Electric Data Research Articles

The Effects of Livestock Grazing on Soil Water Content in a Wetland of Mongolia

Wetlands are critical ecosystems for supporting biodiversity and regulating climate. Over the past century, it has been degraded significantly due to both climatic and human activities. In Mongolia, livestock grazing has drastic impact on this ecosystem. In this study, we seek to understand the effects of grazing on soil water content (SWC) and moisture by contrasting grazed and non-grazed area in a wetland in north-eastern Mongolia. We found that water infiltration was five times lower in grazed area than that of non-grazed area. Soil water content was higher and stable near soil surface (5 cm soil depth) throughout the growing season in grazed area, while it was lower and sensitive to rainfall events in non-grazed area. Also, soil moisture derived from electric resistivity measurement data supports our results of higher SWC in grazed area, relative to non-grazed area, suggesting that a reduction in infiltration is likely due to soil compaction associated with grazing activities.

Artificial Intelligence in power multimodal data analysis

In the past decade, electric power data has undergone a transformation from non-existent to centralized management. The management efficiency and accumulated quantity have reached a high level. Furthermore, this data is widely applied in various fields of the electric power industry and plays a significant role. This article studies the multi-model data problems in many application scenarios in the electric power industry. Combining the characteristics and actual scenarios of the electric power industry, it deeply explores the data sources and characteristics of various tasks, and integrates the path of artificial intelligence analysis in the multi-modal field to explore how to solve related problems in the electric power industry. Additionally, this article further expands on how weakly supervised and semi-supervised methods can be utilized in the electric power industry and proposes suggestions.

Study on the Power Data Assets and Trading System Design of Blockchain

The sensitive data such as electric power data suffer from the easily abuse in the traditional centralized data trading system. To solve this problem, the blockchain-based power data’s capitalization and trading system were proposed in this study. Given the trust for blockchain technology, this work established a multi-party trusty network between multiple parties in the distributed ledger with cryptographic algorithms, and distributed multi-party consensus protocol. The system development with Hyperledger’s consortium blockchain framework Fabric made the power data safe, transparent and immutable. The local development environment for testing included a operating system MAC OS 10.14.6, six-core CPU 3.0 GHz, RAM 16 GB. A single machine is configured with multiple block chain network nodes, an ordering node, and two organizations Org0 and Org1, each of which has two nodes Peer0 and Peer1, and adopts the Solo consensus mechanism. The experimental results show that the system run well in the integrated test, satisfied the design requirements, and had low maintenance costs and improved security. Compared with the traditional data trading system, the blockchain-based power data’s capitalization and trading system demonstrated the comprehensive functionality and security advantages in the study.

Analysis of the Harmfulness of Abnormal Riding Behaviors of Electric Bicycles Based on Improved Multiclass Logistic Regression Model

To analyze the harmfulness of abnormal riding behaviors of electric bicycles in-depth, the research focuses on the 2022 electric bicycle accident data in a specific city in China. Based on an improved multiclass logistic regression model, the relationship between different abnormal riding behaviors and the severity of electric bicycle traffic accidents is explored. Firstly, the severity of accidents is categorized into three levels as the dependent variable, while driver attributes and various hazardous driving behaviors serve as independent variables to construct the multiclass logistic regression model. Secondly, the model is optimized by eliminating irrelevant independent variables and improving the link function. Finally, the harmfulness of abnormal riding behaviors of electric bicycles is analyzed based on the results of the regression model. The results indicate that eight factors significantly influence the dependent variable, with three factors, including driving under the influence of alcohol, being more likely to lead to fatal accidents, requiring focused attention for intervention and regulation.

Monitoring the Production Information of Conventional Machining Equipment Based on Edge Computing.

A production status monitoring method based on edge computing is proposed for traditional machining offline equipment to address the deficiencies that traditional machining offline equipment have, which cannot automatically count the number of parts produced, obtain part processing time information, and discern anomalous operation status. Firstly, the total current signal of the collected equipment was filtered to extract the processing segment data. The processing segment data were then used to manually calibrate the feature vector of the equipment for specific parts and processes, and the feature vector was used as a reference to match with the real-time electric current data on the edge device to identify and obtain the processing start time, processing end time, and anomalous marks for each part. Finally, the information was uploaded to further obtain the part processing time, loading and unloading standby time, and the cause of the anomaly. To verify the reliability of the method, a prototype system was built, and extensive experiments were conducted on many different types of equipment in an auto parts manufacturer. The experimental results show that the proposed monitoring algorithm based on the calibration vector can stably and effectively identify the production information of each part on an independently developed edge device.

