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This paper applies Bayesian inference with normal-normal conjugate to forecast renewable energy generation. The generation forecasts a probability distribution rather than a quantitative value. An assumed normal distribution is initialized for renewable energy generation. This assumed normal distribution's parameters, the mean μ, and the standard deviation σ, are inferred by Bayesian inference afterward. However, applying Bayesian inference barely shall encounter an intractable integral. To circumvent the intractable integral, this paper considers the normal-normal conjugate method. This method fixes the assumed normal distribution's σ and characterizes μ as another normal distribution and then infers the latter normal distribution parameters. A case study of waste-to-energy generation forecast in Taiwan is investigated in this paper. It has been found from the investigation that the Bayesian inferred probability distribution outperforms the assumed one. [ABSTRACT FROM AUTHOR]

This paper accentuates the study of LLC resonant converter by a comparative analysis of the properties of LLC resonant and pulse width modulation direct current-direct current converters. Lately, LLC resonant converters have become more appealing and desirable in many applications than other pulse width modulation converters (e.g., Buck, Boost, Cuk) for their soft-switching techniques like zero voltage switching, zero current switching as well as low electromagnetic interference. This paper presents an analysis of efficiency variation, output voltage's ripple, and various transient performances like percentage overshoot, and the settling time with the variation of load current in a comparative way between LLC resonant converter's buck, boost operation, and conventional pulse width modulation converter's buck, boost operation. Feedback prop ortio nal-i ntegr al-d eriva tive duty cycle controller is used in all converter topologies for some specific analysis. A fixed input voltage of 100 V is selected for simulation and an output voltage of 24 V for buck operation and 120 V for boost operation are chosen. For simulation purposes, MATLAB/SIMULINK software is used. [ABSTRACT FROM AUTHOR]

Reconfiguration of the power distribution system allows for minimal real power losses and compliance with the required bus voltage limits of the power system. This paper proposes the network reconfiguration of the IEEE 33-bus radial distribution system using heuristic optimization algorithms. This problem-solving approach has the advantage of identifying the optimal system configuration solution by evaluating all possible solutions to the problem. The least power losses, voltage deviation, and compliance with the required bus voltage limits of the power system determine the optimal solution. To solve this problem, three types of heuristic optimization algorithms are used: Particle Swarm Optimization (PSO), Multi-Verse Optimization (MVO), and Grey Wolf Optimization (GWO). Using heuristic optimization algorithms to solve the reconfiguration problem, the active power losses of the power distribution system decreased by 46%. The optimal results obtained using the GWO algorithm are compiled in less time than other algorithms. [ABSTRACT FROM AUTHOR]

Control of robots to ensure safe operation has been a current research topic ever since the manipulators were utilized in industrial applications. Humans and robots work together collaboratively more often these days. Providing a safer operation has become an issue that requires more attention. In this paper, the application of a control algorithm that combines a proportional plus derivative controller with an overshoot avoidance trajectory tracking mechanism is introduced. The operation of the algorithm is illustrated on a two-link planar manipulator. The performance is shown both by means of graphs of end effector trajectories and mean absolute error metric. The results indicate that depending on the gain of the proportional plus derivative controller, desired trajectories can be followed very closely and the error becomes negligibly small. [ABSTRACT FROM AUTHOR]

This paper explores the complex interactions that exist between national ancillary service markets and local flexibility in Türkiye's changing power landscape. The significance of grid flexibility is escalating, especially with the country's transition from a predictable energy generation model to one predominantly fueled by intermittent renewable sources, coupled with the imminent integration of nuclear power. Ancillary services, encompassing functions such as frequency control, voltage regulation, and system restart, play a pivotal role in upholding grid stability. The study particularly underscores the significance of frequencyrelated ancillary services in preserving grid equilibrium. Moreover, it emphasizes how important electricity distribution businesses are as major players in improving grid stability. These businesses can actively participate in ancillary service markets and support grid stability by utilizing local flexibility solutions like distributed generation, energy storage, and demand response. This benefits the grid as well as consumers. The central objective of the study is to explore the synergies between local flexibility solutions and national ancillary markets. A case study is employed to evaluate the potential benefits and profitability associated with the integration of these components for an electrical distribution company. A combination solution utilizing solar panels, battery storage systems, electric vehicle charging infrastructure, and a natural gas turbine within the gearbox company's network is the subject of a case study that is presented. The concept of local flexibility and its mutually beneficial relationship with the national auxiliary service markets are also clarified in the study. The findings demonstrate the potential profitability and advantages of battery storage for the national energy grid. A substantial increase in prices for Frequency Restoration Reserve is imperative to render the natural gas turbine economically sustainable. [ABSTRACT FROM AUTHOR]

This paper investigates outage management in electricity distribution networks through the application of artificial intelligence techniques. The core of the system utilizes a diverse dataset compiled from outage management system records, weather forecasts, and geographical data to predict potential electricity outages. The data is rigorously analyzed to determine correlations between various weather conditions and outage occurrences, with particular emphasis on the impact of wind speed and storm conditions. The predictive model, a cornerstone of this research, employs a hybrid artificial intelligence algorithm that integrates outputs from convolutional neural networks, recursive neural networks, and extreme gradient boosting. The predictions are further refined using a feedforward neural network and distributed to specific districts based on historical data trends. Comparative analysis against a naive model based on historical averages highlights the superior performance of the hybrid model, showcasing its reduced error rates and enhanced predictive accuracy. This decision support system not only provides reliable outage predictions but also facilitates more effective management strategies, thus improving operational efficiencies and customer service in electricity distribution. The findings underscore the potential of advanced analytics in transforming utility management and pave the way for further innovations in smart grid technology and outage prevention strategies. [ABSTRACT FROM AUTHOR]

Estimating the state of charge (SOC) of a battery in the lithium-ion batteries used in hybrid vehicles is of high significance to ensure safe operation and to prevent overmuch charging and discharging. Despite the great importance of the parameter SOC, this parameter cannot be directly measured from the battery terminals. Thus, there is the need to estimate it. So far, various methods for estimating the SOC of lithium-ion batteries have been introduced. In this paper, by using the estimator of recursive least squares, firstly, the battery parameters are estimated and calculated and also the modified particle filter has been used for estimating the SOC of the battery. The standard particle filter has the problem of particle degeneracy phenomenon that decreases the estimation accuracy. Thus, in modified filter based PSO, the difference evolutionary algorithm (DEA) and Markov chain Monte Carlo (MCMC) applied to the standard PF that causes the estimation of SOC more accurate and consistent. To obtain an accurate and reliable method for estimating SOC, the desired battery must be modeled that in this paper, the first order resistor capacitor (RC) model has been used to have the high accuracy and low calculation complexity. This model is able to model the dynamic behavior of battery and for this reason it is suitable for hybrid vehicles application. To evaluate the performance of the proposed method, this method is compared with other methods that results represent the effective performance of the proposed method compared to other methods. [ABSTRACT FROM AUTHOR]

Conventional protection systems in distribution systems are capable of identifying low-impedance faults since such faults produce high fault current levels. But in a high impedance fault case protection schemes fail to detect a fault due to the fact that such faults do not generate enough fault current to operate conventional protection devices and fuses. This paper presents a novel algorithm for high impedance fault detection in medium voltage distribution systems. The proposed algorithm leverages multi-resolution analysis using discrete wavelet transform analysis for improved reliability and accuracy, extracting d5 coefficients at the transformer secondary terminal of all phases and the neutral, calculating their energies and integrating and calculating the average slope and comparing them to a threshold slope value in order to take the right decision for high impedance fault detection. The research explores the algorithm's effectiveness through extensive testing using MATLAB/Simulink software, demonstrating its potential as a valuable tool for enhancing the reliability of medium voltage distribution systems. [ABSTRACT FROM AUTHOR]

