Identification of Cascading Failure Propagation Under Extreme Weather Conditions

As a fundamental infrastructure, power systems play a vital role in modern society, but it can be damaged by different adverse events e.g. natural, accidental, and malicious, of which the adverse natural events, especially extreme weathers, with huge destructive force can bring tremendous damages and economic losses. The high exposure and comprehensive geographical coverage of the power system make it highly vulnerable to extreme weathers, resulting in equipment damage which leads to cascading failures and blackouts. Traditional methods only focus on modeling and analysing the reliability of the power system under extreme weathers, without focusing on the propagation of the cascades. In this thesis, innovative methods of studying the cascading failure were proposed, and further extend to collectively consider the impact of extreme weathers on the transmission networks. The proposed models were further validated by applying them to a study system (IEEE-30 bus system) and a real system (Italian transmission network). A so called normal failure model based on probabilistic graphs was proposed to describe how a cascading failure propagates under a contingency analysis. This model employed Monte Carlo simulation to consider most of the possible operating conditions to establish directed probabilistic graphs to identify the cascading propa-gation by tripping all branches one by one under each operating condition. Obviously, the results of the model can clearly and legibly show the main cascading path of a given network without considering the initial operating condition and the triggering contingency. Further, an index based on branch vulnerability was designed to select the triggering event to increase the effectiveness of the failure in the simulation. Furthermore, by integrating a probabilistic model of extreme weather impact into the normal failure model, the extreme weather model was proposed based on failure networks, which maps a physical electricity network into a graph in the cascading propagation dimensions. Based on the generated failure networks, a new method based on clustering techniques was proposed to fast track the cascading failure path from any initial contingencies without recalculating the cascading failure in the physical network. The high similarity of the simulation results on the IEEE 30 bus system from the two proposed models indicates the validity of the models. Further, to demonstrate the extreme weather model, we selected a winter storm, which could happen in Northwest of Italy as an example. The data of snowfall on the Alps was collected and modeled by probability density function and probability mass function. By applying the proposed extreme weather model, the propagation paths can be predicted. The values of the study provide two powerful tools which can 1) clearly present the inherent characteristic of any one given network, i.e. main propagation paths exist regardless of the initial network and failure condition; 2) fast and reasonably predict the cascading paths in a network under extreme weather conditions

DeepSig: Deep learning improves signal peptide detection in proteins

Motivation: The identification of signal peptides in protein sequences is an important step toward protein localization and function characterization. Results: Here, we present DeepSig, an improved approach for signal peptide detection and cleavage-site prediction based on deep learning methods. Comparative benchmarks performed on an updated independent dataset of proteins show that DeepSig is the current best performing method, scoring better than other available state-of-the-art approaches on both signal peptide detection and precise cleavage-site identification. Availability and implementation: DeepSig is available as both standalone program and web server at https://deepsig.biocomp.unibo.it. All datasets used in this study can be obtained from the same website

Cascading Randomized Weighted Majority: A New Online Ensemble Learning Algorithm

With the increasing volume of data in the world, the best approach for learning from this data is to exploit an online learning algorithm. Online ensemble methods are online algorithms which take advantage of an ensemble of classifiers to predict labels of data. Prediction with expert advice is a well-studied problem in the online ensemble learning literature. The Weighted Majority algorithm and the randomized weighted majority (RWM) are the most well-known solutions to this problem, aiming to converge to the best expert. Since among some expert, the best one does not necessarily have the minimum error in all regions of data space, defining specific regions and converging to the best expert in each of these regions will lead to a better result. In this paper, we aim to resolve this defect of RWM algorithms by proposing a novel online ensemble algorithm to the problem of prediction with expert advice. We propose a cascading version of RWM to achieve not only better experimental results but also a better error bound for sufficiently large datasets.Comment: 15 pages, 3 figure