Reliability evaluation of IGBT power module on electric vehicle using big data

There are challenges to the reliability evaluation for insulated gate bipolar transistors(IGBT)on electric vehicles,such as junction temperature measurement,computational and storage resources.In this paper,a junction temperature estimation approach based on neural network without additional cost is proposed and the lifetime calculation for IGBT using electric vehicle big data is performed.The direct current(DC)voltage,operation current,switching frequency,negative thermal coefficient thermistor(NTC)temperature and IGBT lifetime are inputs.And the junction temperature(T_(j))is output.With the rain flow counting method,the classified irregular temperatures are brought into the life model for the failure cycles.The fatigue accumulation method is then used to calculate the IGBT lifetime.To solve the limited computational and storage resources of electric vehicle controllers,the operation of IGBT lifetime calculation is running on a big data platform.The lifetime is then transmitted wirelessly to electric vehicles as input for neural network.Thus the junction temperature of IGBT under long-term operating conditions can be accurately estimated.A test platform of the motor controller combined with the vehicle big data server is built for the IGBT accelerated aging test.Subsequently,the IGBT lifetime predictions are derived from the junction temperature estimation by the neural network method and the thermal network method.The experiment shows that the lifetime prediction based on a neural network with big data demonstrates a higher accuracy than that of the thermal network,which improves the reliability evaluation of system.

Research on Interpolation Method for Missing Electricity Consumption Data

Missing value is one of the main factors that cause dirty data.Without high-quality data,there will be no reliable analysis results and precise decision-making.Therefore,the data warehouse needs to integrate high-quality data consistently.In the power system,the electricity consumption data of some large users cannot be normally collected resulting in missing data,which affects the calculation of power supply and eventually leads to a large error in the daily power line loss rate.For the problem of missing electricity consumption data,this study proposes a group method of data handling(GMDH)based data interpolation method in distribution power networks and applies it in the analysis of actually collected electricity data.First,the dependent and independent variables are defined from the original data,and the upper and lower limits of missing values are determined according to prior knowledge or existing data information.All missing data are randomly interpolated within the upper and lower limits.Then,the GMDH network is established to obtain the optimal complexity model,which is used to predict the missing data to replace the last imputed electricity consumption data.At last,this process is implemented iteratively until the missing values do not change.Under a relatively small noise level(α=0.25),the proposed approach achieves a maximum error of no more than 0.605%.Experimental findings demonstrate the efficacy and feasibility of the proposed approach,which realizes the transformation from incomplete data to complete data.Also,this proposed data interpolation approach provides a strong basis for the electricity theft diagnosis and metering fault analysis of electricity enterprises.

Obtaining 2D Soil Resistance Profiles from the Integration of Electrical Resistivity Data and Standard Penetration Test (SPT) and Light Dynamic Penetrometer (DPL) Resistance Tests—Applications in Mass Movements Studies

In Brazil and various regions globally, the initiation of landslides is frequently associated with rainfall;yet the spatial arrangement of geological structures and stratification considerably influences landslide occurrences. The multifaceted nature of these influences makes the surveillance of mass movements a highly intricate task, requiring an understanding of numerous interdependent variables. Recent years have seen an emergence in scholarly research aimed at integrating geophysical and geotechnical methodologies. The conjoint examination of geophysical and geotechnical data offers an enhanced perspective into subsurface structures. Within this work, a methodology is proposed for the synchronous analysis of electrical resistivity geophysical data and geotechnical data, specifically those extracted from the Light Dynamic Penetrometer (DPL) and Standard Penetration Test (SPT). This study involved a linear fitting process to correlate resistivity with N10/SPT N-values from DPL/SPT soundings, culminating in a 2D profile of N10/SPT N-values predicated on electrical profiles. The findings of this research furnish invaluable insights into slope stability by allowing for a two-dimensional representation of penetration resistance properties. Through the synthesis of geophysical and geotechnical data, this project aims to augment the comprehension of subsurface conditions, with potential implications for refining landslide risk evaluations. This endeavor offers insight into the formulation of more effective and precise slope management protocols and disaster prevention strategies.

