Publikační činnost Katedry elektroenergetiky / Publications of Department of Electrical Power Engineering (410)

Permanent URI for this collectionhttp://hdl.handle.net/10084/70101

Kolekce obsahuje bibliografické záznamy publikační činnosti (článků) akademických pracovníků Katedry elektroenergetiky (410) v časopisech registrovaných ve Web of Science od roku 2003 po současnost.
Do kolekce jsou zařazeny:
a) publikace, u nichž je v originálních dokumentech jako působiště autora (adresa) uvedena Vysoká škola báňská-Technická univerzita Ostrava (VŠB-TUO),
b) publikace, u nichž v originálních dokumentech není v adrese VŠB-TUO uvedena, ale autoři prokazatelně v době jejich zpracování a uveřejnění působili na VŠB-TUO.

Bibliografické záznamy byly původně vytvořeny v kolekci Publikační činnost akademických pracovníků VŠB-TUO, která sleduje publikování akademických pracovníků od roku 1990.

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Now showing 1 - 20 out of 111 results

A novel approach to utilization vehicle to grid technology in microgrid environment
(Elsevier, 2024) Blažek, Vojtěch; Vantuch, Tomáš; Slanina, Zdeněk; Vysocký, Jan; Prokop, Lukáš; Mišák, Stanislav; Piecha, Marian; Walendziuk, Wojciech
This article presents a novel approach to the Vehicle To Grid (V2G) technology in a microgrid with a Demand Side Response (DSR) algorithm. The research describes the microgrid control system used on a physical testing platform. The platform simulates a small-scale microgrid with a photovoltaic plant (PV) as its primary stochastic energy source. The local control system is based on a Demand Side Response algorithm called Active Demand Side Management (ADSM). The ADSM algorithm is implemented with a non-dominated sorting genetic algorithm II (NSGA-2). The article presents the study of the microgrid operation using the results of two experiments. The first experiment includes three scenarios representing electricity consumption in three ordinary households, exploiting a small-scale microgrid during four seasons. Every scenario compares the microgrid’s insufficient energy with and without optimization, with an EV and without EV, and with the tariff mode (energy supply from the distribution network in a chosen time). The second experiment deals with the effect of the size of the static battery in the microgrid on insufficient energy and the efficiency of the optimization itself. The results reveal a fundamentally positive impact of optimizing the control system, which uses an EV, on the potential insufficient energy in the microgrid platform.
On the forecastability of solar energy generation by rooftop panels pointed in different directions
(IEEE, 2024) Jasiński, Michał; Homaee, Omid; Opałkowski, Daniel; Najafi, Arsalan; Leonowicz, Zbigniew
By increasing the penetration of small-scale rooftop solar panels, forecasting their output has become important to both homeowners and distribution systems operators. In many areas, the roof of residential houses is not such that all solar panels are installed pointing in one direction; so, they are installed pointing in different directions. In this letter, the effect of this phenomenon on the forecastability of the day-ahead solar panels' power output is experimentally investigated. To perform day-ahead energy forecasting, a feedforward artificial neural network (ANN) is created using historical data and weather conditions of a similar day along with the forecast weather conditions of the day for which the forecast is to be performed. A similar day selection algorithm based on Euclidean distance is used to determine the reference day. Two forecasting approaches have been compared: forecasting each panel output and forecasting the total output. Moreover, Long short-term memory (LSTM) is used to validate the conclusion made by the feedforward ANN. The results evidently show that considering different directions of the solar panels increases the forecastability of the rooftop solar power plant.