Atmospheric and ionospheric waves induced by the Hunga eruption on 15 January 2022; Doppler sounding and infrasound

SUMMARY The massive explosive eruption of the Hunga volcano on 15 January 2022 generated atmospheric waves that were recorded around the globe and affected the ionosphere. The paper focuses on observations of atmospheric waves in the troposphere and ionosphere in Europe, however, a comparison with observations in East Asia, South Africa and South America is also provided. Unlike most recent studies of waves in the ionosphere based on the detection of changes in the total electron content, this study builds on detection of ionospheric motions at specific altitudes using continuous Doppler sounding. In addition, much attention is paid to long-period infrasound (periods longer than ∼50 s), which in Europe is observed simultaneously in the troposphere and ionosphere about an hour after the arrival of the first horizontally propagating pressure pulse (Lamb wave). It is shown that the long-period infrasound propagated approximately along the shorter great circle path, similar to the previously detected pressure pulse in the troposphere. It is suggested that the infrasound propagated in the ionosphere probably due to imperfect refraction in the lower thermosphere. The observation of infrasound in the ionosphere at such large distances from the source (over 16 000 km) is rare and differs from ionospheric infrasound detected at large distances from the epicenters of strong earthquakes, because in the latter case the infrasound is generated locally by seismic waves. An unusually large traveling ionospheric disturbance (TID) observed in Europe and associated with the pressure pulse from the Hunga eruption is also discussed. Doppler sounders in East Asia, South Africa and South America did not record such a significant TID. However, TIDs were observed in East Asia around times when Lamb waves passed the magnetically conjugate points. A probable observation of wave in the mesopause region in Europe approximately 25 min after the arrival of pressure pulse in the troposphere using a 23.4 kHz signal from a transmitter 557 km away and a coincident pulse in electric field data are also discussed.

A novel technique for detecting electricity theft in secure smart grids using CNN and XG-boost

Electricity theft is one of the main nontechnical losses (NTLs) in distributed networks which cause significant harm to the power grids. As power grids provide the centralized power to all the connected consumers, therefore, any fraudulent consumption can cause harm to the power grids which can damage the whole electric power supply and can influence its quality. The detection of such fraudulent consumers becomes difficult when there is a large amount of data. Smart grids can be used to solve this problem as it provides a two-way electricity flow which allows someone to detect, reenact and apply new changes to the electric data flow. The existing systems for electricity theft detection, works on the principle of one dimensional (1-D) electric data, which provides poor accuracy in theft detection. Therefore, an ensemble model based on convolutional neural network and extreme gradient boosting (CNN-XGB) model is presented in this paper. In this model both one dimensional (1-D) and two-dimensional (2-D) electricity consumption data are used to pass to the CNN model. Proposed model achieved the accuracy of 92% for electricity theft detection, which is better than existing models.

Charging demand prediction in Beijing based on real-world electric vehicle data

The accurate estimation and prediction of charging demand play an essential role in charging infrastructure planning, power grid laying and efficient operations. In this paper, three-month real-world travel and charging records of 25,489 electric passenger vehicles in Beijing are utilized to quantitatively study the travel patterns and charging behaviors of electric vehicles and support charging demand prediction. A “multi-level & multi-dimension” travel and charging characteristic parameters extraction framework is proposed with >60 electric vehicle behavior characteristic parameters extracted from time, spatial, and energy dimensions. Then a two-step GMM and K-means-based clustering model is proposed to cluster the electric vehicle users into six categories with different usage habits, attributes, and characteristics. A trip-chain-simulation-based charging demand prediction model is further established to generate a week-period continuous activities simulation for specific category vehicles, in which the interdependences of travel and charging characteristic parameters are considered to make the trip chain simulation closer to reality. The city charging demands are aggregated from individual charging prediction results at a spatial resolution of 0.46 km and time resolution of 15 min quantitatively; the spatiotemporal aggregation results show that the proposed model can realize high accuracy prediction for real-world charging demands. The results are expected to provide the foundation for research on charging infrastructure planning and the impact of charging behaviors on the grid load.