Changes in plasma calcitonin gene-related peptide and serum neuron specific enolase in rats with acute cerebral ischemia after low-frequency electrical stimulation with different waveforms and intensities

Following acute cerebral ischemia in rats, plasma calcitonin gene-related peptide decreased and the level of serum neuron specific enolase and the volume of the infarction increased. Square-wave and triangular-wave electrical stimulation with low or high intensities could increase the plasma calcitonin gene-related peptide, decrease the serum neuron specific enolase and reduce the infarction volume in the brain in rats with cerebral ischemia. There was no significant difference between different wave forms and intensities. The experimental findings indicate that low-frequency electrical stimulation with varying waveforms and intensities can treat acute cerebral ischemia in rats.

Effects of cervical low-frequency electrical stimulation with various waveforms and densities on body mass,liver and kidney function,and death rate in ischemic stroke rats

Low-frequency electrical stimulation has resulted in favorable effects in the treatment of post-stroke dysphagia. However, the safety of cervical low-frequency electrical stimulation remains unclear because of numerous nerves and blood vessels in the neck. In the present study, rats with ischemic stroke underwent low-frequency electrical stimulation, and systemic and local effects of electrical stimulation at different densities and waveforms were investigated. Electrical stimulation resulted in no significant effects on body mass, liver or kidney function, or mortality rate. In addition, no significant adverse reaction was observed, despite overly high intensity of low-frequency electrical stimulation, which induced laryngismus, results from the present study suggested that it is safe to stimulate the neck with a low-frequency electricity under certain intensities.

Design and Implementation of a Battery Big Data Platform Through Intelligent Connected Electric Vehicles

The development of a battery management algorithm is highly dependent on high-quality battery operation data,especially the data in extreme conditions such as low temperatures.The data in faults are also essential for failure and safety management research.This study developed a battery big data platform to realize vehicle operation,energy interaction and data management.First,we developed an electric vehicle with vehicle navigation and position detection and designed an environmental cabin that allows the vehicle to operate autonomously.Second,charging and heating systems based on wireless energy transfer were developed and equipped on the vehicle to investigate optimal charging and heating methods of the batteries in the vehicle.Third,the data transmission network was designed,a real-time monitoring interface was developed,and the self-developed battery management system was used to measure,collect,upload,and store battery operation data in real time.Finally,experimental validation was performed on the platform.Results demonstrate the efficiency and reliability of the platform.Battery state of charge estimation is used as an example to illustrate the availability of battery operation data.

Research on the optimization strategy of customers’electricity consumption based on big data

Current power systems face significant challenges in supporting large-scale access to new energy sources,and the potential of existing flexible resources needs to be fully explored from the power supply,grid,and customer perspectives.This paper proposes a multi-objective electricity consumption optimization strategy considering the correlation between equipment and electricity consumption.It constructs a multi-objective electricity consumption optimization model that considers the correlation between equipment and electricity consumption to maximize economy and comfort.The results show that the proposed method can accurately assess the potential for electricity consumption optimization and obtain an optimal multi-objective electricity consumption strategy based on customers’actual electricity consumption demand.

Machine Learning Mapping of Soil Apparent Electrical Conductivity on a Research Farm in Mississippi

Open-source and free tools are readily available to the public to process data and assist producers in making management decisions related to agricultural landscapes. On-the-go soil sensors are being used as a proxy to develop digital soil maps because of the data they can collect and their ability to cover a large area quickly. Machine learning, a subcomponent of artificial intelligence, makes predictions from data. Intermixing open-source tools, on-the-go sensor technologies, and machine learning may improve Mississippi soil mapping and crop production. This study aimed to evaluate machine learning for mapping apparent soil electrical conductivity (ECa) collected with an on-the-go sensor system at two sites (i.e., MF2, MF9) on a research farm in Mississippi. Machine learning tools (support vector machine) incorporated in Smart-Map, an open-source application, were used to evaluate the sites and derive the apparent electrical conductivity maps. Autocorrelation of the shallow (ECas) and deep (ECad) readings was statistically significant at both locations (Moran’s I, p 0.001);however, the spatial correlation was greater at MF2. According to the leave-one-out cross-validation results, the best models were developed for ECas versus ECad. Spatial patterns were observed for the ECas and ECad readings in both fields. The patterns observed for the ECad readings were more distinct than the ECas measurements. The research results indicated that machine learning was valuable for deriving apparent electrical conductivity maps in two Mississippi fields. Location and depth played a role in the machine learner’s ability to develop maps.