In ensuring sustainable power delivery under rapid growth in demand, modern power grids are characterized by advanced solutions such as flexible alternating current transmission systems and distributed generation. However, flexible alternating current transmission systems and distributed generations are often planned by their respective system operators, ignoring their coordination and impacting system-wide performance. This paper develops a bi-level optimization approach for flexible alternating current transmission systems and distributed generation coordination in an integrated transmission and distribution network to improve available transfer capability, power losses, and voltage deviation. The approach comprises inner and outer optimization. Inner optimization implements a hybrid of particle swarm optimization and Active Power Flow Performance Index for flexible alternating current transmission systems' planning. At the same time, the outer optimization employs multi-objective particle swarm optimization, which targets distributed generation planning at the distribution network--the integrated transmission and distribution network models' both transmission and distribution section. To demonstrate the effectiveness of the developed approach, two models of distributed generations, only real power and real and reactive power injections, were separately coordinated with a thyristor-controlled series compensator and static synchronous series compensator. Results show superior available transfer capability enhancement with thyristor-controlled series compensator-power injectionsDG and static synchronous series compensator-power injectionsDG, compared to the non-coordinated scenario. Pareto front plots of available transfer capability, power losses, and voltage deviation are such that after some maximum available transfer capability, the slope of the Pareto approaches zero. [ABSTRACT FROM AUTHOR]

In this paper, a new three-phase unbalanced distribution load flow (DLF) technique is proposed to study the impact of distributed generations (DGs) installed in the distribution networks in mitigating the voltage sag. Distributed generations are modeled as variable reactive power and constant power factor source with both wind- and solar-powered DG in the case study. The proposed DLF technique is based on the application of set theory. Effect of DGs in mitigating voltage sag is mathematically explained with the help of phasor diagram. The proposed technique is tested over standard IEEE 13 and IEEE 123 bus distribution networks with programming in MATLAB platform. To justify the claims, different cases have been studied like load increment and increasing maximum power limit of DGs. Also, a comparison is drawn between proposed DLF technique and traditional backward–forward sweep method where the efficacy of proposed technique is proved over the traditional method. [ABSTRACT FROM AUTHOR]

The increasing demand for sustainable energy solutions has sparked a growing interest in solar power plants (SPPs) as a viable option for reducing reliance on conventional energy sources. With the integration of SPP, the potential arises for a decrease in grid dependency during the day and improved stability in demand-side load profiles. To enhance the contribution of SPP toward sustainable energy solutions, it is crucial to explore effective load management strategies, including peak clipping. This paper investigates peak clipping as one of the demand-side management (DSM) techniques facilitated by SPP. The objective of this study is to provide comprehensive insights into the practical application of peak clipping techniques and their impact on improving the load factor (LF). To achieve this objective, the analysis in this study utilizes real data derived from an operational SPP located in the central campus of Manisa Celal Bayar University. To evaluate the impact of SPP on peak clipping and LF, a series of diverse case studies which encompass a range of campus scenarios are undertaken. The aim is to thoroughly analyze and understand how SPP systems affect peak clipping and LF under different circumstances. The analysis reveals significant improvements in LF resulting from the integration of the SPP, ranging from 1.04% to 10.62%. Results show that transition to online education and reduced campus population positively impacted LF during coronavirus disease 2019 compared to pre-pandemic times. These findings align with the dynamics of the campus lifestyle and underscore the favorable effects of these factors on LF. [ABSTRACT FROM AUTHOR]

As wind power generation increases, power system operators are challenged by the detailed modeling and simulation of wind power plants to realize the behavior and to maintain the stability of their power systems. In order to investigate the performance of the wind power plants, dynamic root mean square (RMS, 50 Hz phasor dynamics) and electromagnetic transient (EMT) models are very crucial with standards and guidelines. These dynamic models have been developed in the last two decades by the wind turbine manufacturers and plant and power system tool developers. In accordance with this progress, Australian Energy Market Operator (AEMO) has published a dynamic modeling acceptance test (DMAT) to assess the accuracy, consistency, and robustness of RMS and EMT models used for power system analysis. In this paper, DMAT is summarized to introduce how AEMO guides the modeling aspects of wind power plants in their power system. Additional inputs have been discussed to improve the modeling perspective for the future guidelines. [ABSTRACT FROM AUTHOR]