On the forecastability of solar energy generation by rooftop panels pointed in different directions
(IEEE, 2024) Jasiński, Michał; Homaee, Omid; Opałkowski, Daniel; Najafi, Arsalan; Leonowicz, Zbigniew
By increasing the penetration of small-scale rooftop solar panels, forecasting their output has become important to both homeowners and distribution systems operators. In many areas, the roof of residential houses is not such that all solar panels are installed pointing in one direction; so, they are installed pointing in different directions. In this letter, the effect of this phenomenon on the forecastability of the day-ahead solar panels' power output is experimentally investigated. To perform day-ahead energy forecasting, a feedforward artificial neural network (ANN) is created using historical data and weather conditions of a similar day along with the forecast weather conditions of the day for which the forecast is to be performed. A similar day selection algorithm based on Euclidean distance is used to determine the reference day. Two forecasting approaches have been compared: forecasting each panel output and forecasting the total output. Moreover, Long short-term memory (LSTM) is used to validate the conclusion made by the feedforward ANN. The results evidently show that considering different directions of the solar panels increases the forecastability of the rooftop solar power plant.
Application of extreme learning machine-autoencoder to medium term electricity price forecasting
(IEEE, 2023) Najafi, Arsalan; Homaee, Omid; Golshan, Mehdi; Jasiński, Michał; Leonowicz, Zbigniew
Electricity market prices are highly volatile, highly frequent, non-linear, and non-stationary which makes forecasting very complicated. Although accurate forecasting plays a crucial role in the electricity market for traders, retailers, large consumers as well as generation companies in terms of economic efficiency and power systems safety. Hence, this article proposes a new forecasting approach for medium-term electricity market prices based on an extreme learning machine-autoencoder (ELM-AE). The main idea behind this is to use trained weights for hidden layers instead of randomly generated weights. The input hidden layer weights are obtained by solving a network with the same input outputs by the autoencoder method. Then, the obtained output weights are used again as the input weights for a new ELM network. To do so, a data-set is created using input data, where the ahead 24 hours are forecasted based on the previous 168 data. The simulations have been performed on New York Independent System Operator prices and compared with the classic ELM demonstrating the high accuracy of the proposed method in both training and testing.
Experimental measurement of a pulling force and determination of a friction coefficient during driven transport rollers’ movement
(Elsevier, 2023) Hrabovský, Leopold; Mlčák, Tomáš; Molnár, Vieroslav; Fedorko, Gabriel; Michalik, Peter
The paper presents an experimental measurement of a pulling force and determination of a friction coefficient during driven transport rollers’ movement in a continuous transport system. To implement an experimental measurement, a laboratory machine was designed, enabling an experimental detection of a friction coefficient during movement in the contact surface of the transported load and the roller casings. The values of friction coefficient were determined for three material types: wood, plastic and rubber against the acrylic paint sprayed on a steel roller’s casing. Experimental pulling force measurement with a tensometric load sensor was realized for three different angles of the roller conveyor’s inclination. The results made it possible to determine a mean value of friction coefficient during movement between the contact surfaces of the three materials and the coating of driven rollers casings. The results can be implemented to digitize and monitor operation of electric motor machines.
Exploitation Perspective Index as a support of the management of the transformer fleet
(MDPI, 2023) Kunicki, Michał; Borucki, Sebastian; Fulneček, Jan
This paper presents an alternative approach to the Transformer Assessment Index (TAI) by proposing a relatively simple rating method called the Exploitation Perspective Index (EPI). The method provides two numerical indicators: the first reflects the overall technical condition of the particular unit, and the second shows the condition of the unit in the context of the entire fleet. The objective of the EPI method is to support the decision-making process regarding the technical condition assessment of each of the transformers in the target population, considering not only technical but also economic aspects of transformer maintenance. Application of the method is described step by step, including input data, parametrization of the weights, and interpretation of the output results it provides. The proposed method is evaluated by two representative use cases and compared with two other methods. As a result, EPI confirms its applicability, and it has already been successfully implemented by the electric power industry. EPI can be potentially freely adopted for any transformer fleet, as well as for the specific situation of the utility, by adjusting the relevant parameters.
