IEEE TRANSACTIONS ON POWER SYSTEMSIEEE电力系统汇刊
IEEE TRANSACTIONS ON POWER SYSTEMS(英文缩写 IEEE T POWER SYST),ISSN 0885-8950,eISSN 1558-0679,中文译名:IEEE电力系统汇刊 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-11-01 至 2026-10-31，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 7.326 | Q1 |
| 2022 | 6.600 | Q1 |
| 2023 | 6.500 | Q1 |
| 2024 | 7.200 | Q1 |
| 2025 | 8.700 | Q1 |
IEEE TRANSACTIONS ON POWER SYSTEMS 最新收录文献
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1. Adaptive Methods for Short-Term Electricity Load Forecasting During COVID-19 Lockdown in France.
1. 法国COVID-19封锁期间短期电力负荷预测的自适应方法PMID:日期:2021-03-22The coronavirus disease 2019 (COVID-19) pandemic has urged many governments in the world to enforce a strict lockdown where all nonessential businesses are closed and citizens are ordered to stay at home. One of the consequences of this policy is a significant change in electricity consumption patterns. Since load forecasting models rely on calendar or meteorological information and are trained on historical data, they fail to capture the significant break caused by the lockdown and have exhibited poor performances since the beginning of the pandemic. In this paper we introduce two methods to adapt generalized additive models, alleviating the aforementioned issue. Using Kalman filters and fine-tuning allows to adapt quickly to new electricity consumption patterns without requiring exogenous information. The proposed methods are applied to forecast the electricity demand during the French lockdown period, where they demonstrate their ability to significantly reduce prediction errors compared to traditional models. Finally, expert aggregation is used to leverage the specificities of each predictions and enhance results even further.
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2. A Markovian influence graph formed from utility line outage data to mitigate large cascades.
2. 基于电力线路停电数据构建马尔可夫影响图以缓解大规模级联故障PMID:日期:2020-01-30We use observed transmission line outage data to make a Markovian influence graph that describes the probabili- ties of transitions between generations of cascading line outages. Each generation of a cascade consists of a single line outage or multiple line outages. The new influence graph defines a Markov chain and generalizes previous influence graphs by including multiple line outages as Markov chain states. The generalized influence graph can reproduce the distribution of cascade size in the utility data. In particular, it can estimate the probabilities of small, medium and large cascades. The influence graph has the key advantage of allowing the effect of mitigations to be analyzed and readily tested, which is not available from the observed data. We exploit the asymptotic properties of the Markov chain to find the lines most involved in large cascades and show how upgrades to these critical lines can reduce the probability of large cascades.
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3. MMSE-based analytical estimator for uncertain power system with limited number of measurements.
PMID:日期:2018The expected penetration of a large number of renewable distributed energy resources (DER's) is driving next generation power systems toward uncertainties that can have a huge impact on the reliability and complexities of state estimation. Therefore, the stochastic power flow (SPF) and forecasting-aided state estimation of power systems integrating DER's are becoming a major challenge for operation of the future grid. In this paper we propose a new state estimation method referred to as 'mean squared estimator' (MSE) to deal with the uncertain nature of the power system parameters. The estimator benefits from the prior study of SPF, which involves the probability density functions (PDF's) of the system parameters. The main advantage of this estimator is based on its ability to instantaneously incorporate the dynamics of the power system. Moreover, the analytical formula of MSE expresses the mean value of the estimated parameters corrected by an additional term that takes into account the measurement of the parameters. It is shown that the proposed MSE can provide an accurate state estimation with a limited number of measurements with guaranteed convergence. MSE has been tested using IEEE 14, 30, 39 and 118 bus models for different measurement redundancies. The results have been compared to methods such as weighted least square (WLS), unscented Kalman filter (UKF) and compressive sensing-based UKF (CS-UKF). The numerical results show superior performances, especially under a limited number of measurements where WLS and UKF may lead to divergence.
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4. Dynamic Appliances Scheduling in Collaborative MicroGrids System.
PMID:日期:2016-09-23In this paper a new approach which is based on a collaborative system of MicroGrids (MG's), is proposed to enable household appliance scheduling. To achieve this, appliances are categorized into flexible and non-flexible Deferrable Loads (DL's), according to their electrical components. We propose a dynamic scheduling algorithm where users can systematically manage the operation of their electric appliances. The main challenge is to develop a flattening function calculus (reshaping) for both flexible and non-flexible DL's. In addition, implementation of the proposed algorithm would require dynamically analyzing two successive multi-objective optimization (MOO) problems. The first targets the activation schedule of non-flexible DL's and the second deals with the power profiles of flexible DL's. The MOO problems are resolved by using a fast and elitist multi-objective genetic algorithm (NSGA-II). Finally, in order to show the efficiency of the proposed approach, a case study of a collaborative system that consists of 40 MG's registered in the load curve for the flattening program has been developed. The results verify that the load curve can indeed become very flat by applying the proposed scheduling approach.