Three-Vector-Based Low Complexity Model Predictive Control for Soft Open Point

Soft open point (SOP) can improve the flexibility and reliability of power supplies; thus, they are widely used in distribution network systems. Traditional single-vector model predictive control (SV-MPC) can quickly and flexibly control the power and current at both ports of the SOP. However, SV-MPC can only select one voltage vector in a sampling time, producing large current ripples, and power fluctuations. In order to solve the above problems, this paper proposes a three-vector-based low complexity model predictive control (TV-MPC). In the proposed control method, two effective voltage vectors and one zero voltage vector are selected in a sampling time. For the two-port SOP, methods are given to judge the sectors on both sides and select the voltage vectors. Furthermore, the calculation method of the distribution time is proposed as well. Finally, the effectiveness of the proposed method is verified by steady-state and dynamic-state simulation results compared with the SV-MPC.

Development of a Robust Scalar Control System for an Induction Squirrel-cage Motor Based on a Linearized Vector Model

The paper considers the problem of developing a digital system for an induction motor speed control which has a sensor and a speed regulator to increase accuracy of speed control. Speed control is carried out by a scalar method due to consistent change in the stator frequency and voltage. To obtain the uniformity of the motor overload capability in a given range the control mode is used associated with maintaining uniformity of flux linkage of the motor stator. Induction motor scalar models do not possess high accuracy and their parameters and their parameters can vary over a wide range, which complicates the controller design and achievement of robustness of the speed control system. To eliminate these disadvantages, it is proposed to use a vector model in a rotating coordinate system having subjected it to linearization at different points of the operating mode with the account of the adopted law of frequency control, to ensure robust absolute stability of the system on the basis of application of a graphical method for constructing a modified amplitude-phase characteristic.

Optimization of the impact measurement of market structure on liquidity and volatility

Liquidity and volatility are the two barometers that allow stock markets to appreciate in terms of attractiveness, profitability and efficiency. Several macroeconomic and microstructure variables condition the level of liquidity that directly impact the asset allocation decisions of different investor profiles − institutional and individuals − and therefore the dynamics of the market as a whole. Volatility is the regulatory component that provides information on the level of risk that characterizes the market. Thus, the appreciation of these two elements is of considerable help to fund managers looking to optimize their equity pockets. In this work, we will use the liquidity ratio as a proxy variable for the liquidity of the Moroccan stock market, to estimate the indicators and factors that determine its short- and long-term variability. The appropriate econometric method would be to estimate an error correction vector model (ECVM) which has the property of determining the long- and short-term relationships between the variables. The volatility of the MASI index will be the subject of a second estimate to capture the shape of the function of its evolution.

Professional Learning That Works

Chapter 1 kicks off the introductory chapters and provides readers the opportunity to become familiar with current research on the state of teacher professional development in the United States. The authors present and provide commentary on the literature over the past two decades showing the prevalence of “one-and-done” professional development sessions and the lack of individualized and meaningful learning experiences for educators. The authors highlight some barriers systems face in providing the kind of professional development that research shows is best for educator and student learning, and invite readers to think about the state of professional development in their own systems. The authors also highlight and define key terms that are important for building an understanding of the VECTOR model and the challenges it addresses.

Aid, Multidimensional Poverty and Growth: Reversing the Micro-Macro Paradox in Guinea, Liberia and Sierra Leone

This article analyses whether Official Development Assistance (ODA) is linked to multidimensional poverty indicators in the context of the 2030 Agenda for Sustainable Development, the Sustainable Development Indictors and the principles stated by the Global Partnership for Effective Development Co-operation. Focused on three western Sub-Saharan Africa and least developing countries such as Guinea, Liberia and Sierra Leone, this article uses Error Correction Vector Model to estimate if ODA and economic growth are cointegrated and a sectoral and spatial analysis to check if ODA are linked to Multidimensional Poverty Indicators in the sample countries. Despite the 2014 Ebola outbreak, the three countries have achieved noticeable good results in poverty alleviation. Results shows a certain macro-micro paradox because, despite a common trend between aid and growth identified at the macro level, we cannot find any sign of ODA contributions to the multidimensional poverty indicators when the micro level analysis is carried out. Our results may serve to increase the level of implementation of the ownership principle for effective development co-operation and achieve a significant improvement of several goals and targets included on the 2030 Agenda.