Accurate State of Charge Estimation for Real-World Battery Systems Using a Novel Grid Search and Cross Validated Optimised LSTM Neural Network

State of charge (SOC) is one of the most important parameters in battery management systems, and the accurate and stable estimation of battery SOC for real-world electric vehicles remains a great challenge. This paper proposes a long short-term memory network based on grid search and cross-validation optimisation to estimate the SOC of real-world battery systems. The real-world electric vehicle data are divided into parking charging, travel charging, and finish charging cases. Meanwhile, the parameters associated with the SOC estimation under each operating condition are extracted by the Pearson correlation analysis. Moreover, the hyperparameters of the long short-term memory network are optimised by grid search and cross-validation to improve the accuracy of the model estimation. Moreover, the gaussian noise algorithm is used for data augmentation to improve the accuracy and robustness of SOC estimation under the working conditions of the small dataset. The results indicate that the absolute error of SOC estimation is within 4% for the small dataset and within 2% for the large dataset. More importantly, the robustness and effectiveness of the proposed method are validated based on operational data from three different real-world electric vehicles, and the mean square error of SOC estimation does not exceed 0.006. This paper aims to provide guidance for the SOC estimation of real-world electric vehicles.

Observation and Modeling of Dart Leader Development in an Altitude‐Triggered Lightning Flash

AbstractThe dart leader development process of an altitude‐triggered lightning flash with eight return strokes was analyzed. The analysis, based on the high‐speed camera and electric field data, revealed three different processes of dart leaders: (a) normal dart leader process; (b) dart leader process with chasing leader; and (c) dart leader process with bidirectional leader. These dart leaders are very different in terms of the average 2‐D speed and the electric field change. Moreover, in the bidirectional leader process, the 2‐D speed of the upward positive leader is greater than that of the downward negative leader. A piecewise‐speed source‐charge model was used to simulate the electric field change of the normal dart leader. The superposition of downward positive leader was used to simulate the fading and decaying process of the leader tail, which appeared in the chasing leader and bidirectional leader. The bidirectional leader model was used to simulate the bidirectional leader process, and the final simulation results are in good agreement with the measured electric field.

Magnetization loss and transport current loss in ReBCO racetrack coils carrying stationary current in time-varying magnetic field at 4.2 K

ReBCO racetrack coils may be used in high-dynamic superconducting linear motor systems, typically replacing either permanent- or electromagnets in the DC stator. Even so, in order to achieve a significant increase in force density, the superconductor needs to carry a high transport current while simultaneously experiencing the time-varying magnetic field from the copper mover coils. To aid with the design of such devices, a 2D numerical model has been developed that predicts the AC loss under motor-relevant conditions, i.e. under the combined influence of a stationary transport current and an alternating external magnetic field. The main aim of the experiments described in this paper is to validate this model with dedicated AC loss measurements. To this end, we constructed a set-up that simultaneously measures magnetization-, transport current- and overall AC loss. Two identical insulated sub-scale ReBCO racetrack coils were tested at 4.2 K while carrying a stationary transport current of up to 700 A in a sinusoidal, alternating magnetic field up to 1.5 T, applied perpendicular to the broad face of the windings. Just like with metallic superconductors, the transport current significantly increases the AC loss level and lowers the penetration field. The inductive, electric and calorimetric data were found to be consistent with each other, validating the experimental calibration methods involved. Furthermore, the numerical model accurately predicted all AC loss components in the coils without any fitting to the data and can thus reliably be used in the design of superconducting machines.

