwidth to improve stability. Thus, the dc voltage control carried out on the MSC should be superior to the GSC under weak grid conditions.

Numerous disasters potentially influence critical infrastruc-tures of an electric power system. Generally, disasters to power systems include extreme natural disasters and artificial attacks such as high-impact/low-probability events. If a disas-ter is severe, it could cause power outages. Therefore, it is im-portant to enhance the resilience of power systems. Improving the resilience of a power system is to propose defense strate-gies, prevent voltage collapse and recover from disconnection immediately. Many countries have paid attentions to the resil-ience related issues for preventing disasters. Although resili-ence has been widely used in power systems, there is no any uniform strategy that was proposed to reduce power system risks. This paper aims to review risk assessments about vari-ous disaster types, indexes and corresponding frameworks. Moreover, this paper discusses about the reliability, availabil-ity and challenges of applying various methods to enhance power system resilience.

The installed capacity of solar power generation has been increased in recent years. The intermittent characteristics of solar generation pose large challenges on power system operations. Therefore, accurate solar power forecasting technologies are significant in modern power systems. In this paper, various solar power forecasting methods are summarized and compared. They include time series statistical methods, physical methods, ensemble methods and others. In addition, this paper summaries the optimization methods for designing the parameters of forecasting models. This work also investigates important factors that influences solar power forecasts and then discusses the input selection for PV power forecasting models. Due to forecasting uncertainties, the comparison between probabilistic and deterministic forecasting models is also discussed. Finally, the data pre-processing and post-pro-cessing techniques are also summarized in this paper.

Mobile robots rely heavily on the creation of the scene map and the positioning in the map in an unknown environment, but no matter what type of map is created, it is inevitably affected by cumulative errors. This presents a huge challenge for loop closure detection technology. Using traditional loop closure detection methods to perform scene recognition is difficult to extract the appearance changes caused by time, weather, or seasonal conditions in the image and deep semantic information, and the speed of extracting image features is slow, which is difficult to meet the real-time performance of robots. Because of the success of deep convolutional neural networks(CNN), it is possible to enrich the information of image features. First of all, this paper uses the pre-trained CNN model SSE-Net to extract the deep visual appearance and semantic features of the image, and obtain the feature description vector. Then, after product quantization(PQ) and encoding, the final pair of candidate frames is quickly searched and matched to obtain the most similar pair of candidate frames and judged as a loop . After the verification of the New collage dataset and the City Center dataset, this algorithm has achieved a good Precision-Recall rate and a faster speed compared with the recently proposed large-scale convolution network VGG16 method and traditional feature extraction methods.

This paper proposes an improved method for estimating the state of charge (SOC) of lithium-ion battery. Firstly, a first-order resistor and capacitance (RC) model is introduced. Secondly, the SOC and open-circuit voltage (OCV) relationship is identified through the constant current charge-discharge test, and the least-squares algorithm is used to identify the model parameters. Thirdly, an improved adaptive approach is proposed to solve the problems of particle swarm optimization (PSO), and adaptive particle swarm optimized particle filtering (APSO-PF) is proposed to estimate the SOC of li-ion battery Finally, two dynamic operation conditions are given to show the efficiency of APSO-PF by comparing with the application of particle filter (PF), particle swarm optimized particle filtering (PSO-PF) and APSO-PF in SOC estimation.

The development of e-commerce and the Internet has promoted the rise of the short video shopping platform called TikTok. The increasing scale of trading on the TikTok platform has aroused the increasing concern of people regarding practice and theory, but the influence mechanism of its precision marketing on consumer buying behaviour remains unclear. Combined with the ‘Stimulus–Organism—Response’ framework and Eroglu’s consumer online shopping response model, this study takes precision marketing as an independent variable and online consumer behaviour as a dependent variable. The perceived value of online shopping consumers is the mediating variable, whereas their extroversion is the moderating variable to establish the model. Through a network questionnaire, information is collected from 251 consumers. The positive correlation between precision marketing and the perceived value of online shopping consumers and purchase behaviour is confirmed by empirical research; the relationship between precision marketing and online consumer behaviour is mediated by consumer perceived value. The extraversion of online shopping consumers has a positive moderating effect on the relationship between their perceived value and precision marketing, including the above mediating effect on such a perceived value. The above conclusions provide theoretical basis and practical enlightenment for the network platform to improve its precision marketing effect.

