Electric Power Management Research Articles

Efficient Hybrid Electric Vehicle Power Management: Dual Battery Energy Storage Empowered by Bidirectional DC–DC Converter

ABSTRACTThis work offers a fuel cell power system with the ability to distribute power to the load from the electrical source and charge an auxiliary battery utilizing regenerative power flows created by the load. The approach is established on a bidirectional closed‐loop DC converter. A bidirectional DC–DC converter is presented as a means of achieving extremely high voltage energy storage systems (ESSs) for a DC bus or supply of electricity in power applications. This paper presents a novel dual‐active‐bridge (DAB) bidirectional DC–DC converter power management system for hybrid electric vehicles (HEVs). The proposed system makes it possible to charge an additional battery with regenerative power flows and distributes power from the electrical source to the load efficiently. The two main stages of the DAB converter, which are the focus of this work, are an interleaved buck/boost converter on the battery and a three‐phase wye‐wye series resonating converter on the DC bus. Each switch's current stress is greatly reduced by this design, which lowers transmission losses and enhances thermal performance. The interleaved buck conversion on the battery allows for lesser current stress in each switch, resulting in lower transmission loss. The increasing complexity and power of automotive embedded electronic systems have made the use of more potent power electronic converters in automobiles necessary. In recent years, many dual volt (42 V/14 V) bidirectional inverter topologies for automotive systems have been presented. However, the majority of them are either inefficient or use a huge number of transistors and magnetic devices in both parallel and series arrangements. As a result, in this study, a bidirectional high‐efficiency inverter with fewer components is provided. The design, modes of operation, and performance metrics of the DAB converter are examined, emphasizing its ability to achieve zero‐voltage switching (ZVS) and zero current switching (ZCS) throughout its operating range. The suggested system seeks to maximize EV power management, guaranteeing high dependability and efficiency. To test all of the aforementioned qualities, an evaluation version was created, with an average efficiency of 97.5%. This research could have a substantial impact on the advancement of power electronic converters for automotive applications, leading to better EV power management, increased system reliability, and increased overall efficiency.

Dynamic stability analysis of the aircraft electrical power system in the more electric aircraft concept

This paper presents the modelling and analysis process of the electrical power management system on board an electrified aircraft conforming to the More/All Electric Aircraft concept. The main objective of the study is to assess the stability of the Electric Power Distribution System under different operational conditions, including the aspect of constant power loads. The paper describes the structure of the system, in which the key role is played by a synchronous generator and a three-phase rectifier converting alternating current AC into direct current DC. To solve the problem, modeling methods in the Matlab/Simulink environment were used, which enabled the analysis of the dynamic properties of the system and the assessment of stability under various types of loads. The obtained test results show that the presence of constant power loads can lead to instability in the EPDS system, manifested by voltage and current oscillations exceeding the standards specified in the MIL-STD-704E standard. The paper also provides tools for predicting system stability without the need to conduct full stability tests, which is important for the design of modern aviation power systems. The final section of the work presents the research results obtained and the resulting significant insights and practical conclusions.

Innovative Feature Analysis of Electric Vehicle Technology, Charging Infrastructure, Power management, and Control Methods

Lowering the dependence on fossil fuels and reducing pollution from greenhouse gas (GHG) emissions is incredibly achievable through electric vehicle (EVs) technology. EV technology is an innovation that uses electricity, rather than fossil fuels, to power and refuel (recharge) vehicles. The adoption and development of EVs should lead to a decline in future demand for fossil fuels, which are finite in supply and exhaustible. Inherent challenges in EV technology, such as inadequate supply of critical minerals, power grid overload, battery technology constraints, extended charging durations, insufficient charging infrastructures, high initial costs, and limited driving range, must be addressed. The technology of charging infrastructures cannot be over-emphasized in EV technology. EV technology, charging infrastructures, vis-à-vis the impact of their integration into the grid is investigated. Effective control strategies and power management systems (PMSs) are required to optimize energy use to improve EVs' efficiency and lifetime. This research uses comprehensive analysis methods to assess various control strategies, PMSs, and their effects on EV integration into the grid.

