Electric Units Research Articles

Reliability analysis of the vehicle door system EDCU based on Weibull distribution

The paper established a two-parameter Weibull distribution model to analyze the failure law of key components of metro vehicles and employed the least squares method to fit the parameters of the Weibull function. The paper provided a detailed introduction of the model's parameter fitting method based on actual historical failure data. Taking the EDCU (Electrical Door Control Unit) as an example, the failure rate, cumulative failure rate, and reliability of EDCU for 3 different vehicle models were calculated, and the calculation results were verified to conform to the Weibull distribution through Q-Q diagrams. The results show that although the designed service life of EDCU of vehicle door system is 15 years, the reliability of the EDCU decreases significantly around 10 years. The EDCU reliability of the three vehicle models declines by 22%, 4.2%, 10.6% and the failure rates are 2.7%, 0.57%, 1.13%. Based on the reliability of EDCU mentioned above, while ensuring the reliability of components, the maintenance methods of components can be optimized to reduce maintenance costs.

Development of New Electricity System Marginal Price Forecasting Models Using Statistical and Artificial Intelligence Methods

The System Marginal Price (SMP) is the cost of the last unit of electricity supplied to the grid, reflecting the supply–demand equilibrium and serving as a key indicator of market conditions. Accurate SMP forecasting is essential for ensuring market stability and economic efficiency. This study addresses the challenges of SMP prediction in Turkey by proposing a comprehensive forecasting framework that integrates machine learning, deep learning, and statistical models. Advanced feature selection techniques, such as Minimum Redundancy Maximum Relevance (mRMR) and Maximum Likelihood Feature Selector (MLFS), are employed to refine model inputs. The framework incorporates time series methods like Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Convolutional LSTM (ConvLSTM) to capture complex temporal patterns, alongside models such as Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Extreme Learning Machine (ELM) for modeling non-linear relationships. Model performance was evaluated using the Mean Absolute Percentage Error (MAPE) across regular weekdays, weekends, and public holidays. XGBoost combined with MLFS consistently achieved the lowest MAPE values, demonstrating exceptional accuracy and robustness. Among all of the models, XGBoost combined with MLFS consistently achieved the lowest MAPE values, demonstrating superior accuracy and robustness. The results highlight the inadequacy of traditional models like ARIMA and SARIMA in capturing non-linear and highly volatile patterns, reinforcing the necessity of using advanced techniques for effective SMP forecasting. Overall, this study presents a novel and comprehensive approach tailored for complex electricity markets, significantly enhancing predictive reliability by incorporating economic indicators and sophisticated feature selection methods.

Probabilistic reliability model of electrical motors in submersible well pumping units used in the uranium mining by in-situ leaching

Relevance. A submersible electric motor is the most vulnerable component of an electric centrifugal pumping unit used in in-situ leaching uranium mining. The accuracy of calculating the need for new electric motors directly affects the profitability of the uranium production. Aim. Development of a probabilistic reliability model for a submersible electric motor that provides the calculation of its failure probability for the upcoming period of time and allows estimating with sufficient accuracy the number of motors required to replace the failed ones. Methods. Statistical methods, survival analysis, statistical hypothesis testing. Results and conclusions. The authors conducted a study of three probabilistic reliability models for electric motors of submersible well pumping units used in in-situ leaching uranium mining. As a result of the study, the maximum likelihood estimates of model parameters were determined for each model; the adequacy of the models under study was checked based on nonparametric goodness-of-fit tests. Despite the fact that the test results did not allow excluding any of the considered models, a model based on a mixture of two Weibull distributions was selected as a probabilistic reliability model demonstrating higher consistency with real data, compared to the other models. At the same time, the analysis of the components of the mixture distribution indicated the presence of a group of electric motors with an uncharacteristically low time-to-failure value compared to the average value calculated for the entire set of equipment under study, and made it possible to estimate the share of such motors, which amounted to 8% of the total population. Possible reasons explaining such heterogeneity of data, according to the authors, are hidden manufacturing defects or more severe operating conditions in which some submersible electric motors operate.

Negative impact of vibration on process pipelines of a compressor station with electrically driven gas pumping units

Relevance. In gas industry, the amount of equipment that has outlived its intended service life is increasing every year. In accordance with the law “On Industrial Safety of Hazardous Production Facilities,” compressor stations are dangerous objects, the reliable operation of which largely determines the condition of main gas pipelines. Technological pipelines of a compressor station are important objects of this system. They are subject to strict requirements due to the action of static and dynamic loads on them. Reliability of compressor stations is based on the results of a study of the condition of the object and the main factors affecting increased wear of equipment. The solution to wear problem is timely monitoring of equipment, in particular level measurement and determining the causes of pipeline vibrations using vibration diagnostics methods, and frequency and modal analysis methods. Aim. To study the causes of increased dynamic loads on the technological piping that arise during the operation of electric and gas pumping units at a compressor station using vibration monitoring methods. Methods. Vibration monitoring methods to analyze the causes of increased vibration values in the process piping of a compressor station with an electric gas pumping unit. Results and conclusions. The authors have measured vibration in a wide frequency band and assessed the technological condition of the main equipment of the compressor station with EGPA-6.3/8200-56/1.44-R based on the regulatory documentation STO Gazprom 2-2.3-324-2009. The main frequencies of the root-mean-square value of the vibration velocity, at which the highest vibration values were observed, were identified, and a modal analysis was performed using ANSYS software. Based on the results of natural studies and modal analysis, it was concluded about the occurrence of resonant high-frequency oscillations, multiple of the rotor rotation frequency.

Techno-economic viability of sustainable solar co-generation using CPV cells in parabolic dish concentrators: A 20-year perspective

Solar co-generation is a cutting-edge technology that enables the instantaneous production of electricity and heat energy by employing concentrated solar power (CSP) systems. This study presents the development and experimental analysis of a novel small-scale solar co-generation system, utilizing concentrated photovoltaic (CPV) cells integrated into a solar paraboloidal dish concentrator (SPDC) for simultaneous electricity and heat production. The system, designed with a high concentration ratio, employs a CPV module composed of four 1 cm × 1 cm cells mounted on a flat receiver of a parabolic dish concentrator with an aperture area of 12.6 m2. Experimental results, obtained over three consecutive days, demonstrate that the CPV module achieves an electric power output ranging from 480 to 645 W daily, while the SPDC system generates thermal energy between 395 and 725 W. The economic analysis indicates a cost of ₹10.75 per unit of electricity, with a payback period of approximately 13.5 years, assuming a 20-year system lifespan. Additionally, the reliability of the CPV module and the impact of varying encapsulation materials were critically assessed, providing insights into potential improvements and long-term performance. The findings underscore the viability and economic potential of solar co-generation systems employing CPV technology, contributing to the advancement of sustainable energy solutions.

Analysis of Impact of Control Strategies on Integrated Electric Propulsion System Performance During Icebreaking Process

Developing an efficient power system is an important way for icebreakers to respond to high maneuverability and strong fluctuation loads under icebreaking conditions. The performance of power systems under short-period, regularly fluctuating load-sea conditions has been intensively studied. However, the performance of the power system in the face of a long-period, stochastic multi-frequency fluctuation icebreaking process has not been fully explored, especially the parameter uncertainty and battery cycle life. In this study, an integrated electric propulsion system with an optimal control strategy is suggested for improving the power system’s dynamic performance and battery cycle life. First, an energy flow model with a diesel–electric unit as the main body and coupled energy storage system/hybrid energy storage system has been constructed. A comparative analysis of rule-based and optimization-based energy management strategies has been performed, and an optimized strategy with dynamic programming as global regulation at the upper level and model predictive control at the lower level is suggested to integrate the slow and fast dynamic powers and achieve adaptability to strong fluctuation loads. In this control strategy, the uncertainties of energy storage system/hybrid energy storage system parameters have been introduced to eliminate their impact on the system performance. Then, the icebreaking process with multi-frequency fluctuation has been simulated, and the hybrid energy storage system with battery and supercapacitor is recommended to reach multi-objective with the lowest power fluctuation of diesel–electric unit, highest efficiency, and the minimum battery degradation. Finally, the fuel oil consumption and emissions of the hybrid energy storage system have been discussed, and the optimized strategy can save fuel oil by up to 5.33% and reduce the CO2 emission by 22% during the icebreaking process, exhibiting great potential in the environmental friendliness and significant advantages in terms of low fuel oil consumption.

