Electric Data Research Articles

Risk Assessment and Prediction of Underground Utility Tunnels Based on Bayesian Network: A Case Study in Beijing, China

The underground utility tunnels accommodate various types of urban lifelines, which are of great significance for improving the living standards of the citizens. With the rapid development of underground utility tunnels, the large-scale underground utility tunnel systems are gradually becoming the operational lifeblood of China’s large cities. Currently, most of the underground utility tunnels’ risks are estimated and analyzed from a static perspective, and the analysis results are one-sided. This study proposes a dynamic risk evaluation framework. A risk assessment and sensitivity analysis framework based on Bayesian network is established in this study. Combined with the groundwater and electric tunnel risk accident case study, the operation and maintenance data of Beijing Future Science and Technology City from 2010 to 2018 are collected for learning to obtain the conditional probability of the Bayesian network node by using the K2 algorithm. The overall evolution process from the beginning to the end of groundwater tunnel accidents is clearly described and displayed. Through sensitivity analysis and critical path analysis, the critical points of an accident and the probabilities of risk occurrence are identified and predicted. This proposed framework could facilitate the underground utility tunnel management for controlling risk resources, mitigating risk damage and reducing risk losses.

Lightning stroke rates, atmospheric electric potential gradient data, and Schumann resonance data corresponding to the eruption of the Hunga Tonga - Hunga Ha'apai volcano on January 15, 2022

Lightning stroke rates, atmospheric electric potential gradient data, and Schumann resonance data corresponding to the eruption of the Hunga Tonga - Hunga Ha'apai volcano on January 15, 2022

Analysis of Electric Vehicle Collisions in the United States: An Epidemiological Study.

Despite the increase in electric vehicle sales in the US, their impact on injuries and fatalities is still understudied. We aim to evaluate injuries and fatalities associated with electric vehicle collisions in the US. The study utilized electric vehicle injury and fatality data from the Fatality Analysis Reporting System (FARS). All electric vehicle models available within the FARS database and sold in the US from 2014 to 2020 were selected. Electric vehicle models were matched to analogous motor vehicles when possible. No significant increase in electric vehicle fatality per capita (FPC) was found during the study period (2014: .41 vs 2020: 1.42, per 100000 electric cars, P = .080). However, 82% of all fatalities occurred on non-intersectional local roadways with 46% occurring in the presence of speeding, 14% in the presence of fire, and 38% involving a driver with an elevated blood alcohol content (BAC). The Tesla Model S, Kia Niro, and Hyundai IONIQ accounted for the most fatality per capita (17.89 vs 10.27 vs 8.42, per 100000 electric cars). Upon comparison of electric vehicles to analogous motor vehicles produced within the same year, the Hyundai IONIQ had a significantly lower FPC compared to the Hyundai Elantra (7.33 vs 23.51, per 100000 electric cars P = .034). While no significant increase in electric vehicle fatality per capita (FPC) was found, the total number of electric vehicle fatalities did increase significantly during the study period (2014-2020). Furthermore, a significant proportion of these fatalities is directly related to speeding, fire, and intoxicated driving.

Optimization of Condensate Transfer Pump in the Hydrocarbon Condensate Stabilization Unit of Natural Gas Liquefaction Plant – An Industrial Case

PT. Badak NGL is a commercial LNG producer located in Bontang, Indonesia. In recent years, the decline in LNG production of PT. Badak NGL results in a lower hydrocarbon condensate flow rate. To prevent power losses since its pump operated at below rating, a new operation scheme was proposed. This new scheme adjusts the way of pump is operated without any alteration in terms of pump characteristics. Three pumps operated intermittently for 8 hours per day at a maximum flow rate. The pump power consumption was calculated based on electric current data monitored relay panel. The results show that the pump energy saved by 60% with the absolute value of annual energy efficiency of 957.7 MWh. This study contributes to reducing the greenhouse effect by 412 tons eq CO and the annual production cost of around USD 130,000. The new scheme reliability examines by evaluating pump vibration, pump performance, hydrocarbon condensate quality, and condensate tank liquid level. The result shows that the pump’s vibration in the range of 0.21 in/sec with a steady winding temperature.

