Transient Voltage Research Articles

Analysis of Control Strategy Based on P&G Algorithm for the Switching Component of Wireless Solar Power Transfer

Previous researchers have studied wireless power transfer (WPT), while the current study focused on converting DC voltage to AC voltage on the transmitting coil and transferring it to the receiving coil. Previous studies did not discuss the control technique to drive the switching component on the converter circuit, especially the change controller in DC voltage and current affecting the WPT performance. This paper discussed a control strategy based on the P&O algorithm for driving MOSFETs on the half H-bridge inverter of a wireless solar power transfer (WSPT) system. This system is constructed by PV modules as the main DC voltage source, a control strategy based on the perturb and observe (P&O) algorithm, a half H-bridge inverter, and a transmitting and receiving circuit. The WSPT system was modelled using MATLAB SIMULINK with various solar irradiance, and its performance was assessed. The results show that the change in the total output voltage and power of PV modules can be controlled by the P&O algorithm as the signal input of the PWM generator applied to drive the MOSFETs. There are no transient values of voltage waveform on the transmitting and receiving circuit when the transient total output voltage of PV modules occurs. It indicates that the P&O algorithm can effectively manage the PWM generator for its input signal, including the total output voltage and current produced by PV modules.

Plateau depolarizations in spontaneously active neurons detected by calcium or voltage imaging

In calcium imaging studies, Ca2+ transients are commonly interpreted as neuronal action potentials (APs). However, our findings demonstrate that robust optical Ca2+ transients primarily stem from complex “AP-Plateaus”, while simple APs lacking underlying depolarization envelopes produce much weaker photonic signatures. Under challenging in vivo conditions, these “AP-Plateaus” are likely to surpass noise levels, thus dominating the Ca2+ recordings. In spontaneously active neuronal culture, optical Ca2+ transients (OGB1-AM, GCaMP6f) exhibited approximately tenfold greater amplitude and twofold longer half-width compared to optical voltage transients (ArcLightD). The amplitude of the ArcLightD signal exhibited a strong correlation with the duration of the underlying membrane depolarization, and a weaker correlation with the presence of a fast sodium AP. Specifically, ArcLightD exhibited robust responsiveness to the slow “foot” but not the fast “trunk” of the neuronal AP. Particularly potent stimulators of optical signals in both Ca2+ and voltage imaging modalities were APs combined with plateau potentials (AP-Plateaus), resembling dendritic Ca2+ spikes or “UP states” in pyramidal neurons. Interestingly, even the spikeless plateaus (amplitude > 10 mV, duration > 200 ms) could generate conspicuous Ca2+ optical signals in neurons. Therefore, in certain circumstances, Ca2+ transients should not be interpreted solely as indicators of neuronal AP firing.

Transient voltage stability assessment and margin calculation based on disturbance signal energy feature learning

With the increasing variation of the network topology and the high complexity of the processing measurement data, the transient voltage stability assessment of the new power system is facing significant challenges in low accuracy and high time costs. To address the shortcomings of the existing method and apply it to online assessment, this paper proposes an assessment method based on feature learning for disturbance signal energy (DSE) from bus voltages. Firstly, the relationship between DSE and system transient voltage stability is established, and the calculation of DSE from bus voltage time series is detailed. Subsequently, a transient voltage stability assessment method based on the ID3 Decision Tree algorithm and DSE is proposed. Finally, by employing the Support Vector Machine (SVM) to construct the optimal boundary in the feature space formed by the key buses, the transient voltage stability margin (TVSM) for specific scenarios is proposed. Simulation results on the IEEE 39-bus system demonstrate that the proposed method can rapidly and accurately assess the transient voltage stability of the system and calculate the stability margin, providing intuitive and interpretable results with high engineering application value.

Hilbert-Huang Transform and Machine Learning Based Electromechanical Analysis of Induction Machine under Power Quality Disturbances

Monitoring and predicting Power Quality (PQ) is crucial for quickly minimizing risk, protecting induction machines (IMs), and increasing productivity. This paper proposes an electromechanical analytical framework for IM working with various PQ disturbances based on machine learning (ML). This work collects the motor characteristics and PQ data, motor vibration signals, and other sensor data to identify the failures. The complicated electromechanical data is broken down into intrinsic mode functions (IMFs) using the Hilbert-Huang Transform (HHT), which reveals the modes present in the signals. This frequency-domain data is retrieved, paired with time-domain characteristics, and used as input features for ML algorithms. Support Vector Machines (SVMs) trained on labeled datasets in which features extracted from PQ disturbances are used to categorize the disturbances into several groups. The PQ disturbances caused by voltage sags, swells, harmonics, and transients can all be efficiently classified using SVM classifiers, allowing for real-time determination of the kind of disturbance influencing the IM. The accuracy for the SVM in the proposed scheme is 97.2 %. The SVM method is trained with PQ data and classification results using MATLAB classifier and Python software.

