Research on Low Voltage Series Arc Fault Prediction Method Based on Multidimensional Time-Frequency Domain Characteristics

The load types in low-voltage distribution systems are diverse.Some loads have current signals that are similar to series fault arcs,making it difficult to effectively detect fault arcs during their occurrence and sustained combustion,which can easily lead to serious electrical fire accidents.To address this issue,this paper establishes a fault arc prototype experimental platform,selects multiple commonly used loads for fault arc experiments,and collects data in both normal and fault states.By analyzing waveform characteristics and selecting fault discrimination feature indicators,corresponding feature values are extracted for qualitative analysis to explore changes in timefrequency characteristics of current before and after faults.Multiple features are then selected to form a multidimensional feature vector space to effectively reduce arc misjudgments and construct a fault discrimination feature database.Based on this,a fault arc hazard prediction model is built using random forests.The model’s multiple hyperparameters are simultaneously optimized through grid search,aiming tominimize node information entropy and complete model training,thereby enhancing model robustness and generalization ability.Through experimental verification,the proposed method accurately predicts and classifies fault arcs of different load types,with an average accuracy at least 1%higher than that of the commonly used fault predictionmethods compared in the paper.

Data-driven Anomaly Detection Method Based on Similarities of Multiple Wind Turbines

The operating conditions of wind turbines(WTs)in the same wind farm(WF)may share similarities due to their shared manufacturing process,control strategy,and operating environment.However,the similarities of WTs are seldom considered in WT anomaly detection,resulting in the disregard of useful information.This paper proposes a method to improve the reliability and accuracy of WT anomaly detection using the supervisory control and data acquisition(SCADA)data of multiple WTs in the same WF.First,a similarity assessment method based on a comparison of different observation time series is proposed,which objectively quantifies the similarities of WT operating conditions.Then,the SCADA data of the target WT and selected WTs that are similar are used to establish several estimation models through a long short-term memory(LSTM)algorithm.LSTM models that exhibit good estimation performance are used to construct a combined estimation model that estimates the variations in the monitored variables of the target WT.Finally,an anomaly detection method that jointly compares the effective value and information entropy of the residuals is proposed to identify anomalies.The effectiveness and accuracy of the proposed method are verified using the data of two actual WFs.

Modelling and simulation study on dynamic pollution accumulation process of composite insulator

With the widespread application of composite insulators in transmission lines,exploring the accumulation mechanism of pollution particles on composite insulator surfaces is of importance to ensure the safe and steady operation of the power system.Addressing the current theoretical shortcomings,this study categorises the accumulation process of particles on the insulator surface into three stages,namely‘spatial motion’,‘surface collision’,and‘surface motion’.The motion and rotation velocities in a multi-physics field are calculated in the spatial motion stage.In the surface collision stage,a parameter called‘neck height’is introduced to determine the optimum mechanics theory,and the normal deposition criterion is established.For the surface motion stage,the sliding displacement and rolling displacement on the surface are calculated based on the rotation speed of the particles.A dynamic pollution accumulation model of the composite insulator is estab-lished based on the normal deposition criterion and tangential displacement.Finally,numerical simulations are performed by using the finite element method.Simulation results show that the proposed model agrees with the actual insulator pollution accu-mulation,and the deposition model is still applicable for various types of composite insulators operating in different applied voltages.The deposition probability of particles increases with the increasing particle size.In the surface motion stage,particle displacement increases with particle size and wind velocity.

A Generalized Wind Turbine Anomaly Detection Method Based on Combined Probability Estimation Model

Anomaly detection based on the data collected from the supervisory control and data acquisition(SCADA)system is crucial to reduce the failure rate of wind turbines(WTs).The difficulty of this kind of methods is to dynamically identify the threshold for anomaly detection under changing operating conditions.In this paper,a generalized WT anomaly detection method based on the combined probability estimation model(CPEM)is proposed.The CPEM can estimate the conditional probability density function(PDF)of the target variable given changing conditions.Its generalization and accuracy are better than those of the independent probability estimation model because it combines the advantages of various kinds of probability estimation models through linear combination.By using the CPEM,the normal operating bounds under different operating conditions can be obtained,which dynamically form the thresholds for anomaly detection.Meanwhile,with respect to the thresholds,hypothesis testing(HT)is adopted to identify the anomaly by inspecting whether the observations exceed the thresholds at a given significance level,providing sound mathematical support for anomaly detection and making the detection results more reliable.The effectiveness of the proposed method is tested by using the actual data of WTs with known faults.The results illustrate that the proposed method can detect the abnormal operating state of the gearbox and generator much more early than the system fault alarm.

An Intrusion Detection Method for Advanced Metering Infrastructure System Based on Federated Learning

An advanced metering infrastructure(AMI)system plays a key role in the smart grid(SG),but it is vulnerable to cyberattacks.Current detection methods for AMI cyberattacks mainly focus on the data center or a distributed independent node.On one hand,it is difficult to train an excellent detection intrusion model on a self-learning independent node.On the other hand,large amounts of data are shared over the network and uploaded to a central node for training.These processes may compromise data privacy,cause communication delay,and incur high communication costs.With these limitations,we propose an intrusion detection method for AMI system based on federated learning(FL).The intrusion detection system is deployed in the data concentrators for training,and only its model parameters are communicated to the data center.Furthermore,the data center distributes the learning to each data concentrator through aggregation and weight assignments for collaborative learning.An optimized deep neural network(DNN)is exploited for this proposed method,and extensive experiments based on the NSL-KDD dataset are carried out.From the results,this proposed method improves detection performance and reduces computation costs,communication delays,and communication overheads while guaranteeing data privacy.