Mathematical Methods for the Study of Toponims with Lost Semantics

The article discusses methods for restoring the lost meaning of old toponyms. This task can be solved using a systematic approach. The study used methods of permutation and transformation of consonants in the backbone of a toponym, methods of di- and trichotomy of a word, formation of a table of all possible transformants, a method for finding associates for all transformants of a word using the vector model of the Russian language, a method for clustering the found associates, a method for determining the frequency of repetition of associates in the corpus of the Russian language, which allows you to calculate the probabilities of the appearance of various clusters, a method of lexical analysis of Old Church Slavonic languages, including Old Russian and Sanskrit. The use of a set of methods made it possible to restore the lost meaning of the toponym Moscow. It turned out that with a probability of more than 80 % the name of our capital goes back to the name of the commander and spiritual leader of the first half of the 15th century, who had the nicknames Mosokh, Meshekh, Moses Khan (Prince). The army of medieval Russia (Tatar-Mongolia) created by this man defeated Byzantium and subjugated Western Europe. During the Romanov dynasty, Western European scholars-Russophobes deleted the name of the commander from the history of Russia. As a result, the toponym Moscow has lost its original meaning. The article also restored the lost meaning of the two most important toponyms of Eastern Siberia - Lake Baikal and the Angara River. It turned out that the lake got its name from the old Russian exclamation Bai-ka-al = ay, how divine! The name of the Angara River in Old Russian means the mountains of Christ.

A Novel Downward-Looking Linear Array SAR Imaging Method Based on Multiple Measurement Vector Model with L 2 , 1 -Norm

Downward-looking linear array synthetic aperture radar (DLLA SAR) is a kind of three-dimensional (3-D) radar imaging system. To obtain the superresolution along the crosstrack direction of DLLA SAR, the sparse regularization models with single measurement vector (SMV) have been widely applied. However, the robustness of the sparse regularization models with SMV is unsatisfactory, especially in the low signal-to-noise rate (SNR) environment. To solve this problem, we proposed a novel imaging method for DLLA SAR based on the multiple measurement vector (MMV) model with L 2 , 1 -norm. At first, we exchange the processing order between the along-track (AT) domain and the crosstrack (CT) domain to keep the same sparse structure of the signal in the crosstrack domain so that we can establish the imaging problem as a sparse regularization model based on the MMV model. Moreover, the mixed L 2 , 1 -norm is introduced into the regularization term of the MMV model. Finally, the modified orthogonal matching pursuit (OMP) algorithm is designed for the MMV model with the L 2 , 1 -norm. The simulations verify that the proposed method has better performance in the lower SNR environment and requires lower computation compared with the conventional methods.

Analysis of Coaxiality-Transmission Efficiency Vector Model of RV Reducer Based on ADAMS

Aiming at the influence of the coaxiality error of the transmission system on the detection accuracy of the RV reducer performance test device, taking RV-20E reducer is used as the research object, combined with the ADAMS dynamic simulation software, the RV reducer dynamic transmission coaxiality and transmission efficiency vector model is established, and the coaxiality of the transmission system of this model is simulated under different error ranges and no load. The transmission efficiency is 32.94% when coaxiality is within the allowable error range. The results verify the accuracy of the model. At the same time, when the concentricity exceeds the allowable range of error, it will have a great impact on the transmission efficiency. The design of the coaxiality adjustment mechanism of the RV reducer performance detection device has certain theoretical significance and practical value.

Vector model of error correction in modeling the relationship between bank lending indicators and the growth rates of the Russian economy

The article discusses the problem of modeling the functional dependence of the volume of loans issued and the growth rate of the Russian economy. The relevance of this study is due to the need to identify the factors affecting the growth of the indicator of real gross domestic product. The analyzed domestic and foreign sources do not provide a single set of factors affecting the growth rate of the national economy, highlighting financial mechanisms, production processes, lending volumes to individuals and legal entities, investment activity, etc. The article notes the problem of the formation of a multifactor model of the influence of various parameters on the indicator of the growth rate of the national economy. The process of modeling the functional dependence of GDP on the elements of the influence vector is complicated by the specifics of statistical data. In order to avoid such problems associated with series cointegration, a vector error correction model was proposed. The model was tested on statistical data of organizations in the banking sector and enterprises of the real sector of the Russian economy. It was revealed that under the existing conditions of organizing the lending process, the banking system works not so much to increase production volumes as to maintain the level of the financial condition of economic entities (maintaining the level of liquidity and increasing financial investments), while not contributing to a change in the overall structure of the Russian economy. The developed vector model of error correction makes it possible to measure deviations from equilibrium and the rate of its recovery, which indicates the greater efficiency of this technique. At the same time, the above results allow us to conclude that in the presence of a positive relationship between the volume of lending and the main indicators, the degree of influence of lending on economic growth is generally insignificant