Numerical simulation and experimental study on the effect of cathode relative humidity on the performance of the PEMFC with dead-ended anode

ABSTRACT In the operation of the proton exchange membrane fuel cell (PEMFC) with dead-ended anode (DEA), the cathode relative humidity affects the accumulation state of water and nitrogen on the anode side, which in turn seriously affects the cell’s working performance. In order to investigate the mechanism of the cathode relative humidity on the PEMFC performance, numerical simulation and segmented in-situ measurement methods were used in this study. The results of the study showed that the lower cathode relative humidity causes insufficient water content in the PEMFC. This can further lead to localized dehydration of the membrane, which causes degradation of the DEA-PEMFC performance. Moreover, the higher cathode relative humidity increases the water content on the cathode side of the PEMFC. This not only increases the membrane permeability to nitrogen but also accelerates the water concentration back-diffusion process. The water and nitrogen that penetrate into the anode side can occupy the gas transport channel and hinder hydrogen transport, resulting in the insufficient localized gas supply in the DEA-PEMFC. This finally results in lower transient voltages and significantly shorter stabilized operation times. Therefore, reasonable control of cathode relative humidity is required to ensure long-term efficient and stable operation of the DEA-PEMFC.

Coordinated Control of Transient Voltage Support in Doubly Fed Induction Generators

The large-scale integration of wind power significantly alters the voltage dynamic characteristics of power systems. Wind turbines have a weak ability to withstand grid disturbances and have difficulty in providing effective reactive power support during transient periods. The sensitivity of wind turbines to the grid voltage significantly increases the probability of large-scale, cascading off-grid events. This paper proposes a coordinated control strategy to enhance the transient reactive power support capability of doubly fed wind farms. The additional stator current demagnetization control reduces the risk of a crowbar protection action after a fault and ensures that the unit power is controllable. Based on the voltage–reactive power coupling relationship, each unit can produce reactive power according to the voltage–reactive power sensitivity matrix during the transient period. After the reactive power output of the unit reaches the limit, transient active and reactive combined control is further adopted to reduce the active power output of the unit to a certain extent and improve the reactive power support capability. Finally, two cases are built in the PSCAD to verify the effectiveness of the proposed control strategy. The results show that the proposed control strategy can enable the wind farm to output more reactive power to the grid during the transient period, effectively supporting the system voltage during the transient process and avoiding further deterioration of the fault.

Two-Stage Optimal Configuration Strategy of Distributed Synchronous Condensers at the Sending End of Large-Scale Wind Power Generation Bases

The transmission end of large-scale wind power generation bases faces challenges such as high AC-DC coupling strength, low system inertia, and weak voltage support capabilities. Deploying distributed synchronous condensers (SCs) within and around wind farms can effectively provide transient reactive power support, enhance grid system inertia at the transmission end, and improve dynamic frequency support capabilities. However, the high investment and maintenance costs of SCs hinder their large-scale deployment, necessitating the investigation of optimal SC configuration strategies at critical nodes in the transmission grid. Initially, a node inertia model was developed to identify weaknesses in dynamic frequency support, and a critical inertia constraint based on node frequency stability was proposed. Subsequently, a multi-timescale reactive power response model was formulated to quantify the impact on short-circuit ratio improvement and transient overvoltage suppression. Finally, a two-stage optimal configuration strategy for distributed SCs at the transmission end was proposed, considering dynamic frequency support and transient voltage stability. In the first stage, the optimal SC configuration aimed to maximize system inertia improvement per unit investment to meet dynamic frequency support requirements. In the second stage, the configuration results from the first stage were adjusted by incorporating constraints for enhancing the multiple renewable short-circuit ratio (MRSCR) and suppressing transient overvoltage. The proposed model was validated using the feeder grid of a large energy base in western China. The results demonstrate that the optimal configuration scheme effectively suppressed transient overvoltage at the generator end and significantly enhanced the system’s dynamic frequency support strength.