This paper investigates the impacts of solar irradiance, ambient temperature, and solar inverter efficiency on the performance of a photovoltaic system. The analysis of the system has been performed on the unbalanced IEEE 13-node test feeder. The loads connected to the feeder are modeled as constant impedance, constant current, constant power (ZIP) loads, and the daily load profiles of three customers (commercial, industrial, and residential) are employed. A daily power flow simulation at 1-minute intervals has been carried out using Open Distribution System Simulator (OpenDSS). The PV system performance has been evaluated using numerical simulations under three weather conditions, namely sunny, semi-cloudy, and overcast. The results show that the PV system generates more power on sunny and semi-cloudy days while the PV output power is very low on an overcast day due to the extremely low solar irradiance. In addition, the energy demand from the substation and the power loss has been reduced with the deployment of the photovoltaic system into the distribution system. Moreover, when the ambient temperature and inverter efficiency are considered, the PV system produced more power in sunny and overcast conditions compared to when these factors are not taken into consideration or assumed the temperature is constant throughout the day. This result shows the effect of the temperature of the selected region on the performance of the PV system. The losses due to the inverter and temperature can be reduced by appropriately sizing the inverter and choosing a proper location with high solar irradiance and low temperature, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes a method for thermal optimization for the radial flux permanent magnet synchronous motor (PMSM) used in light electric vehicles. Thermal effects cause many negative impacts, especially losses in electrical machines. These effects cause the permanent magnets to deteriorate and the motor to become inoperable in PMSMs. Therefore, it is important to optimize the operating internal temperatures of PMSM. In this study, it is suggested that the permanent magnets of the PMSM should be made in pieces in the axial direction in order to reduce the operating temperature value. The simulated design is a radial flux PMSM used in light electric vehicles with a power of 3.2 kW, 150 V, and 1000 rpm. ANSYS Electronics Desktop, a finite element method-based software, was used for electromagnetic field analysis, and ANSYS Motor-Cad software was used for thermal simulation. The simulation results show that the axial division of the permanent magnets reduces the PMSM internal temperature value. [ABSTRACT FROM AUTHOR]

Despite the deep interest of materials scientists in cadmium telluride (CdTe) crystal growth, there is no single source to which researchers can turn for comprehensive knowledge of CdTe compound semiconductor synthesis protocols, physical characteristics, and material performance. Considering this, the present review work focuses on bridging these shortcomings. The CdTe crystals with direct band gap (Eg) have been in limelight in the photovoltaic application (PV) as the optoelectronic properties such as Eg (1.49 eV), absorption coefficient (~105 cm−1 ), p-type conductivity, carrier concentration (6 × 1016 cm−3), and appreciably high mobility (1040 cm² /V s) at the room temperature are reported, which are optimum for solar cells. Additionally, Cd-based crystals have also been widely studied and implemented in the field of α and γ-ray radiation detectors because of their extraordinary advantages like a large atomic number, low weight, high mechanical as well as thermal stability, flexibility, and the availability of the constituent materials. Cadmium telluride has demerits like toxicity (ecological damage) and high chances for defects/dislocations due to high thermal conductivity at melting temperature and high melting temperature, which will complicate the growth of stoichiometric CdTe crystals at high temperatures. Even though CdTe has the aforementioned issues, on the other hand, the absorber material reached a milestone (solar cell efficiency in the order of 22%), which enhances the attractiveness of this binary compound more and more in the field of crystal growth technology. In this regard, the review work focused on the periodic evolution of the growth protocols until now for the production of bulk, good quality CdTe crystals. The different synthesis methods, characterization, and recent progress in the field of crystalline CdTe were discussed briefly. Important physicochemical characteristics are presented in the tables and remaining issues due to chemical in-homogeneity, defects, imperfections, etc., have been discussed, which could facilitate the researchers who are working on this class of compound semiconductors. The applications of CdTe crystals to PV fields are also discussed separately in this review paper. [ABSTRACT FROM AUTHOR]

