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The time-dependent quantum transportation through a metal/polymer/metal system is theoretically investigated on the basis of a Su–Schrieffer–Heeger model combined with the hierarchical equations of motion formalism. Using a non-adiabatic dynamical method, the evolution of the electron subspace and lattice atoms with time can be obtained. It is found that the calculated transient currents vary with time and reach stable values after a response time under the bias voltages. However, the stable current as the system reaches its dynamical steady state exhibits a discrepancy between two sweep directions of the bias voltage, which results in pronounced electrical hysteresis loops in the current–voltage curve. By analyzing the evolution of instantaneous energy eigenstates, the occupation number of the instantaneous eigenstates, and the lattice of the polymer, we show that the formation of excitons and the delay of their annihilation are responsible for the hysteretic current–voltage characteristics, where electron–phonon interactions play the key factor. Furthermore, the hysteresis width and amplitude can also be modulated by the strength of the electron–phonon coupling, level-width broadening function, and temperature. We hope these results about past condition-dependent switching performance at a sweep voltage can provide further insight into some of the basic issues of interest in hysteresis processes in conducting polymers. [ABSTRACT FROM AUTHOR]

The global surge in electric vehicle (EV) adoption has driven significant research into electric vehicle charging stations (EVCS) due to their environmentally friendly attributes, including low CO₂ emissions. However, integrating EVCS into existing distribution grids presents challenges such as power losses and voltage instability, especially with the increasing incorporation of renewable distributed generation (RDG) sources and battery energy storage systems (BESS). This study introduces a novel honey badger optimization algorithm (HBOA), designed to enhance solution convergence and optimize multi-objective criteria efficiently. HBOA strategically places EVCS while considering vehicle-to-grid (V2G) capabilities and user driving behaviors over a full 24-hour cycle, effectively addressing uncertainties and dynamic conditions. Simulations on modified IEEE 69-bus and Indian 28-bus radial distribution systems (RDS) demonstrate significant results: in the IEEE 69-bus system, power loss is reduced by 62.0%, the voltage stability index (VSI) increases from 0.7139 to 0.8311, and CO₂ emissions decrease by 66.0%. In the Indian 28-bus system, power loss decreases by 55.5%, with VSI improving from 0.7394 to 0.9964, leading to a 50.0% reduction in CO₂ emissions. The proposed smart microgrid (MG) structure incorporates interconnected MGs for residential, commercial, and industrial sectors, emphasizing the efficacy of RDGs in mitigating the impact of EVCS on RDS. The advantages of the HBOA method lie in its superior optimization capabilities, which enhance system performance, reduce operational costs, and promote sustainability, highlighting the proposed methodology's potential for future integration of EVCS in distribution networks. [ABSTRACT FROM AUTHOR]

Small island communities often rely on groundwater as their primary source of fresh water. However, the limited land area and high proportion of coastal zones pose unique challenges to groundwater management. A detailed understanding of the subsurface structure can provide valuable insights into aquifer structure, groundwater vulnerability, saltwater intrusion, and the location of water resources. These insights can guide groundwater management strategies, for example, pollution regulation, promotion of sustainable agriculture, establishment of coastal buffer zones, and re‐naturalization of land cover. Ordinarily, structural characterization relies on geological mapping and boreholes, however, such approaches can have insufficient spatial resolution to aid groundwater management. In this study, transient electromagnetic (TEM) methods are used to map the subsurface of a small, 13.2 km2, Danish Island. The approach successfully identified two previously unknown paleochannels, where the interface between Quaternary aquifer units and an underlying Paleogene Clay aquiclude had maximum depths of 100 and 160 m below sea level. Before this, the interface was assumed to be 15 to 25 m below sea level: therefore, these paleochannels present substantial potential groundwater resources. Resolving geological heterogeneity within the Quaternary deposits was less successful and future work will focus on addressing these limitations. Nonetheless, in several locations, evidence of saltwater intrusion was observed within the Quaternary units. This work demonstrates how TEM mapping can identify water resources, define aquifer boundaries, and aid water management decisions. Such approaches could be applied in other areas, particularly small islands, where similar groundwater challenges exist. [ABSTRACT FROM AUTHOR]

This manuscript addresses the issue of electromagnetic radiation interference experienced by secondary equipment in ultra-high voltage (UHV) fixed series capacitors (FSCs) under electromagnetic transient overvoltage conditions, which cannot be easily determined. To tackle this, a simulation and analysis method for the electromagnetic interference characteristics of secondary equipment is proposed. First, a primary system simulation model of UHV FSC is established, including modeling the platform's multi-conductor system. The electromagnetic transient overvoltage signals between the low-voltage busbar and the high-potential platform are then simulated and analyzed under two conditions: spark gap triggering and disconnector operation. Next, a finite element model of secondary equipment is created to simulate and analyze the electric field distribution of different materials in the area of the measuring box. The shielding effectiveness of the measuring box is calculated using the overvoltage signals at the measuring box location as excitation. This method allows for the simulation of the electric field distribution in the measuring box area for different materials and calculates the shielding efficiency of the measuring box. It effectively simulates the complex electromagnetic environment of secondary equipment, assesses the electromagnetic shielding efficiency of the measuring box, and provides a theoretical basis for analyzing and improving the anti-interference characteristics of the measuring box. [ABSTRACT FROM AUTHOR]

This article presents the measurement verification of a novel strategy for inrush current reduction in an unloaded three-phase Dy transformer. The strategy combines appropriate pre-magnetization of transformer cores with an original control switching system using initial phase values of the supply voltage as control variables. Measurements were recorded for primary voltages and currents as well as secondary voltages during transient states at start-up under no-load conditions. Various inrush scenarios were examined across the full angular spectrum of initial phase angles, both polarities, and in regard to different pre-magnetization current values. A detailed analysis of the inrush currents was performed using proprietary automated software based on the recorded data. A comparative study with a nonlinear mathematical model of the transformer was also conducted. Additionally, key technical aspects of the designed system for implementing the proposed pre-magnetization strategy with controlled voltage energization are discussed. [ABSTRACT FROM AUTHOR]

