Hasil untuk "Distribution or transmission of electric power"

H.M.Jeewantha De Silva, Yi Zhang

Fahimeh Orvati Nia, Shima Salehi, Joshua Peeples

Advanced metering infrastructure (AMI) provides high-resolution electricity consumption data that can enhance monitoring, diagnosis, and decision making in modern power distribution systems. Detecting anomalies in these time-series measurements is challenging due to nonlinear, nonstationary, and multi-scale temporal behavior across diverse building types and operating conditions. This work presents a systematic, power-system-oriented evaluation of a GAN-LSTM framework for smart meter anomaly detection using the Large-scale Energy Anomaly Detection (LEAD) dataset, which contains one year of hourly measurements from 406 buildings. The proposed pipeline applies consistent preprocessing, temporal windowing, and threshold selection across all methods, and compares the GAN-LSTM approach against six widely used baselines, including statistical, kernel-based, reconstruction-based, and GAN-based models. Experimental results demonstrate that the GAN-LSTM significantly improves detection performance, achieving an F1-score of 0.89. These findings highlight the potential of adversarial temporal modeling as a practical tool for supporting asset monitoring, non-technical loss detection, and situational awareness in real-world power distribution networks. The code for this work is publicly available

Kaiyang Huang, Min Xiong, Yang Liu et al.

As inverter-based resources (IBRs) penetrate power systems, the dynamics become more complex, exhibiting multiple timescales, including electromagnetic transient (EMT) dynamics of power electronic controllers and electromechanical dynamics of synchronous generators. Consequently, the power system model becomes highly stiff, posing a challenge for efficient simulation using existing methods that focus on dynamics within a single timescale. This paper proposes a Heterogeneous Multiscale Method for highly efficient multi-timescale simulation of a power system represented by its EMT model. The new method alternates between the microscopic EMT model of the system and an automatically reduced macroscopic model, varying the step size accordingly to achieve significant acceleration while maintaining accuracy in both fast and slow dynamics of interests. It also incorporates a semi-analytical solution method to enable a more adaptive variable-step mechanism. The new simulation method is illustrated using a two-area system and is then tested on a detailed EMT model of the IEEE 39-bus system.

Pinjie Xie, Shuangshuang Gao, F. Sun

Abstract China predominantly relies on thermal power generation to meet its power requirement, which has led to a major increase in total CO2 emission and poses a huge threat to the development of power industry. In order to reduce CO2 emission in power industry and develop economy simultaneously, it is necessary to study how to achieve the strong decoupling relationship between CO2 emission in power industry and GDP in China. However, such studies are relatively limited so far. Thus, this paper mainly inquiries the major driving factors on the decoupling during the period of 1985–2016. First, the decoupling state in China is quantified by using the Tapio decoupling indicator. Then, this paper carries out the decomposition of the decoupling index to explore the driving factors affecting the decoupling by the logarithmic mean divisia index (LMDI) method and Kaya identity equation. Finally, energy consumption in power generation, thermal power structure, power generation structure, transmission and distribution loss, electrification, energy intensity and economic scale are explored to discuss the decoupling relationship with respect to the CO2 emission reduction. The results show that the decoupling relationship is in an alternating state between weak decoupling and expansive negative decoupling in 1985–2016. And the influences of seven factors on the decoupling relationship are of difference. Wherein, energy intensity as well as energy consumption in power generation, promotes the decoupling while both the economic scale and electrification are the two main factors that inhibit the decoupling. The other three factors have relatively weak effects. Therefore, based on the empirical results, this paper puts forward some policy suggestions to effectively promote the decoupling between China’s electric CO2 emission and economic growth.

Miao Yu, Jianqun Sun, Shuoshuo Tian et al.