This paper presents compared performances of three metaheuristic algorithms in determining the cost of hybrid renewable energy system. Using genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony (ABC), the best affordable sizes of solar photovoltaic array, battery bank, and a minimum-rated diesel generator that could be hybridized to meet the demand of a community in Southwest Nigeria were determined. Load profile, solar radiation, and temperature data were employed as required inputs, and the parameters of the algorithms were properly set to ensure the best result. Bonferroni– Holm method was deployed to ascertain the statistical significance among the algorithms. It was found that ABC produced the best configuration comprising 427 numbers of solar photovoltaic panels, 19 battery units, and 163.2 kW-rated diesel generator. With this, a total annualized cost of $167 284 and 0.2443 estimated cost of energy were obtained. These were the lowest when compared with PSO and GA. The t-test between PSO and ABC are both 5.83 × 10−10 < 0.01666667, between ABC and GA are 6.09 × 10−6 <0.01666667 and 6.09 × 10−6 <0.025, while between GA and PSO are 9.13 × 10−1 > 0.01666667 and 9.13 × 10−1 > 0.05. PSO/ABC and ABC/GA groups are clarified significant, while GA/PSO group is insignificant; post hoc test reveals that ABC produced the best result. Hence, a reliable and sustainable power supply at a reduced cost is guaranteed for the community. [ABSTRACT FROM AUTHOR]

This study presents a comparative analysis of coupled and uncoupled inductors within an interleaved boost converter. The study includes the derivation of steady-state variables and magnetic equations for each inductor configuration. To mitigate input current ripple issues in the conventional boost converter, interleaving techniques are employed. Additionally, the volume concerns associated with interleaving are addressed through the application of inductor coupling techniques. An interleaved boost converter topology with an input voltage of 200 V, an output voltage of 400 V, and a power rating of 3700 W has been designed with different inductor configurations. The advantages and disadvantages of each configuration have been comparatively analyzed. The utilization of coupled inductors results in a more compact, efficient, and cost-effective design when contrasted with the uncoupled configuration. The magnetic flux densities of each inductor configuration are compared to evaluate performance outcomes. To ensure accurate core loss calculations in ANSYS Maxwell, core loss coefficients provided by the manufacturer were not directly used. Instead, new coefficients were derived by generating data sets at five different frequencies. The finite element method (FEM) is used for the analysis of the proposed inductors, and analytical loss calculations are validated. [ABSTRACT FROM AUTHOR]

The rise in energy costs and growing environmental concerns are driving investments in energy-saving technologies, particularly in the maritime industry, including fishing, marine operations, and shipbuilding. Shaft generators are highly favored for their ease of installation, cost-effectiveness, reliability, and low maintenance costs as an energy production system of choice. These systems are well-suited to meet the energy demands of military vessels. As current energy costs and environmental pressures continue to escalate, shaft generators are recognized as pivotal technologies supporting energy conservation efforts. This study provides a comprehensive comparative analysis of the design and energy efficiency of two different shaft generator models employed on military ships. Using ANYSYS/Maxwell for design and analysis, the study evaluates power, torque, and efficiency across both models. The results emphasize that the efficiency of a shaft generator depends mainly on two factors: the number of motor poles and the operating frequency of the system. Moreover, the efficiency tends to decrease as the number of motor poles increases, but it improves as the system frequency rises. [ABSTRACT FROM AUTHOR]

The increasing importance of batteries across diverse sectors, spanning from consumer electronics to electric vehicles, underscores the critical necessity for precise battery models. This review delineates four primary categories of battery models: empirical, equivalent circuit, data-driven, and physics-based models. Empirical models like the Nernst and Shepherd models offer simplicity but lack precision. Equivalent circuit models strike a balance between simplicity and accuracy, albeit with validation constraints. Data-driven methods leverage machine learning to predict battery performance accurately but require high-quality datasets. Physics-based models integrate fundamental electrochemical processes for detailed understanding, albeit with heightened computational complexity. Comparative analyses, with a focus on lithium-ion batteries, reveal trade-offs between computational efficiency and accuracy. The Single Particle Model and its extension single particle model with electrolyte dynamics emerge as efficient options, with single particle model with electrolyte dynamics showing promising accuracy akin to Single Particle Model. Additionally, comparisons across different battery chemistries unveil varying levels of modeling precision. This article compares different electrochemical modeling techniques across chemistries to discern optimal methods. The electrochemical model, which is one of the battery modeling techniques, has been examined and investigated in detail in this study and has contributed to the literature on how the model with which chemistry works with which electrochemical model. In addition, this study contributed to the existing lithium ferro-phosphate chemistry modeling with optimization technique in pybamm. The synthesis offers insights into diverse modeling methodologies and their implications for battery research and development, guiding future investigations toward more tailored modeling strategies for specific applications. [ABSTRACT FROM AUTHOR]