Data processing techniques in transient electromagnetic (TEM) methods face numerous challenges, such as the problem of 3-D imaging and the demand for new data processing techniques arising from the development of small-loop TEM devices. We have developed an imaging algorithm based on gradient features derived from TEM data to address these challenges. We found that normalising the gradient field of TEM responses under appropriate background conditions enables imaging of the target. Numerical experiments conducted under theoretical models have validated the feasibility of this approach and highlighted the significance of background fields. We further designed more complex models and achieved 3-D imaging of synthetic data within seconds, demonstrating the method's high efficiency and applicability to high-dimensional data. Subsequently, we applied the algorithm to challenging small-loop TEM data and demonstrated its superior performance compared to the inversion method, which presents a novel approach for processing data from small-loop TEM devices. This study demonstrates that the algorithm possesses characteristics of high efficiency and wide applicability, suitable for TEM data from various dimensions and types of devices. Furthermore, it holds promising potential in assisting the enhancement of the efficiency of the three-dimensional inversion of TEM data. [ABSTRACT FROM AUTHOR]

In recent years, the surge in the adoption of electric vehicles has played a vital role in reducing fossil fuel consumption and greenhouse gas emissions. However, limited cross-national research has been conducted on the determinants of electric vehicle adoption in developing and developed countries. This study examines the factors influencing the intention to adopt electric vehicles in India (378 participants) and Spain (265 participants). This study develops an integrated model that combines the unified theory of acceptance and use of technology (UTAUT2) and the value-belief-norm (VBN) model while accounting for the impact of national culture. The model is tested using structural equation modeling. The results indicate the integrated UTAUT2-VBN model is a valuable tool for explaining the differences in adoption intention across cultures. Moreover, the national cultural system plays a significant moderating role in most relationships within the model. This study offers valuable insights into the factors influencing electric vehicle adoption in different cultural contexts, which can inform policies and strategies to promote sustainable transportation. [ABSTRACT FROM AUTHOR]

The high penetration of renewable energy sources (RES) results in a low‐inertia, weak power grid. To mitigate this and restore system inertia, it has been widely proposed to operate the inverters of RES units to mimic synchronous generators; this technology is known as a virtual synchronous generator (VSG). In weak grids there is, however, strong coupling between active power (P$P$) and reactive power (Q$Q$), and any VSG technique therefore requires PQ$PQ$ decoupling in order to operate effectively. This article proposes a new PQ$PQ$ decoupling technique based on the transformation of the PQ$PQ$ power circle of a VSG connected to a weak grid: first, the power circle is translated from its designed position to that of a conventional synchronous generator (SG) connected to a strong grid, achieving partialPQ$PQ$ decoupling. Then, to achieve full PQ$PQ$ decoupling, the authors propose further to modulate the radius of the translated PQ$PQ$ power circle; this is achieved using a series resistance‐capacitance–inductance (RCL$RCL$) circuit which is virtually implemented in the VSG controller. The efficacy of the proposed scheme is validated using a modified synchronverter connected to the weak grid in representative loading scenarios. It is demonstrated that the technique achieves a decoupled PQ$PQ$ control for the synchronverter connected in a weak grid. Moreover, the modified synchronverter is capable of supporting frequency and voltage regulation in the grid without inducing large transient grid currents during mild frequency and voltage variations. [ABSTRACT FROM AUTHOR]

Initial symptoms of neurodegenerative diseases are often defined by the loss of the most vulnerable neural populations specific to each disorder. In the early stages of Alzheimer's disease, vulnerable circuits in the temporal lobe exhibit diminished activity prior to overt degeneration. It remains unclear whether these functional changes contribute to regional vulnerability or are simply a consequence of pathology. We previously found that entorhinal neurons in the temporal cortex undergo cell death following transient suppression of electrical activity, suggesting a causal role for activity disruption in neurodegeneration. Here we demonstrate that electrical arrest of this circuit stimulates the injury‐response transcription factor c‐Jun. Entorhinal silencing induces transcriptional changes consistent with c‐Jun activation that share characteristics of gene signatures in other neuronal populations vulnerable to Alzheimer's disease. Despite its established role in the neuronal injury response, inhibiting c‐Jun failed to ameliorate entorhinal degeneration following activity disruption. Finally, we present preliminary evidence of integrated stress response activity that may serve as an alternative hypothesis to what drives entorhinal degeneration after silencing. Our data demonstrate that c‐Jun is activated in response to neuronal silencing in the entorhinal cortex but is decoupled from subsequent neurodegeneration. [ABSTRACT FROM AUTHOR]

With the phasor measurement units (PMUs) being widely utilized in power systems, a large amount of data can be stored. If transient stability assessment (TSA) method based on the deep learning model is trained by this dataset, it requires high computation cost. Furthermore, the fact that unstable cases rarely occur would lead to an imbalanced dataset. Thus, power system transient stability status prediction has the bias problem caused by the imbalance of sample size and class importance. Faced with such a problem, a TSA model based on the sample selection method is proposed in this paper. Sample selection aims to optimize the training set to speed up the training process while improving the preference of the TSA model. The typical samples which can accurately express the spatial distribution of the raw dataset are selected by the proposed method. Primarily, based on the location of training samples in the feature space, the border samples are selected by trained support vector machine (SVM), and the edge samples are selected by the assistance of the approximated tangent hyperplane of a class surface. Then, the selected samples are input to stacked sparse autoencoder (SSAE) as the final classifier. Simulation results in the IEEE 39‐bus system and the realistic regional power system of Eastern China show the high performance of the proposed method. [ABSTRACT FROM AUTHOR]

Accurate and fast transient stability assessment (TSA) of power systems is crucial for safe operation. However, deep learning-based methods require long training and fail to simultaneously extract the spatiotemporal characteristics of the transient process in power systems, limiting their performance in prediction. This paper proposes a novel TSA method based on a spatiotemporal graph convolutional network with graph simplification. First, based on the topology and node information entropy of power grids, as well as the power flow of each node, the input characteristic matrix is compressed to accelerate evaluation. Then, a high-performance TSA model combining a graph convolutional network and a Gated Convolutional Network is constructed to extract the spatial features of the power grid and the temporal features of the transient process. This model establishes a mapping relationship between spatiotemporal features and their transient stability. Finally, the focal loss function has been improved to dynamically adjust the influence of samples with different levels of difficulty on model training, adaptively addressing the challenge of sample imbalance. This improvement reduces misclassification rates and enhances overall accuracy. Case studies on the IEEE 39-bus system demonstrate that the proposed method is rapid, reliable, and generalizable. [ABSTRACT FROM AUTHOR]