Power system abnormal pattern detection for new energy big data

Abstract The energy crisis is a problem that countries all over the world pay more and more attention to, and a series of ecological problems caused by it have become increasingly prominent. It is difficult for traditional fossil fuels to maintain a healthy and coordinated sustainable development of society and economy. The establishment of a sustainable energy system has become the development trend of various countries to solve energy problems. Electric energy is a secondary energy that all primary energy can be converted into, and an irreplaceable consumable for all industrial technologies and people’s lives. Electric power data has the characteristics of large rate span, numerous data sources, complicated interaction methods, and various types of data. The existence of abnormal data in the power system will greatly reduce the accuracy of the system state estimation and the state estimation convergence rate. This paper introduces the power grid industrial control system, combines the data flow of power big data, and analyzes the abnormal information detection process in detail. It takes the data stream acquired by the acquisition unit PMU of the wide area measurement system as the research object. The rapid development of the Hadoop big data platform provides important technical support for the research of power grid big data. Based on the Hadoop platform, the clustering algorithm is used to complete the anomaly detection of real-time data. The LOF algorithm has poor performance when dealing with a large amount of high-dimensional data, and has high time and space complexity. In order to make up for the shortcomings of the LOF algorithm, this paper uses the K-means clustering algorithm to propose an improved algorithm K-LOF of the density-based local abnormal factor detection algorithm LOF, and optimizes the neighborhood query process. It is verified by experiments that the K-LOF algorithm can effectively reduce the time complexity of the anomaly detection algorithm and improve the detection accuracy by 2–4.2%.

Reliability, Applications and Challenges of GaN HEMT Technology for Modern Power Devices: A Review

A new generation of high-efficiency power devices is being developed using wide bandgap (WBG) semiconductors, like GaN and SiC, which are emerging as attractive alternatives to silicon. The recent interest in GaN has been piqued by its excellent material characteristics, including its high critical electric field, high saturation velocity, high electron mobility, and outstanding thermal stability. Therefore, the superior performance is represented by GaN-based high electron mobility transistor (HEMT) devices. They can perform at higher currents, voltages, temperatures, and frequencies, making them suitable devices for the next generation of high-efficiency power converter applications, including electric vehicles, phone chargers, renewable energy, and data centers. Thus, this review article will provide a basic overview of the various technological and scientific elements of the current GaN HEMTs technology. First, the present advancements in the GaN market and its primary application areas are briefly summarized. After that, the GaN is compared with other devices, and the GaN HEMT device’s operational material properties with different heterostructures are discussed. Then, the normally-off GaN HEMT technology with their different types are considered, especially on the recessed gate metal insulator semiconductor high electron mobility transistor (MISHEMT) and p-GaN. Hereafter, this review also discusses the reliability concerns of the GaN HEMT which are caused by trap effects like a drain, gate lag, and current collapse with numerous types of degradation. Eventually, the breakdown voltage of the GaN HEMT with some challenges has been studied.

Electrical conductivity and relaxation in lithium-doped barium vanadate glasses investigated by impedance spectroscopy

We present herein a detailed study of the electric and dielectric properties of glasses with the composition 60V2O5–(40–x)BaO–xLi2O, where x = 10, 15, 20, or 25 mol%. The conduction and relaxation mechanisms have been studied by impedance spectroscopy over the frequency range from 40 Hz to 10 MHz and at various temperatures. With constant transition metal oxide content, the conductivity variation in the glasses is assumed to be mainly due to an ionic contribution. The dependence of electrical conductivity on frequency has been analysed using Jonscher's power law. Properties of the glasses, such as electric modulus loss and relaxation mechanism, have been analysed in terms of electric modulus using the Kohlrausch–William–Watts (KWW) equation, which describes the relaxation behaviour of the mobile ions as non-Debye in nature. Scaling of ac conductivity and electric modulus data has shown that conductivity relaxation is independent of temperature. The non-monotonic variation in the stretching parameter β has been found to show a positive correlation with various activation energies, namely electrical activation energy, relaxation activation energy, and the activation energy from the Anderson–Stuart model. The anomalous behaviour of activation energy with respect to conductivity has been substantiated through structural insight. The conductivity was found to be highest in glasses with 25 mol% Li2O, while coupling between the mobile ions was best for glasses with 20 mol% Li2O.

