Operational Forecasting of Wind Speed for an Self-Contained Power Assembly of a Traction Substation

Currently, the prospects of creating hybrid power assemblies using renewable energy sources, including wind energy, and energy storage systems based on hydrogen energy technologies are being considered. To control such an energy storage system, it is necessary to perform operational renewable sources generation forecasting, particularly forecasting of wind power assemblies. Their production depends on the speed and direction of the wind. The article presents the results of solving the problem of operational forecasting of wind speed for a hybrid power assembly project aimed at increasing the capacity of the railway section between Yaya and Izhmorskaya stations (Kemerovo region of the Russian Federation). Hourly data of wind speeds and directions for 15 years have been analyzed, a neural network model has been built, and a compact architecture of a multilayer perceptron has been proposed for short-term forecasting of wind speed and direction for 1 and 6 hours ahead. The model that has been developed allows minimizing the risks of overfitting and loss of forecasting accuracy due to changes in the operating conditions of the model over time. A specific feature of this work is the stability investigation of the model trained on the data of long-term observations to long-term changes, as well as the analysis of the possibilities of improving the accuracy of forecasting due to regular further training of the model on newly available data. The nature of the influence of the size of the training sample and the self-adaptation of the model on the accuracy of forecasting and the stability of its work on the horizon of several years has been established. It is shown that in order to ensure high accuracy and stability of the neural network model of wind speed forecasting, long-term meteorological observations data are required. © Belarusian National Technical University, 2023

Оперативное прогнозирование скорости ветра для автономной энергетической установки тяговой железнодорожной подстанции

Currently, the prospects of creating hybrid power assemblies using renewable energy sources, including wind energy, and energy storage systems based on hydrogen energy technologies are being considered. To control such an energy storage system, it is necessary to perform operational renewable sources generation forecasting, particularly forecasting of wind power assemblies. Their production depends on the speed and direction of the wind. The article presents the results of solving the problem of operational forecasting of wind speed for a hybrid power assembly project aimed at increasing the capacity of the railway section between Yaya and Izhmorskaya stations (Kemerovo region of the Russian Federation). Hourly data of wind speeds and directions for 15 years have been analyzed, a neural network model has been built, and a compact architecture of a multilayer perceptron has been proposed for short-term forecasting of wind speed and direction for 1 and 6 hours ahead. The model that has been developed allows minimizing the risks of overfitting and loss of forecasting accuracy due to changes in the operating conditions of the model over time. A specific feature of this work is the stability investigation of the model trained on the data of long-term observations to long-term changes, as well as the analysis of the possibilities of improving the accuracy of forecasting due to regular further training of the model on newly available data. The nature of the influence of the size of the training sample and the self-adaptation of the model on the accuracy of forecasting and the stability of its work on the horizon of several years has been established. It is shown that in order to ensure high accuracy and stability of the neural network model of wind speed forecasting, long-term meteorological observations data are required.В настоящее время рассматриваются перспективы создания гибридных энергетических установок с использованием возобновляемых источников энергии, в том числе энергии ветра, и систем накопления энергии на базе технологий водородной энергетики. Для управления такой системой накопления энергии необходимо оперативное прогнозирование генерации от возобновляемых источников, в частности ветровых энергетических установок. Их выработка зависит от скорости и направления ветра. В статье представлены результаты решения задачи оперативного прогнозирования скорости ветра для проекта гибридной энергетической установки, направленной на повышение пропускной способности железнодорожного участка между станциями Яя и Ижморская (Кемеровская область Российской Федерации). Проанализированы почасовые данные скоростей и направлений ветра за 15 лет, построена нейросетевая модель и предложена компактная архитектура многослойного перцептрона для краткосрочного прогнозирования скорости и направления ветра на 1 и 6 ч вперед. Разработанная модель позволяет минимизировать риски переобучения и потери точности прогнозирования из-за изменения условий работы модели со временем. Особенность данной статьи заключается в исследовании устойчивости модели, обученной на данных многолетних наблюдений, к долгосрочным изменениям, а также анализе возможностей повышения точности прогнозирования за счет регулярного дообучения модели на вновь поступающих данных. Установлен характер влияния размера обучающей выборки и самоадаптации модели на точность прогнозирования и устойчивость ее работы на горизонте в несколько лет. Показано, что для обеспечения высокой точности и устойчивости нейросетевой модели прогнозирования скорости ветра необходимы данные многолетних метеорологических наблюдений