The use of multi-terminal high voltage direct current (HVDC) power transmission systems is being adopted in many new links between different generation and consumption areas due to their high efficiency. In these systems, cable energization must be performed at the rated voltage. Healthy energizations at the rated voltage result in large inrush currents, especially in long cables, primarily due to ground capacitance. State-of-the-art protection functions struggle to distinguish between transients caused by switching and those associated with ground faults, leading to potential unwanted tripping of the protection systems. To prevent this, tripping is usually blocked during the energization transient, which delays fault detection and clearing. This paper presents a novel method for prompt discrimination between healthy and faulty energizations. The proposed method outperforms conventional protection functions as this discrimination allows for earlier and more reliable tripping, thus avoiding extensive damage to the cable and the converter due to trip blocking. The method is based on the transient analysis of the current in the cable shields, therefore, another technical advantage is that high voltage-insulated measuring devices are not required. Two distinct tripping criteria are proposed: one attending to the change in current polarity, and the other to the change in current derivative sign. Extensive computer simulations and laboratory tests confirmed the correct operation in both cases. [ABSTRACT FROM AUTHOR]

Operating overvoltage occurs when the pantograph or main breaker of an electric locomotive is operated, which is prone to causing insulation failure of high-voltage equipment. The HXD1 electric locomotive is taken as the research object in this paper to explore the characteristics and influencing factors of operating overvoltage. Under pantograph lifting, main breaker closing, main breaker opening, and pantograph dropping, operating overvoltage waveform in the high-voltage system is recorded by a high-speed oscilloscope and resistance–capacitance voltage divider to analyze the overvoltage characteristics and distribution law. Tested data show that the amplitude of operating overvoltage is in the range of 80 to 330 kV with ultra-high steepness, which is similar to the Very Fast Transient Overvoltage (VFTO) in power systems. The maximum overvoltage during the entire test occurred during the main breaker closing and its amplitude is 328.60 kV with a steepness of 4.21 × 104 kV/μs. The max overvoltage of the other operations (pantograph lifting, main breaker opening, and pantograph dropping) are 280.60 kV, 194.73 kV, and 305.56 kV with ultra-high steepness. High-amplitude overvoltage is predominantly located at the pantograph, while the low-amplitude sort is mainly observed around other high-voltage equipment. The result indicates that operating overvoltage belongs to ultra-fast transient overvoltage and its amplitude and steepness are higher than existing research. [ABSTRACT FROM AUTHOR]

Coal-bearing strata belong to sedimentary strata, and there are multiple aquifers. The accurate detection of deep aquifers is helpful to the safe mining of the working face. In order to provide guidance for the interpretation of the surface-to-underground transient electromagnetic method (SUTEM) that can be used to detect deep aquifers, we used theoretical analysis and numerical simulation methods in this study. Taking uniform half-spaces, single aquifers, and double aquifers as examples, we systematically studied the data characteristics and degree of influence of SUTEM under the influence of shallow aquifers. The results indicate the following: Under the influence of the primary field distribution, the x or y component of the induced electromotive force received by the underground receiving point has a positive and negative inflection point, which increases the difficulty of data interpretation, and the z component is easier to use for data interpretation. The influence of the aquifer on the early data of the underground receiving point is much greater than that of the ground receiving point, and the late influence is closer to the ground receiving point. The change in resistivity of the shallow aquifer has the greatest influence on the ability of each measuring point to detect the data of the deep aquifer; this influence is followed by change in thickness, and change in depth has the least influence on the detection capability of each measuring point. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive saturation module to enhance the transient stability of grid‐following inverters after voltage‐dip inception and fault‐clearance moment. The equal‐area criterion reveals that a large acceleration area will be obtained due to the step change of current reference according to grid codes, which may lead to the loss of synchronism with the grid. It is found that the saturation module used with the phase‐locked loop can enhance the transient stability performance by reducing the acceleration area. To simultaneously adapt to the two cases (the inception and clearance of the voltage‐dip fault), an adaptive saturation module that can automatically adjust the clamping mode is proposed, which is the main contribution of the paper. The selection of the threshold value for the saturation module is also presented. A comprehensive comparison is made between the proposed solution and state‐of‐art solutions. Finally, the effectiveness of the proposed control strategy is confirmed by the experimental tests. [ABSTRACT FROM AUTHOR]

In transient electromagnetic surveys, the collected data inevitably contain noise originating from both natural and cultural sources. This noise has the potential to mask transient electromagnetic responses linked to geological features, thereby posing challenges in accurately interpreting subsurface structures. Hence, the implementation of effective noise reduction techniques is crucial in ensuring the accuracy and reliability of inversion outcomes in transient electromagnetic surveys. This study introduces a novel approach that merges k‐means clustering with locally weighted linear regression to denoise transient electromagnetic data. The results from synthetic examples illustrate that the k‐means locally weighted linear regression method can predict transient electromagnetic data closely resembling true values, similar to the long short‐term memory autoencoder. Occam's inversion results derived from denoised data using both the k‐means locally weighted linear regression and long short‐term memory–autoencoder methods can well reflect the true model. Notably, a key advantage of the k‐means locally weighted linear regression method is its independence from labelled data as the sample set. The k‐means locally weighted linear regression method was applied to field data collected at the Narenbaolige coalfield in Inner Mongolia, China. Occam's inversion models generated from the denoised field data delineate the boundary between the basaltic body and sedimentary rocks, aligning with drilling data. The inversion models derived from the noisy field data also can capture this boundary, but deep section views reveal the presence of numerous intricate high‐resistivity anomalous bodies. These observations highlight the effectiveness of the k‐means locally weighted linear regression method in denoising transient electromagnetic data. [ABSTRACT FROM AUTHOR]

In recent years, there has been a noticeable surge in electric power load due to economic development and improved living standards. The growing need for smart power solutions, such as leveraging user electricity data to forecast power peaks and utilizing power data statistics to enhance end-user services, has been on the rise. However, the misuse and unauthorized access of data have prompted stringent regulations to safeguard data integrity. This paper presents a novel decentralized collaborative machine learning framework aimed at predicting peak power loads while protecting the privacy of users' power data. In this scheme, multiple users engage in collaborative machine learning training within a peer-to-peer network free from a centralized server, with the objective of predicting peak power loads without compromising users' local data privacy. The proposed approach leverages blockchain technology and advanced cryptographic techniques, including multi-key homomorphic encryption and consistent hashing. Key contributions of this framework include the development of a secure dual-aggregate node aggregation algorithm and the establishment of a verifiable process within a decentralized architecture. Experimental validation has been conducted to assess the feasibility and effectiveness of the proposed scheme, demonstrating its potential to address the challenges associated with predicting peak power loads securely and preserving user data privacy. [ABSTRACT FROM AUTHOR]