Marine self-potential measurement tool for autonomous underwater vehicle.

Marine self-potential (SP) measurement is commonly conducted for seafloor sulfide detection and hydrothermal vent studies in deep water using instruments towed close to the seafloor. However, this method has the following shortcomings: (1) It relies on ships for deep towing, and the need for a dedicated ship time lowers its efficiency. (2) Owing to complex topography, most towed instruments are located far from the seafloor to ensure safety, resulting in large effective signal attenuation and low signal-to-noise ratio. (3) The measurement direction is generally a single axis, with only the electric field of the axial component observed, providing limited information. With the gradual maturity of autonomous underwater vehicle (AUV) technology, it has become possible to mount marine SP measurement tools on AUVs for detection. Compared with conventional methods, this method has significant advantages in terms of efficiency, signal-to-noise ratio, and multicomponent observation. The proposed tool is a lightweight underwater device having a compact design and low power consumption, making it suitable for AUVs. The overall volume of the tool is D50mm × L350mm, and the underwater weight is 0.6kg. Chopper amplification technology ensures the low-noise measurement of electric field signals. In addition, the reformed electrodes enhance stability, thereby reducing the mechanical vibration noise. Laboratory test results show that the noise of the data logger is 7.8 nV/rt (Hz)@1Hz. The marine test conducted in the southwest Indian Ocean verified the reliability of the proposed marine SP measurement tool. The maximum working depth was 4000m. The test lasted ∼25 h, and the effective electric field data were collected for ∼17 h. This survey found a maximum SP anomaly of 0.55 mV/m in the Yuhuang hydrothermal field, which provided effective data support for the discovery of new seafloor sulfide anomalies.

Thermal neural networks: Lumped-parameter thermal modeling with state-space machine learning

With electric power systems becoming more compact with higher power density, the relevance of thermal stress and precise real-time-capable model-based thermal monitoring increases. Previous work on thermal modeling by lumped-parameter thermal networks (LPTNs) suffers from mandatory expert knowledge for their design and from uncertainty regarding the required power loss model. In contrast, deep learning-based temperature models cannot be designed with the low amount of model parameters as in a LPTN at equal estimation accuracy. In this work, the thermal neural network (TNN) is introduced, which unifies both, consolidated knowledge in the form of heat-transfer-based LPTNs, and data-driven nonlinear function approximation with supervised machine learning. The TNN approach overcomes the drawbacks of previous paradigms by having physically interpretable states through its state-space representation, is end-to-end differentiable through an automatic differentiation framework, and requires no material, geometry, nor expert knowledge for its design. Experiments on an electric motor data set show that a TNN achieves higher temperature estimation accuracies than previous white-/gray- or black-box models with a mean squared error of 3.18 K2 and a worst-case error of 5.84 K at 64 model parameters.

Observation of Source Plasma and Field Variations of a Substorm Brightening Aurora at L ∼ 6 by a Ground‐Based Camera and the Arase Satellite on 12 October 2017

AbstractAuroral brightening is one of the most common phenomena that occur during substorm onset and is usually recognized as a projection of the substorm‐associated magnetospheric plasma dynamics to the ionosphere. However, electromagnetic fields and plasma features associated with the substorm brightening arc have not been well understood. In this study, we present a comprehensive observation of the source plasma and field variations of a substorm brightening aurora in the inner magnetosphere. We performed a unique conjugate observation of a substorm brightening auroral arc observed by a ground‐based camera and by the Arase satellite in the magnetospheric source region at L ∼ 6. The event was observed at Tromsø (69.6°N, 19.2°E), Norway, on 12 October 2017. The brightening arc indicates east‐west structures with longitudinal scales of ∼0.5°–2.0°. Field‐aligned bi‐directional electrons with an energy range between 66 and 1,800 eV were detected by the satellite, simultaneously with the appearance of the brightening arc in the camera. These electrons were probably supplied from the auroral brightening region in the ionosphere, indicating that the satellite was on the same field line of the brightening aurora. The magnetic and electric field data show characteristic fluctuations and earthward Poynting flux around the time that the satellite crossed the aurora. Anti‐phase oscillations between the thermal pressure and the magnetic pressure are also reported. Based on these observations, we suggest the possibility that a ballooning instability occurred in the source region of the substorm brightening arc in the inner magnetosphere at L ∼ 6.

