Distributed State Estimation With Phasor Measurement Units (Pmu) For Power Systems

Wide-area monitoring for the power system is a key tool for preventing the power system from system wide failure. State Estimation (SE) is an essential and practical monitoring tool that has been widely used to provide estimated values for each quantity within energy management systems (EMS) in the control center. However, monitoring larger power systems coordinated by regional transmission operators has placed an enormous operational burden on current SE techniques. A distributed state estimation (DSE) algorithm with a hierarchical structure designed for the power system industry is much more computationally efficient and robust especially for monitoring a wide-area power system. Moreover, considering the deregulation of the power system industry, this method does not require sensitive data exchange between smaller areas that may be competing entities. The use of phasor measurement units (PMUs) in the SE algorithm has proven to improve the performance in terms of accuracy and converging speed. Being able to synchronize the measurements between different areas, PMUs are perfectly suited for distributed state estimation. This dissertation investigates the benefits of the DSE using PMU over a serial state estimator in wide area monitoring. A new method has been developed using available PMU data to calculate the reference angle differences between decomposed power systems in various situations, such as when the specific PMU data of the global slack bus cannot be obtained. The algorithms were tested on six bus, I standard 30 bus and I 118-bus test cases. The proposed distributed state estimator has also been implemented in a test bed to work with a power system real-time digital simulator (RTDS) that simulates the physical power system. PMUs made by SEL and GE are used to provide real-time inputs to the distributed state estimator. Simulation results demonstrated the benefits of the PMU and distributed SE techniques. Additionally a constructed test bed verified and validated the proposed algorithms and can be used for different smart grid tests

Multiagent model and mean field theory of complex auction dynamics

Acknowledgements We are grateful to Ms Yinan Zhao for providing the data and to Yuzhong Chen and Cancan Zhou for discussions and suggestions. This work was supported by ARO under Grant No. W911NF-14-1-0504 and by NSFC under Grants Nos. 11275003 and 61174165. The visit of QC to Arizona State University was partially sponsored by the State Scholarship Fund of China.Peer reviewedPublisher PD

QueryNet: Attack by Multi-Identity Surrogates

Deep Neural Networks (DNNs) are acknowledged as vulnerable to adversarial attacks, while the existing black-box attacks require extensive queries on the victim DNN to achieve high success rates. For query-efficiency, surrogate models of the victim are used to generate transferable Adversarial Examples (AEs) because of their Gradient Similarity (GS), i.e., surrogates' attack gradients are similar to the victim's ones. However, it is generally neglected to exploit their similarity on outputs, namely the Prediction Similarity (PS), to filter out inefficient queries by surrogates without querying the victim. To jointly utilize and also optimize surrogates' GS and PS, we develop QueryNet, a unified attack framework that can significantly reduce queries. QueryNet creatively attacks by multi-identity surrogates, i.e., crafts several AEs for one sample by different surrogates, and also uses surrogates to decide on the most promising AE for the query. After that, the victim's query feedback is accumulated to optimize not only surrogates' parameters but also their architectures, enhancing both the GS and the PS. Although QueryNet has no access to pre-trained surrogates' prior, it reduces queries by averagely about an order of magnitude compared to alternatives within an acceptable time, according to our comprehensive experiments: 11 victims (including two commercial models) on MNIST/CIFAR10/ImageNet, allowing only 8-bit image queries, and no access to the victim's training data. The code is available at https://github.com/Sizhe-Chen/QueryNet.Comment: QueryNet reduces queries by about an order of magnitude against SOTA black-box attack

Environmental regulation, innovation quality and firms’ competitivity-Quasi-natural experiment based on China’s carbon emissions trading pilot

In the study of the “Porter Hypothesis”, scholars explored the impact of different forms of innovation on the firms’ competitivity, but did not distinguish between innovations on the difference in patent quality. In addition, relevant research only regards innovation as a mediator between environmental regulation and competitivity, and doesn’t take into account innovation induced by environmental regulation, can only promote competitivity under the constraints of environmental regulation. That is to say, environmental regulation not only induces innovation, but also moderates innovation to promote competitivity. In view of this, we use panel data of A-share listed firms in China from 2006 to 2016, and adopt propensity score matching and different in different (PSM-DID) model to empirically test the inductive effect and moderating effect. The results show that CETS cannot only improve the quantity and quality, but also significantly enhance the firms’ market value; innovation itself cannot enhance the firms’ market value, but the interaction with CETS can promote the firms’ market value. In addition, the CETS has a stronger inductive effect on innovation of state-owned shares firms, but the positive moderating effect on high-quality innovation and competitivity only exists in non-state-owned shares firms