Modern power systems are high-dimensional, strongly coupled nonlinear systems with complex and diverse dynamic characteristics. The polynomial model of the power system is a key focus in stability research. Therefore, this paper presents a study on the approximate transient stability solution targeting the fault process in power systems. Firstly, based on the inherent sinusoidal coupling characteristics of the power system swing equations, a generalized polynomial matrix description in perturbation form is presented using the Taylor expansion formula. Secondly, considering the staged characteristics of the stability process in power systems, the approximate solutions of the polynomial model during and after the fault are provided using coordinate transformation and regular perturbation techniques. Then, the structural characteristics of the approximate solutions are analyzed, revealing the mathematical basis of the stable motion patterns of the power grid, and a monotonicity rule of the system's power angle oscillation amplitude is discovered. Finally, the effectiveness of the proposed methods and analyses is further validated by numerical examples of the IEEE 3-machine 9-bus system and IEEE 10-machine 39-bus system. [ABSTRACT FROM AUTHOR]

The conventional transient electromagnetic inversion method has a low calculation speed and precision and is susceptible to falling into local minima, which does not meet the fine detection requirements of urban underground space. In this study, we proposed a novel inversion method based on convolutional bidirectional long short-term memory neural networks for shallow subsurface transient electromagnetic inversion. This network structure possessed strong spatial feature extraction capabilities and a proficient understanding of sequential data, thereby addressing the issues of slow conventional inversion computations and inadequate inversion accuracy. Utilizing the apparent resistivity from a three-layer model as the sample input and the real model as the target, the network was trained using batch normalization and dropout techniques to accelerate the convergence rate. The resulting model achieved real-time inversion speeds and high accuracy, with robust generalization capabilities and adaptability to new data. To assess the inversion performance, we used a novel 1-D inversion error calculation index, the correlation area loss error, for a more accurate measurement. Numerical simulation experiments showed that the proposed method required only 2.121 s to invert data from 100 observation points. The inversion efficiency was significantly superior to the conventional methods, maintaining excellent accuracy while effectively discerning subsurface electrical stratification in geophysics. Applying convolutional bidirectional long short-term memory neural networks to multidimensional and field data yielded results superior to those of conventional inversion, demonstrating the promising applicability and generalization of this approach. This study offers an efficient solution for shallow subsurface transient electromagnetic exploration and holds potential for application in other areas. [ABSTRACT FROM AUTHOR]

Neural action potentials (APs) are difficult to interpret as signal encoders and/or computational primitives. Their relationships with stimuli and behaviors are obscured by the staggering complexity of nervous systems themselves. We can reduce this complexity by observing that "simpler" neuron-less organisms also transduce stimuli into transient electrical pulses that affect their behaviors. Without a complicated nervous system, APs are often easier to understand as signal/response mechanisms. We review examples of nonneural stimulus transductions in domains of life largely neglected by theoretical neuroscience: bacteria, protozoans, plants, fungi, and neuron-less animals. We report properties of those electrical signals—for example, amplitudes, durations, ionic bases, refractory periods, and particularly their ecological purposes. We compare those properties with those of neurons to infer the tasks and selection pressures that neurons satisfy. Throughout the tree of life, nonneural stimulus transductions time behavioral responses to environmental changes. Nonneural organisms represent the presence or absence of a stimulus with the presence or absence of an electrical signal. Their transductions usually exhibit high sensitivity and specificity to a stimulus, but are often slow compared to neurons. Neurons appear to be sacrificing the specificity of their stimulus transductions for sensitivity and speed. We interpret cellular stimulus transductions as a cell's assertion that it detected something important at that moment in time. In particular, we consider neural APs as fast but noisy detection assertions. We infer that a principal goal of nervous systems is to detect extremely weak signals from noisy sensory spikes under enormous time pressure. We discuss neural computation proposals that address this goal by casting neurons as devices that implement online, analog, probabilistic computations with their membrane potentials. Those proposals imply a measurable relationship between afferent neural spiking statistics and efferent neural membrane electrophysiology. [ABSTRACT FROM AUTHOR]

Inverse problems are typically tackled using deterministic optimization methods that may become trapped in a local minimum or probabilistic methods that can be computationally demanding. In this study, we explore the potential of the back propagation neural network (BPNN) optimized by the genetic algorithm (GA) for onshore transient electromagnetic (TEM) inversion. The GA is employed to optimize the initial parameters of the BPNN, enhancing its global optimization ability. Once the BPNN optimized by GA (GA-BPNN) is properly trained, it can provide the distribution of subsurface electrical conductivity (σ) in 0.1 s. We train the GA-BPNN using synthetic datasets generated by TEM forward modeling and assess its reliability using both synthetic and field data. Theoretical simulations demonstrate that compared with BPNN, the error of GA-BPNN on the inversion results of six samples is reduced by 23.2%. Furthermore, this method can provide reliable results even in the presence of noise in the TEM response. Finally, applying this inversion method to karst exploration with measured data proves the reliability and robustness of the proposed method. The proposed method can support quasi-real-time imaging of subsurface structures and provides a powerful tool for the interpretation of field TEM data. [ABSTRACT FROM AUTHOR]