Abstract The recognition of the transient dominant instability mode is of great significance for rapidly and accurately formulating transient emergency decisions in power systems. In response to the challenge of accurately distinguishing between angle instability and voltage instability, which are coupled in actual power grids, this paper explores the mapping relationship between simulation data and the stable state of the system, as well as the dominant instability mode. The method enables real‐time identification of the dominant instability mode, which bypasses complex physical mechanisms. Firstly, spatio‐temporal feature mining is conducted, where convolutional neural networks are employed to learn crucial local features of transient curves, and bidirectional gated recurrent unit s utilized to learn transient features over time sequences. Next, a multihead attention mechanism is introduced to enhance sensitivity to important time steps in the sequence data. Finally, the transit search optimization algorithm optimizes the global model parameters, further increasing the accuracy of the model. Using the IEEE 10‐machine and 39‐node system as an example for simulation, the results validate that the proposed method exhibits significant advantages in terms of accuracy and applicability compared with other machine learning methods.

Jordon Ashley Grant, Iver Bakken Sperstad, Vijay Venu Vadlamudi et al.

Abstract Condition monitoring data can be used to assess the health of a power system component but is rarely used to assess the reliability of the power system they are part of. For high‐voltage circuit breakers (HVCBs), the susceptibility to various failures can be assessed by examining trip coil current (TCC) measurements. HVCBs have two failure modes, failure to trip on command and tripping without a command, which are triggered by various failure mechanisms that in turn may depend on the technical condition of the HVCBs. The aim of this article is to demonstrate a methodology for quantifying the impact that the technical condition of HVCBs has on the power system reliability indices. An equivalent transmission line failure rate considering protection system failures, including failures related to HVCBs, can be calculated by computing contributions from various fault types (FTs). This article proposes a framework that can quantify the frequency of the FTs that are affected by HVCB condition. The system‐level effects are then evaluated using approximate methods for power system reliability evaluation. To demonstrate the principles and benefits of this methodology, a case study is presented that incorporates HVCB condition data from the Icelandic transmission grid into an illustrative 4‐bus test system; a dataset of 83 TCC measurements from 38 HVCBs from the Icelandic Transmission System Operator is used, together with an outage database of life histories of 464 HVCBs from the Icelandic transmission grid. Results indicate that the condition deterioration associated with the probability of an HVCB failing to trip on command can significantly degrade the reliability indices.

Botong Li, Yuqi Li, Bin Li et al.

Abstract Fast and accurate fault direction criteria are crucial for efficient protection in hybrid multi‐terminal HVDC transmission systems. The current variation method is commonly used to identify fault direction, but it is affected by fault resistance and distributed capacitance. This paper proposes a new direction criterion based on the backward fault travelling wave, which has stronger resistance to interference from transition resistance and operates at a faster speed than other methods. Additionally, this paper provides the time‐domain expression of the travelling wave after multiple reflections between the fault point and the converter station port, enabling the calculation of the threshold setting for the criterion. The new direction criterion is tested in PSCAD/EMTDC, and simulation results demonstrate that it can accurately identify fault direction and has a strong ability to withstand transition resistance.

Tushar Kanti Roy, Samson S. Yu, Md. Apel Mahmud et al.

Abstract Maintaining stability in modern power systems is challenging due to complex structures, rising power demand, and load disturbances. The integration of renewable energy sources further threatens stability by causing imbalances between generation and demand. Conventional load frequency stabilization methods fall short in such scenarios. This paper proposes an optimal fractional‐order proportional‐integral‐derivative‐acceleration (FOPIDA) controller, enhanced by a robust adaptive neuro‐fuzzy inference system (ANFIS), to improve load frequency control and reliability in power systems with wind and solar generators. First, the dynamical model of a multi‐area interconnected power system, including a thermal power plant, wind turbine, and solar photovoltaic generators, is developed. A decentralized ANFIS‐FOPIDA controller is then designed for load frequency control objectives. The gains of this controller are optimized using the whale optimization algorithm (WOA), focusing on frequency deviation and tie‐line power exchange. Simulations on a New England IEEE 10‐generator 39‐bus power system demonstrate the approach's effectiveness under various disturbances, including random load‐generation disturbances and nonlinear generation behaviors. Comparisons with other strategies, such as fractional order (FO) beetle swarm optimization algorithm (FOBSOA)‐FOPIDA, WOA‐PIDA, and WOA‐ANFIS‐PIDA, and recent control approaches highlight the superior performance of the WOA‐ANFIS‐FOPIDA method in enhancing power system stability.