A Short-Term Power Load Forecasting Method of Based on the CEEMDAN-MVO-GRU

Given that the power load data are stochastic and it is difficult to obtain accurate forecasting results by a single algorithm. In this study, a combined forecasting method for short-term power load was proposed based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Multiverse optimization algorithm (MVO), and the Gated Recurrent Unit (GRU) based on Rectified Adam (RAdam) optimizer. Firstly, the model uses the CEEMDAN algorithm to decompose the original electric load data into subsequences of different frequencies, and the dominant factors are extracted from the subsequences. Then, a GRU network based on the RAdam optimizer was built to perform the forecasting of the subsequences using the existing subsequences data and the associated influencing factors as the data set. Meanwhile, the parameters of the GRU network were optimized with the MVO optimization algorithm for the prediction problems of different subsequences. Finally, the prediction results of each subsequence were superimposed to obtain the final prediction results. The proposed combined prediction method was implemented in a case study of a substation in Weinan, China, and the prediction accuracy was compared with the traditional prediction method. The prediction accuracy index shows that the Root Mean Square Error of the prediction results of the proposed model is 80.18% lower than that of the traditional method, and the prediction accuracy error is controlled within 2%, indicating that the proposed model is better than the traditional method. This will have a favorable impact on the safe and stable operation of the power grid.

Traffic flow prediction based on electric alarm data analysis

Traffic flow prediction based on electric alarm data analysis

Load Regulation Method of Interaction Between Electric Vehicle and Power Grid Based on LSTM

In response to the problem of disorderly charging of scaled new energy vehicles, which causes an increase in the intermittency and randomness of the overall load and then leads to the instability of the distribution network, an edge-control-based load regulation method for scaled vehicle-network interaction is proposed. Firstly, the sequence of influencing factors is constructed, and the data is processed by using weighted Marxist distance to realize the accurate extraction of electric vehicle load power data. Secondly, feature extraction of the data by using Long Short-Term Memory (LSTM) to realize fast and accurate prediction of regional load. Then, an edge-side regulation model is constructed with the objective function of minimizing network loss and minimizing peak-to-valley difference and solved by the wolf pack algorithm. Then, the edge controller is used to control the tide of each pile-end management unit to realize the effective cooperative regulation of each station-pile-net. Finally, using the edge controller physical device with an example of the distribution network in a region of Tianjin, it is verified that the method has good prediction accuracy and ensures the reasonable deployment as well as stability of the distribution network.

Geotechnical Screening of Linear Earth Structures: Electric and Seismic Streamer Data for Hydraulic Conductivity Assessment of the Arignano Earth Dam, Italy

River embankments and small earth dams are linear retaining structures commonly used to protect densely populated areas from flood phenomena and to provide water reservoirs for human or agricultural use. Their continuity and uniformity are fundamental to their structural efficiency. Due to their significant length and the localized nature of potential weakness points, their characterization cannot rely only on local geotechnical investigations: it requires the application of efficient and affordable investigation methods. The need for new screening tools is becoming increasingly important worldwide because most river embankments and small earth dams are reaching their design life limit due to aging. This study used a new electric streamer and a seismic streamer for the combined measurement of resistivity and shear wave velocity to investigate the Arignano earth dam (Piedmont Region, northwestern Italy), a historical reservoir used for agricultural purposes. A procedure is also proposed to assess hydraulic conductivity from the measured geophysical parameters. The results of this assessment were compared with available geotechnical investigations and also used for calibrating the proposed procedure. Results are in good agreement when compared with local geotechnical investigations. The proposed procedure can therefore provide engineers and local authorities with information to plan maintenance or urgent measures for reducing flood risk.

CD-Guide: A Dispatching and Charging Approach for Electric Taxicabs

Previous methods for passenger demand inference are unable to capture the effect of all possible random factors (e.g., accident and weather), hence resulting in insufficient accuracy. Moreover, due to the lack of charging optimization, existing taxicab dispatching methods cannot be applied to electric taxicabs directly. We propose CD-Guide, which provides Charging and Dispatching Guide for electric taxicabs based on customized selection and training of historical passenger demand data, multiobjective optimization, and reinforcement learning (RL). By analyzing a large-scale electric taxicab data set, we found that: 1) the histogram of passengers’ origin buildings is effective in illustrating the suitability of historical data for learning; 2) passenger demands in different regions vary a lot due to various random factors; and 3) charging time must be considered in dispatching electric taxicabs. We first develop a passenger demand inference model based on customized selection and training of suitable historical passenger demand data. Then, we develop two taxicab guidance methods that utilize multiobjective optimization and RL, respectively, to maximize the taxicab’s likelihood of finding passengers, maximally prevent the taxicab from missing passengers due to charging, and, meanwhile, maintain the continuous service of the taxicab. Extensive experiments on real-world data sets demonstrate that compared with the state of the art, CD-Guide increases the total number of served passengers by 100%, and the minimum State-of-Charge of all taxicabs by 75% during all time slots.