Electrical Data Matrix Decomposition in Smart Grid

As the development of smart grid and energy internet, this leads to a significantincrease in the amount of data transmitted in real time. Due to the mismatch withcommunication networks that were not designed to carry high-speed and real time data,data losses and data quality degradation may happen constantly. For this problem,according to the strong spatial and temporal correlation of electricity data which isgenerated by human’s actions and feelings, we build a low-rank electricity data matrixwhere the row is time and the column is user. Inspired by matrix decomposition, we dividethe low-rank electricity data matrix into the multiply of two small matrices and use theknown data to approximate the low-rank electricity data matrix and recover the missedelectrical data. Based on the real electricity data, we analyze the low-rankness of theelectricity data matrix and perform the Matrix Decomposition-based method on the realdata. The experimental results verify the efficiency and efficiency of the proposed scheme.

Effect of low-frequency pulse percutaneous electric stimulation on peripheral nerve injuries at different sites

BACKGROUND： The postoperative recovery of nerve function in patients with peripheral nerve injury is always an important problem to solve after treatment. The electric stimulation induced electromagnetic field can nourish nerve, postpone muscular atrophy, and help the postoperative neuromuscular function. OBJECTIVE： To observe the effects of low-frequency pulse percutaneous electric stimulation on the functional recovery of postoperative patients with peripheral nerve injury, and quantitatively evaluate the results of electromyogram （EMG） examination before and after treatment. DESIGN ： A retrospective case analysis SETTING： The Sixth People＇s Hospital affiliated to Shanghai Jiaotong University PARTICIPANTS： Nineteen postoperative inpatients with peripheral nerve injury were selected from the De- partment of Orthopaedics, the Sixth People＇s Hospital affiliated to Shanghai Jiaotong University from June 2005 to January 2006, including 13 males and 6 females aged 24-62 years with an average of 36 years old. There were 3 cases of brachial plexus nerve injury, 3 of median nerve injury, 7 of radial nerve injury, 3 of ul- nar nerve injury and 3 of common peroneal nerve injury, and all the patients received probing nerve fiber restoration. Their main preoperative manifestations were dennervation, pain in limbs, motor and sensory disturbances. All the 19 patients were informed with the therapeutic program and items for evaluation. METHODS： ① Low-frequency pulse percutaneous electric stimulation apparatus： The patients were given electric stimulation with the TERESA cantata instrument （TERESA-0, Shanghai Teresa Health Technology, Co., Ltd.）. The patients were stimulated with symmetric square waves of 1-111 Hz, and the intensity was 1.2-5.0 mA, and it was gradually adjusted according to the recovered conditions of neural regeneration following the principle that the intensity was strong enough and the patients felt no obvious upset. They were treated for 4- 24 weeks, 10-30 minutes for each time, 1-3 times a day, and 6 weeks as a course. ② EMG examination was applied to evaluate the recoveries of recruitment, motor conduction velocity （MCV） and sensory conduction velocity （SCV） before and after treatment. The patients were examined with the EMG apparatus （DIS- A2000C, Danmark） before and after the treatment of percutaneous electric stimulation. ③Standards for evaluating the effects included cured （complete recovery of motor functions, muscle strength of grade 5, no abnormality in EMG examination）, obviously effective [general recovery of motor function, muscle strength of grade 4, no or a few denervation potentials, motor conduction velocity （MCV） and sensory conduction velocity （SCV）], improved （partial recovery of motor function, muscle strength of grade 3, denervation potentials and reinneration potentials, slowed MCV and SCV, invalid （no obvious changes of motor function）. MAIN OUTCOME MEASURES： ① Ameliorated degree of the nerve function of the postoperative patients with peripheral nerve injury treated with percutaneous electric stimulation; ② Changes of EMG examination before and after treatment. RESULTS： All the 19 postoperative patients with peripheral nerve injury were involved in the analysis of results. ① Comparison of nerve function before and after treatment in 19 patients with peripheral nerve injury of different sites： For the patients with radial nerve injury （n=7）, the nerve functions all completely recovered after 8-week treatment, and the cured and obvious rate was 100% （7/7）; For the patients with brachial plexus nerve injury （n=3）, 1 case had no obvious improvement, and the cured and obvious rate was 67% （2/3）; For the patients with common peroneal nerve injury （n=3）, the extension of foot dorsum generally recovered in 1 case of nerve contusion after 4-week treatment, and the cured and obvious rate was 67% （2/3）; For the patients with median nerve injury （n=3）, muscle strength was obviously recovered, and the cured and obvious rate was 100% （3/3）; For the patients with ulnar nerve injury （n=3）, 1 case only had recovery of partial senses, and the cured and obvious rate was 67% （2/3）. Totally 9 cases were cured, 7 were obviously effective, 1 was improved, and only 2 were invalid. After 4 courses, the cured rate of damaged nerve function after four courses was 47% （9/19）, and effective rate was 89% （17/19）.② Comparison of EMG examination before and after treatment： Before and after percutaneous electric stimulation, he effective rates of recruitment, MCV and SCV were 89% （17/19）, 58% （11/19）, 47% （9/19） respectively, and there were extremely obvious differences （P〈 0.01）. CONCLUSION： ①Low-frequency pulse percutaneous electric stimulation can improve the nerve function of postoperative patients with peripheral nerve injury of different sites, especially that the injuries of radial nerve and median nerve recover more obviously. ②Percutaneous electric stimulation can ameliorate the indexes of EMG examination, especially the recruitment, in postoperative patients with peripheral nerve injury.