Optimal scheduling of boiler electrification for process decarbonization

AbstractProcess heat electrification offers the prospect of deep decarbonization of the chemical and allied industries. Replacing fossil fuel‐fired boilers with electric units can reduce carbon emissions if the power mix has a large share of renewables. For multinational firms with plants in multiple locations, the electrification decisions should be scheduled based on grid carbon intensity projections and should also be coordinated among these subsidiaries. In addition, carbon credits can be traded among the multiple sites to allow lagging plants to reduce their carbon footprints. A novel mathematical model has been developed to optimize process heat electrification plans in multinational corporations. The model determines the optimal timing of electrification at each location, and also the necessary level of carbon trading among subsidiaries. An illustrative case study demonstrates how the model can be used to generate electrification plans that are superior to those based on simple heuristics.

Paradigm on Levenberg–Marquardt neural algorithm analysis of heat conduction optimization for ternary hybrid nanofluid with entropy generation

AbstractThe significance of the present article is to enhance the thermal management and energy efficiency of complex engineering infrastructures such as energy storage systems, modern electric vehicles, thermal insulations, heavy‐duty machinery, and production units. This research aims to understand the intricate relationship between the thermal conductivity performance of ternary () hybrid nanomaterial and entropy generation to optimize material design and efficacy. A synergetic combination of three distinct nanomaterials silicon dioxide, ferric oxide, and titanium oxide with ethylene glycol and water in the ratio 3:2 as a base solvent is comprised of contributing unique thermophysical properties. To elucidate the impact of this hybrid composition on thermal conductivity, various factors are analyzed. The advanced computational technique of Artificial intelligent feed‐forward neural network (AIFFNN) is utilized. The problem governed the system of PDEs, which is transformed into ODEs by dimensionless similarity. Adams method provided the dataset which is filtered and embedded into Marquardt–Levenberg Algorithm (LMA). The study examines the role of nanomaterial constituents, morphology, and boundary conditions on thermal performance and entropy generation. Graphical analysis of velocity, temperature, and entropy is provided with respect to varying parameters, including surface absorption (λ), magnetic strength (Tesla M), radiation parameter (Rd), Brownian motion (Br), and Eckert number (Ec). The findings have practical significance for optimizing material design in engineering and industrial applications.

Optimizing the performance parameters of vacuum evaporation technology for management of anaerobic digestate in a waste water treatment plant using fuzzy MCDM method

Evaporation technology in waste water treatment plants is used to treat excess liquid digestate, eventually reaping the essential nutrients from the process. In the present study, the input conditions have been optimized for implementing and achieving optimal performance of the digestate evaporation technology used in waste water treatment plants by employing fuzzy multi-criteria decision making method. Three input parameters, namely the Temperature, the Pressure, and the Mass Flow rate, each at three levels are considered. Experiments are conducted as per the Taguchi’s L9 standard orthogonal array. The performance of the evaporator is examined based on a few response characteristics, such as payback period, thermal energy consumption, power consumption and cost per unit of electricity. Temperature's impact on multi performance stands out significantly, contributing the most (70.52 %), followed by mass flow rate (18.95 %), and pressure (5.37 %). Result of the study reveals that within the investigated range of input parameters, temperature at 80 ℃, pressure at 0.3 bar and mass flow rate at 20 kg/h is the optimal combination. The results of the present study will help in enhancing the efficiency and effectiveness of the evaporation process, leading to improved digestate treatment that supports circular economy initiatives.