Electric Energy Monitoring Node Design Based on NodeMCU and MQTT Protocol

Intelligent electricity management systems can be widely used for monitoring the regional power consumption of enterprises, factories, campuses, properties, etc. To meet the current demand of electricity consumers for monitoring the power data of each node, a node design method based on Modbus and MQTT protocols for monitoring power consumption has been proposed. It can monitor the power, current, voltage, electricity, and other related parameters of the power nodes so that the electricity consumers can control the electric energy data of each power node in real time. The test results show that the node scheme can effectively monitor the data of each power node and realize the real-time monitoring of power consumption data, which shows that the design scheme is effective and feasible.

Industrial park electric power load pattern recognition: An ensemble clustering-based framework

Electric power load pattern recognition from various accumulated load data is performed for energy efficiency improvement, power system operation support, and demand side management. Industrial park is a typical energy consumption scenario, in which there are many different types of energy users. In this study, a framework based on ensemble clustering is proposed for electric power load pattern recognition in industrial parks. The proposed framework can not only accelerate the ensemble clustering process but also improve the clustering performance. Specifically, we focus on the electric power load pattern recognition of business office industrial parks with air conditioners as the main energy-consuming device. The hourly air conditioning load data and total electric power load data of eight buildings in an industrial park in Suzhou, China, from January 1, 2019, to December 31, 2019, were used in the experiment. The results showed the effectiveness of the proposed framework in recognizing the electric power load patterns in industrial parks. The identified electric power load patterns could be potentially used to support more efficient and targeted industrial park energy management decision-making.

A New Method for Modeling and Parameter Identification of Positively Charged Downward Lightning Leader Based on Remote Lightning Electric Field Signatures Recorded in the ELF/MF Range and 3D Doppler Radar Scanning Data

The aim of this paper is modeling and parameter identification of the leader stage preceding the positive stroke of natural cloud-to-ground lightning. This research is based on electric field and 3D Doppler radar data recorded during thunderstorms in Poland, as supplied by database information from long range lightning location systems. The numerical simulation performed in the MATLAB platform showed that a high number of assumed model parameters, such as non-uniform leader tip speed and lightning channel charge density, allowed us to obtain greater compliance between simulated and measured electric field signatures than in the classical approach. The proposed model can be implemented in lightning location systems and forest fire warning systems, operating globally to reduce the risk of damages caused by positive cloud-to-ground flashes being one of the most dangerous type of lightning events. An alternative application of the model can be for research, including ground-based lightning data supplementation for the corresponding satellite and airborne registrations.

Development of the Methodology for Pipe Burst Detection in Multi-Regional Water Supply Networks Using Sensor Network Maps and Deep Neural Networks

Multi-regional waterworks are large-scale facilities for supplying tap water to the public and industrial parks, and interruptions in the water supply due to leaks result in massive social and economic damages. Accordingly, real-time, around-the-clock accident monitoring is necessary to minimize secondary damage. In the present study, a section of a large-scale waterworks transmission mains system with frequent changes in its physical boundaries was defined for sensor network map-based deep learning input and output. A deep neural network (DNN)-based pressure prediction model, able to detect pipe burst accidents in real-time using short-term data collected over periods within 1 month, was developed. A sensor network map refers to a sensor-based hierarchy diagram, which is expressed using a hydraulically divided area. A hydraulically independent area can be determined using known value information (e.g., the known flow, pressure, and total head) in a complex water supply system. The input data used for the deep learning model training were: the water levels measured at 1 min intervals, flow rates, ambient pressure, pump operation state, and electric valve opening data. To verify the developed methodology, two sets of real-world data from past burst accidents in different multi-regional waterworks systems were used. The results showed that the difference between the pressure as measured by pressure meters and an estimated pressure was extremely small before an accident, and that the difference would reach a maximum at the time point when an accident occurs. It was confirmed that an approximate estimation of an accident occurrence and accident location could be estimated based on predicted pressure meter data. The developed methodology predicts a mutual influence between pressure meters and, therefore, has the advantage of not requiring past data covering long time periods. The proposed methodology can be applied immediately and used in currently operational large-scale water transmission main systems.