Intelligent Energy Utilization Analysis Using IUA-SMD Model Based Optimization Technique for Smart Metering Data

Smart metering has gained considerable attention as a research focus due to its reliability and energy-efficient nature compared to traditional electromechanical metering systems. Existing methods primarily focus on data management,rather than emphasizing efficiency. Accurate prediction of electricity consumption is crucial for enabling intelligent grid operations,including resource planning and demandsupply balancing. Smart metering solutions offer users the benefits of effectively interpreting their energy utilization and optimizing costs. Motivated by this,this paper presents an Intelligent Energy Utilization Analysis using Smart Metering Data(IUA-SMD)model to determine energy consumption patterns. The proposed IUA-SMD model comprises three major processes:data Pre-processing,feature extraction,and classification,with parameter optimization. We employ the extreme learning machine(ELM)based classification approach within the IUA-SMD model to derive optimal energy utilization labels. Additionally,we apply the shell game optimization(SGO)algorithm to enhance the classification efficiency of the ELM by optimizing its parameters. The effectiveness of the IUA-SMD model is evaluated using an extensive dataset of smart metering data,and the results are analyzed in terms of accuracy and mean square error(MSE). The proposed model demonstrates superior performance,achieving a maximum accuracy of65.917% and a minimum MSE of0.096. These results highlight the potential of the IUA-SMD model for enabling efficient energy utilization through intelligent analysis of smart metering data.

Research on Total Electric Field Prediction Method of Ultra-High Voltage Direct Current Transmission Line Based on Stacking Algorithm

Ultra-high voltage(UHV)transmission lines are an important part of China’s power grid and are often surrounded by a complex electromagnetic environment.The ground total electric field is considered a main electromagnetic environment indicator of UHV transmission lines and is currently employed for reliable long-term operation of the power grid.Yet,the accurate prediction of the ground total electric field remains a technical challenge.In this work,we collected the total electric field data from the Ningdong-Zhejiang±800 kV UHVDC transmission project,as of the Ling Shao line,and perform an outlier analysis of the total electric field data.We show that the Local Outlier Factor(LOF)elimination algorithm has a small average difference and overcomes the performance of Density-Based Spatial Clustering of Applications with Noise(DBSCAN)and Isolated Forest elimination algorithms.Moreover,the Stacking algorithm has been found to have superior prediction accuracy than a variety of similar prediction algorithms,including the traditional finite element.The low prediction error of the Stacking algorithm highlights the superior ability to accurately forecast the ground total electric field of UHVDC transmission lines.

