Hasil untuk "Distribution or transmission of electric power"

S. Frank, Ingrida Steponavice, Steffen Rebennack

Jongoh Baek, Luke Lowery, Adam B. Birchfield

Accurately predicting the frequency nadir and estimating the overall frequency trajectory are crucial analytical tasks in power system planning. Given the large number of operating scenarios and contingency events that must be evaluated, low-order frequency nadir prediction (FNP) models have been recently developed to avoid the computational burden of full dynamic simulations in large, complex systems. However, a major technical limitation of existing FNP models is their inability to capture inherent discontinuities such as limits, piecewise functions, and deadbands that strongly influence the actual frequency dynamics. To overcome these challenges, this paper proposes a discontinuity-aware frequency nadir prediction (DA-FNP) model that explicitly implements discontinuity constraints into the frequency response estimation. By implementing these discontinuities, the model not only predicts the system frequency trajectory with high fidelity but also identifies which generators are subject to enforced constraints. This capability provides new insights for system planners, enabling a more realistic evaluation of frequency security margins and resource adequacy in future power systems with high renewable penetration. The methodology is validated against detailed dynamic simulations on both small- and large-scale synthetic grids. The case study demonstrates significant enhancement on the accuracy of system configuration and system frequency trajectory, while retaining computational efficiency of low-order models. Furthermore, the approach offers a practical and scalable tool for planning studies in large, complex power systems.

Kim Thai Chai

Firdous Kausar, Sajid Hussain, Karl Walker et al.

False Data Injection Attacks (FDIAs) pose a substantial risk to the reliability and stability of Cyber-Physical Power Systems (CPPS). While federated learning (FL) has emerged as a promising approach to detect such attacks without exposing sensitive data, security concerns remain in FL, including untrusted central aggregators and potential malicious client updates. This research integrate a private Ethereum blockchain layer and homomorphic encryption into a secure FL framework for FDIA detection to verify model updates and authenticate participating nodes. We design smart contracts to immutably log model update hashes and enforce client authentication, enhancing traceability and tamper-resistance. A prototype implementation uses Ethereum smart contracts for model update verification and client identity management. We simulate the blockchain-integrated FL on a cyber-physical power system dataset using three detection models &#x2013; XGBoost, LSTM, and a Transformer &#x2013; and analyze the blockchain-induced latency and communication overhead under a specific network configuration. Results show that the blockchain layer has negligible impact on detection accuracy (global AUC <inline-formula> <tex-math notation="LaTeX">$\sim 0.94 \text {-}0.96$ </tex-math></inline-formula> across models) while introducing a moderate training time overhead (<inline-formula> <tex-math notation="LaTeX">$\sim 13- -40\%$ </tex-math></inline-formula> increase in training duration due to block confirmation delays). The proposed research demonstrates a viable approach to blockchain-aided federated learning for critical infrastructure security, combining data privacy, model integrity, and participant trust in a unified framework.

Hang Li, Bei Han, Guojie Li et al.

Abstract Charging behaviours of electric vehicles (EVs) exhibit substantial randomness, making accurate prediction or modelling challenging. Furthermore, as the number of EVs continues to increase, charging stations are diversifying their offerings to accommodate distinct charging characteristics, addressing a wide spectrum of EV charging needs. Previous research mostly focused on the randomness of EVs while neglecting the heterogeneity in charging infrastructure. Therefore, this paper introduces a decentralized collaborative optimal method for EV charging stations, taking into account the varying facility types and the power limitations. First, a decentralized collaborative framework is proposed. The energy boundary model and the average laxity of EVs contribute to transforming the optimization problem into a Markov Decision Process (MDP) with uncertain transitions. Then, multi‐agent deep deterministic policy gradient multi‐individuals (MADDPG‐MI) algorithm is developed to train several heterogeneous agents presenting different types of charging facilities. Each agent makes decisions for multiple homogenous charging piles. Numerous simulation studies validate that the proposed method can effectively reduce charging costs and manages in scenarios involving either homogeneous or multiple heterogeneous charging facilities. Moreover, the MADDPG‐MI algorithm demonstrates performance consistency among multiple decision‐making units while consuming lower training resources offering enhanced scalability.

Antonio Bracale, Pierluigi Caramia, Giovanni Mercurio Casolino et al.