Design and Optimization of Steel Structures for Solar Panel Systems

Abstract: Structural components are fundamental for supporting mechanical machines in industrial settings. These structures are designed not only to bear the weight of the machines but also to withstand the vibrations generated during operation. The project at hand focuses on integrating basic structural sections to create a complete mechanical framework. Our goal is to collaborate with an industry specializing in support structures for various electrical units. From our initial analysis, we observed that many of these industries rely on conventional manufacturing practices and past experience to design structures, often leading to overdesign. No finite element analysis (FEA) or theoretical calculations are typically applied. We propose introducing modern FEA techniques and concepts to optimize and refine these designs for improved efficiency.

Meta Analysis of the Effect of Applying the Problem Based Learning Model in Physics Learning to Improve Students' Ability to Solve Problems

The meta-analysis method in this research was used to determine how problem-based learning activities influence problem-solving abilities. Analysis is carried out based on material units and levels/classes. Based on the results of the analysis, it was found that the mechanical material unit had an effect size of 0.83 in the high category, for the electric magnetic material unit it had an effect size value of 1.12 in the very high category, for the optical wave material unit it had an effect size of 1.66 with very high category, for thermodynamic material units it has an effect size of 1.41 in the very high category, and for fluid material units it has an effect size of 1.56 in the very high category. Then for class X it has an effect size of 1.19 in the very high category, for class XI it has an effect size of 1.35 in the very high category and for class In conclusion, for the optical wave material unit, it has an effect size of 1.81 which has a very high influence on students' ability to solve problems and for class XI level it has an effect size of 1.35 which has a very high influence on students' ability to solve problems.

Optimal scheduling of a multi-energy complementary system simultaneously considering the trading of carbon emission and green certificate

This paper uses a multi-energy complementary system composed of thermal, wind, photovoltaic power generation, and electric energy storage units to participate in four market mechanisms and construct the optimal scheduling model of these trading mechanisms. These four mechanisms are carbon emission trading, green certificate trading, these two trading that are not joint, and joint. Under the latter two mechanisms, the excess carbon emissions of the system can be reduced to 9258t, and the utilization rate of renewable energy can reach 100 %. However, the operating costs under the fourth mechanism are 9695.80 $ lower than the third one because 5947 pieces of green certificates are converted into carbon emission rights. When the carbon price rises, the cost of the fourth mechanism increases slower than that of the third mechanism. The conversion volume of green certificates increases from 5947 to 10947, and the cost difference between the two increases to 67558.33 $. When green certificate prices rise, the cost under the fourth trading mechanism decreases faster. As that cost steadily declines, the carbon emission rights are converted into 907 green certificates, and the cost difference between this and the third mechanism increases to 4695.52 $.

Nonlinear flow modeling of electro hydrostatic pump unit based on Gauss Newton iterative method for high performance control

For the electric hydrostatic pump unit (EPU), which is a crucial component in the electro hydraulic servo pump control system (EHSPCS), nonlinear flow modelling is a key challenge to achieve high performance operation, especially in position control. For the nonlinear flow of EPUs, simple linearization combined with compensation has become a popular control method recently. However, the control performance is constrained by the reliance on deviation correction. This paper proposes utilizing the Gauss Newton iterative method to investigate the nonlinear flow modeling in the EPU, aiming to obtain the mapping relationship between the load flow and operating parameters in the EPU, thereby directly improving the control performance. A nonlinear flow model is built by utilizing the Gauss Newton iterative method based on test data obtained from various operating conditions, and the mapping relationship is presented accordingly. Experimental studies show that compared to well-established methods, this model exhibits high accuracy and practicality in improving the control performance, particularly in position control of the EPU.