A Novel Single Phase Grounding Fault Protection Scheme Without Threshold Setting for Neutral Ineffectively Earthed Power Systems

The setting values of thresholds for fault feature parameters are critical in all kinds of protection schemes.When the detected feature parameter value exceeds the setting value,the protection will trip.However,the setting value based conventional protection schemes sometimes cannot satisfy the protection requirements of neutral ineffectively earthed power systems(NIEPS)due to wide variations in operating conditions and the complexities of fault cases.In this paper,a novel single phase grounding fault protection scheme without threshold setting is proposed.The fault detection is achieved based on operating states rather than setting values.A fuzzy c-means algorithm is used to divide the operating state of the protected feeder into non-fault states and fault states.The cluster center of each state is then obtained by classifying the historical feature samples of the protected feeder extracted under various operating conditions into their corresponding states in a constructed multi-dimensional fault feature space.The distances between the detected feature samples and the cluster centers of the non-fault and the fault states are calculated.If the distance to the fault state is shorter than that to the non-fault state,a fault is detected.Otherwise,the feeder is considered normal.A PSCAD/EMTDC simulator is used to simulate a 35 kV NIEPS under various operating conditions,non-linear loads,and complex fault cases.Results show that the proposed single phase grounding fault protection scheme without threshold setting can protect the system correctly under all kinds of faults.

Research on multi-terminal traveling wave fault location method in complicated networks based on cloud computing platform

Cloud computing technology is used in traveling wave fault location,which establishes a new technology platform for multi-terminal traveling wave fault location in complicated power systems.In this paper,multi-terminal traveling wave fault location network is developed,and massive data storage,management,and algorithm realization are implemented in the cloud computing platform.Based on network topology structure,the section connecting points for any lines and corresponding detection placement in the loop are determined first.The loop is divided into different sections,in which the shortest transmission path for any of the fault points is directly and uniquely obtained.In order to minimize the number of traveling wave acquisition unit(TWU),multi-objective optimal configuration model for TWU is then set up based on network full observability.Finally,according to the TWU distribution,fault section can be located by using temporal correlation,and the final fault location point can be precisely calculated by fusing all the times recorded in TWU.PSCAD/EMTDC simulation results show that the proposed method can quickly,accurately,and reliably locate the fault point under limited TWU with optimal placement.

Signaling pathways in the regulation of cytokine release syndrome in human diseases and intervention therapy

Cytokine release syndrome(CRS)embodies a mixture of clinical manifestations,including elevated circulating cytokine levels,acute systemic inflammatory symptoms and secondary organ dysfunction,which was first described in the context of acute graft-versus-host disease after allogeneic hematopoietic stem-cell transplantation and was later observed in pandemics of influenza,SARS-CoV and COVID-19,immunotherapy of tumor,after chimeric antigen receptor T(CAR-T)therapy,and in monogenic disorders and autoimmune diseases.Particularly,severe CRS is a very significant and life-threatening complication,which is clinically characterized by persistent high fever,hyperinflammation,and severe organ dysfunction.However,CRS is a double-edged sword,which may be both helpful in controlling tumors/viruses/infections and harmful to the host.Although a high incidence and high levels of cytokines are features of CRS,the detailed kinetics and specific mechanisms of CRS in human diseases and intervention therapy remain unclear.In the present review,we have summarized the most recent advances related to the clinical features and management of CRS as well as cutting-edge technologies to elucidate the mechanisms of CRS.Considering that CRS is the major adverse event in human diseases and intervention therapy,our review delineates the characteristics,kinetics,signaling pathways,and potential mechanisms of CRS,which shows its clinical relevance for achieving both favorable efficacy and low toxicity.

Correction:Signaling pathways in the regulation of cytokine release syndrome in human diseases and intervention therapy

Since the publication of this review1,the authors noticed one inadvertent mistake occurred in Fig.6c that needs to be corrected.The correct Fig.6 is provided as follows.In detail,"Human-derived CAR-T cells"has been replaced by"Mouse-derived CAR-T cells"in the revised fig.6c.The key findings of the article are not affected by these corrections.The original review article has been corrected.

Efficient expansion of rare human circulating hematopoietic stem/progenitor cells in steady-state blood using a polypeptide-forming 3D culture

Although widely applied in treating hematopoietic malignancies,transplantation of hematopoietic stem/progenitor cells(HSPCs)is impeded by HSPC shortage.Whether circulating HSPCs(cHSPCs)in steady-state blood could be used as an alternative source remains largely elusive.Here we develop a three-dimensional culture system(3DCS)including arginine,glycine,aspartate,and a series of factors.Fourteen-day culture of peripheral blood mononuclear cells(PBMNCs)in 3DCS led to 125-and 70-fold increase of the frequency and number of CD34+cells.Further,3DCS-expanded cHSPCs exhibited the similar reconstitution rate com-pared to CD34+HSPCs in bone marrow.Mechanistically,3DCS fabricated an immunomodulatory niche,secreting cytokines as TNF to support cHSPC survival and proliferation.Finally,3DCS could also promote the expansion of cHSPCs in patients who failed in HSPC mobilization.Our 3DCS successfully expands rare cHSPCs,providing an alternative source for the HSPC therapy,particularly for the patients/donors who have failed in HSPC mobilization.