The analysis of power quality disturbances in distribution systems has gained significance with the diffusion of electric vehicles (EVs). Waveform distortions are interesting since EV currents introduce distortions with spectral components in both low and high-frequency bands. This paper develops specific indices to assess cumulative emissions from single-phase EV on-board chargers, extending the aggregation and diversity factors to the supra-harmonic range. The methodology accounts for variables such as EV charging powers, upstream network impedance, and number of EVs. A simplified time-domain model of a low-power unidirectional converter, commonly used for EV battery charging, is employed to balance circuit complexity and computational effort. This model allows for sensitivity analyses of key parameters influencing charger emissions. Numerical applications are carried out for both individuals and groups of EV chargers at a charging station. Results highlight the need for careful quantification of aggregated EV emissions, showing that supra-harmonic emissions are highly sensitive to variations in the power absorbed by EV chargers. Notably, their cumulative impact is much lower when chargers operate at different power levels than when all chargers operate at the same power level. These findings underscore the importance of accurately assessing the impact of EV charging on power quality.

Jie Zhao, Chenhao Wang, Biao Zhao et al.

Abstract The high uncertainty of wind power output greatly affects the rapid reactive power optimization of power systems. This paper proposes a neural network‐based comprehensive reactive power optimization method for large‐scale wind power grids, effectively addressing the challenges of rapid reactive power optimization in power systems. Firstly, by constructing typical wind‐power‐load scenarios, the generalization ability of the neural network is improved. Then, focusing on the comprehensive reactive power optimization problem after integrating typical wind‐power‐load scenarios into the system, the improved Harris hawks optimization algorithm (HHO) is compared with the particle swarm optimization algorithm and traditional HHO algorithm, highlighting its advantages. Finally, HHO is utilized for solving, thereby constructing a comprehensive reactive power optimization strategy tag set. Furthermore, through deep fitting of the neural network between the power grid operating state and the comprehensive reactive power optimization strategy, the computational complexity and decision‐making time of reactive power optimization are reduced.

Kun‐Long Chen, Jui‐Hsiang Chen

Abstract In this study, a long‐distance non‐contact current measurement method is proposed based on the non‐contact sensor array measurement technology, combined with a new current algorithm. The sensor array is a vertical array composed of three three‐axis magnetic field (MF) sensors that is placed on the ground plane below the measured overhead line. The proposed current algorithm can optimize the geometry of the overhead line using the spatial MF values measured by the sensor array and then the three‐phase currents can be calculated. To improve the accuracy of current measurement, the spatial MF model in the algorithm considers the influence of cable sag. It enables a more accurate coupling matrix between the measured overhead line and the sensor array to be established. Additionally, the current algorithm architecture is designed using a composite algorithm to more optimally obtain the best three‐phase current values. A scale‐down overhead‐line measurement system is developed to test the proposed current measurement method. The results show whether a cable sag exists when the three‐phase currents are approximately balanced, with errors less than 2.0%. Moreover, it can be determined whether cable sag exists when the three‐phase currents are unbalanced, with less than 3.4% errors.

Mohammad Karkhane, Sadjaad Ozgoli

In the smart grid era, Short-Term Load Forecasting (STLF) is the building block of a secure, reliable, and economical power system. Therefore, researchers have spent much time trying different methods to improve load forecasting accuracy. Despite the advances in the STLF area, load forecasting is still difficult. This difficulty comes from two facts: 1- The behavior of the electric load is complex and shows different levels of seasonality; 2- The electric load is strongly influenced by other external factors such as meteorological variables and calendar variables. To overcome these issues, in this paper, a two-stage Kalman filter-based method is used to enhance the accuracy of STLF. In the first stage of the proposed method, the Kalman filter and Rauch-Tung-Striebel smoother are applied to the short windows of the past electric load series to obtain an initial prediction of the load series. To produce the final forecast, in the second stage, the initial prediction of the load series along with other calendar and meteorological variables are used to form a load forecasting model whose parameters are obtained based on another Kalman filter. The effectiveness of the proposed method is evaluated by performing a case study on the real dataset from a power utility in Iran, which shows the excellent performance of the proposed method with 1.98% mean absolute error.

Yujun Li, Jingrui Liu, Yonghui Liu et al.

Abstract This paper demonstrates that the switching of one grid‐connected virtual synchronous generator (VSG) between two control modes, namely constant voltage control (CVC) and current limiting control (CLC) happens at the derived two switching lines. Based on the current limiting inequality, the traditional current‐switched model is transferred to the angle‐switched model proposed here, and the system can be studied as one switched dynamic system. Based on this model, the transient stability of VSG with controller limits is investigated. This is achieved by constructing Lyapunov functions for each subsystem and deriving the relationship between the values of Lyapunov functions constructed under different conditions at the switching moment. The stability of the system is ensured when the Lyapunov function of each subsystem presents a decreasing trend in two consecutive switching intervals. On this basis, the stability boundary of the switching system is derived. Further analysis shows that optimal adjustment of the saturation current angle can make the system reach the maximum stability boundary. Finally, the numerical simulations and experimental tests verify the correctness of the proposed analysis.