Power Factor Optimization with Cos Phi Mode Trainee

The power factor cos phi measures the efficiency of using electrical power in a system. A low power factor indicates the presence of high reactive power consumption, which can lead to increased energy losses and operational costs. To overcome this problem, an effective power factor regulation system is required. This research designed and developed the Cos Phi Trainer, which operates in two modes, manual and automatic, to improve the power factor at an electrical load. The system is designed to give users a practical understanding of measuring and improving power factors by setting up compensation capacitors. In manual mode, the user directly sets the value of the capacitor used to fix the power factor. While in automatic mode, the system uses current and voltage sensors to measure the power factor value in real-time and automatically adjusts the capacitor to achieve the optimal power factor. The trainer has an easy-to-use interface and a measurement panel that displays important parameters such as current, voltage, active power, reactive power, and power factor. The test results show that the system can significantly increase the power factor in manual and automatic modes. Thus, this Cos Phi Trainer is an effective tool for training in the laboratory of Indorama Engineering Polytechnic and teaching about electrical power management and energy loss reduction in the electrical system. Keywords: Power factor, manual, active power, reactive power, trainer

Analysis of thermal stability and enthalpy in SSD systems with various thickening agents

Abstract Phase change materials (PCMs) play a vital role in the improvement of efficiency of cold storage air conditioning for demand side management of electrical power. To enhance the thermal stability and the enthalpy of the system, the separation of Sodium Sulfate Decahydrate (SSD) hydrate crystals from their aqueous solution is a significant factor that needs to be addressed. The melt blending technique has been used to explore the effect of various thickening agents, namely Carboxymethyl Cellulose (CMC), Xanthan Gum (XG), and Sodium Alginate (SA) on the thermal stability and enthalpy of the system. The choice of the best thickening agent and its optimum concentration led to the enhancement in the thermal stability and enthalpy of the system. Experimentally, among the three thickeners, CMC proved to be the best thickening agent that remarkably improved the thermal physical properties of the SSD formulation. The paper described the detailed experimental procedures, including melt blending set-up, thermal characterization unit, and cyclic stability test unit. The experimental results showed that the formulation, SSD-CMC at 2 wt% CMC and 50% SSD significant, improved in their thermal physical performance, wherein the enthalpy decreased from 104.23 kJ/kg to 83.71 kJ/kg over fifty cycles, which are the 19.69% of the decay for such a long cycling. It was then concluded that the results would improve the effectiveness of thermal energy storage materials and that phase change materials should be optimized to be used in sustainable and efficient cooling applications and useful for the improvement of the storage capacity of thermal energy storage solutions.

Transformasi Proteksi Tegangan: Sistem Monitoring IoT untuk Pemantauan Real-Time

Electric energy is a vital resource extensively used in various sectors such as industry, offices, agriculture, and trade. Its significance in supporting a nation's economy is undeniable, making the monitoring of power consumption and quality crucial. This study focuses on designing and implementing an Internet of Things (IoT)-based monitoring system for overvoltage protection in an electrical power system. The power system incorporates an Over Voltage Relay (OVR) as a protective measure against voltage fluctuations that may harm the equipment. The OVR operates based on electromagnetic principles, promptly responding to deviations in voltage beyond specified limits. Additionally, the system integrates the PZEM-004T module for measuring voltage, current, power, frequency, energy, and power factor, providing comprehensive insights into the electrical system's performance. The heart of the IoT-based monitoring system is the ESP32-32D module, facilitating real-time monitoring and remote control through the Blynk application. The implemented system successfully detects overvoltage conditions, automatically triggers protective measures, and allows users to monitor the power system through an intuitive interface.The study concludes with an evaluation of the average voltage measured by the OVR, emphasizing the system's reliability and potential for further industrial applications. The average OVR voltage, determined to be approximately 239 Volts, provides a comprehensive overview of measurement consistency throughout the experiments. This research contributes to the enhancement of electrical power management, ensuring the reliability and efficiency of overvoltage protection through IoT-based monitoring systems. Further evaluations and advancements could optimize the system's performance for broader industrial applications.

