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In fault diagnosis, partial least squares (PLS) is a popular data-driven method to identify abnormal key performance indicators (KPI). However, there are two problems in fault diagnosis when using PLS, including inaccurate fault subspace extraction and unidentified false alarms. In the first problem, the improved PLS (IPLS) model is adopted to obtain a precise subspace through orthogonal decomposition. In addition, to eliminate the normal value in fault data, a quality-related fault subspace (QRFS) extraction method is proposed, which can extract a purer quality-related fault subspace. In the second problem, to provide feedback for false alarms, a modified IPLS (M-IPLS) model is proposed to extract the quality-unrelated fault subspace. Based on the proposed fault subspace extraction methods, the fault can be reconstructed by a lower dimensional fault subspace and false alarms with feedback can improve the efficiency of diagnosis. Finally, two examples, including a numerical simulation and the Tennessee Eastman process (TEP), are used to show the effectiveness of the proposed method.

With the development of energy internet and power market, the operation regulation and pricing mechanism of traditional virtual power plants are improved to adapt to the new environment. In this paper, a bi-level multi-time scale scheduling method based on bidding for multi-operator virtual power plant is proposed to provide a framework for solving the interest distribution between operators and optimal scheduling problems of multiple-operator virtual power plant. An operator power allocation and internal electricity price formation method based on bidding equilibrium is proposed in the upper level, which introduces the fluctuation cost coefficient to express the influence of the uncertainty of renewable energy power generation on the bidding process. A multi-time scale optimal scheduling method combining scheduling model and adjustment strategy is established in the lower level. A default penalty mechanism in the scheduling model is used to ensure that operators provide the electricity allocated from the bidding process and considering the influence of demand response based on internal electricity price on adjustment strategy. Simulation results show that the proposed method can realize the optimal distribution of operators’ power generation and form the internal electricity price that reflects the internal supply and demand level of virtual power plant. Besides, it can reduce the impact of uncertainty on dispatching results and improve the application range of virtual power plant to enhance the competitiveness of virtual power plant in market transactions.

It is commonly believed that the purpose of the image fusion is to merge as much information, such as contour, texture and intensity distribution information from original images, as possible into the fusion image. Most of the existing methods treat different source images equally with certain feature extracting operation during the fusion process. However, as for the infrared (IR) and visible image fusion problem, the features of images taken from two imaging devices with different sensitive wave bands are different, sometimes even adverse. We can’t extract and preserve the opposite information at the same time. To keep the targets salient in clutter background and visual friendly, in this paper, a novel IR and visible image fusion method called structure transferring fusion method (STF) is first proposed. Firstly, the structure-transferring model is built to transfer the grayscale structure from the visible input image into the IR image. Secondly, infrared detail enhancing strategy is carried out to supplement the missing details of the IR image. Experimental results reveal that the proposed STF method is both effective and efficient for IR and visible image fusion. The final fusion image with conspicuous targets and vivid texture is conducive to night vision surveillance for human observers.

As the distribution network structure becomes increasingly complex, this paper proposes a distributed state estimation for distribution network with phasor measurement units (PMUs) information to improve the performance of state estimation. This proposed method takes the PMU installation locations as alternative points of the partition, and takes the scale of sub-zone, the number of real-time measurement, and the DG configuration position as the partition criteria, and then conducts distributed state estimation. During the state estimation, the root bus voltage and branch current are used as state variables to reserve the advantage of measurement conversion technology, and the traditional measurement data and PMU measurement data are converted into corresponding currents, which simplifies the Jacobian matrix, thereby reducing iterations and improving the calculation speed of state estimation. Finally, the IEEE 69-bus radial distribution network is used to verify the effectiveness of the proposed method.

With the access of the renewable DGs such as wind turbines and photovoltaic generations, network operation state is uncertain due to the randomness of these renewable DGs. This paper proposes a novel optimal method of the active distribution network (ADN) considering the uncertain conditions and the coordination control of source-network-load. By optimizing the controllable distributed power output, controlling the network switches, and managing the demand-side load synchronously, the impact of distributed renewable energies can be reduced and the reliable operation of ADN can be ensured. The chance constrained programming is used to deal with the uncertainties. The proposed model is settled by the improved teaching-learning-based optimization algorithm (ITLBO) and the performance of the algorithm is verified by the comparison with the TLBO in the modified IEEE 33-bus distribution system.