A review on resonant inductive coupling pad design for wireless electric vehicle charging application
(Elsevier, 2023) Rahulkumar, J.; Narayanamoorthi, R.; Vishnuram, Pradeep; Balaji, C.; Goňo, Tomáš; Dočkal, Tomáš; Goňo, Radomír; Krejčí, Petr
Wireless Resonant Inductive Power Transfer (WRIPT) system is a recently emerging technology for Electrical Vehicles (EVs) charging applications in the automobile industry. WRIPT EV charging system is suitable for low-speed EVs, industrial lift EVs trucks, commercial EVs, marine-operated EVs, and other earth-moving EVs applications. The WRIPT system enables user-friendly EV charging, In-motion WRIPT EV charging system increases the travel range anxiety of the EVs for long distances and reduces the EV charging time duration. Also, this in-motion WRIPT charging system supports the EV with limited onboard battery capacity, with reduced size and weight of the battery. However, this WRIPT EV charging system suffers from low power transfer efficiency (PTE), when the intermediate air gap increases between the Tx and Rx coils or when the coupled coils are misaligned. Hence, designing an efficient magnetic coupling geometrical structure coil for a Tx and Rx power pad is one of the prime requirements of the WRIPT system. This article focuses on presents a review of WRIPT power pads for static and in-motion (quasi-dynamic and dynamic) EV charging applications. It describes the layers in inductive power pad construction arrangement, a selection of materials for developing an effective inductive power pad, and the current state of the art of power pad geometries. Also, the review focuses on WRIPT systems with various power capacities and their parameter specifications, along with prototypes available in various industries globally with system standards. Finally, the challenges with the WRIPT system and its future opportunities or research gaps for investigations are discussed in this article. The main objective of the WRIPT power pad is to achieve a much higher magnetic flux density (B) with minimum coil outer dimension, better tolerance misalignment, and well interoperability performance in coupling architecture. This survey helps the readers to understand the technical implementation of WRIPT coils and identify scientific or technical insights into WRIPT power pads.
Analyzing the effect of lightning channel impedance on the lightning overvoltages in wind turbines
(IEEE, 2023) Nasiri, Mohammad Javad; Homaee, Omid; Najafi, Arsalan; Jasiński, Michał; Leonowicz, Zbigniew
Lightning is a natural phenomenon that can cause serious damage to power systems including wind farms. Wind Turbines (WTs) are tall structures and are often installed in areas with high lightning activity levels which makes them exposed to direct strikes. One of the parameters affecting the performance of the lightning protection system is the impedance of the lightning channel. In this paper, the effects of this impedance on the lightning overvoltages in WTs, and the energy absorbed by Surge Arresters (SAs), as the main lightning protection device, are investigated. The case study is carried out on a WT with 2 MVA of rated power where a direct lightning strike with a maximum current magnitude of 50 kA is struck to one of the WT's blades. Accurate models, designed for transient studies, are used for modeling different parts of the WT. The results of different scenarios are compared, and it is shown that as the value of lightning channel impedance gets higher, the overvoltage caused by the corresponding lightning strike, with the same current magnitude, increases too. In addition, it is shown that the higher the lightning channel impedance, the higher the absorbed energy by the arrester. This shows the importance of the lightning channel impedance's effect on high-voltage studies of WTs, as it can help in selecting proper lightning protection devices.