The Development of Bi-LSTM Based on Fault Diagnosis Scheme in MVDC System

Diagnosing faults is crucial for ensuring the safety and reliability of medium-voltage direct current (MVDC) systems. In this study, we propose a bidirectional long short-term memory (Bi-LSTM)-based fault diagnosis scheme for the accurate classification of faults occurring in MVDC systems. First, to ensure stability in case a fault occurs, we modeled an MVDC system that included a resistor-based fault current limiter (R-FCL) and a direct current circuit breaker (DCCB). A discrete wavelet transform (DWT) extracted the transient voltages and currents measured using DC lines and AC grids in the frequency–time domain. Based on the digital signal normalized by the DWT, using the measurement data, the Bi-LSTM algorithm was used to classify and learn the types and locations of faults, such as DC line (PTP, P-PTG, and N-PTG) and internal inverter faults. The effectiveness of the proposed fault diagnosis scheme was validated through comparative analysis within the four-terminal MVDC system, demonstrating superior accuracy and a faster diagnosis time compared to those of the existing schemes that utilize other AI algorithms, such as the CNN and LSTM. According to the test results, the proposed fault diagnosis scheme detects MVDC faults and shows a high recognition accuracy of 97.7%. Additionally, when applying the Bi-LSTM-based fault diagnosis scheme, it was confirmed that not only the training diagnosis time (TraDT) but also the average diagnosis time (AvgDT) were 0.03 ms and 0.05 ms faster than LSTM and CNN, respectively. The results validate the superior fault clarification and fast diagnosis performance of the proposed scheme over those of the other methods.

Synergistic Active Heterostructure Design for Enhanced Two Electron Oxygen Reduction via Chemical and Electrochemical Reconstruction of Heterosulfides.

Transition metal sulfides, particularly heterostructures, represent a promising class of electrocatalysts for two electron oxygen reduction (2e- ORR), however, understanding the dynamic structural evolution of these catalysts during alkaline ORR remains relatively unexplored. Herein, NiS2/In2.77S4 heterostructure was synthesized as a precatalyst and through a series of comprehensive ex-situ and in-situ characterizations, including X-ray absorption spectroscopy, Raman spectroscopy, transient photo-induced voltage measurements, electron energy loss spectroscopy, and spherical aberration-corrected electron microscopy, it was revealed that nickel/indium (oxy)hydroxides (NiOOH/In(OH)3) could be evolved from the initial NiS2/In2.77S4 via both electrochemical and chemical-driven methods. The electrochemical-driven phase featured abundant bridging oxygen-deficient [NiO6]-[InO6] units at the interfaces of NiOOH/In(OH)3, facilitating a synergistic effect between active Ni and In sites, thus enabling an enhanced alkaline 2e- ORR capability than that of chemical-driven process. Remarkably, electrochemically induced NiOOH/In(OH)3 exhibited exceptional performance, achieving H2O2 selectivity of >90% across the wide potential window (up to 0.4 V) with a peak selectivity of >99%. Notably, within the flow cell, a current density exceeding 200 mA cm-2 was sustained for over 20 h, together with an impressive Faradaic efficiency of approximately 90% and a hydrogen peroxide production rate surpassing 4 mol g-1 h-1.

Neural networks based surrogate modeling for efficient uncertainty quantification and calibration of MEMS accelerometers

This paper addresses the computational challenges inherent in the stochastic characterization and uncertainty quantification of Micro-Electro-Mechanical Systems (MEMS) capacitive accelerometers. Traditional methods, such as Markov Chain Monte Carlo (MCMC) algorithms, are often constrained by the computational intensity required for high-fidelity (e.g., finite element) simulations. To overcome these limitations, we propose to use supervised learning-based surrogate models, specifically artificial neural networks, to effectively approximate the response of MEMS capacitive accelerometers. Our approach involves training the surrogate models with data derived from initial high-fidelity finite element analyses (FEA), providing rich datasets to be generated in an offline phase. The surrogate models replicate the FEA accuracy in predicting the behavior of the accelerometer under a wide range of fabrication parameters, thereby reducing the online computational cost without compromising accuracy. This enables extensive and efficient stochastic analyses of complex MEMS devices, offering a flexible framework for their characterization. A key application of our framework is demonstrated in estimating the sensitivity of an accelerometer, accounting for unknown mechanical offsets, over-etching, and thickness variations. We employ an MCMC approach to estimate the posterior distribution of the device’s unknown fabrication parameters, informed by its response to transient voltage signals. The integration of surrogate models for mapping fabrication parameters to device responses, and subsequently to sensitivity measures, greatly enhances both backward and forward uncertainty quantification, yielding accurate results while significantly improving the efficiency and effectiveness of the characterization process. This process allows for the reconstruction of device sensitivity using only voltage signals, without the need for direct mechanical acceleration stimuli.

