ترغب بنشر مسار تعليمي؟ اضغط هنا

A Hybrid Framework for Topology Identification of Distribution Grid with Renewables Integration

162   0   0.0 ( 0 )
 نشر من قبل Xing He
 تاريخ النشر 2020
  مجال البحث الاحصاء الرياضي
والبحث باللغة English




اسأل ChatGPT حول البحث

Topology identification (TI) is a key task for state estimation (SE) in distribution grids, especially the one with high-penetration renewables. The uncertainties, initiated by the time-series behavior of renewables, will almost certainly lead to bad TI results without a proper treatment. These uncertainties are analytically intractable under conventional framework--they are usually jointly spatial-temporal dependent, and hence cannot be simply treated as white noise. For this purpose, a hybrid framework is suggested in this paper to handle these uncertainties in a systematic and theoretical way; in particular, big data analytics are studied to harness the jointly spatial-temporal statistical properties of those uncertainties. With some prior knowledge, a model bank is built first to store the countable typical models of network configurations; therefore, the difference between the SE outputs of each bank model and our observation is capable of being defined as a matrix variate--the so-called random matrix. In order to gain insight into the random matrix, a well-designed metric space is needed. Auto-regression (AR) model, factor analysis (FA), and random matrix theory (RMT) are tied together for the metric space design, followed by jointly temporal-spatial analysis of those matrices which is conducted in a high-dimensional (vector) space. Under the proposed framework, some big data analytics and theoretical results are obtained to improve the TI performance. Our framework is validated using IEEE standard distribution network with some field data in practice.



قيم البحث

اقرأ أيضاً

169 - Stan Zachary , Chris Dent 2014
In recent years there has been a resurgence of interest in generation adequacy risk assessment, due to the need to include variable generation renewables within such calculations. This paper will describe new statistical approaches to estimating the joint distribution of demand and available VG capacity; this is required for the LOLE calculations used in many statutory adequacy studies, for example those of GB and PJM. The most popular estimation technique in the VG-integration literature is `hindcast, in which the historic joint distribution of demand and available VG is used as a predictive distribution. Through the use of bootstrap statistical analysis, this paper will show that due to extreme sparsity of data on times of high demand and low VG, hindcast results can suffer from sampling uncertainty to the extent that they have little practical meaning. An alternative estimation approach, in which a marginal distribution of available VG is rescaled according to demand level, is thus proposed. This reduces sampling uncertainty at the expense of the additional model structure assumption, and further provides a means of assessing the sensitivity of model outputs to the VG-demand relationship by varying the function of demand by which the marginal VG distribution is rescaled.
With more distributed energy resources (DERs) connected to distribution grids, better monitoring and control are needed, where identifying the topology accurately is the prerequisite. However, due to frequent re-configurations, operators usually cann ot know a complete structure in distribution grids. Luckily, the growing data from smart sensors, restricted by Ohm law, provides the possibility of topology inference. In this paper, we show how line parameters of Ohm equation can be estimated for topology identification even when there are hidden nodes. Specifically, the introduced learning method recursively conducts hidden-node detection and impedance calculation. However, the assumptions on uncorrelated data, availability of phasor measurements, and a balanced system, are not met in practices, causing large errors. To resolve these problems, we employ Cholesky whitening first with a proof for measurement decorrelations. For increasing robustness further, we show how to handle practical scenarios when only measurement magnitudes are available or when the grid is three-phase unbalanced. Numerical performance is verified on multi-size distribution grids with both simulation and real-world data.
The Pacific Northwest Smart Grid Demonstration was an electricity grid modernization project conducted in the Northwest U.S. This paper presents the analysis of renewable generation at the Renewable Energy Park located in the City of Ellensburg, WA. The community energy park concept is an intriguing model for community investment in renewable resources,but the lessons in this paper should be considered.
Robotic telescopes and grid technology have made significant progress in recent years. Both innovations offer important advantages over conventional technologies, particularly in combination with one another. Here, we introduce robotic telescopes use d by the Astrophysical Institute Potsdam as ideal instruments for building a robotic telescope network. We also discuss the grid architecture and protocols facilitating the network integration that is being developed by the German AstroGrid-D project. Finally, we present three user interfaces employed for this purpose.
A/B testing, also known as controlled experiment, bucket testing or splitting testing, has been widely used for evaluating a new feature, service or product in the data-driven decision processes of online websites. The goal of A/B testing is to estim ate or test the difference between the treatment effects of the old and new variations. It is a well-studied two-sample comparison problem if each users response is influenced by her treatment only. However, in many applications of A/B testing, especially those in HIVE of Yahoo and other social networks of Microsoft, Facebook, LinkedIn, Twitter and Google, users in the social networks influence their friends via underlying social interactions, and the conventional A/B testing methods fail to work. This paper considers the network A/B testing problem and provide a general framework consisting of five steps: data sampling, probabilistic model, parameter inference, computing average treatment effect and hypothesis test. The framework performs well for network A/B testing in simulation studies.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا