Sensitivity analysis applied to computer-aided circuit design

Imperial Users onl

Conservation in signal processing systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 205-209).Conservation principles have played a key role in the development and analysis of many existing engineering systems and algorithms. In electrical network theory for example, many of the useful theorems regarding the stability, robustness, and variational properties of circuits can be derived in terms of Tellegen's theorem, which states that a wide range of quantities, including power, are conserved. Conservation principles also lay the groundwork for a number of results related to control theory, algorithms for optimization, and efficient filter implementations, suggesting potential opportunity in developing a cohesive signal processing framework within which to view these principles. This thesis makes progress toward that goal, providing a unified treatment of a class of conservation principles that occur in signal processing systems. The main contributions in the thesis can be broadly categorized as pertaining to a mathematical formulation of a class of conservation principles, the synthesis and identification of these principles in signal processing systems, a variational interpretation of these principles, and the use of these principles in designing and gaining insight into various algorithms. In illustrating the use of the framework, examples related to linear and nonlinear signal-flow graph analysis, robust filter architectures, and algorithms for distributed control are provided.by Thomas A. Baran.Ph.D

Termolecular Association of Ions in Gases

Issued as Technical reports [nos. 1-4], and Final report, Project no. G-41-61

Model-based and Model-free Approaches for Power System Security Assessment

Continuous security assessment of a power system is necessary to insure a reliable, stable, and continuous supply of electrical power to customers. To this end, this dissertation identifies and explores some of the various challenges encountered in the field of power system security assessment. Accordingly, several model-based and/or model-free approaches were developed to overcome these challenges. First, a voltage stability index, named TAVSI, is proposed. This index has three important features: TAVSI applies to general load models including ZIP, exponential, and induction motor loads; TAVSI can be used for both measurement-based and model-based voltage stability assessment; and finally, TAVSI is calculated based on normalized sensitivities which enables identification of weak buses and the definition of a global instability threshold. TAVSI was tested on both the IEEE 14-bus and the 181-bus WECC systems. Results show that TAVSI gives a reliable assessment of system stability. Second, a data-driven and model-based hybrid reinforcement learning approach is proposed for training a control agent to re-dispatch generators’ output power in order to relieve stressed branches. For large power systems, the agent’s action space is highly dimensioned which challenges the successful training of data-driven agents. Therefore, we propose a hybrid approach where model-based actions are utilized to help the agent learn an optimal control policy. The proposed approach was tested and compared to the generic data-driven DDPG-based approach on the IEEE 118-bus system and a larger 2749-bus real-world system. Results show that the hybrid approach performs well for large power systems and that it is superior to the DDPG-based approach. Finally, a Convolutional Neural Network (CNN) based approach is proposed as a faster alternative to the classical AC power flow-based contingency screening. The proposed approach is investigated on both the IEEE 118-bus system and the Texas 2000-bus synthetic system. For such large systems, the implementation of the proposed approach came with several challenges, such as computational burden, learning from imbalanced datasets, and performance evaluation of trained models. Accordingly, this work contributes a set of novel techniques and best practices that enables both efficient and successful implementation of CNN-based multi-contingency classifiers for large power systems

Modeling and inversion of self-potential data

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Earth, Atmospheric, and Planetary Sciences, 2007.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 235-251).This dissertation presents data processing techniques relevant to the acquisition, modeling, and inversion of self-potential data. The primary goal is to facilitate the interpretation of self-potentials in terms of the underlying mechanisms that generate the measured signal. The central component of this work describes a methodology for inverting self-potential data to recover the three-dimensional distribution of causative sources in the earth. This approach is general in that it is not specific to a particular forcing mechanism, and is therefore applicable to a wide variety of problems. Self-potential source inversion is formulated as a linear problem by seeking the distribution of source amplitudes within a discretized model that satisfies the measured data. One complicating factor is that the potentials are a function of the earth resistivity structure and the unknown sources. The influence of imperfect resistivity information in the inverse problem is derived, and illustrated through several synthetic examples. Source inversion is an ill-posed and non-unique problem, which is addressed by incorporating model regularization into the inverse problem. A non-traditional regularization method, termed "minimum support," is utilized to recover a spatially compact source model rather than one that satisfies more commonly used smoothness constraints. Spatial compactness is often an appropriate form of prior information for the inverse source problem. Minimum support regularization makes the inverse problem non-linear, and therefore requires an iterative solution technique similar to iteratively re-weighted least squares (IRLS) methods.(cont.) Synthetic and field data examples are studied to illustrate the efficacy of this method and the influence of noise, with applications to hydrogeologic and electrochemical self-potential source mechanisms. Finally, a novel technique for pre-processing self-potential data collected with arbitrarily complicated survey geometries is presented. This approach overcomes the inability of traditional processing methods to produce a unique map of the potential field when multiple lines of data form interconnected loops. The data are processed simultaneously to minimize mis-ties on a survey-wide basis using either an 12 or 11 measure of misfit, and simplifies to traditional methods in the absence of survey complexity. The 11 measure requires IRLS solution methods, but is more reliable in the presence of data outliers.by Burke J. Minsley.Ph.D

Course Catalogue of the Massachusetts Institute of Technology 1962 - 1963

The Massachusetts Institute of Technology Bulletin. The General Catalog Issue 1962-1963. Includes an index to members of the staff, an alphabetical list of subjects and alphabetical index; this issue also includes a campus directory and two appendices: student aid and prizes; student housing. Digitized from microfiche copies. Digital version may contain microfiche headers and targets