Research on the Application of Virtual Reality Technology in the Design and Evaluation of Interactive Interfaces for Renewable Energy Systems

In this investigation, we delve deeply into the visual interaction design of electric vehicle power management systems, particularly how we can integrate sustainable development principles to enhance the user experience and promote environmentally friendly travel. Initially, we take a look at the present situation and challenges faced by electric vehicle power management systems. We identify shortcomings in traditional design approaches that fail to prioritize sustainability, such as low energy efficiency and challenges in accessing user-friendly information. To validate the effectiveness of our proposed method, we conduct case studies and user tests. The results reveal that incorporating sustainable development elements into the visual interaction design not only significantly boosts user satisfaction and trust in the power management system but also helps reduce energy consumption and environmental pollution.

Prediction of Load Demand in Home Area Network Using LSTM

Load forecasting is one of the most important tools for the energy management system in the modern world. Forecasting power consumption and load demand calculation became an interesting topic for the stakeholders in the electricity market. Decision-making in purchasing and generating electric power, load switching, and demand side management are dependent on load forecasting. This research work focuses on the prediction of power consumption in a Home Area Network (HAN) using time series forecasting methods like Long short-term memory (LSTM) neural Networks. Our goal was to design a model that could precisely forecast the electrical load required in a Home Area Network. Utility companies and homeowners can utilize this model to better plan and control their power usage. It can assist in lowering the likelihood of power outages and enhancing the effectiveness of the electrical system. The Root Mean Squared Error (RMSE) is used as the performance measure in our work. The dataset considered is from the UCI repository with 350400 rows and 4 parameters such as active power, and 3 submetering values, to achieve realistic data for training the model used the encoder-decoder LSTM model for load prediction and achieved an RMSE value of 23 and an MAE value of 18.6

Performance of Hybrid Nanofluids of Mineral Oil and Vegetable oil based Ester

The prosperity and economic development of a nation depend on its capability to maintain reliable and high-quality electric power management. It is achieved through the availability of extremely reliable ancillary electrical equipment. A power transformer is one such piece of crucial equipment that transfers electricity from one circuit to another without altering the frequency. Mineral oil has been conventionally used as a liquid dielectric in transformers since the last century. However, in recent days, vegetable oil-based esters have emerged as a favourable alternative to mineral oils due to their biodegradability and sustainability. The introduction of nanotechnology led to massive studies to enhance the quality of liquid dielectrics. Further, the addition of more than one nanoparticle to the base fluid has broadened the scope of nanofluids’ property enhancement. This study focused on the assessment of the dielectric properties of hybrid nanofluids consisting of ZnO and reduced graphene. Mineral oil and vegetable-based transformer oil were chosen as base fluids to prepare the nanofluids. The mass of the added nanoparticles was 0.01%. The ratio between the nanoparticles was selected as 1:1. The hybrid nanofluids were characterized for breakdown voltage viscosity at temperatures ranging from 40 °C to 100 °C, interfacial tension, tan delta, and resistivity. The overall properties of hybrid nanofluids were found to be improved due to the inclusion of ZnO and reduced graphene.

Diamonds in the Current: Navigating Challenges for the Integration of Diamond in Power Electronics

Power electronics, a pivotal field orchestrating electrical energy flow in the modern world, deals with efficient conversion, control, and management of electrical power across diverse applications. While its scope encompasses circuits for energy conversion, its challenges include transporting electrical energy over extended distances. Focusing on electrical‐to‐electrical conversions, the goal is minimal loss for delivering maximum power. The article explores the intricacies of power electronics, presenting key equations, and concepts. The current global power electronics market witnesses growth, driven by demand for energy‐efficient technologies, renewables integration, and the rise of electric vehicles. Future trends indicate continued growth, driven by renewable energy systems, and electric vehicles. Wide bandgap semiconductors, play a crucial role, with ultra‐wide bandgap semiconductors like diamond emerging as potential disruptors. A comparative analysis of semiconductor properties reveals diamond's unique attributes. Despite its challenges, diamond shows promise for power electronic applications, with ongoing research on components like Schottky diodes. Thermal considerations, substrate limitations, and dislocation challenges are discussed, emphasizing the need for advancements to harness the full potential of diamond in power electronics. Finally, some inputs about the importance of overcoming these challenges for the successful integration of diamond in power electronic systems are given.