Stochastic stability of viscoelastic systems

Many new materials used in mechanical and structural engineering exhibit viscoelastic properties, that is, stress depends on the past time history of strain, and vice versa. Investigating the behaviour of viscoelastic materials under dynamical loads is of great theoretical and practical importance for structural design, vibration reduction, and other engineering applications. The objective of this thesis is to find how viscoelasticity affects the stability of structures under random loads. The time history dependence of viscoelasticity renders the equations of motion of viscoelastic bodies in the form of integro-partial differential equations, which are more difficult to study compared to those of elastic bodies. The method of stochastic averaging, which has been proved to be an effective tool in the study of dynamical systems, is applied to simplify some single degree-of-freedom linear viscoelastic systems parametrically excited by wide-band noise and narrow-band noise. The solutions of the averaged systems are diffusion processes characterized by Itô differential equations. Therefore, the stability of the solutions is determined in the sense of the moment Lyapunov exponents and Lyapunov exponents, which characterize the moment stability and the almost-sure stability, respectively. The moment Lyapunov exponents may be obtained by solving the averaged Itô equations directly, or by solving the eigenvalue problems governing the moment Lyapunov exponents. Monte Carlo simulation is applied to study the behaviour of stochastic dynamical systems numerically. Estimating the moments of solutions through sample average may lead to erroneous results under the circumstances that systems exhibit large deviations. An improved algorithm for simulating the moment Lyapunov exponents of linear homogeneous stochastic systems is presented. Under certain conditions, the logarithm of norm of a solution converges weakly to normal distribution after suitably normalized. This property, along with the results of Komlós-Major-Tusnády for sums of independent random variables, are applied to construct the algorithm. The numerical results obtained from the improved algorithm are used to determine the accuracy of the approximate analytical moment Lyapunov exponents obtained from the averaged systems. In this way the effectiveness of the stochastic averaging method is confirmed. The world is essentially nonlinear. A single degree-of-freedom viscoelastic system with cubic nonlinearity under wide-band noise excitation is studied in this thesis. The approximated nonlinear stochastic system is obtained through the stochastic averaging method. Stability and bifurcation properties of the averaged system are verified by numerical simulation. The existence of nonlinearity makes the system stable in one of the two stationary states

SwG-former: Sliding-window Graph Convolutional Network Integrated with Conformer for Sound Event Localization and Detection

Sound event localization and detection (SELD) is a joint task of sound event detection (SED) and direction of arrival (DoA) estimation. SED mainly relies on temporal dependencies to distinguish different sound classes, while DoA estimation depends on spatial correlations to estimate source directions. To jointly optimize two subtasks, the SELD system should extract spatial correlations and model temporal dependencies simultaneously. However, numerous models mainly extract spatial correlations and model temporal dependencies separately. In this paper, the interdependence of spatial-temporal information in audio signals is exploited for simultaneous extraction to enhance the model performance. In response, a novel graph representation leveraging graph convolutional network (GCN) in non-Euclidean space is developed to extract spatial-temporal information concurrently. A sliding-window graph (SwG) module is designed based on the graph representation. It exploits sliding-windows with different sizes to learn temporal context information and dynamically constructs graph vertices in the frequency-channel (F-C) domain to capture spatial correlations. Furthermore, as the cornerstone of message passing, a robust Conv2dAgg function is proposed and embedded into the SwG module to aggregate the features of neighbor vertices. To improve the performance of SELD in a natural spatial acoustic environment, a general and efficient SwG-former model is proposed by integrating the SwG module with the Conformer. It exhibits superior performance in comparison to recent advanced SELD models. To further validate the generality and efficiency of the SwG-former, it is seamlessly integrated into the event-independent network version 2 (EINV2) called SwG-EINV2. The SwG-EINV2 surpasses the state-of-the-art (SOTA) methods under the same acoustic environment

A Novel Segmentation Approach Combining Region- and Edge-Based Information for Ultrasound Images

Ultrasound imaging has become one of the most popular medical imaging modalities with numerous diagnostic applications. However, ultrasound (US) image segmentation, which is the essential process for further analysis, is a challenging task due to the poor image quality

Going Far Boosts Attack Transferability, but Do Not Do It

Deep Neural Networks (DNNs) could be easily fooled by Adversarial Examples (AEs) with an imperceptible difference to original ones in human eyes. Also, the AEs from attacking one surrogate DNN tend to cheat other black-box DNNs as well, i.e., the attack transferability. Existing works reveal that adopting certain optimization algorithms in attack improves transferability, but the underlying reasons have not been thoroughly studied. In this paper, we investigate the impacts of optimization on attack transferability by comprehensive experiments concerning 7 optimization algorithms, 4 surrogates, and 9 black-box models. Through the thorough empirical analysis from three perspectives, we surprisingly find that the varied transferability of AEs from optimization algorithms is strongly related to the corresponding Root Mean Square Error (RMSE) from their original samples. On such a basis, one could simply approach high transferability by attacking until RMSE decreases, which motives us to propose a LArge RMSE Attack (LARA). Although LARA significantly improves transferability by 20%, it is insufficient to exploit the vulnerability of DNNs, leading to a natural urge that the strength of all attacks should be measured by both the widely used $\ell_\infty$ bound and the RMSE addressed in this paper, so that tricky enhancement of transferability would be avoided