Use of electric potential difference in audio magnetotelluric (AMT) geophysics for groundwater exploration

Due to recent advancements in technology, a variety of Audio Magnetotelluric (AMT) geophysical equipment using electric potential difference (V) as the main parameter for groundwater exploration is now in use. Studies on the application of electric potential difference in Audio Magnetotelluric Geophysics for groundwater exploration are yet to be published. This paper primarily provides an overview of the theoretical background on the use of the electric potential difference in Audio Magnetotelluric geophysics for groundwater exploration and shares recent field results from the application of the technology. In the field test, analysis of the lithology data collected during the drilling of two groundwater production boreholes provides meaning to the electric potential difference data collected prior to the drilling. Results show that the electric potential difference measured with the Audio Magnetotelluric Groundwater Detector correlated with lithology and groundwater occurrence at two observation sites. The water strikes occurred in sandstone formation of various degrees of disintegration and were associated with contrasts of low electric potential differences in the range of 0.005–0.019 mV. The finding suggests that the changes in electric potential difference can be used to delineate potential aquifers and vertical variation of geology. The expectations are that this presentation will stimulate further research to understand the applications of Audio Magnetotelluric electric potential difference technology for groundwater exploration in different hydrogeological settings.

Reactive Power Compensation Using Electric Vehicle and Data Center by Integrating Virtual Power Plant

This article proposes a virtual power plant (VPP) theory for reactive power support consisting of electric vehicle (EV) and data center (DC) UPS battery energy storage in the power system. As enhanced grid regulations have been developed to enable large-scale distributed generation, progressive controllers are required for reactive power control considering nonlinear and industrial type load. Data center batteries are deep cycle batteries; they discharge over a much longer time and EV batteries also have great potential to absorb/release electricity from/back to the grid when needed. This paper presents the bidirectional VPP operation with a novel virtual synchronous generator (VSG)-driven reactive power controller for reactive power support. Four case studies are performed in MATLAB/Simulink platform to evaluate the performance characteristics, which include grid import/export operations, time-varying nonlinear load conditions, nonlinear industrial load conditions, and transient disturbance conditions. The obtained results validate the reactive power support in all discussed case studies.

Methods of Pre-Clustering and Generating Time Series Images for Detecting Anomalies in Electric Power Usage Data

As electricity supply expands, it is essential for providers to predict and analyze consumer electricity patterns to plan effective electricity supply policies. In general, electricity consumption data take the form of time series data, and to analyze the data, it is first necessary to check if there is no data contamination. For this, the process of verifying that there are no abnormalities in the data is essential. Especially for power data, anomalies are often recorded over multiple time units rather than a single point. In addition, due to various external factors, each set of power consumption data does not have consistent data features, so the importance of pre-clustering is highlighted. In this paper, we propose a method using a CNN model using pre-clustering-based time series images to detect anomalies in time series power usage data. For pre-clustering, the performances were compared using k-means, k-shapes clustering, and SOM algorithms. After pre-clustering, a method using the ARIMA model, a statistical technique for anomaly detection, and a CNN-based model by converting time series data into images compared the methods used. As a result, the pre-clustered data produced higher accuracy anomaly detection results than the non-clustered data, and the CNN-based binary classification model using time series images had higher accuracy than the ARIMA model.