Evolution of a summer peak intelligent controller (SPIC) for residential distribution networks
(MDPI, 2023) Parangusam, Kanakaraj; Lekshmana, Ramesh; Goňo, Tomáš; Goňo, Radomír
Electricity demand has increased tremendously in recent years, due to the fact that all sectors require energy for their operation. Due to the increased amount of modern home appliances on the market, residential areas consume a significant amount of energy. This article focuses on the residential community to reduce peak load on residential distribution networks. Mostly, the residential consumer’s power demand increases more during the summer season due to many air conditioners (AC) operating in residential homes. This paper proposes a novel summer peak intelli gent controller (SPIC) algorithm to reduce summer peak load in residential distribution transformers (RDT). This proposed SPIC algorithm is implemented in a multi-home energy management system (MHEMS) with a four-home hardware prototype and a real-time TNEB system. This hardware prototype is divided into two different cases, one with and one without taking user comfort into account. When considering consumer comfort, all residential homes reduce their peak load almost equally. The maximum and minimum contribution percentages in Case 2 are 29.82% and 19.30%, respectively. Additionally, the real-time TNEB system is addressed in two different cases: with and without incentive-based programs. In the real-time TNEB system during peak hours, the novel SPIC algorithm reduces peak demand in Case 1 by 113.70 kW, and Case 2 further reduces it to 118.80 kW. The peak load decrease in Case 2 during peak hours is 4.5% greater than in Case 1. In addition, we conducted a residential consumer opinion survey to validate the acceptance rate of the proposed design and algorithm.
Automatic identifier of socket for electrical vehicles using SWIN-Transformer and SimAM attention mechanism-based EVS YOLO
(IEEE, 2023) Mahaadevan, V. C.; Narayanamoorthi, R.; Goňo, Radomír; Moldřík, Petr
Electric vehicle (EV) technology is emerging as one of the most promising solutions for green transportation. The same growth occurs in the charging infrastructure development and automating the EV charging process. Globally, EVs has different types of charging sockets and it’s located at the various positions in the Vehicle. In simple, EV has a diversity in socket type and socket location. Hence, correctly identifying the socket type and location is mandatory to automate the charging process. The recent development in computer vision and robotic systems helps to automate EV charging without human intervention. Image processing and deep learning-based socket identification can help the EV charging infrastructure providers automate the process. Moreover, the deep learning techniques should be simple enough to implement in the real-time processing boards for experimental viability. Hence, this paper proposes a new You Only Look Once (YOLO) model called the Electric Vehicle Socket (EVS) YOLO that uses YOLOv5 as its base architecture with the addition of a vision-type transformer called the SWIN-Transformer and an attention mechanism called SimAM for better performance of the model in detecting the correct charging port. A dataset of 2700 images with six types of classes has been used to test the model, and the EVS -YOLO also evaluated with varying mechanisms of attention positioned at various places along the head. The paper contrasts the suggested model with alternative deep learning architectures and analyzes respective performances.
Voltage drop estimation during shore connection with the use of motor drives modified as static frequency converters
(MDPI, 2023) Vrzala, Matouš; Goňo, Radomír; Stacho, Břetislav; Lukianov, Semen
Ship-to-shore connection is an important technological element that reduces air pollution in ports. Therefore, ports install facilities that allow mooring ships to connect to the port distribution network. By 2025, this will be mandatory for all ports in Europe. This can be a challenging task in most ports due to the different frequency of the network and ship frequency. This problem can be solved by the use of grid-forming static frequency converters. This solution also brings some other advantages: The ship is not threatened by high shore short-circuit currents, and the port distribution network is not affected by the character of the ship load. However, frequency converter software must include a droop control algorithm to ensure that voltage deviations do not exceed the allowed limits during transients. Typical frequency converters used for shore connection are those developed as static frequency converters (SFCs). However, those converters were not developed for large power outputs, which are needed to power large vessels, such as ferries or cruise ships. This paper proposes motor drives that were modified to operate as SFCs. This approach has quite a lot of advantages which are described in this article. This paper describes both a standard shore connection system without a frequency converter and a solution that includes static frequency converters. The paper then focusses on voltage deviation estimations during connection/disconnection of large load (ferry or cruise ship) to static frequency converters. In this work, a high-voltage shore connection (HVSC) simulation model is developed, including a frequency converter, a shoreside transformer, medium-voltage (MV) connection cables, and a power system of the ship, to analyze in detail the behavior of the system in the case of connection or disconnection of the ship load. The model was made in DIgSILENT PowerFactory for the case of a commercial port in southern France. The model gives credible estimations of voltage drops/surges during transient and steady states.
