Power Loss Research Articles

IMPLEMENTASI SISTEM MONITORING AIR DENGANSENSOR ULTRASONIK HC-SR04 DAN WATER FLOW DI ASONE HIDROPONIK BERBASIS INTERNET OF THINGS (IoT) MENGGUNAKAN ENERGI PANEL SURYA(PLTS)

The cultivation system using hydroponics is now widely popular among the community because it does not require large areas of land. Using the hydroponic system results in faster and optimal harvests. However, the busy activities of urban communities often cause them to fail in implementing it due to constraints in controlling and monitoring hydroponic plants. Additionally, PLN electricity is essential for operating the water pump, and the electricity load and tariff increases, along with sudden blackouts, significantly impact business actors. To address this problem, this research creates an Internet of Things (IoT)-based water monitoring system for ACONE Hydroponics partners with solar panel (PLTS) energy sources as an alternative energy in the event of a sudden PLN power outage. The sensors used in this system include the HCR-04 ultrasonic sensor to monitor water availability and the water flow sensor to measure water flow (discharge). Sensor data is processed by the ESP32 microcontroller and sent to the Blynk cloud platform via an internet connection and displayed on an LCD. System testing shows that the system can send sensor data to the cloud platform stably and in real-time. Data visualization on the cloud platform is easy to understand and helps hydroponic owners monitor water conditions in hydroponic systems. This water monitoring system can help users optimize water use in hydroponic systems.

Cooperative energy optimal control involving optimization of longitudinal motion, powertrain, and air conditioning systems

This paper is concerned with the optimal control strategies for the longitudinal control, powertrain, and air conditioning (A/C) system of connected four-wheel hub-drive electric vehicles (EVs). A hierarchical control framework is developed to enhance the energy economy of the vehicle. Real-time connected information is utilized in the upper layer to determine the travel mode. Then, a multi-objective motion planning system (MOMPS) is proposed to plan the optimal acceleration trajectory. In the lower layer, an offline global optimization approach is employed to find the torque combinations that minimize the total power loss. The proposed A/C controller operates based on the bi-level model predictive control (Bi-level MPC) algorithm. A novel prediction model is developed to enable the A/C system to decrease energy consumption by leveraging the speed of the vehicle. The performance of the MOMPS is evaluated using urban test road data, demonstrating that the MOMPS can balance multiple objectives compared to global dynamic programing (Global DP) and the intelligent driver model (IDM). In addition, the proposed torque distribution strategy results in a 4.98% energy-savings rate through comparison with the even torque distribution strategy. Moreover, the A/C controller proposed in this paper can optimize energy consumption by 13.57% compared to a baseline strategy that maintains a constant setting.

Implementation of fuzzy logic controller for positive output LUO converter

This paper describes the fuzzy logic controller (FLC) for a positive output LUO converter (POLC), which uses a voltage controller to achieve the output voltage regulation. The fourth-order POLC DC–DC converter produces positive output voltage utilizing the voltage lift technique. Due to the converter’s switching functions and its inherently time-varying nature, it exhibits a highly nonlinear dynamic response. To enhance the dynamic performance of the converter, controllers are designed using small signal model and frequency response analysis. The closed loop is used to evaluate converter performance in the presence of variations in reference voltage, source voltage and load disturbances. The power loss analysis of the converter is performed. The comparative results show that FLC gives good dynamic response for wide range of variations than the proportional–integral (PI) controller. The prototype model is built using dsPIC (30F4011) microcontroller, and the experimental results are presented to validate the simulation results.

Simulation and Application of a New Type of Energy-Saving Steel Claw for Aluminum Electrolysis Cells

Aluminum electrolysis is a typical industry with high energy consumption, and the energy saving of aluminum electrolysis cells is conducive to the sustainable development of the ecological environment. The current density distribution on the steel claws of conventional aluminum electrolysis cells is uneven, resulting in a large amount of power loss. Therefore, a new type of current-equalized steel claw (CESC) is designed in this paper. The ANSYS simulation study shows that the CESC can achieve a more uniform current density distribution and reduce the voltage drop by about 36 mV compared with the traditional steel claw (TSC). In addition, the use of CESC optimizes the temperature distribution of the steel claws and reduces the risk of cracking and deformation. The results of the industrial application tests are highly consistent with the simulation results, confirming the accuracy of the simulation results. The economic benefit analysis shows that using CESC saves 114.1 kWh of electricity per ton of aluminum produced. If this technology can be promoted throughout China, it is expected to save up to 4.75 billion kWh of electricity annually. The development of CESC is promising and of great significance for improving the overall technical level of the aluminum electrolysis industry.