Geospatial Assessment and Mapping of Suitable Sites for a Utility-scale Solar PV Farm

Geospatial and multi-criteria decision-making techniques are applied to process and analyse datasets for determining suitable areas for multiple utilityscale solar photovoltaic farms in the city of Akure, Ondo State, southwestern Nigeria. Data processed include local electric power distribution system data, Shuttle Radar Topographic Mission elevation data, Landsat 8 and solar global horizontal irradiance. Multi-criteria decision-making techniques adopted are the analytical hierarchy process, reclassification, and overlay. These techniques were carried out considering criteria for siting solar photovoltaic farms. Criteria considered in this study are climate, topography, economic, environmental impact operational and technical while sub-criteria are solar global horizontal irradiance, slope, proximity and land cover. The outcome of the study shows that the study area covering a total extent of ~33,200 ha, 15%, 8%, 13% and 64% are highly suitable, suitable, moderately suitable, and unsuitable respectively for siting utility-scale solar photovoltaic farms within the study area. The study reveals the potential of multiple utility-scale solar photovoltaic farms in the study area. However, the proportions of areas suitable for solar photovoltaic farms are quite lower compared to findings from similar studies conducted in northwestern Nigeria. The study recommends solar photovoltaic sources as an alternative energy source in and around the study area.

An overview of load frequency control for grid using various control techniques

Nowadays Electrical power generation using renewable energy systems brings more novel ideas to satisfy the power demand worldwide. Microgrid is one of the ideas that ensures stability and reliability of the power system. Due to seasonal or climatic effect, the power generated by the renewable energy sources has issue in providing a constant power output and hence produces the power imbalance between the source to load. Furthermore, the system frequency cannot be kept constant in all cases. In this case, an electric vehicle (EV) can serve as a power generation source to support the power grid and meet the Load Frequency Control requirement (LFC). Due to their fast-regulating characteristics, electric vehicles provide frequency regulation services. Electric vehicles are used in both primary and secondary frequency management in power systems to quickly suppress frequency oscillations caused by changes in load. The control centre gathers and updates real-time Electric Vehicle data, such as information regarding state of charge and the state of each Electric Vehicle, in order to decide on load frequency regulation dispatch to Electric Vehicles. This paper compares and consolidate the performance of Load frequency control through Conventional plants and Renewable energy systems. The graphical abstract of power system grid is shown in Figure.1

주택용 계시별 요금제 (ToU) 대응을 위한 스마트플러그의 불규칙 전력 시계열 데이터 분류 AI 개발

본 연구에서는 우리나라 차후 도입 예정인 가정용 ToU(Time of Use) 요금 체계에서 스마트플러그에 체결된 전자기기의 요금 절감 가능 여부를 판별하는 AI를 개발하였다. 이를 위해, 스마트플러그에 체결된 전자기기의 사용 패턴을 파악하고 분석하는데 필요한 전력 사용 데이터를 약 3분 간격으로 11개월 동안 167개의 스마트플러그로 수집하였다. 이 과정에서 시계열 데이터에 불규칙성이 야기 되었고, 이를 해결하기 위하여 소모 전력의 백분위수를 추출하여 학습 데이터를 확보하였다. 완전 연결 인공신경망을 통해 데이터를 학습하였고 AI의 이진분류 정확도는 91.2%로 나타났다. 최종적으로 스마트플러그를 ToU 적용 절감 가능 여부에 따라 두 그룹으로 나누어 평균단가의 분포(평균 및 분산) 비교를 통해 인공지능의 적합성 여부를 평가하였다.

Randomized high order fuzzy cognitive maps as reservoir computing models: A first introduction and applications

Fuzzy Cognitive Maps (FCMs) have emerged as an interpretable signed weighted digraph method consisting of nodes (concepts) and weights which represent the dependencies among the concepts. Although FCMs have attained considerable achievements in various time series prediction applications, designing an FCM model with time-efficient training method is still an open challenge. Thus, this paper introduces a novel univariate time series forecasting technique, which is composed of a group of randomized high order FCM models labeled R-HFCM. The novelty of the proposed R-HFCM model is relevant to merging the concepts of FCM and Echo State Network (ESN) as an efficient and particular family of Reservoir Computing (RC) models, where the least squares algorithm is applied to train the model. From another perspective, the structure of R-HFCM consists of the input layer, reservoir layer, and output layer in which only the output layer is trainable while the weights of each sub-reservoir components are selected randomly and kept constant during the training process. As case studies, in this paper we consider solar energy forecasting with public data for Brazilian solar stations, hourly electric load of the power supply company of the city of Johor in Malaysia, solar energy dataset from United States National Renewable Energy Laboratory (NREL), electric load data from the Global Energy Forecasting Competition 2012 (GEFCom 2012), and PJM hourly energy consumption data. The experiments also include the effect of the map size, activation function, the number of order and the size of the reservoir on the accuracy of R-HFCM method. The obtained results confirm the outperformance of the proposed R-HFCM model in comparison to the other methods. This study provides evidence that FCM can be a new way to implement a reservoir of dynamics in time series modeling. The Python code of the model is publicly available for research replication.