Micro-hydraulic energy system for electric power production and DSM in buildings

In this article a procedure of electric power obtaining is presented, based on the micro-hydraulic application derived from the supply pressure of water in the cities; this constitute a step more toward the objective of sustainable buildings (energy and environmentally speaking), and is integrated into the generat electrical power management in biuldings with the main renewable energy systems for electrical supply (basicaly wind and solar energy). The proposed system can be more important in non-developed countries, where the electric supply is not totally reliable, and the water supply of the buildings is made by using water tanks on the top.

Optimization of photovoltaic solar electric power for renewable energy generation and DSM strategies in singular apartment buildings.

This work presents a procedure of optimization (algorithms) for the supervision, control, and management of photovoltaic solar electric power (PVSEP) in singular buildings dedicated mainly to apartments. The basic principles of performance are kept within two big guidelines denominated Renewable Energy Generation (REG) and Demand Side Management (DSM). From the point of view of GER it is assumed that the more electric power is generated -and therefore sent to the electric net- the more income will be obtained. On the other hand, distribution electric nets recently are having problems of apparent impossibility to attend to the demand peaks. For these reasons, from the energetic efficiency point of view it is very interesting to develop a system able to reduce the demand peaks of the buildings, that is to say, to use DSM techniques. Some procedures (algorithms) useful to justify the installation of photovoltaic panels in buildings have been obtained and are presented in this paper. These procedures are expected to be the core of quality scientific production in our group, including some patented designs and utility models.

Electrical Power Management by Flexible Operation of a Continuous Gas Export Plant

Electrical Power Management by Flexible Operation of a Continuous Gas Export Plant

Intermittent control switch characteristics of triboelectric electric hybrid energy harvesting devices and power management circuits

Renewable energy sources such as wind, vibration, and water wave energy are widely available in everyday life, where there are also scenarios where multiple renewable energy sources coexist. If multiple...

Recent advancements in piezoelectric energy harvesting for implantable medical devices

Biomedical implantable devices like deep brain stimulators, implantable cardioverter-defibrillators and cardiac pacemakers are essential for treating the human heart and brain-related diseases. In the past few decades, a considerable amount of research has focused on improving bio-implant technologies. Conventional bio implant devices consist of an external generator like a battery to power the system, which requires replacement after a particular time. Therefore, in recent years, self-powered implants with various energy harvesting techniques have been proposed to avoid frequent surgery for battery replacement and to miniaturise the implant systems. However, the research communities have yet to explore all the limitations and possibilities of improvement on such energy-scavenging technologies, especially when the application is in vivo. Several aspects of recent developments in energy harvesting technologies feasible for biomedical implantable devices are reported systematically. A detailed review of piezoelectric energy harvester mechanism and miniaturisation, electric output and power management and biocompatibility of an energy harvester for implantable medical devices in vitro and in vivo environments. Furthermore, the piezoelectric energy harvester’s durability, packaging material, connection and evaluation criteria are discussed.

