اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدLine Outage Detection and Localization via Synchrophasor Measurement
80
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
Since transmission lines are crucial links in the power system, one line outage event may bring about interruption or even cascading failure of the power system. If a quick and accurate line outage detection and localization can be achieved, the system operator can take necessary actions in time to mitigate the negative impact. Therefore, the objective of this paper is to study a method for line outage detection and localization via synchrophasor measurements. The density of deployed phasor measurement units (PMUs) is increasing recently, which greatly improves the visibility of the power grid. Taking advantage of the high-resolution synchrophasor data, the proposed method utilizes frequency measurement for line outage detection and power change for localization. The procedure of the proposed method is given. Compared with conventional methods, it does not require the pre-knowledge on the system. Simulation study validates the effectiveness of the proposed method.
قيم البحث
اقرأ أيضاً
This paper proposes a data-driven algorithm of locating the source of forced oscillations and suggests the physical interpretation of the method. By leveraging the sparsity of the forced oscillation sources along with the low-rank nature of synchroph
asor data, the problem of source localization under resonance conditions is cast as computing the sparse and low-rank components using Robust Principal Component Analysis (RPCA), which can be efficiently solved by the exact Augmented Lagrange Multiplier method. Based on this problem formulation, an efficient and practically implementable algorithm is proposed to pinpoint the forced oscillation source during real-time operation. Furthermore, we provide theoretical insights into the efficacy of the proposed approach by use of physical model-based analysis, in specific by establishing the fact that the rank of the resonance component matrix is at most 2. The effectiveness of the proposed method is validated in the IEEE 68-bus power system and the WECC 179-bus benchmark system.
Line spectral estimation (LSE) from multi snapshot samples is studied utilizing the variational Bayesian methods. Motivated by the recently proposed variational line spectral estimation (VALSE) method for a single snapshot, we develop the multisnapsh
ot VALSE (MVALSE) for multi snapshot scenarios, which is important for array processing. The MVALSE shares the advantages of the VALSE method, such as automatically estimating the model order, noise variance and weight variance, closed-form updates of the posterior probability density function (PDF) of the frequencies. By using multiple snapshots, MVALSE improves the recovery performance and it encodes the prior distribution naturally. Finally, numerical results demonstrate the competitive performance of the MVALSE compared to state-of-the-art methods.
We present a deep learning solution to the problem of localization of magnetoencephalography (MEG) brain signals. The proposed deep model architectures are tuned for single and multiple time point MEG data, and can estimate varying numbers of dipole
sources. Results from simulated MEG data on the cortical surface of a real human subject demonstrated improvements against the popular RAP-MUSIC localization algorithm in specific scenarios with varying SNR levels, inter-source correlation values, and number of sources. Importantly, the deep learning models had robust performance to forward model errors and a significant reduction in computation time, to a fraction of 1 ms, paving the way to real-time MEG source localization.
For enhancing transmission performance, privacy level and energy manipulating efficiency of wireless networks, this paper initiates a novel simultaneous wireless information and power transfer (SWIPT) full-duplex (FD) relaying protocol, termed harves
t-and-opportunistically-relay (HOR). In the proposed HOR protocol, the relay can work opportunistically in either pure energy harvesting (PEH) or the FD SWIPT mode. Due to the FD characteristics, the dynamic fluctuation of Rs residual energy is difficult to quantify and track. To solve this problem, we apply a novel discrete-state Markov Chain (MC) method in which the practical finite-capacity energy storage is considered. Furthermore, to improve the privacy level of the proposed HOR relaying system, covert transmission performance analysis is developed and investigated, where closed-form expressions of optimal detection threshold and minimum detection error probability are derived. Last but not least, with the aid of stationary distribution of the MC, closed-form expression of transmission outage probability is calculated, based on which transmission outage performance is analyzed. Numerical results have validated the correctness of analyses on transmission outage and covert communication. The impacts of key system parameters on the performance of transmission outage and covert communication are given and discussed. Based on mathematical analysis and numerical results, it is fair to say that the proposed HOR model is able to not only reliably enhance the transmission performance via smartly managing residual energy but also efficiently improve the privacy level of the legitimate transmission party via dynamically adjust the optimal detection threshold.
This paper focuses on quantifying the outage performance of terahertz (THz) relaying systems. In this direction, novel closed-form expressions for the outage probability of a dual-hop relaying system, in which both the source-relay and relay-destinat
ion links suffer from fading and stochastic beam misalignment, are extracted. Our results reveal the importance of taking into account the impact of beam misalignment when characterizing the outage performance of the system as well as when selecting the transmission frequencies.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