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A self‐potential (SP) data‐inversion algorithm was developed and tested on an analytical model of electrical‐potential profile data attributed to single and multiple polarized electrical sources. The developed algorithm was then validated by an application to SP‐monitoring field data measured on the floodplain of East Fork Poplar Creek, Oak Ridge, Tennessee, to image electrical sources in areas conducive to preferential flow into the flood plain from the bedrock‐lined riverbed. The algorithm combined stochastic source‐localization by particle‐swarm‐optimization (PSO) of electrical sources characterized by simplified geometries with source tomography by regularized weighted least‐squares minimization of a quadratic objective function. Prior information was incorporated by preconditioning the tomography algorithm by PSO results. Variable percentages of random noise were added to analytical‐model data to evaluate the algorithm performance. Results indicated that true parameters of single‐source models were inverted and approximated with small residual error, whereas inversion of analytical‐model data representing multiple electrical sources accurately approximated the locations of the sources but miscalculated some parameters because of the non‐uniqueness of the inverse‐model solution. Source tomography applied to analytical model data during testing produced a spatially continuous parameter field that identified the locations of point‐scale synthetic dipole sources of electrical current flow with varying degrees of accuracy depending on the prior information incorporated into the tomography. When applied to SP‐monitoring field data, the algorithm imaged electrical sources within a known fault that intersects the bedrock riverbed and flood plain of East Fork Poplar Creek and depicted dynamic electrical conditions attributed to hyporheic exchange. Plain Language Summary: A self‐potential (SP)‐data inversion method was developed and tested on synthetic models of electrical‐potential data and electrical‐potential field data acquired by monitoring voltages through time at multiple locations on the flood plain of a river flowing over a bedrock aquifer. The SP‐data inversion method (a) calculates horizontal electrical‐potential profile data over buried electrical sources with user‐defined properties, (b) finds the locations of unknown electrical sources of electrical‐potential data by particle swarm optimization (PSO), (c) performs electrical‐source imaging using the locations of the sources determined by PSO as a guide, and (d) enables real‐time monitoring of water exchange between a river and a bedrock aquifer. The results obtained by applying the inversion method to SP‐monitoring data depict transient electrical changes in a bedrock aquifer that are affected by changes in streamflow and attributed to water exchange between the river and aquifer along a fault. Key Points: Hyporheic exchange flows in East Fork Poplar Creek occur along fault lines and fractures in the limestone bedrock riverbedSelf‐potential tomography is preconditioned by particle swarm optimization and applied to synthetic models and self‐potential monitoring dataInverse models image electrical sources attributed to groundwater flow along a known fault in the flood plain of East Fork Poplar Creek [ABSTRACT FROM AUTHOR]

Aiming at the noise that may exist in the data collection and transmission of power system synchronous phasor measurement unit (PMU), as well as the imbalance between stable and unstable samples, making the model likely to tilt to most kinds of samples, thus affecting the evaluation results of power system transient stability model, a transient stability assessment method integrating Convolutional Block Attention Module (CBAM) and deep residual shrinkage network is proposed. First, the mapping relationship between the input values and the labels of the stability results is established, and the electrical quantities are transformed into the form of convolutional feature maps to be input into the model. Second, channel attention module and spatial attention module are introduced, and soft threshold function is used to automatically learn the noise threshold and reduce the interference of the noise. As the model tends to favor the majority of the samples during training, gradient harmonized mechanism (GHM) loss function is introduced to improve the attention of the minority of samples as well as the training effect and evaluation performance of the model. Finally, the model is tested by simulation on the IEEE39 node system. [ABSTRACT FROM AUTHOR]

Generating highly organized skeletal muscle tissues that mimics the cellular alignment, maturation, and contraction of native skeletal muscle remains a challenge in disease modeling and regenerative therapies. Existing methodologies are constrained by complexity in fabrication and difficulty in achieving aligned 3D myofibers. Here, a functional skeletal muscle organoid‐on‐a‐chip (SMO) is engineered by establishing mechanical boundary constraints at either end of the cell‐laden extracellular matrix hydrogel within polydimethylsiloxane microstructures to promote the formation of an anisotropic biophysical microenvironment in tissues. The linearly aligned tissue, featuring multinucleated myofibers with distinct cross‐striations, exhibited a positive force‐frequency relationship and stable calcium transients under electrical stimulation. SMOs applicability is demonstrated by systematically evaluating muscle response to varying degrees of intermittent hypoxia. Murine‐ or human‐derived SMOs revealed that, with increasing hypoxia severity, muscles transitioned from a compensatory phase‐characterized by enhanced contractile function, vacuolation and hypertrophic‐like changes in myofibers, fiber type switching, and metabolic shift, to a decompensatory stage, paralleling in vivo muscle responses and highlighting interspecies differences. Human‐derived SMOs are also utilized to assess self‐repair capabilities and pharmaceuticals protective effects on damaged muscle. Together, the platform, with its simplicity of operation and reliable phenotypic readouts, demonstrates significant potential for future disease modeling and regenerative therapies. [ABSTRACT FROM AUTHOR]

The given power and grid frequency disturbances can cause transient oscillations and steady-state deviations in the output power of a virtual synchronous generator (VSG), which can be effectively addressed by adding transient damping. However, this approach may result in significant power overshoot. This article proposes an improved VSG control strategy based on transient electromagnetic power feedback compensation and a small-signal model reduction scheme. Firstly, the grid-connected active closed-loop small-signal models of typical VSG control and transient damping VSG control are established, respectively. The transient oscillation suppression mechanism of active power is revealed through root locus and frequency response analyses, and the power overshoot characteristics of the two control strategies are analysed by combining them with the system of zero points. Secondly, the active transient feedback compensation method and the small-signal model reduction design method are introduced in detail. Finally, comparative analysis experiments are conducted using the Matlab/Simulink and hardware-in-the-loop experimental platform. It is verified that the proposed control strategy can suppress transient oscillations in active power, prevent steady-state deviations, and effectively mitigate the power overshoot problem of the system. [ABSTRACT FROM AUTHOR]

The detection of concealed water-conducting structures is essential for preventing water inrush disasters. Aiming to mitigate the limitations inherent in using any single technique, a comprehensive approach that combines integrated mining geophysical exploration, hydrogeological drilling, and hydrochemical exploration (GDH) is proposed for the exploration of concealed water-conducting structures. By conducting a thorough analysis of the background geological data obtained through surface exploration, potentially concealed water-conducting structures can be predicted. Then, a combination of the seismic reflection method (SRM) and mine transient electromagnetic method (MTEM) can be used to detect the location and water-bearing properties of the target structures. Afterwards, the target drilling areas are defined by the anomalies detected by the integrated mine geophysical technique, and the drilling method can directly acquire the hydrogeological information of water-conducting structures and verify the results of the geophysical methods. By means of hydrochemical analysis, inrush water sources and their runoff conditions can be identified, and the spatial relationship betweenof the source aquifers and mining space can be determined; hence, the properties, scale, and configuration of the water-conducting structures can finally be evaluated. Employing a water-conducting fault in a mine as a case study, we verified that the integrated method overcomes the limitations and possible biases of each method, providing a multiple-method solution that can accurately detect concealed water-conducting structures to help prevent water inrush disasters. [ABSTRACT FROM AUTHOR]

