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Optimal power flow algorithm and analysis in distribution system considering distributed generation
This study investigates the optimal power flow (OPF) problem for distribution networks with the integration of distributed generation (DG). By considering the objectives of minimal line loss, minimal voltage deviation and maximum DG active power output, the proposed OPF formulation is a multi-object optimisation problem. Through normalisation of each objective function, the multi-objective optimisation is transformed to single-objective optimisation. To solve such a non-convex problem, the trust-region sequential quadratic programming (TRSQP) method is proposed, which iteratively approximates the OPF by a quadratic programming with the trust-region guidance. The TRSQP utilises the sensitivity analysis to approximate all the constraints with linear ones, which will reduce the optimisation scale. Active set method is utilised in TRSQP to solve quadratic programming sub-problem. Numerical tests on IEEE 33-, PG&E 69- and actual 292-, 588-, 1180-bus systems show the applicability of the proposed method, and comparisons with the primal–dual interior point method and sequential linear programming method are provided. The initialisation and convergence condition of the proposed method are also discussed. The computational result indicates that the proposed algorithm for DG control optimisation in distribution system is feasible and effective.
Journal Article
Python for finance : mastering data-driven finance
Python has become the programming language of choice for data-driven and AI-first finance. Some of the largest investment banks and hedge funds now use Python and its ecosystem for building core trading and risk management systems. In the second edition of this guide, Yves Hilpisch shows developers and quantitative analysts how to use Python packages and tools for financial data science, algorithmic trading, and computational finance.
Book
Atomic Decomposition by Basis Pursuit
The time-frequency and time-scale communities have recently developed a large number of overcomplete waveform dictionaries-stationary wavelets, wavelet packets, cosine packets, chirplets, and warplets, to name a few. Decomposition into overcomplete systems is not unique, and several methods for decomposition have been proposed, including the method of frames (MOF), matching pursuit (MP), and, for special dictionaries, the best orthogonal basis (BOB). Basis pursuit (BP) is a principle for decomposing a signal into an \"optimal\" superposition of dictionary elements, where optimal means having the smallest l1 norm of coefficients among all such decompositions. We give examples exhibiting several advantages over MOF, MP, and BOB, including better sparsity and superresolution. BP has interesting relations to ideas in areas as diverse as ill-posed problems, abstract harmonic analysis, total variation denoising, and multiscale edge denoising. BP in highly overcomplete dictionaries leads to large-scale optimization problems. With signals of length 8192 and a wavelet packet dictionary, one gets an equivalent linear program of size 8192 by 212,992. Such problems can be attacked successfully only because of recent advances in linear and quadratic programming by interior-point methods. We obtain reasonable success with a primal-dual logarithmic barrier method and conjugategradient solver.
Journal Article
Design patterns in .NET : reusable approaches in C# and F# for object-oriented software design
\"Implement design patterns in .NET using the latest versions of the C# and F# languages. This book provides a comprehensive overview of the field of design patterns as they are used in today's developer toolbox. Using the C# programming language, \"Design patterns in .NET\" explores the classic design pattern implementation and discusses the applicability and relevance of specific language features for the purpose of implementing patterns. You will learn by example, reviewing scenarios where patterns are applicable. MVP and patterns expert Dmitri Nesteruk demonstrates possible implementations of patterns, discusses alternatives and pattern inter-relationships, and illustrates the way that a dedicated refactoring tool (ReSharper) can be used to implement design patterns with ease.\"-- Provided by publisher
Book
A random forest guided tour
The random forest algorithm, proposed by L. Breiman in 2001, has been extremely successful as a general-purpose classification and regression method. The approach, which combines several randomized decision trees and aggregates their predictions by averaging, has shown excellent performance in settings where the number of variables is much larger than the number of observations. Moreover, it is versatile enough to be applied to large-scale problems, is easily adapted to various ad hoc learning tasks, and returns measures of variable importance. The present article reviews the most recent theoretical and methodological developments for random forests. Emphasis is placed on the mathematical forces driving the algorithm, with special attention given to the selection of parameters, the resampling mechanism, and variable importance measures. This review is intended to provide non-experts easy access to the main ideas.