Shengfu Gao, Qunzhan Li, Xiaohong Huang et al.

Abstract To recycle regenerative braking energy (RBE) while reducing demand charge in electrified railway, a co‐phase power supply system with hybrid energy storage system (HESS) is implemented. However, the dynamical degradation characteristic of battery is necessary to be considered in optimal operation of HESS. A bi‐level model considering battery degradation is proposed to obtain optimal sizing and operation of HESS. The proposed model includes a novel real‐time energy management strategy (EMS) using average power as thresholds, to effectively reduce the demand charge and energy consumption charge. Thresholds are dynamically adjusted with the consideration of battery capacity degradation. A real measured load profile from Beijing‐Shanghai high speed railway is studied. The results demonstrate that the proposed EMS performs better than previous EMS in cost saving. The dynamically adjusted thresholds of EMS are proved to be essential under the consideration of battery degradation. With the optimal sizing of the HESS, the traction substation can achieve 8.69% annual saving of demand charge and recycle 52.33% of the RBE. The results also show that a traction substation equipped with the HESS yields higher economic benefit than the energy storage systems equipped with only a battery or a supercapacitor.

Görkem Gök, Özgül Salor, Müslüm Cengiz Taplamacıoğlu

Abstract This paper presents a research work which focuses on generating synthetic data to enrich the training‐set of a deep learning (DL) based classification system to classify power system transient events using PMU frequency measurements. The synthetically improved training‐set is shown to increase the classification performance compared to the case when only the actual‐data training‐set is used. The proposed classification system helps to reveal high‐frequency transient variation information out of PMU measurements collected at a relatively much lower rate, especially when a small set of training‐data exists. Synthetic PMU frequency data is generated based on the DFT analysis statistics on the limited‐size PMU frequency data. Generation of the synthetic data is achieved by re‐synthesis of the PMU frequency data using inverse DFT, which imitates the DFT frequency and phase behaviour for each event type separately. Then the DL structure is trained to classify the power system transients using the synthetically enriched train set. The proposed method of generating synthetically supported training‐set has lower computational complexity compared to the existing methods in the literature and helps to obtain improved classification results. It can be used to increase the classification performances of power quality devices performing transient event analysis, especially for those with access to a limited set of training‐set.

Mohammad Pourheydari, Mostafa Parniani, Mohammad Hasan Ravanji

Abstract This paper investigates the dynamic interactions between large‐scale PV plants and nearby synchronous generators. To this end, a comprehensive model of a PV plant, including the PV arrays characteristic and their voltage controller, DC/DC converter, DC‐link dynamic, DC/AC inverter controllers, and output filter, is obtained. Then, the eigenvalues of the PV plant model are calculated, and it is revealed that the PV plant has a dynamic mode in the sub‐synchronous oscillations (SSOs) range. Furthermore, the modal analysis method is employed, and it is shown that the inductance of the DC/DC converter and the DC‐link capacitor significantly contribute to the PV sub‐synchronous mode. Also, it is shown that this mode oscillations penetrate the grid and increase the risk of sub‐synchronous resonance (SSR). Then, the mechanism of dynamic interactions between large‐scale PV plants and torsional modes of nearby synchronous generators is revealed. This paper also investigates the required conditions for these interactions and shows that solar irradiation level and the DC‐link capacitance are the essential factors that can intensify these interactions.

Hongwen Liu, Xiangjun Zeng, Qing yang et al.

Abstract Current active suppression technology of arc overvoltage in distribution network has small capacity of controllable voltage source and complex control and cannot flexibly adjust the zero‐sequence voltage of neutral point. This study proposes a controllable voltage source system to fully compensate the arc current of ground fault for solving these problems. A controllable voltage source model based on passive supply voltage converter is established, utilising Dyn transformer to carry unbalanced load for a long time. The model output voltage is the same amplitude and frequency as that of single‐phase ground fault phase, but opposite phase. The compensation switch is operated when a ground fault occurs. The output voltage amplitude of the controllable voltage source is controlled, the fault phase voltage is controlled to be 0 in the closed loop, the grounding fault arc current is eliminated and the arc grounding overvoltage is suppressed. The control strategy was verified by the PSCAD/EMTDC simulation model of single‐phase grounding fault arc in 10 kV distribution network and the real single‐phase fault experiment platform of 0.4 kV distribution network. Results proved the model and strategy's accuracy and effectiveness. It provides an engineering practical method for active suppression of arc grounding overvoltage in distribution network.

Zhitao Guan, Dan Wang, Qing Duan et al.