With the large-scale integration of wind power, it is essential to establish an electromagnetic transient (EMT) model of a wind turbine system. Focusing on the problem of the difficulty in obtaining the parameters of the direct-driven permanent magnet synchronous generator (PMSG) model, this manuscript proposes a method based on trajectory sensitivity analysis and improved gray wolf optimization (IGWO) for identifying the parameters of the PMSG EMT model. First, a model of a PMSG wind turbine is established on an EMT simulation platform. Then, the key parameters of the model are determined based on the sensitivity analysis. Five control parameters are selected as the key parameters for their higher sensitivity indexes. Finally, the key parameters are accurately identified, using the proposed IGWO algorithm. The final case study demonstrates that the proposed IGWO algorithm has better optimization performance compared with the GWO algorithm and particle swarm optimization (PSO) algorithm. In addition, the simulation waveforms show that the identified parameters are accurate and applicable to other operating conditions. [ABSTRACT FROM AUTHOR]

It is essential to protect control, regulation and data storage units used in automated robotic manufacturing from the consequences of harmful electrical grid harmonics. In this paper, the effectiveness of active/passive harmonic filters built into LED lamps, test benches, and industrial robots was investigated (in the latter two cases, light and heavy load states were applied). Based on network analysis, it was found that the built-in passive harmonic filters of the luminaries were ineffective because the THD of the current was approximately two or three times the permissible value according to the IEEE 519-2022 standard. It was proved that the built-in active harmonic filter of the test bench at a heavy load worked properly, but at a light load it was not effective, while the magnitude of harmonic distortion of the industrial robot exceeded the allowed level (three times the limit at a light load, seven times the limit at a heavy load). Further measurements were also performed at three locations: an engineering room, a gearbox house machine tool, and an office room (where there were no built-in filters). The results proved that in each case, there was a significant exceedance of the limit value. Therefore, protection against harmonics needs to be installed, and then the measurements must be repeated. [ABSTRACT FROM AUTHOR]

The Meijiayan landslide in Pengshui County within Chongqing City is a medium-scale soil landslide triggered by the excavation of roadbeds. To delve into the influencing factors and assess the stability of this landslide, it is crucial to meticulously map the subterranean geological framework of the area. Such an analysis lays the groundwork for evaluating and mitigating the risks of future landslide instabilities. In this context, the semi-airborne transient electromagnetic method (SATEM), which is complemented by a receiving system mounted on an aerial platform, stands out as an innovative geophysical exploration technique. This method is adept at conducting swift measurements across complex terrains, making it particularly valuable for areas prone to such geological events. This paper presents the utilization of a cutting-edge loop source SATEM system, which was operationalized via a rotor-based unmanned aerial vehicle (UAV). The system was employed to conduct shallow geological structure detection experiments on the Meijiayan landslide. The SATEM detection outcomes have unveiled fluctuations in the electrical distribution across the upper strata, which are indicative of the subsurface geological boundaries, faults, and areas potentially rich in water within the landslide region. These discoveries affirm the viability of utilizing loop source SATEM for the identification of shallow geological structures in regions susceptible to landslides. The findings indicate that while the landslide is currently in a stable condition, it poses a significant risk of movement, especially during the rainy season, with the potential for landslides to be exacerbated by extreme or sustained rainfall events. [ABSTRACT FROM AUTHOR]

In the two‐stage inverter system, due to the space vector modulation (SVM), the midpoint potential of active neutral point clamped three‐level inverter system (ANPC‐TLIS) will have triple frequency fluctuation, under the influence of the closed‐loop control strategy, the fluctuation amplitude of the lithium battery energy storage system (LBESS) output current will increase. To reduce the low‐frequency output current fluctuation, a voltage loop strategy based on active disturbance rejection control (ADRC) with notch filter (NF)is proposed in this paper. In this scheme, the voltage outer‐loop control strategy not only maintains voltage stability, but also reduces the fluctuation amplitude of reference current. In addition, during the electromagnetic transient process, the LBESS output current fluctuation frequency is constantly changing because the power supply frequency required by linear motor is constantly changing. To solve this problem, the inverter output current frequency is feedforward to the LBESS controller, forming a variable frequency notch filter (VFNF). Finally, the effectiveness of the proposed strategy is verified by fixed‐frequency and variable frequency conditions experiments on RT‐LAB experimental platform. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

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A renewable energy-based DC micro grid system supports the DC power flow for domestic and industrial purposes. The DC voltage drawn from the source is to be adjusted by using the power electronic converters. So, the controlling of converter came into existence. In this paper the Flyback converter is modelled to get the rated voltage regulation with the desired output voltage with proper duty cycle at the converter. The nonlinear system is converted into the linear one by using the lie derivative control technique. In this technique the relative values of current loop and voltage loop is linearized to get the desired output voltage at the flyback converter. Thus as compared to PI controller the proposed controller flows good for the transient response of the power system. The boost converter designed in the existing has modelled with the low output range but in the Flyback converter the ability to achieve the voltage regulation in DC micro grid with variable inductance and capacitance is defined. Simulation result shows the modelled flyback converter flows good and accurate as compared to the PI controller with the settling time difference of 85s of voltage dynamics in the DC micro grid system. The steady state and transient response of the system is evaluated by comparing the simulation of PI and the proposed controller used in the real time power transmission system to reduce the power loss at receiving side. [ABSTRACT FROM AUTHOR]

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This paper presents a new method for the analysis of transient signals in the frequency domain based on the Continuous Wavelet Transform (CWT). The proposed case study involves test signals measured from an electronic switch considering open and close operations. The source is connected to inductive, resistive, and capacitive loads. Resonance behaviors are introduced and compared with the Discrete Fourier Transform (DFT). Multiple factors, such as reliability, repeatability, high noise attenuation, and the smoothing of the analyzed spectrum, are considered in this study. This proposed study highlights the effectiveness of CWT in signal processing, especially in obtaining a detailed spectrum that reveals the behavior of electrical circuits. Resonance behaviors were analyzed, demonstrating that the signal processing performed by CWT is better for spectrum analysis than DFT. This study shows the potential of CWT to analyze transient electrical signals, specifically for identifying and characterizing the behavior of load connections and disconnections. [ABSTRACT FROM AUTHOR]