Dynamic model of integrated electricity and district heating for remote communities

District heating networks offer promising solutions for remote communities, providing centralized heat supply, improved efficiency, and diverse energy sources, especially with existing diesel generation. Hence, this paper bridges gaps in the existing literature by developing comprehensive dynamic models of combined district heating networks within existing electric power networks in remote communities, which allows identifying challenges and benefits of district heating networks for these communities. It is shown that district heating networks allow utilizing waste energy to enable energy exchanges between the electricity and heating systems, enabling the provision of necessary ancillary services for remote microgrids with renewable energy sources. The presented dynamic district heating network model incorporates particular considerations in remote, northern communities such as soil limitations, extreme cold conditions, and piping insulation to minimize heat loss. It also addresses accurate sizing of heat pumps based on realistic thermal load requirements, weather conditions, and consumer profiles, proposing demand management controls to enhance frequency regulation for the integration of variable renewable energy sources. The main contributions of the paper include detailed dynamic modeling for district heating network operation, heat pump demand response control system design, and a comparative analysis between centralized district heating networks and decentralized electric thermal storage units that have been deployed for thermal supply in remote areas. The presented dynamic models are applied, tested, and validated in an existing electric microgrid at Kasabonika Lake First Nation in Northern Ontario, showcasing the role of a potential district heating network in facilitating renewable energy sources integration in isolated microgrids.

Electric vehicle supply equipment monitoring and early fault detection through autoencoders

This paper presents a novel approach to detecting anomalies in Electric Vehicle charging unit power profiles using a combination of Autoencoders with LSTM techniques. This study presents a robust methodology, combining the two Machine Learning techniques, for early fault estimation in a real-world case study. The proposed methodology offers significant advantages over existing methods by providing a more comprehensive analysis of anomalous trends. To validate the effectiveness of the proposed methodology, the authors tested it on real Electric Vehicles charging power curves provided by an Italian Distribution System Operator recorded on a historical database and compared the performances with the ones of a traditional anomaly detection technique. The results of the study, tested on Electric Vehicles Supply Equipment or charging stations, demonstrate that the proposed approach is highly effective in detecting anomalous trends in Electric Vehicles charging profiles.

A dynamic reference voltage adjustment strategy for Open-UPQC to increase hosting capacity of electrical distribution networks

Future electrical grids, particularly the distribution networks, may face more severe voltage rises/drops, and in general, more power quality problems in the presence of new loads such as electric vehicle chargers and renewable energy generation units like photovoltaic systems. This necessitates investing in additional high-cost infrastructure to increase the capability of the feeder in hosting higher levels of loads and generation units while the existing capacity is not utilized effectively. In the stated condition, effective voltage stabilization strategies in electrical distribution networks can contribute to hosting capacity improvement and the better utilization of the existing infrastructure. Accordingly, in this paper, the application of Open-UPQC in voltage profile improvement and hosting capacity enhancement is evaluated in low-voltage distribution networks. Furthermore, a dynamic reference voltage adjustment strategy is applied to the device to improve its capabilities in power quality improvement and hosting capacity enhancement. Simulation studies have been implemented to evaluate the capability of Open-UPQC either with static reference voltage or the dynamically-adjusted one in low-voltage networks with real measured data while different cases are assessed regarding the topology and the length of the feeder. The simulation results approved the capability of Open-UPQC especially with the dynamic reference voltage in hosting capacity enhancement while providing the highest level of voltage profile improvement among all the assessed custom power devices in the studied low-voltage networks.

Development of a high-speed imaging system for real time evaluation and monitoring of cardiac engineered tissues.

Stem cell derived cardiac monolayers have high potential for tissue regeneration, in vitro drug testing and disease modeling. However, current differentiation protocols are still sub-optimal, resulting in cultures with variable yields and properties. We propose a high-speed lenseless imaging system, integrated with an electrical stimulation unit, to optimize the generation of these cultures. This tool relies on the variations of cellular patterns, during contraction, measured by digital imaging. The imaging system can monitor cardiac cell sheet function and structure, providing the necessary tools to quickly evaluate engineered monolayer. It can record high speed videos and capture high resolution images, from which tissue spatial organization and contractile characteristics can be obtained. Validation of the system was performed using cardiomyocytes derived from human induced pluripotent stem cell and neonatal rat cardiomyocytes. The imaging system allows the observation, acquisition and analysis of important data relating to contractile activity development of cardiac cells, making it a promising tool for optimization in cardiac tissue engineering.