Background: The involvement of gastrointestinal (GI) symptoms in the progression of COVID-19 patients has not been illustrated, with the association between GI symptoms and severity of illness remaining controversial. The present study aimed to evaluate the association between GI symptoms and the illness progression, severity, and prognosis of COVID-19 patients. Design: This retrospective study recruited 1024 consecutive patients with laboratory-confirmed COVID-19 from three hospitals in Wuhan. The severity of illness was classified as non-severe and severe for analyses. The primary outcome was the association between GI symptoms and the progression from non-severe to severe illness (PNTS) in COVID-19 patients. Results: Of the 934 COVID-19 patients (mean age 59.3 years; 43.7% males), the prevalence of overall and specific GI symptoms at/prior to admission were 59.9% and 13.0%, respectively. Patients with GI symptoms were associated with increased risk of fever (56.1% vs. 48.1%; P=0.02), increased IL-6 (18.2% vs. 11.7%; P=0.04), ground-glass opacity (56.8% vs. 43.1%; P

Metal nanoparticles frequently exhibit good selective extraction ability for Hg(II) in water environment. Here, a kind of Ag-coordination aramid nanofiber-silver nanoparticle composite beads (ANF/Ag) was facilely fabricated using aramid nanofiber (ANF) as the motifs through Ag(I) coordinated self-assembly process. The formation mechanism of ANF/Ag nanoparticle composite beads was established, and the adsorption performances were performed using batch and dynamic experiments, respectively. Ag firstly as promoter coordinated self-assembly of ANF/Ag composite beads, and that can play a role of active sites. ANF/Ag composite showing high adsorption capacities and excellent selectivity towards Hg(II). The adsorption process for Hg(II) followed pseudo-second-order model well, while the dynamic adsorption could be described by the Yoon-Nelson model. Furthermore, adsorption process was a monolayer and chemical adsorption verified by isotherm adsorption. Meanwhile, the maximum adsorption amount of ANF/Ag for Hg(II) was achieved 370.4 mg·g-1. More importantly, ANF/Ag displays remarkable regeneration ability after 5-cycle five cycles of adsorption-desorption. Therefore, this project not only presents an ANF/Ag composite for Hg (II) removal field in environmental pollutions control, but also opens a new design field of ANF material.

Multiple energy storage devices in multi-energy microgrid are beneficial to smooth the fluctuation of renewable energy, improve the reliability of energy supply and energy economy. Taking the multi-energy microgrid with wind-solar power generation and electricity/heat/gas load as the research object, an energy storage optimization method of microgrid considering multi-energy coupling demand response (DR) is proposed in the paper. Firstly, the multi-objective optimization model of multiple energy storage capacity planning based on coupled DR was established with the objective of minimizing economic cost and carbon emission. Then, adaptive dynamic weighting factors are used to adapt to the flexibility of planning scenarios. At last, the economic performance and carbon emissions of the multi-energy microgrid before and after the application of coupled demand response are studied, and the configuration of energy storage system are compared with a numerical example to verify the proposed method effectiveness.

Wind farms are included in the grid Automatic Generation Control (AGC) will help for power system control. In order to minimize the imbalance between the active output of wind farm and the reference value, a power automatic control strategy for wind farm was proposed in the paper, which is considered the dispatching of safe operation of grid and the number of start and stop of wind turbines. The wind turbines in the wind farm were divided into three different categories according to the output characteristics and operation state of the wind turbines. By combining with the energy storage control of wind farm, the selection of power regulation turbines was carried out while considering the constraints such as the climbing rate and the power distribution value of the wind turbines. the proposed method effectiveness was verified with simulation example analysis, which could take full account of the individual information of wind turbines and make the output power adjustment of wind farm reach the optimization effect both in economy and safety.