The lock-on effect of the gallium arsenide photoconductive semiconductor switch (GaAs PCSS) at repetition rate aggravates the current crowding and electric field distortion, which significantly increases the risk of switch damage or even failure. Therefore, it is of great significance to investigate the carrier transport and the heat generation mechanism for improving the performance and longevity of GaAs PCSS in lock-on mode. The internal physical process of an opposed-electrode GaAs PCSS at low optical energy and strong electric field is analyzed and discussed by experiment and simulation. A device-circuit hybrid simulation is employed to investigate the transient electric field, carrier concentration, and lattice temperature distribution within the GaAs PCSS in lock-on mode. The device temperature exhibits a positive correlation with the applied bias electric field, resulting in a peak temperature of 1037.25 K at an electric field of 38 kV cm−1. The temperature distribution within the GaAs PCSS indicates a greater possibility for thermal breakdown and damage near the electrodes. [ABSTRACT FROM AUTHOR]

The joint inversion of radio magnetotelluric and electrical resistivity tomography data has the potential to reduce the uncertainties in the subsurface conductivity model. This is particularly beneficial when the data sets offer complementary information about the subsurface. However, the traditional gradient-based inversion methods pose challenges in quantifying uncertainty, as they yield a single model with limited appraisal of parameter uncertainty. The Bayesian inversion approach stands out for its capacity to provide quantitative assessments of uncertainty in the inverted model parameters. This is accomplished by generating an ensemble of models, leading to a posterior distribution that encapsulates both prior information concerning model parameters and the data set information. We have implemented a transdimensional Markov chain Monte Carlo algorithm to perform the joint inversion of radio magnetotelluric and electrical resistivity tomography data. Through synthetic data studies, we illustrate how the inclusion of two complementary data sets can effectively reduce uncertainties in model parameters and how the model parameter uncertainties can be quantified. Subsequently, the developed algorithm is tested using exemplary field data from a waste site near Roorkee, India. Intensive prior geoelectric and transient electromagnetic as well as radio magnetotelluric studies investigated possible waste water seepage with a potential to contaminate the shallow aquifers. The derived subsurface structure from our transdimensional Bayesian results compare well with the deterministic results for the exemplary profile, but in addition provide comprehensive uncertainty estimates. [ABSTRACT FROM AUTHOR]

In recent years, in order to reduce the damage from the blackout accident, the demand of the power system for its basic resistance ability has gradually increased. Therefore, this paper proposes a double‐layer optimization method for the prevention of cascading failure of the power system that takes into account the transient stability constraint for cascading trips that directly lead to the accident. In terms of models, the paper reconstructs an outer model for optimizing the safety margin based on the inner model that solves the safety margin of cascading failure prevention. In terms of algorithms, the paper uses the Extended Particle Swarm Optimization (EPSO) algorithm with higher precision and accuracy to solve the inner model and innovatively introduced the Practical Dynamic Security Region (PDSR), which uses PSASP software for offline calculation and online application, as a dynamic analysis method to quickly realize transient stability constraint to solve the outer model. Three evaluation indexes of the preventive optimization effect can be obtained by this method; dispatchers can plan more flexible and reliable optimal dispatching schemes through these indexes, to comprehensively improve the safety margin of cascading failure prevention in the current operating state and enhance the risk resistance ability of the power grid. This paper has used MATLAB and PSASP to perform simulation analysis using the IEEE39 node system as a test example to verify the effectiveness and superiority of the proposed model and algorithm. [ABSTRACT FROM AUTHOR]

With the penetration of the high proportion of power electronics interfaced energy resources, the frequency stability issue in power systems is becoming increasingly prominent. This paper proposes a coordinated control strategy of multiple voltage source converter based high voltage direct current transmission (VSC‐HVDC) to simultaneously improve the transient frequency and steady‐state frequency after a severe disturbance. Firstly, a modified average system frequency response (MASFR) model is constructed with reserve power limitation, which takes into account the frequency responses of different types of generators (thermal, hydro and new energy resources). Secondly, according to the frequency stability requirements, the corresponding constraints, which consider the overload capacity of VSC‐HVDC, the AC transmission capacity and the frequency of AC grids on both sides of VSC‐HVDC, are formulated. Thirdly, for the transient frequency control, the coordinated optimization control model of multiple VSC‐HVDC is established, which aims to minimize the control energy considering the transient frequency threshold. For the steady‐state frequency control, the coordinated optimization control model is fomulated, which aims to minimize the control cost while considering the steady‐state frequency threshold. The two control schemes achieve seamless and smooth switching. Finally, the correctness and effectiveness of the proposed control strategy are verified in the improved IEEE 39 bus system. [ABSTRACT FROM AUTHOR]

This paper proposes a design method for an additional damping reduced-order controller based on the projective control theorem. Improvements are made to the projective theorem to achieve additional damping control through Controllable-Line-Commutated Converter (CLCC)-based High Voltage Direct Current (HVDC), which suppresses low-frequency oscillations. Using small disturbance identification techniques, the system's linear model is first identified, and the state feedback control law of the system is then determined using the state-space pole assignment control method. Then, by retaining the dominant oscillation modes of the closed-loop system through the improved projective theorem, the state feedback is converted into output feedback. Ultimately, the sixth-order state feedback controller is reduced to a second-order one and applied in the CLCC HVDC additional damping strategy to suppress low-frequency oscillations. Simulation results based on the electromagnetic transient simulation software PSCAD demonstrate that the designed CLCC HVDC additional damping reduced-order projective control exhibits good suppression performance, strong robustness, and low order, which are of significant importance for engineering practice. [ABSTRACT FROM AUTHOR]