Subsurface structure of the proposed Sirente meteorite crater: insights from ERT synthetic modelling

The Sirente main crater is a ≈ 130 m wide, in plan view droplet-shaped depression with an elevated rim, surrounded by 30 smaller depressions. It was proposed to be of meteorite impact origin. Given the age of formation in the 3rd to 5th centuries A.D., the inferred catastrophic origin was related to the celestial sign (“Chi Rho”) said to have been seen by Emperor Constantine in 312 A.D. and suggested to have changed the course of both Roman and Christian history. However, the meteoritic origin is not yet confirmed. This paper presents new results from synthetic modelling of Electric Resistivity Tomography field data collected at the Sirente main crater which provide further clues around the controversy of its origin. This study arises from the need to validate the observed structural features which include possible upturned strata (i.e., overturning of strata below impact crater rims) and compaction-fissure-like features below and just outside the crater rim, well-developed “breccia lens”, as well as an ejecta layer, and provide key indicators for objective and quantitative interpretation of the measured resistivity pattern. The results from this study are consistent with the hypothesis of a small impact crater in a low-strength target, with a relatively shallow apparent crater and do not support other proposed mechanisms of formation such as karst, mud volcano or merely anthropogenic origin.

Clustering-based interval prediction of electric load using multi-objective pathfinder algorithm and Elman neural network

Interval prediction of electric load has aroused widespread concern by the power industry because of variability and uncertainty. To quantify the potential uncertainty associated with prediction, this paper proposes a clustering-based approach to construct prediction intervals (PIs) for electric load data. The singular spectrum analysis (SSA) and k-means clustering are firstly performed to decompose the original data due to the high volatility and nonlinearity of load data. Then, we improve the multi-objective pathfinder algorithm (MOPATH) by using crowding degree of population in order to prevent premature, and further utilize the Elman neural network (ELMAN) optimized by IMOPATH to obtain the subseries PIs of electric load data. In addition, the interval width, coverage probability and deviation are used as three optimization objectives. Finally, the IMOPATH, as an ensemble approach, is applied to ensemble the three PIs together and achieves the final PIs. To verify the performance of the SSA-IMOPATH-ELMAN approach, the proposed approach is compared with 41 models. The forecasting outcomes indicate that PIs of the proposed approach have higher coverage probability, narrower width and lower deviation degree than other benchmark models. Moreover, the proposed approach has good performance on robustness and sensibility.

Electrical and Magnetic Transport Properties of Co2VGa Half-Metallic Heusler Alloy.

This study performed a systematic experimental investigation into the structural, magnetic, and transport properties of the Co2VGa Heusler alloy, which was theoretically predicted to exhibit half-metallic ferromagnetism. It has been experimentally found that the studied alloy has a relatively high-ordered L21 cubic structure at room temperature and orders ferromagnetically below ~350 K. Interestingly, by fitting the electric transport data with the properly governing equations in two different temperature regions, the two-magnon scattering process (the dependence) appears in the temperature range from 30 to 75 K. Moreover, the magnetoresistance effect changes from a negative value to a positive value when the temperature is below 100 K. Such experimental findings provide indirect evidence that the half-metallic nature of this alloy is retained only when the temperature is below 100 K. On the other hand, the magnetic transport measurements indicate that the anomalous Hall coefficient of this alloy increases when the temperature increases and reaches a relatively high value (~8.3 μΩ·cm/T) at 300 K due to its lower saturated magnetization. By analyzing the anomalous Hall resistivity scale with the longitudinal resistivity, it was also found that the anomalous Hall effect can be ascribed to the combined effect of extrinsic skew scattering and intrinsic Berry curvature, but the latter contribution plays a dominant role.