arXiv subjects

Rui Yao

Publications and source records attributed to Rui Yao.

75 records · Page 5Linked to original sources

arXiv2017

Towards Simulation and Risk Assessment of Weather-Related Cascading Outages

Weather and environmental factors are verified to have played significant roles in historical major cascading outages and blackouts. Therefore, in the simulation and risk assessment of cascading outages in power systems, it is necessary to consider the weather and environmental effects. This paper proposes a method for the risk assessment of weather-related cascading outages. Based on the analysis of historical outage records and temperature-dependent physical outage mechanisms of transmission lines, an outage rate model considering weather condition and conductor temperature is proposed, and the analytical form of outage probability of lines are derived. With the weather-dependent outage model, a two-stage risk assessment method based on Markovian tree (MT) search is proposed, which consists of offline full assessment, and online efficient update of risk assessment results and continued MT search using updated NWP data. The test cases on NPCC 140-bus test system model in winter and summer scenarios verify the advantages of the proposed risk assessment method in both accuracy and efficiency.

cs.CE↗

arXiv2017

Efficient Dense Labeling of Human Activity Sequences from Wearables using Fully Convolutional Networks

Recognizing human activities in a sequence is a challenging area of research in ubiquitous computing. Most approaches use a fixed size sliding window over consecutive samples to extract features---either handcrafted or learned features---and predict a single label for all samples in the window. Two key problems emanate from this approach: i) the samples in one window may not always share the same label. Consequently, using one label for all samples within a window inevitably lead to loss of information; ii) the testing phase is constrained by the window size selected during training while the best window size is difficult to tune in practice. We propose an efficient algorithm that can predict the label of each sample, which we call dense labeling, in a sequence of human activities of arbitrary length using a fully convolutional network. In particular, our approach overcomes the problems posed by the sliding window step. Additionally, our algorithm learns both the features and classifier automatically. We release a new daily activity dataset based on a wearable sensor with hospitalized patients. We conduct extensive experiments and demonstrate that our proposed approach is able to outperform the state-of-the-arts in terms of classification and label misalignment measures on three challenging datasets: Opportunity, Hand Gesture, and our new dataset.

cs.CV↗

arXiv2016

Risk Assessment of Multi-timescale Cascading Outages based on Markovian Tree Search

In the risk assessment of cascading outages, the rationality of simulation and efficiency of computation are both of great significance. To overcome the drawback of sampling-based methods that huge computation resources are required and the shortcoming of initial contingency selection practices that the dependencies in sequences of outages are omitted, this paper proposes a novel risk assessment approach by searching on Markovian Tree. The Markovian tree model is reformulated from the quasi-dynamic multi-timescale simulation model proposed recently to ensure reasonable modeling and simulation of cascading outages. Then a tree search scheme is established to avoid duplicated simulations on same cascade paths, significantly saving computation time. To accelerate the convergence of risk assessment, a risk estimation index is proposed to guide the search for states with major contributions to the risk, and the risk assessment is realized based on the risk estimation index with a forward tree search and backward update algorithm. The effectiveness of the proposed method is illustrated on a 4-node power system, and its convergence profile as well as efficiency is demonstrated on the RTS-96 test system.

eess.SY↗