The significance and rapid rise of distributed power generation utilizing alternative energy sources have been receiving a lot of attention today. Due to the high worldwide demand for clean and sustainable energy, microgrid systems have emerged as a promising solution to improve energy reliability while promoting the grid's insertion of renewable energy sources. To maximize energy production, storage, and distribution, the thesis revolves around the design and simulation of a solar -wind -batt ery-d iesel generator hybrid microgrid system for the Havza Waste Water Treatment Plant located in Izmir, Turkey. HOMER Pro program is used in this report, which is a sophisticated tool commonly used for microgrid analysis and optimization. The economic analysis and emission rates are obtained for the system. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries have recently become the focus of research in vehicle applications due to their numerous advantages. Lithium-ion batteries have higher specific energy, better energy density, and a lower self-discharge rate than other secondary batteries, making them appropriate for electric vehicles and hybrid electric vehicles. Nonetheless, worries about safety, cost, charging time, and recycling have hampered the commercial usage of lithium-ion batteries for automotive applications. An accurate battery model on a simulation platform is required for the development of an effective battery system. In this study, a battery model is built in MATLAB/Simulink. Two variations are available: one with a series-parallel battery arrangement and a single model without configuration. The structure of the proposed model is provided and explained in detail. Based on the test results, the developed battery model was validated. A comparison shows that the model created can accurately predict current, voltage, and power performance. This model is designed for Eaton Electromechanical Battery Li-ion 18650 batteries but is also said to work with other types of batteries. The simulation takes into account the battery's state of charge, current, voltage, and power requirements. [ABSTRACT FROM AUTHOR]

In recent years, increasing the rate of green energy use in order to meet the increasing energy demand and combat global warming has become one of the important goals of the countries. For this reason, the integration of renewable energy sources as distributed generation has become increasingly popular. In this study, hybrid renewable energy systems were designed for the electrification of a 100-household area in the Sarayköy district of Denizli province, Turkey, and the Hybrid Optimization Model for Electric Renewables program was used to optimize the required component outputs to to achieve the best economic and environmental results. A total of six hybrid renewable energy system designs, three grid-connected and three stand-alone, were created with different combinations of components such as photovoltaic panel, wind turbine, diesel generator, battery energy storage system, and converter. The most economical design was the grid-connected system with only solar energy with a unit energy cost of 0.0362 $/kWh, while the most cost-effective was the stand-alone system containing solar energy, wind energy, and batteries with 1.61 $/kWh. In terms of the environment, on the contrary, off-grid systems emit less carbon dioxide, while on-grid systems emit more carbon dioxide. [ABSTRACT FROM AUTHOR]

Power transformers are one of the most important and costly equipment of electrical networks. Possible malfunctions that may occur in a power transformer may cause power outages as well as large energy losses. Therefore, it is important to detect the fault in transformers in advance. One of the commonly used methods for fault diagnosis in transformers is to make analyses based on gas concentrations that occur at the time of failure. This method is called dissolved gas analysis (DGA) which is based on measuring gas formation in the transformer insulating fluid during or before the fault. In this study, gas data obtained from DGA was used as the inputs of the chosen machine learning algorithms, and their diagnostic performances were measured. First, the International Electrotechnical Commission Technical Committee (IEC TC)-10 data set which is very popular in the literature was used, and then the real data set obtained from the Turkish Electricity System was applied. Since the data set consists of different sizes, it greatly affects the performance of classification algorithms. Different data preprocessing methods were applied to increase the performance of the algorithms, and how they affect the performance of the algorithms was examined. [ABSTRACT FROM AUTHOR]