Qian Zhang, P. R. Kumar, Le Xie

This study addresses the transmission value of energy storage in electric grids. The inherent connection between storage and transmission infrastructure is captured from a "cumulative energy" perspective, which enables the reformulating of the conventional optimization problem by employing line power flow as the decision variable. The study also establishes the theoretical limitations of both storage and transmission lines that can be replaced by each other, providing explicit closed-form expressions for the minimum capacity needed. As a key departure from conventional practice in which transmission lines are designed according to the peak power delivery needs, with sufficient storage capacity, the transmission line capacity can be designed based on the average power delivery needs. The models of this paper only rely on a few basic assumptions, paving the way for understanding future storage as a transmission asset market design. Numerical experiments based on 2-bus, modified RTS 24-bus, RTS-GMLC, and Texas synthetic power systems illustrate the results.

Bénédicte Jourdier

Abstract. As variable renewable energies are developing, their impacts on the electric system are growing. To anticipate these impacts, prospective studies may use wind power production simulations in the form of 1 h or 30 min time series that are often based on reanalysis wind-speed data. The purpose of this study is to assess how several wind-speed datasets are performing when used to simulate wind-power production at the local scale, when no observation is available to use bias correction methods. The study evaluates two global reanalysis (MERRA-2 from NASA and ERA5 from ECMWF), two high-resolution models (COSMO-REA6 reanalysis from DWD, AROME NWP model from Météo-France) and the New European Wind Atlas mesoscale data. The study is conducted over continental France. In a first part, wind-speed measurements (between 55 and 100 m above ground) at eight locations are directly compared to modelled wind speeds. In a second part, 30 min wind-power productions are simulated for every wind farm in France and compared to two open datasets of observed production published by the distribution and transmission system operators, either at the local scale in terms of annual bias, or aggregated at the regional scale, in terms of bias, correlations and diurnal cycles. ERA5 is very skilled, despite its low resolution compared to the regional models, but it underestimates wind speeds, especially in mountainous areas. AROME and COSMO-REA6 have better skills in complex areas and have generally low biases. MERRA-2 and NEWA have large biases and overestimate wind speeds especially at night. Several problems affecting diurnal cycles are detected in ERA5 and COSMO-REA6.

Guilherme Vieira Hollweg, Van-Hai Bui, Felipe Leno Da Silva et al.

This paper presents a novel approach for grid-injected current control of DC-AC converters using a robust model reference adaptive controller (RMRAC) with deep symbolic optimization (DSO). Grid voltages are known to be time-varying and can contain distortions, unbalances, and harmonics, which can lead to poor tracking and high total harmonic distortion (THD). The proposed adaptive control structure addresses this issue by enabling or disabling harmonics compensation blocks based on the grid voltage’s characteristics. The DSO framework is implemented to generate an equivalent mathematical expression of the grid voltages, which is then incorporated into the RMRAC-based controller. The controller is then able to reconfigure itself to adequately compensate for high harmonics present in the grid, reducing computational complexity and improving performance. A controller-hardware-in-the-loop (C-HIL) environment with a Typhoon HIL 604 and a TSM320F28335 DSP is implemented to demonstrate that the proposed RMRAC-based structure with DSO outperforms both the same adaptive structure without DSO and a superior RMRAC-based controller. The proposed approach has potential applications in less-inertia power grids, where efficient and accurate control of grid-connected converters is crucial.

Zhaoxun Liu, Liang Qin, Yiyuan Zhou et al.