Abstract Energy routers based on the electronic power transformer are suitable for the AC–DC hybrid grid with multiple voltage levels, but their structures are complex. This paper proposes a novel energy router based on the multi‐winding line frequency transformer. By a combination of a multi‐winding line frequency transformer and power electronic devices, the proposed energy router can take advantage of the high reliability of the multi‐winding line frequency transformer and the high controllability of power electronic devices. The proposed energy router is suitable for the AC–DC hybrid grid with multiple voltage levels and has the characteristic of a simple structure. The simulations and experimental results demonstrate the effectiveness of the proposed energy router.

Zhongxue Chang, Zhihao Zeng, Minna Dou et al.

Abstract Single phase line‐broken (SPLB) fault is one kind of high impedance fault (HIF). When a SPLB fault occurs, there are four scenarios: SPLB fault with neither side conductors grounded (SPLB‐NSCG), with source side conductor grounded only (SPLB‐SSCG), with load side conductor grounded only (SPLB‐LSCG), with both side conductors grounded (SPLB‐BSCG). Traditional HIF detection methods using current or voltage characteristics have low sensitivity to SPLB fault. In this paper, voltage characteristics are analysed firstly in the cases of the first three scenarios of SPLB fault, respectively. Theoretical analysis finds that the voltage at both the sides of the disconnection point is significantly different. A high sensitive SPLB fault detection method based on differential voltage is proposed for radial distribution network with different data synchronization ability. Finally, SPLB fault location methods based on centralized intelligent mode feeder automation (FA) and intelligent distributed mode FA are proposed. Simulations based on PSCAD verify that the proposed SPLB fault detection method is feasible, highly sensitive and immune to the neutral grounded mode, fault scenarios and fault resistance.

Guoliang Tian, Zhengchun Du, Yujun Li

Abstract This paper proposes a novel primary frequency regulation (PFR) scheme for multi‐infeed high voltage direct current (HVDC) (MIDC) system. First, a dynamic model for frequency response in the MIDC system is carried out to involve line‐commutated‐coveter (LCC)‐HVDCs in frequency regulation. Then, a disturbance observer (DO) is proposed to estimate both the power imbalance and the full order states of the system. By combining a Kalman filter, this DO gets an accurate estimation quickly with local frequency measurement. On the basis of the estimated disturbance and state variables, feedforward control schemes can be concluded to fully utilize the fast adjustability of LCC‐HVDCs. In this paper, a model predictive control based scheme is designed to coordinate LCC‐HVDCs under constraints of HVDC capacity and frequency boundaries. As a result, the optimal power‐sharing property of LCC‐HVDCs is achieved to enhance the PFR dynamic performance of the AC main system. Further, the stability of the proposed method is analysed using the Lyapunov method. Finally, numerical simulations are concluded to verify the effectiveness and the merits of the proposed method, by comparing it to typical droop controls.

Xiaodong Lv, Lifen Yuan, Zhen Cheng et al.

Abstract With the development of smart meters and other intelligent electronic devices, more and more data‐driven fault location methods based on wide area measurement are emerging. However, these diagnostic methods for dealing with the whole tested system often appear complex. This paper presents an innovative subsystems‐based fault location strategy in distribution grid by the sparsity promoted Bayesian learning algorithm. To avoid taking measurement for the whole distribution system, the fault‐included subsystem is selected according to the distribution characteristics of negative sequence voltage. Then the data for fault location is measured by allocating meters in subsystem, which can reduce the number of required meters. For accurately estimating the fault location, a sparse prior is proposed for the Bayesian learning, which could improve the accuracy of the fault location algorithm by about 4%. The performance is tested on a 12.66‐kV, 69‐bus distribution system in response to various fault scenarios. The results show that the accuracy of the proposed method for the fault section location can reach 90%. It also verifies the robustness and accuracy for fault line location, faced different fault types, fault resistance, noise, etc.

Thomas Treider, Hans Kristian Høidalen

Abstract Earth faults is a challenging fault type to locate in resonant grounded networks due to their naturally low fault current, and the problem increases with an increased fault impedance. This paper describes the detailed implementation and laboratory testing of a method for detection, location and clearing of earth faults with very small fault currents. The method consists of two indicators used in the fault detection stage, where their simultaneous operation ensures selective fault detection and faulty feeder selection. One of these indicators also enables continuous fault indication throughout a sectionalizing process. The laboratory tests demonstrate that both indicators function as intended, and it is the current sensors which ultimately limit the attainable sensitivity. Faults up to 15 kΩ were detected successfully in the laboratory network based on phase current measurements, while the sectionalizing indicator showed much higher sensitivity and functioned as intended in a 50 kΩ fault. Measurements from one field test in a 22 kV network corroborate the laboratory results and demonstrate the expected earth fault indicator response.

S. Khokhar, Abdullah Asuhaimi B. Mohd Zin, A. Mokhtar et al.