The probing depth of the transient electromagnetic method (TEM) refers to the depth range at which the underground conductivity changes can be effectively detected. It typically ranges from tens of meters to several kilometers and is influenced by factors such as instrument parameters and the conductivity of the subsurface structure. Rapid and accurate probing depth is useful for the selection of appropriate inversion parameters and improving survey accuracy. However, mainstream methods suffer from issues such as low computational precision, large uncertainties, or high computational requirements, making them unsuitable for processing massive airborne electromagnetic data. In this study, we propose a prediction model based on deep learning that can directly compute the probing depth from the TEM responses, and its effectiveness and accuracy are validated through synthetic models and field measurements. We compared the performance of classic deep learning models, including CNN, RESNET, and RNN, and found that RNN performed the best overall on both synthetic and field data. Furthermore, we apply this algorithm to deep learning-based ATEM inversion by constraining the one-dimensional resistivity model depths in the training set, to reduce the non-uniqueness of the inversion, accelerate the convergence, and improve its prediction accuracy. [ABSTRACT FROM AUTHOR]

Current researches mainly focus on the participation of wind farms in primary frequency regulation, including overspeed load shedding control, propeller control and their coordinated control, etc. The frequency support is realized by reserving reserve capacity of wind turbines, but the influence of the dynamic characteristics of maximum power point tracking (MPPT) on the overall frequency regulation characteristics of wind turbines during frequency support is ignored. To this end, firstly, the frequency response model of the system is constructed, and the main factors that affect the frequency dynamic characteristics are revealed. Secondly, the principle of the grid-forming VSC with the function of self-inertial synchronization is introduced, the influence of the dynamic characteristics of MPPT on the frequency regulation characteristics of the wind turbine generator is clarified. Finally, based on the PSCAD/EMTDC electromagnetic transient simulation software platform, the influence of the dynamic characteristics of MPPT on the frequency regulation characteristics are verified by combining the inertia self-synchronous network control technology. The simulation results indicate that with the increase of setting of droop control, the decrease of output power of MPPT becomes large so as to weaken the frequency support capability. [ABSTRACT FROM AUTHOR]

Abstract—The paper presents the algorithm for eliminating structured noise (SN) appearing in the transient electromagnetic (TEM) response curves during correlation processing of recorded pseudo noise signals. The algorithm is based on the detection and elimination of pulses of structured noise (SNP) with energies exceeding optimal detection threshold (calculated from the TEM curve) against the background of the smoothly (slowly) varying TEM sounding signal. The method provides reliable SNP detection with high correct recognition and low false alarm rate as well the subsequent elimination of SN field from the recorded TEM curves. For automatic calculation of SNP optimal detection threshold, it is proposed to use the criterion that the noise and interference signals remaining in the TEM curve after SNP removal are maximally close to normal distribution. Selection and setting of the optimal detection threshold is performed by successive sweep through its values (from maximum to minimum) with a given step which determines the accuracy of detection threshold. The procedure of selecting the detection threshold is reduced to finding, at each step, the maximum value of the Pearson correlation coefficient between the probability density distribution functions of amplitudes of noise and interference signals remaining in TEM curve after SN removal and the calculated normal distribution. This approach has made data processing maximally automated and increased its speed, which is very important in the field work. The algorithm was tested on TEM signals recorded at Chunkurchak experimental site in the field work with electromagnetic measurement system using pseudo noise signals (EMS PNS). Application of the algorithm has significantly improved (approximately by a factor of 450) the signal to noise ratio in the TEM response curve in the interval of late times. [ABSTRACT FROM AUTHOR]

In 2018 the first subglacial lake in the Canadian Arctic was proposed to exist beneath the Devon Ice Cap, based on the analysis of airborne radar data. Here, we report a new interpretation of the subglacial material beneath the Devon Ice Cap, supported by data acquired from multiple surface-based geophysical methods in 2022. The geophysical data recorded included 9 km of active-source seismic-reflection profiles, seven transient electromagnetic (TEM) soundings, and 17 magnetotellurics (MT) stations. These surface-based geophysical datasets were collected above the inferred locations of the subglacial lakes and show no evidence for the presence of subglacial water. The acoustic impedance of the subglacial material, estimated from the seismic data, is 9.49 ± 1.92 × 10 6 kg m -2 s -1 , comparable to consolidated or frozen sediment. The resistivity models obtained by inversion of both the TEM and MT measurements show the presence of highly resistive rock layers (1000–100 000 Ω m) directly beneath the ice. Re-evaluation of the airborne reflectivity data shows that the radar attenuation rates were likely overestimated, leading to an overestimation of the basal reflectivity in the original radar studies. Here, we derive new radar attenuation rates using the temperature- and chemistry-dependent Arrhenius equation, and when applied to correct the returned bed power, the bed power does not meet the basal reflectivity threshold expected over subglacial water. Thus, the radar interpretation is now consistent with the seismic and electromagnetic observations of dry or frozen, non-conductive basal material. [ABSTRACT FROM AUTHOR]

Independent lightning rods are often installed in substation grounding systems for lightning protection. The concentrated discharge channel formed when a lightning current flows to the ground through a grounding electrode will cause electromagnetic interference to the directly buried secondary cable. And the three-dimensional structure of the discharge channel will affect the transient electromagnetic field distribution, thereby affecting the electromagnetic transients on cable shields. In order to explore the influence of soil discharge phenomena on the electromagnetic interference of the directly buried secondary cable, in this paper, we carried out experiments on cables in two different grounding modes, single-ended and double-ended grounding, and captured image of soil discharge channels. The results show that the cable grounding mode will affect the coupling mode that causes the shielding layer current. The relative spatial position of the soil discharge channel and the cable has a significant impact on the magnitude of the shielding layer current under both grounding modes. The water content and salt content of the soil also have different degrees of influence on the coupling current of the shielding layer in different grounding modes. [ABSTRACT FROM AUTHOR]

The transient stability assessment based on machine learning faces challenges such as sample data imbalance and poor generalization. To address these problems, this paper proposes an intelligent enhancement method for real-time adaptive assessment of transient stability. In the offline phase, a convolutional neural network (CNN) is used as the base classifier. A model training method based on contrastive learning is introduced, aiming to increase the spatial distance between positive and negative samples in the mapping space. This approach effectively improves the accuracy of the model in recognizing unbalanced samples. In the online phase, when real data with different distribution characteristics from the offline data are encountered, an active transfer strategy is employed to update the model. New system samples are obtained through instance transfer from the original system, and an active sampling strategy considering uncertainty is designed to continuously select high-value samples from the new system for labeling. The model parameters are then updated by fine-tuning. This approach drastically reduces the cost of updating while improving the model's adaptability. Experiments on the IEEE39-node system verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]