Abstract With the increasing proportion of renewable power generations in the power system, the power grid impedance may fluctuate greatly, and it is difficult for the virtual synchronous generator (VSG) with a single control structure to meet the stability requirements. Thus, the control principles of the two types of VSGs are compared to conclude that there are similarities in the control structure of the two VSGs. And the small signal models of the two types of VSGs are established to analyze the stability boundaries, coming to the conclusion that the stability region is complementary. Based on the conclusions, the paper puts forward the idea of switching operation modes: switch to the U‐VSG mode when the grid strength weakens and switch to the PQ‐VSG mode when greater, so that the inverter can keep stable in a wider range of grid strength. Therefore, the characterization method of the switching boundary with hysteresis properties is proposed. Then the improved recursive least squares (RLS) algorithm is introduced to identify the power grid impedance online without additional injected disturbance, based on which the adaptive multi‐mode smooth switching control strategy is proposed. Finally, the effectiveness of the analysis and the control strategy are verified by simulations.

Hui Huang, Yonggang Li, Huazhi Liu

Abstract The multi‐scale flexibility coordination of multiple storages is a key technology to enhance the diversified regulation ability of the power system. This paper first considers the interaction mechanism of multi‐type storage peak regulation time sequences based on the Euclidian distance, dynamic time warping distance, and storage correlation distance. A matching index was proposed to consider the temporal correlation, overall distribution characteristics, and dynamic characteristics of the net load and energy storage. The multitype storage coordination mode, including battery storage, pumped storage, and electric vehicles, was formulated, and a collaborative optimal scheduling system architecture of source‐grid‐load‐storage (SGLS) was constructed. To attain a low‐carbon economy, a collaborative optimal scheduling model of SGLS considering the dynamic time‐series complementarity of multiple energy storage systems was constructed. The Nash equilibrium theory was used to achieve friendly interaction among the source, grid, load, and storage. Then, an improved transfer reinforcement learning algorithm for SGLS was proposed, which used reinforcement learning and transfer learning algorithms combined with K‐means clustering and dual‐structure experience pool technology. The test results of actual regional power grid data indicated that the proposed strategy can effectively reduce the economic and carbon treatment costs of the system and improve the absorption capacity of renewable energy.

Yangzhou Pei, Jun Yang, Jundong Wang et al.

Abstract Undervoltage load shedding (UVLS) is the last line of defense to ensure the safe and stable operation of the power system. The existing UVLS technique has difficulty adapting and generalizing to new topology variation scenarios of the power network, which greatly affects the reliability of the control strategy. This paper proposes a UVLS emergency control scheme based on a graph deep reinforcement learning method named GraphSAGE‐D3QN (graph sample and aggregate‐double dueling deep q network). During offline training, a GraphSAGE‐based feature extraction mechanism of the power grid with topology variation is designed that can better capture the changes in system state characteristics. A novel reinforcement learning framework based on D3QN is developed for UVLS modeling, which reduces overestimations of control action and leads to a better control effect. Then, online emergency decision‐making is achieved. The simulation results on the modified IEEE 39‐bus system and IEEE 300‐bus power system show that the proposed UVLS scheme can always provide more economical and reliable control strategies for power networks with topology variations and achieves better benefits in both adaptability and generalization performances for previously unseen topology variation scenarios.

Mahan A. Mansouri, Ramteen Sioshansi

Recent technical, market, and policy developments in the electricity industry are increasing interest in and need for energy storage. We examine the potential for using the flexibility of an aggregation of tank electric water heaters as a source of virtual energy storage. Specifically, we examine the operational performance of and operating profit that is earned by a fleet of water heaters that provide energy shifting and frequency regulation. We contrast this performance and operating profit to that of a lithium-ion battery. We find that water heaters do not achieve the same level of performance or operating profit that a battery does. However, when accounting for the capital costs of the two technologies, water heaters are superior, insomuch as they have a better cost-to-profit ratio. We find that both water heaters and batteries earn significant operating profits from frequency regulation as opposed to energy shifting. Water heaters have a stronger bias towards frequency regulation, due to temporal constraints on load shifting. Relaxing these constraints improve water-heater performance slightly.

Shuping Gao, Han Zheng, Yue Gao et al.