Regarding the issue of the influence of factors on the braking parameters of M1 category vehicles with electric or hybrid power unit

Problem. The relevance of this work is explained by the fact that there are no works at all in the reference and normative literature on the theory and practice of forensic auto technical examination, and even more so, methodical recommendations regarding the values ​​of the braking parameters of vehicles of the M1 category with an electric power plant. Goal. The purpose of the work is to establish the factors that affect the braking parameters of M1 category vehicles with an electric or hybrid power unit. Methodology. The approaches adopted in the work to solve the set goals are based on the theory of conducting the experiment, the theory of the interaction of the pneumatic tire with the road surface, the theory of the car and the laws of theoretical mechanics. Results. On the basis of the obtained experimental data regarding the value of permanent deceleration and the time value of the increasing deceleration during emergency braking of the M1 category vehicles with an electric or hybrid power unit, the tasks will be solved necessary for conducting forensic engineering and transport examinations for specific road traffic events with the participation of M1 category vehicles with an electric or hybrid power unit. Originality. The results of the study provided an opportunity to get an idea of the influence of factors on the braking parameters of M1 category vehicles with an electric or hybrid power unit. Practical value. The obtained results can be recommended to forensic experts when writing expert opinions or expert studies.

Low-cost Light-emitting Diode Lights Reduce Production Cost of Bibb Lettuce Grown in Aquaponic Systems

In temperate climates, aquaponic producers must use artificial lighting; however, purchasing light-emitting diode (LED) grow lights can be cost-prohibitive. Two aquaponic trials evaluated low-cost LED lighting for the growth of bibb lettuce (Lactuca sativa var. capitata). In trial 1, seven low-cost LED lights were screened and compared with a high-end LED grow light. The three best-performing lights in terms of total plant biomass produced (grams) and total plant biomass per unit of electricity (grams per square meter per kilowatt-hour) were more intensively evaluated in trial 2. These lights included Spider Farmer SF-2000 [SPI ($300)], Fluence RAZRx [FLU ($364)], Designers Fountain 6-Light 5000 K LED High Bay Light [DES ($100)], and the control NeoSol DS [NEO ($1400)]. After 17 days, lettuce grown under NEO achieved higher (P ≤ 0.05) total biomass (939 g) than DES (812 g). There were no differences (P > 0.05) in the average individual plant weights in any treatment. Plant production efficiency (grams per square meter per kilowatt-hour) was higher in DES than NEO (P ≤ 0.05) but was not different from SPI and FLU (P > 0.05). Results indicate that low-cost LEDs are a viable option for producers looking to reduce investment costs while maintaining adequate plant growth. To further describe potential cost savings for producers, a partial budget analysis evaluated the net change in profits and benefit/cost ratio (BCR) of the experimental lights. All showed improved economic results compared with the control (NEO). These studies indicate that low-cost LED lights can support similar plant growth, at higher energy efficiencies, with better economic viability than more expensive grow lights.

Design of the pistol P1 weapon storage system shelf using fingerprint electronic system in the TNI-AD units

One of the most important aspects of preventing illegal usage of firearms is safe storage. An inventive way to guarantee that only authorized owners can access the firearms is to incorporate fingerprint technology into the design of electric firearm storage systems. The use of fingerprint technology to increase the security of gun storage is desperately needed. However, issues with the finger's health, such as stains, cuts, and dampness, can impair the accuracy of the detection. This study employs a quantitative methodology using five fingerprint detection situations to test the accuracy of the system. The Arduino ESP was used as the foundation for the system implementation throughout the nine months that the research was conducted at the Kodiklatad Poltekad Electronics Laboratory. According to the test results, the system has 100% accuracy in identifying five different fingerprint detecting scenarios. But limitations are implied by the system's incapacity to identify damp, damaged, or dusty fingertips. Consequently, further thought must be given to these circumstances in order to improve system reliability. The design of an electric gun storage unit using Arduino ESP-based fingerprint technology has the potential to increase the security of gun storage, but more work is needed to address some issues that limit detection accuracy