Оперативное прогнозирование скорости ветра для автономной энергетической установки тяговой железнодорожной подстанции

В настоящее время рассматриваются перспективы создания гибридных энергетических установок с использованием возобновляемых источников энергии, в том числе энергии ветра, и систем накопления энергии на базе технологий водородной энергетики. Для управления такой системой накопления энергии необходимо оперативное прогнозирование генерации от возобновляемых источников, в частности ветровых энергетических установок. Их выработка зависит от скорости и направления ветра. В статье представлены результаты решения задачи оперативного прогнозирования скорости ветра для проекта гибридной энергетической установки, направленной на повышение пропускной способности железнодорожного участка между станциями Яя и Ижморская (Кемеровская область Российской Федерации). Проанализированы почасовые данные скоростей и направлений ветра за 15 лет, построена нейросетевая модель и предложена компактная архитектура много- слойного перцептрона для краткосрочного прогнозирования скорости и направления ветра на 1 и 6 ч вперед. Разработанная модель позволяет минимизировать риски переобучения и потери точности прогнозирования из-за изменения условий работы модели со временем. Особенность данной статьи заключается в исследовании устойчивости модели, обученной на данных многолетних наблюдений, к долгосрочным изменениям, а также анализе возможностей повышения точности прогнозирования за счет регулярного дообучения модели на вновь поступающих данных. Установлен характер влияния размера обучающей выборки и самоадаптации модели на точность прогнозирования и устойчивость ее работы на горизонте в несколько лет. Показано, что для обеспечения высокой точности и устойчивости нейросетевой модели прогнозирования скорости ветра необходимы данные многолетних метеорологических наблюдений

Будущее радиоуправляемых стрелок с автономным питанием

A promising control method for railways, that takes into account minimization of the number of outdoor technological facilities, use of wireless data transmission technologies, as well as use of renewable energy sources for decentralized switch systems is considered. It is proposed to implement all other commands through the use of cyber-protected radio channel while renouncing to discrete positioning of mobile units and discrete transmission of data on the speed patterns of train movement. The system of switches with autonomous power and use of a radio channel to manage and control the position of switch blades must be adapted in stages, starting from the local level and basing on availability of portable devices and going to comprehensive centralised control system within the station.Описывается перспективный способ управления на железнодорожном транспорте, учитывающий минимизацию числа напольных технологических объектов, использование беспроводных технологий передачи данных, а также применение источников возобновляемой энергии для децентрализованных подсистем стрелочных переводов. Все остальные команды предложено реализовывать путём применения киберзащищённого радиоканала с отказом от дискретного позиционирования подвижных единиц и дискретной передачи данных о скоростных режимах движения поездов. Система стрелок с автономным питанием и использованием радиоканала для управления и контроля положения остряков должна адаптироваться поэтапно, начиная с местного уровня и наличия носимых устройств и заканчивая глобальной централизацией в пределах станции

CognitiveCharge: disconnection tolerant adaptive collaborative and predictive vehicular charging

Electric vehicles (EVs) are rapidly becoming more common and ownership is set to rise globally in coming years. The potential impacts of increased EVs on the electrical grid have been widely investigated and in its current state, existing grid infrastructure will struggle to meet the high demands at peak charging hours. The limited range of electric cars compounds this issue. We therefore propose CognitiveCharge, a novel approach to predictive and adaptive disconnection aware opportunistic energy discovery and transfer for the smart vehicular charging. CognitiveCharge detects and reacts to individual nodes and network regions which are at risk of getting depleted by using implicit predictive hybrid contact and resources congestion heuristics. CognitiveCharge exploits localised relative utility based approach to adaptively offload the energy from parts of the network with energy surplus to depleting areas with non-uniform depletion rates. We evaluate CognitiveCharge using a multi-day traces for the city of San Francisco, USA and Nottingham, UK to compare against existing infrastructure across a range of metrics. CognitiveCharge successfully eliminates congestion at both ad hoc and infrastructure charging points, reduces the time that a vehicle must wait to charge from the point at which it identifies as being in need of energy, and drastically reduces the total number of nodes in need of energy over the evaluation period