Utilization of Data Communication according to the IEC61850 Standard for Nuclear Power Plant Electrical Equipment Testing

This paper deals with an integration of directly measured electrical parameters with data acquired by data communication from protections and terminals into an advanced monitoring system. Based on the periodic test, the authors of this paper present the possibility of an extended evaluation and more accurate analysis of transient and failure events. For periodical testing, as implemented during the commissioning of power plants in the Czech Republic, a monitoring system of electrical equipment has been used, to record the courses of important electrical parameters and thus, proving the proper functioning of complex technological systems in various operation modes. Data from monitoring system were used to prove the successful results of the test or as a base data for further analysis of failures. The monitoring system has proved itself as a very useful device also when recording unexpected failure events, the cause of which was very quickly and accurately detected by the follow-up analysis. Initially, only the voltage and current data from measuring transformers, analogue transducers and contact relays were used as input data for the monitoring system. After the implementation of new digital protection technology and controlling terminals with inner data recorder, the data from digital devices could be also utilized for the monitoring system.

Treatment time influences the effects of a low-frequency pulsed electric field on synthesis of tyrosine hydroxylase and dopamine in PC12 cells

BACKGROUND： Electromagnetic radiation can influence dopamine （DA） synthesis in brain tissues or ceils, but electromagnetic frequencies, intensities, and radiation time can produce different effects. In addition, the signal pathway by which electromagnetic radiation influences DA synthesis remains controversial. OBJECTIVE： To determine tyrosine hydroxylase （TH） expression in PC12 cells and DA levels in cell culture media after different periods of low-frequency pulsed electric field （LF-PEF） stimulation, and to determine how LF-PEF signaling stimulates TH synthesis using inhibitors. DESIGN, TIME AND SETTING： A parallel, controlled, cell experiment was performed at the Laboratory of Cell Biology, School of Life Science, East China Normal University, between January and October 2006. MATERIALS： PC12 cells were purchased from the Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, China. Nerve growth factor was purchased from PeproTech, USA. The protein kinase A inhibitor, H-89, and mitogen-activated protein kinase kinase inhibitor, U0126, were purchased from Sigma, USA. METHODS： （1） Following routine culture in Dulbecco＇s modified eagle medium, primary PC12 cells were stimulated under LF-PEF （pulse frequency 50.Hz, pulse width 20 μs, peak field strength 1 V/m） for 5, 10, 15, 20, and 30 minutes. （2） Inhibitors （H-89 or U0126, 1 μmol/L） were added 30 minutes before LF-PEF stimulation for 10 minutes. MAIN OUTCOME MEASURES： （1） TH expression was determined by Western blot in PC12 cells at 0.5, 1,2, 3, and 4 days after LF-PEF stimulation. Similarly, DA was measured by high-performance liquid chromatography in media at 2, 3, 4, or 5 days after LF-PEE （2） TH expression was detected 1 day after H-89 or U0126 treatment and LF-PEE RESULTS： （1） Short-term LF-PEF stimulation （5 and 10 minutes） increased TH expression and media DA levels after short-term culture （2 days） （P 〈 0.01）, but both parameters decreased with longer culture （3 4 days） （P 〈 0.01）. Long-term LF-PEF stimulation （15, 20, or 30 minutes） decreased TH and DA synthesis, followed by a rapid increase （P 〈 0.01）. （2） H89 could completely inhibit TH expression in PC12 cells stimulated by LF-PEF for 10 minutes, while the inhibition rate of U0126 was 53.2%. CONCLUSION： Short-term LF-PEF first promotes then inhibits, while long-term LF-PEF first inhibits then promotes, TH and DA synthesis. LF-PEF stimulation regulates TH expression primarily by activating protein kinase A to regulate DA synthesis.

Mining Rules from Electrical Load Time Series Data Set

The mining of the rules from the electrical load time series data which are collected from the EMS (Energy Management System) is discussed. The data from the EMS are too huge and sophisticated to be understood and used by the power system engineer, while useful information is hidden in the electrical load data. The authors discuss the use of fuzzy linguistic summary as data mining method to induce the rules from the electrical load time series. The data preprocessing techniques are also discussed in the paper.

