Real-time grid topology modeling and optimization for power transmission systems

Accurately modeling and tactfully switching power grid topology are not only crucial for routine power system operational tasks but also play a critical role in system emergency responses under extreme events. The modern power grids have recently witnessed more frequent occurrences of unintentional topology changes. These changes can be caused by misoperations of substation protection systems, malicious cyber attacks, or natural disasters. Although strategically altering the grid topology through transmission switching can effectively relieve network congestion and thus has the potential to mitigate the impact of these events, the optimal decision is in general difficult to attain due to the uncertainty and variability therein. Therefore, this motivates us to devise efficient algorithms for achieving real-time power grid topology monitoring and optimization. This dissertation first focuses on efficient modeling and monitoring of the bus split event, which is a type of grid topology change caused by circuit breakers in substations. We perform sensitivity analysis to evaluate the grid-wide impact of such events under the bus-branch representation, for which a synchrophasor data enabled identification problem is presented by matching the changes in the measurements. Inspired by this, we next explore the transmission switching problem that can incorporate the substation-level topology changes. Furthermore, to perform reliable and cost-effective transmission switching under the renewable uncertainty, we study the distributionally robust chance-constrained problem, which can provide superior robustness guarantees over the traditional chance-constrained formulation. Finally, to provide effective system responses under extreme weather events, we will also investigate scalable optimization and learning algorithms for quick power grid restoration.Electrical and Computer Engineerin

False Analog Data Injection Attack Towards Topology Errors: Formulation and Feasibility Analysis

In this paper, we propose a class of false analog data injection attack that can misguide the system as if topology errors had occurred. By utilizing the measurement redundancy with respect to the state variables, the adversary who knows the system configuration is shown to be capable of computing the corresponding measurement value with the intentionally misguided topology. The attack is designed such that the state as well as residue distribution after state estimation will converge to those in the system with a topology error. It is shown that the attack can be launched even if the attacker is constrained to some specific meters. The attack is detrimental to the system since manipulation of analog data will lead to a forged digital topology status, and the state after the error is identified and modified will be significantly biased with the intended wrong topology. The feasibility of the proposed attack is demonstrated with an IEEE 14-bus system.Comment: 5 pages, 7 figures, Proc. of 2018 IEEE Power and Energy Society General Meetin

Improving Micro-Expression Recognition with Shift Matrices and Database Combination

Micro-expressions are brief, subtle changes in facial expressions associated with emotional responses, and researchers have worked for decades on automatic recognition of them. As convolutional neural networks have been widely used in many areas of computer vision, such as image recognition and motion detection, it has also drawn the attention of scientists to use it for micro-expression recognition. However, none of them have been able to achieve an accuracy high enough for practical use. One of the biggest problems is the limited number of available datasets. The most popular datasets are SMIC, CASME, CASMEII, and SAMM. Most groups have worked on the datasets separately, but few have tried to combine them. In our approach, we combined the datasets and extracted the shared features. If new datasets under the same classifying rules (FACS) are created in the future, they can easily be combined using our approach. In addition to this novel approach for combining datasets, we use a new way of extracting the features instead of the Local Binary Pattern from Three Orthogonal Planes (LBP-TOP). To be more specific, we create shift matrices, the changing pattern of pixels, to keep the spatial information of the videos. Our highest recorded accuracy from 100 experiments was 88 percent, but we chose to report 72.5 percent. This is the median accuracy and a more convincing result though it’s a little bit lower than the best result to date. However, our f1 score is 72.3 percent and higher than the best result to date. Our paper presents an extendable approach to micro-expression recognition that should increase in accuracy as more datasets become available

Some Sharp Chernoff type inequalities

Two sharp Chernoff type inequalities are obtained for star body in $\mathbb{R}^2$, one of which is an extension of the dual Chernoff-Ou-Pan inequality, and the other is the reverse Chernoff type inequality. Furthermore, we establish a generalized dual symmetric mixed Chernoff inequality for two planar star bodies. As a direct consequence, a new proof of the the dual symmetric mixed isoperimetric inequality is presented

Learning Rich Geographical Representations: Predicting Colorectal Cancer Survival in the State of Iowa

Neural networks are capable of learning rich, nonlinear feature representations shown to be beneficial in many predictive tasks. In this work, we use these models to explore the use of geographical features in predicting colorectal cancer survival curves for patients in the state of Iowa, spanning the years 1989 to 2012. Specifically, we compare model performance using a newly defined metric -- area between the curves (ABC) -- to assess (a) whether survival curves can be reasonably predicted for colorectal cancer patients in the state of Iowa, (b) whether geographical features improve predictive performance, and (c) whether a simple binary representation or richer, spectral clustering-based representation perform better. Our findings suggest that survival curves can be reasonably estimated on average, with predictive performance deviating at the five-year survival mark. We also find that geographical features improve predictive performance, and that the best performance is obtained using richer, spectral analysis-elicited features.Comment: 8 page

A Data-Driven Approach for High-Impedance Fault Localization in Distribution Systems

Accurate and quick identification of high-impedance faults is critical for the reliable operation of distribution systems. Unlike other faults in power grids, HIFs are very difficult to detect by conventional overcurrent relays due to the low fault current. Although HIFs can be affected by various factors, the voltage current characteristics can substantially imply how the system responds to the disturbance and thus provides opportunities to effectively localize HIFs. In this work, we propose a data-driven approach for the identification of HIF events. To tackle the nonlinearity of the voltage current trajectory, first, we formulate optimization problems to approximate the trajectory with piecewise functions. Then we collect the function features of all segments as inputs and use the support vector machine approach to efficiently identify HIFs at different locations. Numerical studies on the IEEE 123-node test feeder demonstrate the validity and accuracy of the proposed approach for real-time HIF identification