In this study, a new modulation method is proposed to reduce the harmonics at the output voltage due to dead time in uninterruptible power supply (UPS) applications. In UPS applications, it is desirable to have low-output voltage harmonics. Dead-time effects in a three-level UPS were investigated for various load conditions. Analysis and simulations related to the proposed method are given and the optimum solution is found by iteration. The validity of the proposed modulation method is tested on two different UPS systems. UPS1 is 20 kVA NPC type, and UPS2 is 160 kVA T type. It is observed that the theoretical and experimental results are in good agreement, and the total harmonic distortion value caused by dead time is significantly reduced at ohmic loads. The proposed method is activated at power factor >0.95 and it improves the output voltage quality as much as the ideal case where the dead time is zero. [ABSTRACT FROM AUTHOR]

KNX is a widely used organization in the world that standardizes the open system communication protocol for smart home and building automation. KNX controls integrated functions such as heating, cooling, ventilation, lighting systems, audio and video services, security, and energy management in all types of residential, commercial, and industrial building structures. KNX power supply is designed to ensure the continuous operation of these automation systems by considering the number of devices connected to the system. According to the KNX standards, in devices that are compliant with these standards, the main current and communication signals required for the operation of the devices use the same line. Conventional power supplies cannot cope with these standards and therefore special power supplies are required. In this study, a high-frequency (100 kHz) and high-efficiency (minimum 85%) flyback converter-based power supply with 155-265 VAC input and 30 V 640 mA output values in accordance with KNX standards has been designed and implemented. [ABSTRACT FROM AUTHOR]

The electric meters used to measure the amount of electricity consumption measure both active and reactive power consumption in the distribution line with current transformers. As in every analog measurement process, in every analog measurement, existing of undesired components such as noises, interferences and harmonics are caused mistakes. In order to remove this undesirable information included in the measurement from the measurement results, digital filters are used in applications because of their performance and their easily replaceable features. In this study, we aimed to remove unwanted components such as noise and interference from the measurement signal. The subject of a commercial project is to design a digital filter in MATLAB® environment, which allows filtering the measurements of reactive power consumption in power distribution lines with less than 1% error. To ensure whether designed filter satisfies for mentioned criteria or not, we used the Gaussian model as a noise model because Gauss noise is spreaded whole frequency spectrum. While the power signal modeled in the design is a sine wave of IPP = 2 A 50 Hz, Gaussian noise with different variances and mean values is used in modeling the unwanted components. [ABSTRACT FROM AUTHOR]

The use of renewable energy sources (RESs) can ensure both lower cost of energy and improvement in voltage levels in the grid. Likewise, batteries can help achieve technical improvement and cost reduction. However, the full benefits of integrating RESs and batteries into the grid can only be realized by a suitable energy management system (EMS). In this work, a predictive EMS is developed to optimally operate a microgrid (MG) with photovoltaics and batteries while satisfying voltage constraints in the distribution grid. First, mathematical models of the MG components are obtained. Then, proper load flow method is selected for the network structure. Using component models and the load flow method, a day-ahead EMS is posed as an optimization problem. Since the power flow calculations are nonlinear, the optimization problem is constructed as a nonlinear program. Simulation studies are performed to analyze the sensitivity of the cost of operation and power loss in the grid, with respect to different system parameters. It is confirmed that the purchase price of electricity and the amount of photovoltaic panels were the most effective factors on the daily energy cost. [ABSTRACT FROM AUTHOR]