Development of a high-gain step-up DC/DC power converter with magnetic coupling for low-voltage renewable energy
(IEEE, 2023) Du, Ruihong; Samavatian, Vahid; Samavatian, Majid; Goňo, Tomáš; Jasiński, Michał
There exists an extensive range of applications for elevated gain DC/DC converters, as numerous low-voltage resources are exploited for power supply. Therefore, this study introduces a groundbreaking magnetically coupled DC/DC converter specifically designed for resources with low voltage, including micro PV or fuel cell systems. By enduring low current and voltage stresses, the power devices in this converter ensure remarkable efficiency while maintaining proven voltage ratio capability. The operational principles of the converter are thoroughly discussed and supported by the implementation of a 200W-400V prototype. In order to confirm the effectiveness of the converter, a range of experimental tests were carried out in different scenarios, confirming its dependable functionality. This novel converter opens up new possibilities for harnessing the potential of low-voltage resources, offering an efficient and reliable solution for power conversion. The research yields a substantial advancement in renewable energy systems, showcasing the practicality and efficiency of the proposed converter in real-life scenarios, with the suggested converter achieving a remarkable 94.3% maximum efficiency during voltage transfer. The MATLAB software serves as the principal tool for conducting primary simulations and formulating the design equations for the circuit.
Realization of a test tool for diagnosis of contact resistance and measurement of selected types of conductive materials
(MDPI, 2023) Kačor, Petr; Bernat, Petr; Mlčák, Tomáš; Hrabovský, Leopold
Contact connections in electrical machines and apparatus are important elements in the whole power supply network and a high level of reliability is expected there. Contact resistance is a fundamental criterion in the design of an electrical contact or contact system. The contact resistance should be as low as possible to minimize losses due to the current passage and the related heating of the contact connection. The value of the contact resistance depends on the material used, the value of the applied force, the type of contact, and, last but not least, the quality of the surface and chemical layers. In this paper, an initial diagnosis of the contact material is performed based on the determination of the sample’s specific resistivity by the four-wire method and the evaluation of the measurement uncertainty. The work is followed by the design of a testing device that uses crossed bars to measure the change in contact resistance as a function of the magnitude of the applied force. An analysis of the sample mounting method is performed here using FEM simulations of the current field and shows the interaction between the holder and the sample in terms of current line transfer. The proposed system is then used for experimental measurements of the material-dependent coefficient KC for verification of existing or newly developed materials in electrical engineering, where the values of the KC coefficient are not known. Finally, the paper also deals with the measurement of fritting voltage for individual contact pairs having surface quality corresponding to brushing.
Virtual energy storage system for peak shaving and power balancing the generation of a MW photovoltaic plant
(Elsevier, 2023) Burgio, Alessandro; Cimmino, Domenico; Dolatabadi, Mohammad; Jasiński, Michał; Leonowicz, Zbigniew; Siano, Pierlugi
This article proposes a novel control of a Virtual Energy Storage System (VESS) for the correct management of non-programmable renewable sources by coordinating the loads demand and the battery storage systems operations at the residential level. The proposed novel control aims at covering two main gaps in current state-of-the-art VESSs. The first gap is considering a distributed battery storage system instead of a centralized one, the second gap is providing the electricity grid operator with two services instead of one. To this aim, the authors explore a VESS consisting of residential buildings where each apartment is equipped with an air conditioner but also with a battery storage system. The explored VESS provides the grid operator with both peak shaving and power balancing services for the generation of a megawatt photovoltaic plant located near the VESS. The goodness of the proposed coordinated control is demonstrated via numerical experiments and using real data, measured every 15 min in September 2019. The case study consists of a 1.4 MW photovoltaic plant located near a small town, 21 residential buildings with 168 apartments, each equipped with an air conditioner (continuous power is 1.5 kW) and battery energy storage systems (3 kW /2.5 kWh). The numerical results show that the battery energy storage systems are charged correctly during peak hours (the charging power is between 0.45 and 0.90 kW, and the state of charge varies from 20 % to 78 %) and that the residual photovoltaic plant generation resembles a horizontal line. Later, in the early afternoon, the reference temperature of the air conditioners and the charge/discharge of the battery storage systems are suitably adjusted by solving a mixed linear integer programming problem, to balance the reduction in photovoltaic plant generation, which lasts an hour and a half and peaks at 188 kW. Finally, the numerical results also show that the energy that remained in the batteries is entirely consumed by users in the late afternoon or evening and that the amplitude and the duration of the so-called “load rebound” are so slight that no compensation action (e.g., the bath returning or linear recovery strategy) is required for the considered case study.