Magnetic and dielectric properties of low‐loss MnZn ferrites with wide temperature stability

AbstractTo meet the needs for higher energy efficiency and a wide operating temperature range of electric vehicles, the low‐loss MnZn ferrites in a wide temperature range have been developed by optimizing the Fe content and the oxygen partial pressure (PO2) during the sintering process in this work. For the optimal sample, power loss at 300 kHz/100mT is 204 kW/m3 at 25°C and remains below 290 kW/m3 in the wide temperature range from ‐10 to 120°C. The loss separation method was employed to clarify the effects of the Fe content and PO2 on power loss. The equivalent circuit model has been employed to fit the complex impedance and it is found that the increase of PO2 enhances both the grain resistance Rg and the grain boundary resistance Rgb. The enhancement of Rgb is mainly responsible for the reduction of eddy current loss and consequently power loss. Dielectric permittivity is as large as about 15000 in this series of samples due to the electric polarization at the rich grain boundaries. Dielectric loss is very low between ‐50 and 150°C and has little contribution to the energy loss.

Graph theory-enhanced integrated distribution network reconfiguration and distributed generation planning: A comparative techno-economic and environmental impacts analysis

In today's world, eco-friendly solutions are crucial for efficient power delivery and assessing their corresponding economic and environmental benefits is essential. As innovators, it is also imperative to continually improve on existing techniques to solve a problem. Evaluating the existing literature in this area of study, gaps of improving the optimization techniques by reducing the amount of infeasible configurations the reconfiguration procedure encounters was established, additionally, the need to utilize distributed generations that significantly reduce carbon footprint in the environment was also ascertained. Hence, this paper presents an effective integration method for the simultaneous reconfiguration of Radial Distribution Networks (RDNs) and Photovoltaic (PV) DGs allocation, considering the tripodal issues of cost, operational efficiency, and environmental sustainability. A modification of the adaptive mountain gazelle optimizer (AMGO) enhanced with graph theory is deployed for the optimization procedures. The crucial feature of the proposed approach is the reduction of unfeasible configurations throughout the optimization procedure toward satisfying the network's radiality constraints, achieving consistent convergence and reduced computation time. The technical benefits are active power loss minimization, voltage stability, and voltage profile improvement. The economic benefits are analyzed by estimating the purchased power, the associated cost of power losses, and the cost of DGs and switches over a planning period of 20 years. The consequent environmental benefits are analyzed in detail, highlighting the significant reduction in pollutant emissions. The proposed model was tested on the IEEE 33- and 69-bus RDN, considering several scenarios, including synchronous network reconfiguration and DG installations. From the results procured, the simultaneous network reconfiguration and DG allocation provided better outcomes, yielding minimum active power loss of 35.36 kW, minimum voltage of 0.9541 p.u., voltage stability index of 1.9936 p.u., total planning cost of $3.456 million, and emission of 1.744 million lb/hr, respectively, for the 33-bus systems. The corresponding value for the 69-bus system is 32.57 kW, 0.9832 p.u., 2.3847 p.u., $ 2.524 million, and 2.53 million lb/hr, respectively. The proposed model was compared with other reported techniques for performance validation, and its efficacy and superior performance was established.

Digital-Based Learning in Lagging Area: Students' Problems and Expectations

Digital-based learning required by the government since the Covid-19 pandemic in early 2020 until mid-2022 is not fully implemented by teachers and students in lagging area. There are several problems and challenges faced by students in digital-based learning. This study aims to identify the causes of not implementing digital-based learning in the lagging area and analyze the expectations of students. This study was conducted by using quantitative methods. Data were obtained by giving questionnaires, conducting observations and interviews and then analyzed and explained narratively. The results of this study reveal that limited facilities and infrastructure, frequent power outages, limited internet access, there are still many students who do not have laptops and smart devices and lack of responsibility, understanding and teacher skills are obstacles for students in utilizing digital media. Students expect the government's attention to improve facilities, digital literacy of teachers and students and teacher professionalism. In addition, they also want teachers to be more creative in carrying out classroom learning such as the use of various digital media, learning videos, and various online learning technologies.