Photocatalytic disinfection, which is a readily reliable method in most climates, holds great promise to significantly reduce the microbial contamination in modern industry. Here we report that vanadate quantum dots-interspersed graphitic carbon nitride (vanadate QDs/g-C3N4) can achieve efficient inactivation of Salmonella by harvesting a substantial visible light. Detailed characterization through SEM-EDS, TEM, XRD, FT-IR, and XPS confirmed the formation of the composites. Owing to the efficient reactive oxygen species (ROS) production between vanadate QDs and g-C3N4, the bactericidal efficiency of AgVO3 QDs/g-C3N4 could reach 96.4% toward Salmonella in a concentration of 0.75 mg/mL after 10 min visible-light illumination. More importantly, scavenger experiments of different reactive species proved that the photoinduced electron generated at the oxidation site of AgVO3/g-C3N4 play a major role as oxidative species. Fluorescent-based cell live/dead test and membrane potentials were applied to demonstrate the integrity of cell membranes. Furthermore, the SEM technology, PCR and BCA protein assay were employed to verify the bacterial decomposition as well as leakage of bacterial cell contents toward Salmonella. Sterilization experiments of Staphylococcus aureus revealed that our composites have broad spectrum antimicrobial activity for both Gram-negative and Gram-positive bacteria under visible light. The results showed that the generation of high ROS could attack the bacterial cells membrane, and ultimately disrupt the cell metabolism through bacterial contents, which provided a feasible method for eliminating the microbial contaminated water.

Organic-inorganic hybrid perovskite solar cells (PSCs) are one of the most promising technologies in the field of photovoltaics due to its high-power conversion efficiency (PCE) and easy fabrication process. However, its moisture stability has posed a crucial hurdle towards its further commercialization. In this paper, we have introduced an interfacial layer, cerotic acid (CA), inspired from the honeycomb, to modify the surface of perovskite films, thus improving moisture stability. The PCE of the CA modified PSCs retained ~81% of its initial value after aging 30 days at a relative humidity of 35%, in sharp contrast with the pristine devices, only 19% retention of its initial value. In addition, the C=O group in CA was found effectively passivate the unsaturated Pb sites in perovskite films and further contributes to the PCE of devices. Overall, this work has demonstrated a simple, potentially low-cost, and environmentally friendly method to improve not only the photovoltaic performance but also the moisture stability.

Minor component (MC) plays an important role in signal processing and data analysis, so it is a valuable work to develop MC extraction algorithms. Based on the concepts of weighted subspace and optimum theory, a weighted information criterion is proposed for searching the optimum solution of a linear neural network. This information criterion exhibits a unique global minimum attained if and only if the state matrix is composed of the desired MCs of an autocorrelation matrix of an input signal. By using gradient ascent method and recursive least square (RLS) method, two algorithms are developed for multiple MCs extraction. The global convergences of the proposed algorithms are also analyzed by the Lyapunov method. The proposed algorithms can extract the multiple MCs in parallel and has advantage in dealing with high dimension matrices. Since the weighted matrix does not require an accurate value, it facilitates the system design of the proposed algorithms for practical applications. The speed and computation advantages of the proposed algorithms are verified through simulations.

The high overall carrier recombination is widely believed to be a main hurdle towards the high electric power output for perovskite solar cells (PSCs). However, the current leakage at the interfaces of the anode and electron transport layer induced by the carrier recombination has always been overlooked. Herein, we have introduced an ultra-thin lanthanum oxide (La2O3) layer, with a high dielectric constant (κ) and a wide band gap, as an anode modifier to suppress the recombination of electrons and holes. With La2O3 locating at the ITO/SnO2 interface, the power conversion efficiency (PCE) of PSCs was enhanced to 20.23% from 18.45% of the control devices, due to the improved short-circuit current (Jsc) and fill factor (FF). Overall, this work provides a new strategy to further alleviate the hole and electron recombination for developing the high efficiency PSCs.

