Emergency control algorithm for maintaining power stability based on newton's method

The problem of stability boundary evaluation for the current power system state is of paramount importance for power system operation. In addition, maintaining stability after large scale disturbances has come to the fore in recent years. This paper presents the algorithm, which allows us to both evaluate the stability boundary of a power system and to calculate emergency control actions for maintaining stability in the case of blackout. The algorithm is based on Newton's method for solving optimization problems. There are a number of emergency actions algorithms in literature, but most of them use heuristic rules. On the contrary, the proposed method has a reasonable analytical background. Thus, having an adequate power system model, the proposed method is able to calculate more accurate control actions. The paper demonstrates the very basic idea of the approach with the simplest example. © 2014 WIT Press.International Journal of Safety and Security Engineering;International Journal of Sustainable Development and Planning;WIT Transactions on Ecology and the Environmen

Industry experience of developing day-ahead photovoltaic plant forecasting system based on machine learning

This article highlights the industry experience of the development and practical implementation of a short-term photovoltaic forecasting system based on machine learning methods for a real industry-scale photovoltaic power plant implemented in a Russian power system using remote data acquisition. One of the goals of the study is to improve photovoltaic power plants generation forecasting accuracy based on open-source meteorological data, which is provided in regular weather forecasts. In order to improve the robustness of the system in terms of the forecasting accuracy, we apply newly derived feature introduction, a factor obtained as a result of feature engineering procedure, characterizing the relationship between photovoltaic power plant energy production and solar irradiation on a horizontal surface, thus taking into account the impacts of atmospheric and electrical nature. The article scrutinizes the application of different machine learning algorithms, including Random Forest regressor, Gradient Boosting Regressor, Linear Regression and Decision Trees regression, to the remotely obtained data. As a result of the application of the aforementioned approaches together with hyperparameters, tuning and pipelining of the algorithms, the optimal structure, parameters and the application sphere of different regressors were identified for various testing samples. The mathematical model developed within the framework of the study gave us the opportunity to provide robust photovoltaic energy forecasting results with mean accuracy over 92% for mostly-sunny sample days and over 83% for mostly cloudy days with different types of precipitation. © 2020 by the authors. Licensee MDPI, Basel, Switzerland

Behavior of the emission from atomic oxygen of the atmosphere at 557.7 nm during the events of stratospheric warming over East Siberia

The influence of stratospheric warming on the atomic oxygen emission at 557.7-nm wavelength (airglow) was investigated based on data obtained at the Geophysical Observatory of ISTP SB RAS (52°N, 103°E) in [1997][1998][1999][2000][2001][2002][2003][2004][2005]. In this period, several cases of anomalous behavior of the 557.7-nm emission intensity in the absence of strong geomagnetic disturbances have been recorded. It has been found that these cases of the growth of intensity of atmospheric emission at 557.7 nm in the height region of the mesosphere and lower thermosphere, have been caused by strong stratospheric warming when the disturbance covers a wide range of atmospheric altitudes. It should be emphasized that high concentration of centers of stratospheric warming is characteristic of Asia and, in particular, of the East Siberia that can result in the formation of regional peculiarities in characteristics of the airglow