Developed square-root cubature Kalman filter-based solution for improving power system state estimation with unknown inputs and non-Gaussian noise

Understanding the ever-changing dynamics of power systems is crucial, and dynamic state estimation (DSE) plays a vital role in achieving this. However, traditional nonlinear Kalman filters (NKFs) face limitations: lack of access to control inputs and presence of non-Gaussian noise in measurements, impacting their accuracy and robustness. This research introduces a novel robust DSE method that tackles these challenges head-on. For the first time in DSE, it leverages the predictive power of Holt-Winters Triple Exponential Smoothing to model the time-varying behavior of control inputs. This innovative approach allows for the simultaneous estimation of dynamic state variables such as the rotor angle and rotor speed changes, as well as transient voltages and control inputs like mechanical input torque and excitation voltage, even in the presence of non-Gaussian noise. Furthermore, the method employs modified projection statistics and a Cauchy function. This unique combination effectively bounds the influence of observation outliers while maintaining high statistical estimation efficiency. This innovative approach utilizes a square cubature Kalman filter (SCKF) for enhanced numerical stability. Extensive simulations under various anomalous conditions demonstrate the method's superior accuracy and efficiency in estimating the state vector. These results highlight its potential to significantly improve power system estimation and pave the way for real-time applications.

A new approach towards more accurate modeling of mechanical defects in power transformer windings

In many cases, power transformers can continue their normal operation despite mechanical defects in their windings. However, estimating the lifespan and assessing the risk of using a transformer with mechanical defects depend on detailed studies of insulation aging and the distribution of transient voltages along the winding, and consequently, the likelihood of partial discharge. Risk assessment studies can only be effectively conducted through accurate modeling of transformers across a wide frequency range. In this study, an attempt has been made to investigate the impact of winding mechanical defects on the transformer equivalent circuits parameters using finite element method precisely modeling of transformer windings, without applying approximation or common simplifications. To compare the effect of each mechanical defect on the resistance, inductance, and capacitance matrices arrays, an index called fault classification accuracy has been used. The investigations have shown that the capacitance matrix arrays undergo the most significant changes for all types of modeled defects in this research. To examine the impact of defects on the transformer frequency response, a π coupled model that directly uses output matrices of finite element modeling has been employed. The sensitivity of various components of frequency response has also been investigated for the used model.

A Grouping and Aggregation Modeling Method of Induction Motors for Transient Voltage Stability Analysis

Induction motors are the most common type of motor in power systems, constituting approximately 70–90% of the dynamic loads, making them significant contributors to system dynamics. In transient voltage stability analysis, dynamic equivalent models are commonly used to simplify the representation of a group of induction motors. This paper presents a method for the grouping and aggregation of induction motors at a common bus. Firstly, grouping rules are provided for clustering induction motors into several subgroups based on the mechanical principles of rotor force and motion, and aggregation rules are provided for aggregating a motor subgroup into a single-unit model based on the relationship between voltage drop and power transmission in distribution networks. Secondly, guided by the grouping rules, high-speed remaining electromagnetic torque and low-speed remaining electromagnetic torque are defined as new clustering indicators, and an adaptive K-means clustering method using silhouette coefficient verification is introduced to obtain the optimal motor subgroups. Thirdly, guided by the aggregation rules, a dynamic equivalent method is further introduced to obtain the equivalent single-unit model from a motor subgroup. Lastly, a transient voltage stability simulation in a typical distribution network is presented to illustrate that the proposed clustering and equivalent methods are more reasonable, accurate, and effective than traditional methods, as the obtained model has better dynamic characteristics and can more accurately reproduce the process of voltage collapse.