Abstract In order to improve the ability of the modular multi‐level converter to quickly clear and isolate faults in the multi‐terminal flexible DC grid, this paper analyzes the characteristics of current faults in the multi‐terminal flexible direct current grid, introduces the comprehensive distance of current fault waveform, and proposes a line protection method based on the similarity of current fault waveform. This protection method firstly uses Euclid Dynamic Time Warping (DTW) distance and entropy weight method to process electrical quantity signals, calculates the comprehensive distance of current fault waveform of two converter stations, and builds the protection criterion based on the similarity of current fault waveform. Then, the fault identification is realized by using the calculated comprehensive distance, and the fault pole identification is realized by using the ratio of comprehensive distance between positive and negative poles. Finally, PSCAD was used to build a simulation model to output the fault data under different fault conditions, and MATLAB was used to process the fault data to verify the protection algorithm. The simulation results show that the proposed method has good resistance to high resistance, and its rapidity and adaptability are further improved. Compared with other similarity methods, this method combines Euclidean DTW distance and entropy method to process the signal, realizes the fault pole identification, and can still operate correctly under the interference of 500 Ω transition resistance and 20 dB noise ratio. The research results will provide a theoretical basis for the safe and reliable operation of flexible DC power grid.

Ali Rostami, Navid Rezaei

Abstract The rotor‐circuit (RC) of the doubly fed induction generator‐based wind turbine (DFIG‐WT) consists of various equipment in which faulty conditions must be immediately identified, because this faulty status leads to the loss‐of‐excitation (LOE) phenomenon and may cause some serious damages. In this regard, this paper develops a novel solution to protect the DFIG‐WT during such a condition. For this purpose, a comprehensive investigation on behavior of the DFIG‐WT during the conditions of the RC failures has been carried out. The results show that, the output active power of the machine (Pdfig) can be considered as a suitable initial indicator for detecting the RC failures. However, to reliable detection of the partial‐LOE (PLOE), complete‐LOE (CLOE) and discrimination from the other power system disturbance (PSD), a directional over‐current (DOC) and an under voltage (UV) relays are suggested to be installed in the RC, at both ends of the back‐to‐back converters. Consequently, by detecting the Pdfig decrement, the provided DOC and UV relays in the RC will be triggered to classify the occurred events. The outlined protection scheme is exhaustively investigated on a typical power system which the obtained simulation results show the high reliability in discriminating the RC failures and PSD events with an acceptable detection time.

Hamed Abdollahzadeh

Karen Montano-Martinez, Sushrut Thakar, Shanshan Ma et al.

Reliable and accurate distribution system modeling, including the secondary network, is essential in examining distribution system performance with high penetration of distributed energy resources (DERs). This paper presents a highly automated, novel method to enhance the accuracy of utility distribution feeder models to capture their performance by matching simulation results with corresponding field measurements. The method is demonstrated using an actual feeder from an electrical utility with high penetration of DERs. The method proposed uses advanced metering infrastructure (AMI) voltage and derived active power measurements at the customer level, and data acquisition systems (DAS) measurements at the feeder-head, in conjunction with an AC optimal power flow (ACOPF) to estimate customer active and reactive power consumption over a time horizon, while accounting for unmetered loads. The ACOPF uses the measured voltage magnitudes, derived active power measurements, and the feeder head measurements to obtain a complete active power and reactive power capture of the feeder loads. Additionally, the method proposed estimates both voltage magnitude and angle for each phase at the unbalanced distribution substation. The accuracy of the method developed is verified in two stages: by comparing the time-series power flow results obtained from the enhancement algorithm with OpenDSS results and with the field measurements available. The proposed approach seamlessly manages the data available from the optimization procedure through the final model verification automatically.

Arbel Yaniv, Avi Lin, David Raz et al.

Abstract Power flow calculations are an essential stage in many planning and control applications for distribution systems.To use these in control applications, however, the calculation time needs to be improved, and this can be done by the use of a trained ANN. This paper presents the considerations for constructing ANNs for DSs, and describes a method for training the system in order to support switching events representing a change in topology. The solutions for three DSs, balanced as well as unbalanced, are presented and the various considerations affecting the most appropriate ANN construction are discussed. The results are compared to the solution from the classical complex Newton‐Raphson and the fixed‐point iterative methods. The solutions have very high precision and good results are found for switched laterals. The computational performance is also compared and an improvement of two orders of magnitude is observed.