The FeO content in sinter is one of the important indexes for evaluating the quality of sinter. However, due to the high temperature and harsh environments, which makes the FeO content cannot be detected online in real time. To solve this problem, a method combining heat transfer mechanism and data-driven model is proposed to realize online prediction of FeO content. Firstly, a temperature distribution mechanism model of sintering process is established, in which the sinter is divided into three categories by the maximum temperature. Then, three long short-term memory models are constructed under different conditions to predict the FeO content respectively. The validity and feasibility of the proposed model are verified by a sintering plant application, and the prediction results can provide reliable FeO content information for the sintering site.

Non-orthogonal multiple access (NOMA) has been considered as a promising scheme for enabling ultra-high throughput transmission and massive-connectivity in next generation wireless systems. In this paper, we investigate the secrecy-based NOMA transmission for encountering the eavesdropping attack. Exploiting the NOMA-users simultaneous transmission as an artificial jamming, we investigate the joint optimization of NOMA-users’ power allocations and the secrecy-provisioning, with the objective of the effective secure throughput of NOMA-users while ensuring the fairness among them. Despite the non-convexity of the formulated joint optimization problem, we explore its hidden feature and design a search algorithm to compute the optimal solution. Numerical results are provided to validate the performance of our proposed algorithm.1

Industrial Internet of Things (IIoT) is regarded as a promising transformation technology for manufacturing industry. We expect the information flow through along the process of material flow, gauging and collective devices feed IIoT platform with essential data. AI and big data analytical techniques can then be used to process the data to support a series of decision-making. Users at both managerial and operational levels can take actions accordingly, which significantly improve management efficiency in one organization, enhance supply chain coordination, and attract investment, etc. Powerful as bigdata and AI technology, they cannot operate without data. In centralized, third-party-managed IIoT platform, few manufactures are willing to share their critical data. This study extends IIoT framework with blockchain technology to provide a solution for trustless data sharing. The solution embeds encrypted ledger to avoid credential data tampering, employs m-in-n-out smart contracts to secure data exchange, and utilizes consensus mechanism to improve cyber security. The proposed framework integrates blockchain with IIoT to operate the entire industrial data exchange in a decentralized network, namely Industrial Blockchain of Things (IBoT).

The Age-of-Information (AoI) is a new performance metric recently proposed for measuring the freshness of information in information-update systems. In this work, we conduct a systematic and comparative study to investigate the impact of scheduling policies on the AoI performance in single-server queues and provide useful guidelines for the design of AoI-efficient scheduling policies. Specifically, we first perform extensive simulations to demonstrate that the update-size information can be leveraged for achieving a substantially improved AoI compared to non-size-based (or arrival-time-based) policies. Then, by utilizing both the update-size and arrival-time information, we propose three AoI-based policies. Observing improved AoI performance of policies that allow service preemption and that prioritize informative updates, we further propose preemptive, informative, AoI-based scheduling policies. Our simulation results show that such policies empirically achieve the best AoI performance among all the considered policies. However, compared to the best delay-efficient policies (such as Shortest-Remaining-Processing-Time (SRPT)), the AoI improvement is rather marginal in the settings with exogenous arrivals. Interestingly, we also prove sample-path equivalence between some size-based policies and AoI-based policies. This provides an intuitive explanation for why some size-based policies (such as SRPT) achieve a very good AoI performance., A preliminary version of this work was presented at IEEE INFOCOM 2020 Age of Information Workshop

As the modern battlefield environment becomes more and more complex, the normal operation of radar is facing greater challenges. From the interference level, Most of the electronic jamming can enter the radar receiver through the antenna sidelobe, and the most effective technique against such jamming is antenna sidelobe cancellation. Dechirp processing is a common radar signal processing method. In order to study the influence factors of sidelobe cancellation performance, based on the signal bandwidth, dechirped or not, sampling rate, carrying out formula derivation and simulation experiments, thus forming the comprehensive performance evaluation report of anti-sidelobe interference in full frequency band and multi-bandwidth cancellation mode under dechrip processing system. For the engineering application of broadband interference theory, the model has a guiding significance.