Electric Car Power Management and Monitoring System: Enhancing Efficiency and Battery Life

Electric Car Power Management and Monitoring System: Enhancing Efficiency and Battery Life

Enhancing Performance of Hybrid Electric Vehicle using Optimized Energy Management Methodology

The fuel consumption and the fuel management strategy (PMS) of the hybrid electric vehicle are closely linked (HEV). In this study, a hybrid power management technique and an adaptive neuro-fuzzy inference (ANFIS) method are established. Artificial intelligence represents a huge improvement in electricity management across different energy sources (AI). The main energy source of the hybrid power supply is a proton exchange membrane fuel cell (PEMFC), while its electrical storage devices are a battery bank and an ultracapacitor. The hybrid electric vehicle's power management strategy (PMS) and fuel consumption are closely related (HEV). In this paper, an adaptive neuro-fuzzy inference and hybrid power management strategy (ANFIS) approach is developed. A significant advance in electricity management across multiple energy sources is artificial intelligence (AI). The proton exchange membrane fuel cell (PEMFC) serves as the primary energy source of the hybrid power supply, and the ultracapacitor and battery bank serve as its electrical storage components.

Forecasting future electric power consumption in Busan New Port using a deep learning model

As smart and environmentally friendly technologies and equipment are introduced in the sea port industry, electric power consumption is expected to rapidly increase. However, there is a paucity of research on the creation of electric power management plans, specifically in relation to electric power consumption forecasting, in ports. In order to address this gap, this study forecasts future electric power consumption in Busan New Port (South Korea's largest container port) and, comparing this with the current standard electric power supply capacity, investigated the feasibility of maintaining a stable electric power supply in the future. We applied a Long Short-Term Memory (LSTM) model trained using electric power consumption and throughput data of the last 10 years to forecast the future electric power consumption of Busan New Port. According to the results, electric power consumption is expected to increase at an annual average of 4.9 % until 2040, exceeding the predicted annual 4.7 % increase in throughput during the same period. Given these results, the current standard electric power supply capacity is forecast to reach only 35 % of demand in 2040, indicating that additional electrical power supply facilities will be needed for stable port operation in the future.

EXTRACTION OF POINT CLOUD-BASED INFORMATION FOR POWERLINE CORRIDORS

Abstract. It has been a challenge for electric power management to automatically extract power lines from LiDAR point clouds. However, environmental and technical issues have made management more challenging in complicated areas where power lines are in close proximity to buildings and/or trees. In this study, the structure and types of the data captured by a LiDAR sensor in regions containing line corridors were analysed. The crucial stage is appropriately identifying from the data the essential parts of a power line corridor route. The point cloud dataset used in the study belongs to the Borssele region in Zeeland, the Netherlands. By manually labelling the dataset, three classes were identified: wire, pylon, and others. For the classification of point clouds, the Random Forest method was utilised. To assess the obstacles posed by the class wire, 5 m, 10 m, and 15 m 3D buffer zones are created. The visual presentation of obstacles within the buffer zone is achieved by assigning them a separate class code and indicating that they are inside and partially within. Based on the results, the correctness values of the classes of wire and others are considered to be satisfactory. However, the class pylon contains points with incorrect labels after the classification. As a result, the accuracy of the pylon class is much lower than the accuracy of the other two classes.

Improving power transmission tower state recognition in remote sensing images using cooperative Adaboost-MobileNet

ABSTRACT Tower state recognition is of great importance in smart electric power management. Most previous efforts were devoted on the guyed towers and tower structures or materials to monitor the structural stability of towers through sensors fixed on them. Actually, identifying their states as a whole is also needed for remote management. This study addresses the task of tower state recognition in high-resolution remote-sensing images under the framework of deep learning. To achieve this goal, a new remote-sensing image dataset is constructed. Technically then, an ensemble learning strategy is developed to classify the states of the transmission towers. Specifically, MobileNetV3 is taken as the weak classifier as it is simple yet parameter-light in the family of convolutional networks. Along the line of ensemble learning with the principal of Adaboost in machine learning, an Adaboost-style network paradigm, named as Adaboost-MobileNetV3 in this paper, is finally proposed to recognize the states of towers. To evaluate its effectiveness, a series of deep convolutional networks are compared with the proposed network. The extensive comparative experiments indicate that the proposed Adaboost-MobileNetV3 network is suitable for practical tower state recognition, which achieves about 90% accuracy with at least about 4% higher than these compared models.