Enhancing smart city operation management: integrating energy systems with a subway synergism hub

This paper is centered on establishing a secure framework for the optimal concurrent operation of a smart city, encompassing transportation, water, heat, electrical, and cooling energy systems. The studied smart city includes the microgrid, smart transportation system (STS), energy hub (EH) and smart grid. In this regard, a subway synergism hub (SSH) as a new non-energy system is added to the smart city with the aim of serving the subway's water, heat, electrical and cooling demands as well as diminishing the operation cost of the smart city. The EH within the SSH cooperated with a desalination unit is considered to supply the subway's stations water demand by using the sea water. The investigation of the optimal allocation of the SSH unit for reducing the cost of smart city operation is also conducted by introducing a novel intelligent priority selection (IPS) analytical algorithm. In comparison to common meta-heuristic algorithms for allocation problems, the accurate optimal solution can be found in low runtime by the IPS algorithm. To achieve an accurate model of the smart city, directed acyclic graph (DAG) based blockchain approach is provided which can enhance the data and energy exchanges security within the smart city. This research paper introduces a security framework deployed in a smart city setting to establish a secure platform for energy transactions. The findings validate the effectiveness of this model and highlight the value of the IPS method. The effectiveness of the suggested approach has been assessed using the smart city system is comprised of various sections, including EVs, smart grid, microgrid, and SSH, demonstrating the credibility and accuracy of this study

A Highly Reliable Propulsion System with Onboard Uninterruptible Power Supply for Train Application:Topology and Control

Providing uninterrupted electricity service aboard the urban trains is of vital importance not only for reliable signaling and accurate traffic management but also for ensuring the safety of passengers and supplying emergency equipment such as lighting and signage systems. Hence, to alleviate power shortages caused by power transmission failures while the uninterruptible power supplies installed in the railway stations are not available, this paper suggests an innovative traction drive topology which is equipped by an onboard hybrid energy storage system for railway vehicles. Besides, to limit currents magnitudes and voltages variations of the feeder during train acceleration and to recuperate braking energy during train deceleration, an energy management strategy is presented. Moreover, a new optimal model predictive method is developed to control the currents of converters and storages as well as the speeds of the two open-end-windings permanent-magnet-synchronous-machines in the intended modular drive, under their constraints. Although to improve control dynamic performance, the control laws are designed as a set of piecewise affine functions from the control signals based on an offline procedure, the controller can still withstand real-time non-measurable disturbances. The effectiveness of proposed multifunctional propulsion topology and the feasibility of the designed controller are demonstrated by simulation and experimental results

Recuperation of Regenerative Braking Energy in Electric Rail Transit Systems

Electric rail transit systems are large consumers of energy. In trains with regenerative braking capability, a fraction of the energy used to power a train is regenerated during braking. This regenerated energy, if not properly captured, is typically dumped in the form of heat to avoid overvoltage. Finding a way to recuperate regenerative braking energy can result in substantial economic as well as technical benefits. Regenerative braking energy can be effectively recuperated using wayside energy storage, reversible substations, or hybrid storage/reversible substation systems. In this research study, we compare these recuperation techniques and investigate their application in New York City Transit (NYCT) systems, where most of the regenerative braking energy is currently being wasted. We have developed a detailed transient model to determine the applicability, feasibility, and pros and cons of deploying wayside energy storage, such as batteries, super capacitors or flywheels. This model has been validated using real measurement data on the 7-Line (Flushing), including:1) speed, current, voltage, power and energy train profiles; and 2) 24-hour interval metering data at substations. The validated model has been used to analyze and compare various ESS technologies, including Li-ion Battery, Supercapacitor and Flywheel. In addition, we have developed detailed transient models for reversible substations. A reversible substation, also known as bidirectional or inverting substation, provides a path through an inverter for regenerative braking energy to feedback to the upstream AC grid. This energy can be consumed by AC equipment within passenger stations (e.g., escalators) or fed back to the main grid based on legislations of the electric distribution utility. This study will provide crucial technical as well as financial guidelines for various stakeholders while making investment decisions pertaining to regenerative braking energy