Green synthesis of MeOH derivatives through in situ catalytic transformations of captured CO2 in a membrane integrated photo-microreactor system: A state-of-art review for carbon capture and utilization
(Elsevier, 2023) Chakrabortty, Sankha; Kumar, Ramesh; Nayak, Jayato; Jeon, Byong-Hun; Dargar, Shashi Kant; Tripathy, Suraj K.; Pal, Parimal; Ha, Geon-Soo; Kim, Kwang Ho; Jasiński, Michał
Globally, industrial production sectors have become increasingly concerned about reducing CO2 evolution, through planned carbonization with concurrent substitution of fossil fuels with renewable energy resources, since the release of the Paris climate accord regulations. CO2 is an inexpensive substrate used for the production of useful chemicals and fuels through various chemical and biological processes. As a result, reducing CO2 emissions while producing non-fossil fuels, such as methanol or its derivatives, could be an appealing solution to the global energy problems. The high cetane number, low autoignition temperature, and low extract pollutant value of dimethyl ether, one of the most valuable methanol derivatives, make it a clean and eco-friendly alternative to fossil fuels. Recent literature from the last five years is critically reviewed in the present study to assess the current best practices for CO2 capture and conversion into high value fuels. Particular emphasis has been placed on atmospheric CO2 capture, photoconversion, and the downstream purification of the final product using membrane-based technologies for a sustainable future. Currently, there is a compelling need for an impending transition away from fossil fuel-based technologies toward inventive new technologies using renewable energy sources through carbon management via CO2 conversion and utilization.
Integrating hydrogen technology into active distribution networks: The case of private hydrogen refueling stations
(Elsevier, 2023) Najafi, Arsalan; Homaee, Omid; Jasiński, Michał; Tsaousoglou, Georgios; Leonowicz, Zbigniew
Private hydrogen refueling stations (HRSs) are expected to be an integrated part of active distribution networks (ADNs) in the near future. In this paper, we consider an ADN operator, responsible for serving the network’s electricity demands in a cost-effective manner, while ensuring the network’s operational safety. The ADN’s resources include micro-turbines, energy storage systems, and private HRSs that deliberate over buying hydrogen directly, or converting electricity to hydrogen on-site by using their electrolyzers and hydrogen tanks. Each HRS is after maximizing its profit, stemming from serving the stochastic demands of hydrogen vehicles. In the presence of stochastic hydrogen demands, volatile wholesale electricity market prices, and deliberate, profit-maximizing HRSs, the ADN’s goal takes the form of a stochastic-robust bi-level optimization problem. After a number of reformulations, we bring the problem to a solvable form. Numerical results demonstrate the effectiveness of the model towards integrating private HRSs into ADNs, and maintaining the ADN’s safe operation under severe uncertainties.