Journal Article
mBot for makers : conceive, construct and code your own robots at home or in the classroom
\"The mBot is an educational Arduino robot that helps kids learn programming and electronics, alone or in the classroom. The mBot allows novices to start by tinkering, and to access higher-level features or add new components when inspiration strikes, without soldering or breadboarding! This flexibility allows raw beginners and experienced Makers to work at their own comfort level. Written by educators, this book cuts through much of the confusion resulting from the mBot documentation. It also saves you time when you're scaling up your mBots for home and classroom use by giving you creative project ideas you can use right away.\"--Back cover.
Book
Invariant Causal Prediction for Sequential Data
We investigate the problem of inferring the causal predictors of a response Y from a set of d explanatory variables (X
1
, ..., X
d
). Classical ordinary least-square regression includes all predictors that reduce the variance of Y. Using only the causal predictors instead leads to models that have the advantage of remaining invariant under interventions; loosely speaking they lead to invariance across different \"environments\" or \"heterogeneity patterns.\" More precisely, the conditional distribution of Y given its causal predictors is the same for all observations, provided that there are no interventions on Y. Recent work exploits such a stability to infer causal relations from data with different but known environments. We show that even without having knowledge of the environments or heterogeneity pattern, inferring causal relations is possible for time-ordered (or any other type of sequentially ordered) data. In particular, this allows detecting instantaneous causal relations in multivariate linear time series, which is usually not the case for Granger causality. Besides novel methodology, we provide statistical confidence bounds and asymptotic detection results for inferring causal predictors, and present an application to monetary policy in macroeconomics. Supplementary materials for this article are available online.
Journal Article
Domain-Specific Modeling
\"[The authors] are pioneers. . . . Few in our industry havetheir breadth of knowledge and experience.\"
—From the Foreword by Dave Thomas, Bedarra LabsDomain-Specific Modeling (DSM) is the latest approach tosoftware development, promising to greatly increase the speed andease of software creation. Early adopters of DSM have been enjoyingproductivity increases of 500–1000% in production for over adecade. This book introduces DSM and offers examples from variousfields to illustrate to experienced developers how DSM can improvesoftware development in their teams.Two authorities in the field explain what DSM is, why it works,and how to successfully create and use a DSM solution to improveproductivity and quality. Divided into four parts, the book covers:background and motivation; fundamentals; in-depth examples; andcreating DSM solutions. There is an emphasis throughout the book onpractical guidelines for implementing DSM, including how toidentify the necessary language constructs, how to generate fullcode from models, and how to provide tool support for a new DSMlanguage. The example cases described in the book are available thebook's Website, www.dsmbook.com, along with, an evaluation copy ofthe MetaEdit+ tool (for Windows, Mac OS X, and Linux), which allowsreaders to examine and try out the modeling languages and codegenerators.Domain-Specific Modeling is an essential reference for leaddevelopers, software engineers, architects, methodologists, andtechnical managers who want to learn how to create a DSM solutionand successfully put it into practice.
eBook
Sparse Sliced Inverse Regression via Lasso
For multiple index models, it has recently been shown that the sliced inverse regression (SIR) is consistent for estimating the sufficient dimension reduction (SDR) space if and only if
, where p is the dimension and n is the sample size. Thus, when p is of the same or a higher order of n, additional assumptions such as sparsity must be imposed in order to ensure consistency for SIR. By constructing artificial response variables made up from top eigenvectors of the estimated conditional covariance matrix, we introduce a simple Lasso regression method to obtain an estimate of the SDR space. The resulting algorithm, Lasso-SIR, is shown to be consistent and achieves the optimal convergence rate under certain sparsity conditions when p is of order
, where λ is the generalized signal-to-noise ratio. We also demonstrate the superior performance of Lasso-SIR compared with existing approaches via extensive numerical studies and several real data examples.
Supplementary materials
for this article are available online.
Journal Article