A Self-Biased Triggered Dual-Direction Silicon-Controlled Rectifier Device for Low Supply Voltage Application-Specific Integrated Circuit Electrostatic Discharge Protection

A direct bidirectional current discharge path between the input/output (I/O) and ground (GND) is essential for the robust protection of charging device models (CDM) in the tightly constrained design parameters of advanced low-voltage (LV) processes. Dual-direction silicon controlled rectifiers (DDSCRs) serve as ESD protection devices with high efficiency unit area discharge, enabling bidirectional electrostatic protection. However, the high trigger voltage of conventional DDSCR makes it unsuitable for ASICs used for the preamplification of biomedical signals, which only operate at low supply voltage. To address this issue, a self-biased triggered DDSCR (STDDSCR) structure is proposed to further reduce the trigger voltage. When the ESD pulse comes, the external RC trigger circuit controls the PMOS turn-on by self-bias, and the current release path is opened in advance to reduce the trigger voltage. As the ESD pulse voltage increases, the SCR loop opens to establish positive feedback and drain the amplified current. Additionally, the junction capacitance is decreased through high-resistance epitaxy and low-concentration P-well injection to further lower the trigger voltage. The simulation results of LTspice and TCAD respectively demonstrate that ESD devices can clamp transient high voltages earlier, with low parasitic capacitance and leakage current suitable for ESD protection of high-speed ports up to 1.5 V under normal operating conditions.

Conceptional design for a universal HVDC-HVAC vacuum-interrupter-based circuit breaker

Modern power systems high voltage transmission systems either HVDC or HVAC has mandated the presence of two types of circuit breakers (CB), HVAC-CB and HVDC-CB. That required two different production lines, higher costs and more complicated manufacturing process. A solution is proposed in this paper which is a concept design for a universal HVCB (UHVCB) that is applicable to both HVDC and HVAC system. Such a concept would allow a faster, easier production and more economical unit cost for CBs that would benefit the entire industry from manufacturers to utilities. The design of HVDC-CB was used as the foundation the proposed UHVCB such that L–C branches are used. The UHVCB was tested in both HVAC and HVDC transmission systems. The results showed the reliable performance of UHVCB in both systems. The recorded transient recovery voltage (TRV) was reduced from 750 to 430 kV when UHVCB was used instead of conventional HVCB. The testing included both technical and economic aspects. The performance of the UHVCB was tested by varying the parameters for L–C shunt branches in both systems. That included varying the value of L in range 0.3–1 mH and 10–30 µF for C. The most important conclusion from this paper that a UHVCB that is applicable in HVDC and HVAC systems is achievable and this paper is only an initial step in achieving this goal.

Security-constrained unit commitment model considering frequency and voltage stabilities with multiresource participation

The rapidly increasing proportion of renewable energy sources has led to a reduction in the relative share of synchronous units, which has resulted in a decline in the inertia of the power system and a decrease in its voltage support capacity. This has led to several issues related to the frequency and voltage stabilities of the power system. To ensure these frequency and voltage stabilities, it is necessary to maintain a number of synchronous units online in the day-ahead generation schedule. First, the dynamic frequency change process after a power system fault is discussed, and a linear expression is derived for the frequency stability constraints involving energy storage systems and wind turbines. Second, the short-circuit capacity of the bus is characterized to describe the strength of voltage support, and the minimum short-circuit capacity requirement of the bus is solved based on the transient voltage recovery problem after clearance of the short-circuit faults. The total short-circuit capacity provided by the unit to the bus is then calculated through the network reactance matrix to establish the voltage stability constraint. Subsequently, the security-constrained unit commitment model considering frequency and voltage stabilities (FVS-SCUC) is established. Finally, the effectiveness of the proposed model is demonstrated through a numerical simulation of the IEEE-39 system comprising storage and wind turbines. The model ensures the frequency and voltage securities of the system, and the renewable energy output is improved upon considering energy storage. Thus, the overall system cost was reduced by nearly 30% by considering the frequency regulation effects of the energy storage system and wind turbine as well as the voltage regulation effects of the energy storage system.

Ensembled methodology for the comtrade analysis regarding medium voltage side in wind park

This research proposes a methodology for automated COMTRADE analysis using ensembled algorithms for fault analysis in medium voltage circuits, specifically for wind park feeders. Traditional fault detection methods face significant limitations, such as CT saturation and DC offset, underscoring the need for improved approaches. The proposed methodology integrates Random Committee, XGBoost (XGB), and Light XGB algorithms, optimized through grid search, achieving an accuracy of 99.397 %. This ensemble approach significantly reduces classification errors, false positives, and false negatives compared to single algorithm methods. This research has the analysis of 3929 events for training and 829 fault events for test, it revealed a high correlation between current and fault spectra; the methodology demonstrated superior performance in fault location analysis, with terminal faults showing the most severe transient recovery voltage (TRV) peak values, while kilometer faults exhibited higher transient recovery restoration voltage growth rates. The fault location analysis was conducted by comparing simulation-derived overvoltage with standard TRV graphs, aligned with IEC 62271-100 standards. Circuit breakers exhibited higher TRV tolerance with lower fault currents. Terminal faults, particularly symmetrical three-phase faults, were identified as the most severe, typically occurring at peak TRV values. Its findings reduced classification errors, from 41 to 5 events, and decreased the false negative rate from 1.81 % to 0.24 % compared to Random Forest. Validation with virtual relays showed a reduction in RCE from 5.3 % to 0.63 %.