Due to the increasing energy demand and the climate crisis in the world, the importance of alternative energy generation techniques, which are clean and cheap, is increasing. Since renewable energy sources are clean and sustainable, their integration into the grids at transmission or distribution levels as distributed generation sources provides significant benefits both economically and environmentally. Rooftop solar panels are also an application of small-power renewable distributed generation technologies. In this study, the economic and environmental analysis of the rooftop photovoltaic system designed to increase the green energy usage rate and reduce the greenhouse gases released to the environment has been made. Considering the electricity consumption data and roof area of an office building of a factory located in the north of Turkey, solar radiation data obtained from three different sources such as the General Directorate of Meteorology, National Aeronautics and Space Administration, and Photovoltaic Geographical Information System were used during the analyses carried out in the Hybrid Optimization Models for Energy Resources program. By providing an annual average of 160 000 kWh (11%) clean energy generation, the annual release of pollutants such as 101.353 kg of carbon dioxide, 0.439 kg of sulfur dioxide, and 0.215 kg of nitrogen oxide to the environment has been prevented. [ABSTRACT FROM AUTHOR]

Light electric vehicles (LEV) necessitate power electronic converters that have a high-power density and a high efficiency in order to accommodate the limited space available. The input voltage of a four-switch buck-boost converter can be increased or decreased across a wide range, allowing for energy to be transferred in either direction. This type of converter topology is particularly efficient. In this study, an algorithm is presented that proposes two different designs for a four-switch buck-boost converter within both efficiency and cost constraints. Two 96-12-5 V, 500 W converter that is powered by a 48 V battery have been developed for the purpose of determining whether or not the proposed algorithm is effective. The proposed algorithm chose the switches, inductance, and capacitance based on a previously created database. [ABSTRACT FROM AUTHOR]

In this study, design, optimization, and performance improvement of a 2-kW synchronous reluctance motor (SynRM) have been performed for micro-mobility electric vehicles. The finite-element method has been used for analyses. At first, SynRM has been designed in Ansys Maxwell with initial stator and rotor parameters. Then, stator and rotor parameters of SynRM have been optimized by using a genetic algorithm. Thus, the torque capacity of SynRM has been increased and the torque ripple has been reduced. In addition, the performance of optimized SynRM has been investigated by adding magnets to its rotor. Designed SynRM and permanent magnet-supported SynRM have been compared. It has been seen that the permanent magnet-supported SynRM produces higher torque with higher efficiency and less torque ripple. Simulation results proved that the permanent magnet-supported SynRM can be used successfully in micro-mobility class electric vehicles. [ABSTRACT FROM AUTHOR]

A viable remedy for lowering hazardous greenhouse gas emissions and carbon footprint is through the adoption of electric vehicles (EVs). Electric vehicles minimize fossil fuel reliance and ozone-damaging compounds by supporting large-scale renewable deployment. However, EV modeling and manufacturing are continuing to change despite extensive study on the qualities and characteristics that are evaluated from time to time. This is due to restrictions on EV adoption and their charging infrastructure. The present study addresses the numerous modeling approaches and optimization strategies used in studies of EV, hybrid, plug-in hybrid, battery, and fuel cell EV penetration and adoption rates in the market. The study is unique for a developing country like India in that it addresses crucial challenges in adoption and the lack of charging facilities for EV consumers. In addition, when renewable energy sources are unavailable, the development and deployment of the vehicle-to-grid concept is an innovative strategy to provide auxiliary supply to the grid. It is concluded that considering the unique features of EVs is vital to their adoption and mobility. [ABSTRACT FROM AUTHOR]

The reliability of the transmission system is crucial in terms of both economic and social welfare. On one hand, the transmission lines getting overloaded because of a single outage situation may give rise to even a blackout. On the other hand, the excessive short-circuit current might cause distortion of valuable system devices. Therefore, the static security of the transmission system should be provided so as to protect consumers and system components. The bus splitting optimization (BSO) is a tool that maintains system security by distributing transmission feeders to the suitable busbars. The BSO is a non-convex, combinatorial, and binary problem due to AC power flow equations, short-circuit calculations, and the nature of the representation of the feeder position. Hence, the novel Archimedes optimization algorithm (AOA) is utilized in order to solve the BSO problem instead of the deterministic approaches in this study. Analyses we implemented show that the AOA algorithm has the efficacy and robustness to solve the BSO problem in the Istanbul Anatolian Region of the Turkish Electricity Transmission System and to enhance the power system security. [ABSTRACT FROM AUTHOR]