Applications of Data Mining Theory in Electrical Engineering

In this paper, we adopt a novel applied approach to fault analysis based on data mining theory. In our researches, global information will be introduced into the electric power system, we are using mainly cluster analysis technology of data mining theory to resolve quickly and exactly detection of fault components and fault sections, and finally accomplish fault analysis. The main technical contributions and innovations in this paper include, introducing global information into electrical engineering, developing a new application to fault analysis in electrical engineering. Data mining theory is defined as the process of automatically extracting valid, novel, potentially useful and ultimately comprehensive information from large databases. It has been widely utilized in both academic and applied scientific researches in which the data sets are generated by experiments. Data mining theory will contribute a lot in the study of electrical engineering.

Development of geo-electrical meter based on networking

Further development of earthquake equipments is closely associated with that of computer technology. Because Embedded PC104 module has the equivalent functions of PC,it has been widely used in recent years,and can provide a new and flexible hardware design environment,but its applications in observation instruments of earth-quake precursor are rare. The present paper introduces in detail the realization of a networked geo-electrical meter by applying the low price,high reliability embedded PC104 industrial computer.

Blockchain and MEC-Assisted Reliable Billing Data Transmission over Electric Vehicular Network:An Actor–Critic RL Approach

Recently,electric vehicles(EVs)have been widely used under the call of green travel and environmental protection,and diverse requirements for charging are also increasing gradually.In order to ensure the authenticity and privacy of charging information interaction,blockchain technology is proposed and applied in charging station billing systems.However,there are some issues in blockchain itself,including lower computing efficiency of the nodes and higher energy consumption in the consensus process.To handle the above issues,in this paper,combining blockchain and mobile edge computing(MEC),we develop a reliable billing data transmission scheme to improve the computing capacity of nodes and reduce the energy consumption of the consensus process.By jointly optimizing the primary and replica nodes offloading decisions,block size and block interval,the transaction throughput of the blockchain system is maximized,as well as the latency and energy consumption of the system are minimized.Moreover,we formulate the joint optimization problem as a Markov decision process(MDP).To tackle the dynamic and continuity of the system state,the reinforcement learning(RL)is introduced to solve the MDP problem.Finally,simulation results demonstrate that the performance improvement of the proposed scheme through comparison with other existing schemes.

A data complementary method for thunderstorm point charge localization based on atmospheric electric field apparatus array group

Data loss or distortion causes adverse effects on the accuracy and stability of the thunderstorm point charge localization.To solve this problem,we propose a data complementary method based on the atmospheric electric field apparatus array group.The electric field component measurement model of the atmospheric electric field apparatus is established,and the orientation parameters of the thunderstorm point charge are defined.Based on the mirror method,the thunderstorm point charge coordinates are obtained by using the potential distribution formulas.To test the validity of the basic algorithm,the electric field component measurement error and the localization accuracy are studied.Besides the azimuth angle and the elevation angle,the localization parameters also include the distance from the apparatus to the thunderstorm cloud.Based on a primary electric field apparatus,we establish the array group of apparatuses.Based on this,the data measured by each apparatus is complementarily processed to regain the thunderstorm point charge position.The results show that,compared with the radar map data,this method can accurately reflect the location of the thunderstorm point charge,and has a better localization effect.Additionally,several observation results during thunderstorm weather have been presented.

Design of Parallel Electrical Resistance Tomography System for Measuring Multiphase Flow

ERT(electrical resistance tomography) is effective method for visualization of multiphase flows,offering some advantages of rapid response and low cost,so as to explore the transient hydrodynamics.Aiming at this target,a fully programmable and reconfigurable FPGA(field programmable gate array)-based Compact PCI(peripheral component interconnect) bus linked sixteen-channel ERT system has been presented.The data acquisition system is carefully designed with function modules of signal generator module;Compact PCI transmission module and data processing module(including data sampling,filtering and demodulating).The processing module incorporates a powerful FPGA with Compact PCI bus for communication,and the measurement process management is conducted in FPGA.Image reconstruction algorithms with different speed and accuracy are also coded for this system.The system has been demonstrated in real time(1400 frames per second for 50 kHz excitation) with signal-noise-ratio above 62 dB and repeatability error below 0.7%.Static experiments have been conducted and the images manifested good resolution relative to the actual object distribution.The parallel ERT system has provided alternative experimental platform for the multiphase flow measurements by the dynamic experiments in terms of concentration and velocity.