Dynamic modeling of polymer electrolyte membrane fuel cells under real-world automotive driving cycle with experimental validation on segmented single cell

A 1+1D transient non-isothermal and multiphase polymer electrolyte membrane fuel cell model is developed. The model is validated on experimental data gathered on a segmented single cell and representative of a real-world automotive driving cycle, derived from the stack protocol defined in the ID-FAST H2020 project. During Low-Power operation, cell voltage response is mainly controlled by platinum oxide and water dynamics: the low hydration significantly affects cathode inlet performance. Throughout High-Power operation, instead, voltage transients are ascribable to local mass-transport related phenomena: in particular, liquid water accumulation along the channel strongly decreases the efficiency of cathode outlet region. Finally, the effect of relative humidity at cathode channel inlet is investigated: an increase in the humidification of the air inlet stream from 30% to 50% leads to a better proton conductivity and, therefore, to a significant enhancement of the performance of cathode inlet regions, which also means a slight improvement in the average stack efficiency from 62.2% to 62.8%. Lowering RH of the air-inlet stream from 30% to 15%, a worsening of proton conductivity is observed, which exacerbates performance heterogeneities and leads to a reduction of the average stack efficiency from 62.2% to 61.5%, consistently with experimental results.

Zero-Biased Bionic Fingertip E-Skin with Multimodal Tactile Perception and Artificial Intelligence for Augmented Touch Awareness.

Electronic skins (E-Skins) are crucial for future robotics and wearable devices to interact with and perceive the real world. Prior research faces challenges in achieving comprehensive tactile perception and versatile functionality while keeping system simplicity for lack of multimodal sensing capability in a single sensor. Two kinds of tactile sensors, transient voltage artificial neuron (TVAN) and sustained potential artificial neuron (SPAN), featuring self-generated zero-biased signals are developed to realize synergistic sensing of multimodal information (vibration, material, texture, pressure, and temperature) in a single device instead of complex sensor arrays. Simultaneously, machine learning with feature fusion is applied to fully decode their output information and compensate for the inevitable instability of applied force, speed, etc, in real applications. Integrating TVAN and SPAN, the formed E-Skin achieves holistic touch awareness in only a single unit. It can thoroughly perceive an object through a simple touch without strictly controlled testing conditions, realize the capability to discern surface roughness from 0.8 to 1600µm, hardness from 6HA to 85HD, and correctly distinguish 16 objects with temperature variance from 0 to 80°C. The E-skin also features a simple and scalable fabrication process, which can be integrated into various devices for broad applications.

Simulation on Operating Overvoltage of Dropping Pantograph Based on Pantograph–Catenary Arc and Variable Capacitance Model

When the electric locomotive pantograph is dropping, the interruption of pantograph catenary contact causes electromagnetic oscillation and arcing. The frequent arc burning that occurs due to charge accumulation results in the amplitude of overvoltage increasing gradually, posing a threat to locomotive high-voltage equipment. However, the physical mechanisms and characteristics of overvoltage are still unclear. This paper proposes a simulation model of operating overvoltage due to a dropping pantograph based on the pantograph–catenary arc and variable capacitance. Distributed RLC electromagnetic oscillation is considered, which allows the real-time calculation of arc resistance and capacitance. Under the same working conditions, the error between the simulation and test results is less than 4.0%, which proves the credibility of the model. The variation law of overvoltage under different dropping speeds or catenary phases was investigated, which shows the max amplitude is 298.20 kV and steepness is 2096.80 kV/μs at 0.30 m/s speed. The waveform shows the characteristics of high amplitude and high steepness, similar to very fast transient overvoltage (VFTO). There is a sinusoidal relationship between the catenary phase and overvoltage amplitude. The closer the catenary phase to 90°, the higher the overvoltage amplitude. The research has important guiding significance for the overvoltage formation mechanism of a traction power supply system and the insulation coordination design of high-voltage equipment.