Hole transport materials (HTMs), as a critical role in the hole extraction and transportation processes, highly influence the efficiency and stability of perovskite solar cells (PSCs). Despite that several efficient dopant-free HTMs have been reported, there is still no clear structure-property relationship that could give instructions for the rational molecular design of efficient HTMs. Thus, in this work, a series of donor–acceptor-donor (D–A–D) type carbazole-based small molecules, TM-1 to TM-4, have been carefully designed and synthesized. By varing the electron acceptor unit from benzene to pyridine, pyrazine and diazine, their packing structure in single crystals, optical and electronic properties have shown a great difference. While as dopant-free HTM in p-i-n type PSCs, TM-2 improved the device photovoltaic performance with a power conversion efficiency from 15.02% (based on PEDOT:PSS) to 16.13%. Moreover, the unencapsulated device based on TM-2 retains about 80% of its initial efficiency after 500 h storage in ambient environment, showing the superior stability.

The theft of electricity affects power supply quality and safety of grid operation, and non-technical losses (NTL) have become the major reason of unfair power supply and economic losses for power companies. For more effective electricity theft inspection, an electricity theft detection method based on similarity measure and decision tree combined K-Nearest Neighbor and support vector machine (DT-KSVM) is proposed in the paper. Firstly, the condensed feature set is devised based on feature selection strategy, typical power consumption characteristic curves of users are obtained based on kernel fuzzy C-means algorithm (KFCM). Next, to solve the problem of lack of stealing data and realize the reasonable use of advanced metering infrastructure (AMI). One dimensional Wasserstein generative adversarial networks (1D-WGAN) is used to generate more simulated stealing data. Then the numerical and morphological features in the similarity measurement process are comprehensively considered to conduct preliminary detection of NTL. And DT-KSVM is used to perform secondary detection and identify suspicious customers. At last, simulation experiments verify the effectiveness of the proposed method.

It is usually difficult but important to extract distant targets from sea clutters and clouds since the targets are small compared to the pixel field of view. In this paper, an algorithm based on wavelet transformation is proposed for automatic detection of small targets under the maritime background. We recognize that the distant small targets generally appear near the sea-sky horizon line and noises lie along the direction of sea-sky horizon line. So the sea-sky horizon is located firstly by examining the approximate image of a Haar wavelet decomposition of the original image. And the equation of the sea-sky horizon is set up, no matter whether the sea-sky horizon is horizontal or not. Since the sea-sky horizon is located, not only the potential area but also the strip direction of noise is got. Then the modified mutual wavelet energy combination algorithm is applied to extract targets with targets being marked by red windows. Computer simulations are shown to validate the great adaptability of the sea-sky horizon line detection and the accuracy of the small targets detection. The algorithm should be useful to engineers and scientists to design precise guidance or maritime monitoring system.

Understanding the molecular mechanisms of tumor initiation has significant impact on early cancer detection and intervention. To define the role of KLF4 in pancreatic ductal adenocarcinoma (PDA) initiation, we used molecular biological analyses and mouse models of klf4 gain- and loss-of-function and mutant Kras. KLF4 is upregulated in and required for acinar-to-ductal metaplasia. Klf4 ablation drastically attenuates the formation of pancreatic intraepithelial neoplasia induced by mutant Kras(G12D), whereas upregulation of KLF4 does the opposite. Mutant KRAS and cellular injuries induce KLF4 expression, and ectopic expression of KLF4 in acinar cells reduces acinar lineage- and induces ductal lineage-related marker expression. These results demonstrate that KLF4 induces ductal identity in PanIN initiation and may be a potential target for prevention of PDA initiation.

The safety and reliability of urban power supply are critical to the sustainability of cities and society. Based on the analysis of big data, a power supply reliability evaluation method for urban distribution networks considering uncertain factors is proposed in this paper. The method has good adaptability and can support the analysis of safety improvement measures. By investigating historical data on distribution network topology and parameters, the main influencing factors affecting power supply reliability and the uncertainties of these factors are screened out. An improved Elman neural network (IENN) is used, and the main process of reliability evaluation is obtained for the complex urban distribution network. It can effectively simplify the calculation and includes multiple uncertain factors to improve evaluation accuracy. Case studies with actual urban distribution network data are used to verify the feasibility and effectiveness of the proposed method. Finally, some useful conclusions are given, including the problems of urban distribution network power supply, and the improvement measures for power supply to support the development of sustainable cities and society.