An improvement in dynamic behavior of single phase PM brushless DC motor using deep neural network and mixture of experts
(IEEE, 2023) Zhang, Yang; Goňo, Radomír; Jasiński, Michał
Brushless DC motors play a vital role as a workhorse in many applications, especially home appliances. In the competitive world of the day, a brushless DC motor is a wise choice for many applications because of its high power density, a simple driving circuit, and high efficiency. Accordingly, demonstrating the feasibility of a new controller on this type of motor has undoubtedly paramount importance. Two methods of speed controllers, namely linear-quadratic regulator, and proportional-integral-derivative, are mixed using a mixture of experts (MoE) for a single-phase PM brushless DC external rotor motor. The dynamic model of the SP PM BLDC ER motor characterizes the behavior of the motor, involving cogging torque and electromotive force (EMF) gained from 2D finite element analyses. The motor is supplied by a pulse width modulation inverter with a constant voltage source. The results disclose that the SP PM BLDC performance is enhanced and more robust during load disturbance. ANSYS and MATLAB environments are used for obtaining finite element analysis and dynamic analysis of single-phase PM brushless DC external rotor motors, respectively. The merits of the proposed approach are validated through implementing a low-scale experimental setup.
Global simulation model design of input-serial, output-parallel solid-state transformer for smart grid applications
(MDPI, 2023) Takács, Kristián; Frivaldský, Michal; Kindl, Vladimír; Bernat, Petr
This paper provides an overview of an early attempt at developing a simulation model on a solid-state transformer (SST) based on input-serial and output-parallel (ISOP) topology. The proposed SST is designed as a base for a smart grid (SG). The paper provides a theoretical review of the power converters under consideration, as well as their control techniques. Further, the paper presents a simulation model of the proposed concept with a PLECS circuit simulator. The proposed simulation model examines bidirectional energy flow control between the medium-voltage AC grid and DC smart grid, while evaluating power flow efficiency and qualitative indicators of the AC grid. After the completion of design verification and electrical properties analysis by the PLECS simulation models, the synthesis offers recommendations on the optimal layout of the proposed SST topology for smart grid application.
Short-term load forecasting models: A review of challenges, progress, and the road ahead
(MDPI, 2023) Akhtar, Saima; Shahzad, Sulman; Zaheer, Asad; Ullah, Hafiz Sami; Kilic, Heybet; Goňo, Radomír; Jasiński, Michał; Leonowicz, Zbigniew
Short-term load forecasting (STLF) is critical for the energy industry. Accurate predictions of future electricity demand are necessary to ensure power systems’ reliable and efficient operation. Various STLF models have been proposed in recent years, each with strengths and weaknesses. This paper comprehensively reviews some STLF models, including time series, artificial neural networks (ANNs), regression-based, and hybrid models. It first introduces the fundamental concepts and challenges of STLF, then discusses each model class’s main features and assumptions. The paper compares the models in terms of their accuracy, robustness, computational efficiency, scalability, and adaptability and identifies each approach’s advantages and limitations. Although this study suggests that ANNs and hybrid models may be the most promising ways to achieve accurate and reliable STLF, additional research is required to handle multiple input features, manage massive data sets, and adjust to shifting energy conditions.
The use of the imperialist competitive algorithm in optimising the setting of the tram speed controller in the development of a Matlab-Simulink environment
(Kauno technologijos universitetas, 2023) Kolář, Václav; Demel, Lukáš; Hrbáč, Roman; Cigánek, Jiří; Zajaczek, Stanislav; Ďurica, Maroš
Estimating the electric power used by railway vehicles is an important factor in the planning of future power consumption, looking for possibilities to reduce the use of electric power and therefore also reduce carbon emissions. To improve the estimation, we used the imperialist competitive algorithm in the optimisation process of a mathematical model of a tram vehicle. Specifically, in the setting of the proportional and summation constant of the vehicle speed controller which emulates the activity of the driver in the simulation. Our work presents a new approach to optimising the estimation of energy consumption in tram transport. The method used is based on mathematical modelling and simulation of social development in human society. To obtain the input data for the simulation, we performed a measurement of the reference speed by means of a GPS receiver located in a sample tram vehicle. Subsequently, to verify the model and energy calculation results, we measured the output currents and voltage from the traction converter station at the corresponding time. Our method achieved a 93 % match between the measured and simulated power consumption.

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