In recent years, with the rapid development of the energy Internet and the deepening of the complementary coupling of various energy sources, the concept of multi-energy virtual power plant comes into being. At the same time, insufficient research on optimal scheduling of multi-energy virtual power plants under multiple uncertainties. Here we propose a robust stochastic optimal dispatching method to solve the scheduling problem under multiple uncertainties. For the source side uncertainties, the uncertain set of cardinalities with a robust adjustable coefficient is adopted to describe the output of wind turbines and photovoltaics. For the load side uncertainties, the Wasserstein generative adversarial network with gradient penalty is used to generate electric, thermal, cooling, and natural gas load scenarios, and the K-medoids clustering is used to get typical scenes. A two-stage robust stochastic optimal model of the min-max-min structure was established. Based on the dual transformation theory and the column constraint generation algorithm, the original model was solved alternately. Finally, the effectiveness of the proposed model and algorithm is verified by simulation analysis. The proposed method can get the scheduling scheme with the lowest operating cost in the worst scenario and is conducive to reducing the overall scheduling cost of the system.

With the popularization of smart power consumption technology, users can optimize the use of electrical equipment through a home energy management system (HEMS), thereby reducing electricity costs while maintaining a degree of comfort. In this study, we established an HEMS with distributed power and an electrical vehicle, describing a multi-objective optimization model with risk cost and risk index constraints, which have not been considered in previous work. The problem can be solved with improved genetic methods. First, this study establishes a mathematical model for the typical electrical equipment of specific smart home users. On this basis, with the user’s electricity consumption cost and risk index being considered, we propose a multi-objective optimization model of the user’s overall satisfaction and the corresponding constraints of the various devices in the home. This model is regarded as a two-stage optimization considering both electricity cost and power fluctuation. Finally, we verify the effectiveness of the model and the optimization algorithm through example simulations.

To guarantee the heat demand during winter, most combined heat and power (CHP) units in the integrated energy system (IES) usually work under following heat load (FTL) mode, and the renewable energy accommodation is limited. With the development of Power Internet of Things (PIoT), the information exchange in IES become more frequent. Through flexible interaction between different networks in IES, the accommodation capacity of renewable energy can increase significantly. Therefore, this paper focus on the optimization of IES under the background of PIoT. Firstly, based on the influence of PIoT on IES, a novel integrated demand response (DR) way and the model of the critical components in IES are established. Secondly, a Bi-level economic dispatching method for regional IES is developed, considering the cyber-physical infrastructure of PIoT and IES. The upper level of the dispatching method is used to optimize the overall IES operation; the lower level is to optimize the output of demand-side facilities and integrated DR. Thirdly, with adaptive particle swarm optimization (APSO) algorithm, the solution method for the Bi-level dispatch is established. Finally, the feasibility and effectiveness of the proposed method are verified in a standard IES and a real system in northern China.

The interconnected microgrid system (IMS) is a promising solution for the problem of growing penetration of renewable-based microgrids into the power system. To optimally coordinate the operation of microgrids owned by different owners while considering uncertainties in market environment, a bi-level distributed optimized operation method for IMS with uncertainties is proposed in this paper. A hierarchical and distributed operational communication architecture of IMS is first established. A bi-level distributed optimization model was built for IMS, where at the upper level, the IMS operates purchase-sale mode or demand response mode with the distribution network operator and optimizes the trading power with microgrids to maximize revenue. At the lower level, the chance constraint programming is used to describe and deal with the uncertainty of renewable energy and loads and optimize the output and energy storage of distributed energy with the goal of minimum cost. The analytical target cascading and augmented Lagrange method are combined to decouple and reconstruct the bi-level model for distributed solution and establishing a fair price mechanism. The optimal solutions of the problem are obtained through parallel iteration, in which the price signal plays a coordinated role in the distributed iterative optimization process. Abundant case studies verify the advantages of the model and the performance of the proposed method.

Incentive-based demand response is playing an increasingly important role in ensuring the safe operation of the power grid and reducing system costs, and advances in information and communications technology have made it possible to implement it online. However, in regions where incentive-based demand response has never been implemented, the response behavior of customers is unknown, in this case, how to quickly and accurately set the incentive price is a challenge for service providers. This paper proposes a pricing method that combines long short-term memory networks and reinforcement learning to solve the pricing problem of service providers when the customers’ response behavior is unknown. Taking the total profit of all response time slots in one day as the optimization goal, long and short-term memory networks are used to learn the relationship between customers’ response behavior and incentive price, and reinforcement learning is used to explore and determine the optimal price. The results show that the combination of these two methods can perform virtual exploration of the optimal price, which solves the disadvantage that reinforcement learning can only rely on delayed rewards to perform exploration in the real scene, thereby speeding up the process of setting the optimal price. In addition, because the influence of the incentive prices combination of different time slots on the profit of the service provider is considered, the negative effect of myopia optimization is avoided.

Accurate short-term load forecasting (STLF) is an important basis for daily dispatching of the power grid, but the non-stationary characteristics of the load series add to the challenge of this task. Many researchers have been working to improve the accuracy and speed of forecasting models, but stability is equally important. This paper develops a forecasting method based on error correction using dynamic mode decomposition (DMD) for STLF, including data selection, error forecasting, and error correction. In the data selection stage, three types of data are selected as input data of the model, including previous day data, same day data in previous week and similar day data obtained by grey relational analysis (GRA). In the error forecasting stage, the data driving characteristics of the DMD algorithm is used to capture the potential spatiotemporal dynamics of error series, thereby realizing the error forecasting. In the error correction stage, on the basis of combining the forecasting results of load and error, an extreme value constraint method (EVCM) is developed to further correct the load demand series. Based on the load data of different regions, this paper selects different performance indicators, such as MAPE, MAE, RMSE, Variance and direction accuracy (DA), to prove that the proposed method has the advantages of accuracy and stability.

Aiming at the power supply capability evaluation under the background of controllable source-network-load in power system, a multi-objective power supply capability evaluation method for active distribution network considering the active control cost is proposed. Firstly, uncertain factors such as renewable distributed generations’ output and demand response are modeled. Then, the maximization of regional power supply capacity and the minimization of active control costs are taken as the optimization objective from the perspective of both the planning and operation. Considering the constraints of distributed generations output ability, the network topology, load controllable levels, and so on, a multi-objective optimization uncertainty model for the active distribution network is constructed. In addition, the crossover operator and the selection strategy of NSGA-II are improved based on the non-uniform arithmetic crossover and phase-out strategy, which is used to solve the proposed optimization model. The Pareto optimal solution set obtained by the multi-objective optimization algorithm has a large scale and contains a wealth of information, and a method based on entropy-TOPSIS is also provided to select one eclectic solution set by the operator. Finally, the effectiveness of the proposed evaluation method and the performance of the improved algorithm are verified by the improved IEEE 33-bus distribution system and one of China’s actual power grid.

There are two problems, which should be paid attention to when using kernel partial least squares (KPLS), one is overfitting and another is how to eliminate the useless information mixed in the independent variables X. In this paper, the stochastic gradient boosting (SGB) method is adopted to solve the overfitting problems and a new method called kernel net analyte preprocessing (KNAP) is proposed to remove undesirable systematic variation in X that is unrelated to Y. Thus, by combining the two methods, a final modeling approach named modified KPLS (MKPLS) is proposed. Two simulation experiments are carried out to evaluate the performance of the MKPLS method. The simulation results show that MKPLS method can not only be resistant to overfitting but also improve the prediction accuracy.

With the expansion in scale and complexity of distribution networks, distributed state estimation (DSE), a real-time database for other on-line applications, is becoming popular for large-scale active distribution networks (ADN). Measurements from phasor measurement units (PMUs) with the same time stamp can assist DSE to obtain faster and more accurate estimation; however, the configuration of PMUs and communication links should be updated to support data collection and transmission. This paper proposes an optimal PMUs and communication links placement method for DSE in distribution networks. A network partitioning method is presented with the aim of balancing calculation times among subareas. Then, a binary integer linear programming model that simultaneously considers the optimal placement of PMUs, phasor data concentrators (PDCs) and communication links is proposed. The economy of the configuration scheme is guaranteed on the premise that the network is fully observable. Finally, case studies on the IEEE 33-node, PG&E 69-node and IEEE 123-node systems verify the feasibility of the proposed method.