A Facile Low Prevacuum Treatment to Enhance the Durability of Nonfullerene Organic Solar Cells

Herein, a straightforward vacuum‐assisted method is introduced to enhance the stability of nonfullerene organic solar cells (OSCs). The method, termed “prevacuum” involves subjecting the active layer (D18:Y6) to a low‐pressure vacuum (−1 bar) before thermal annealing at 100 °C. Compared to untreated devices, prevacuum‐treated OSCs exhibit a notable increase in power conversion efficiency from 13.71% to 14.90%. This enhancement is attributed to improved light absorption and charge extraction, as evidenced by external quantum efficiency measurements. Moreover, prevacuum treatment significantly improves device stability under operational conditions, with a 30% power loss occurring after 8.25 h compared to 4.5 h for untreated devices. This improvement is attributed to the removal of volatile components and impurities during the vacuum process, leading to a more hydrophobic and stable active layer. The study demonstrates the efficacy of prevacuum treatment as a simple and accessible method for enhancing the performance and longevity of OSCs, paving the way for their broader application in sustainable energy technologies.

Optimization of high-speed reducer in electric vehicle based on analysis of lubrication

PurposeThe purposes of this paper are optimization of high speed reducer in electric vehicles based on the analysis of lubrication and verification of simulation accuracy and optimization results.Design/methodology/approachThe traditional CFD method presents poor applicability to complex geometric problems due to grid deformity. Therefore, moving particle semi-implicit (MPS) method is applied in this study to simulate lubrication of the reducer and analyze the influence of input speed and lubrication system design on the distribution. According to the results, the reducer is optimized. Meanwhile, the experiments for lubrication and churning power loss is carried out to verify the accuracy of simulation and optimization effects.FindingsThe flow field of lubricant inside the reducer is obtained. The lubrication system of reducer needs to be improved. Simulation and experiment show that the optimization is sufficient and efficient.Originality/valueAccording to the simulation of lubrication, the reducer is optimized. The lubrication experimental setup is established. The conclusion of paper can provide the method and tool for reducer in electric vehicle.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2024-0123/

Multi-objective optimal coordination of electric vehicle charging, power grid, energy storages and renewables

Considering that the grid connection of variable renewable energies (VREs) and the disorderly charging loads of large-scale electric vehicles (EVs) will adversely affect the power grid stability, the optimization strategy of EV charging and grid-connected scheduling are investigated, in which energy storage system is added to balance the demand and supply of the power grid. First of all, considering the profit of EV charging station, the charging cost of EV users and power loss, a multi-objective optimal scheduling model of EV charging, power grid, pumped hydro-storage (PHS) and wind farm is constructed, which is improved from the aspect of wind farm wake. Then, the multi-objective optimization algorithm based on genetic algorithm is used to solve the model to realize the optimal charging of EVs. Finally, the IEEE-13 power grid is used to verify the effectiveness of the proposed multi-objective optimal scheduling model. Combined with a specific example for simulation analysis, the results show that the model can achieve orderly charging of EVs, ensuring the safety of the power grid and promote the use of VREs. In addition, the optimization algorithm can further improve the profit, and reduce the charging cost and power loss.

Disrupted Hippocampal Theta-Gamma Coupling and Spike-Field Coherence Following Experimental Traumatic Brain Injury.

Traumatic brain injury (TBI) often results in persistent learning and memory deficits, likely due to disrupted hippocampal circuitry underlying these processes. Precise temporal control of hippocampal neuronal activity is important for memory encoding and retrieval and is supported by oscillations that dynamically organize single unit firing. Using high-density laminar electrophysiology, we discovered a loss of oscillatory power across CA1 lamina, with a profound, layer-specific reduction in theta-gamma phase amplitude coupling in injured rats. Interneurons from injured animals were less strongly entrained to theta and gamma oscillations, suggesting a mechanism for the loss of coupling, while pyramidal cells were entrained to a later phase of theta. During quiet immobility, we report decreased ripple amplitudes from injured animals during sharp-wave ripple events. These results reveal deficits in information encoding and retrieval schemes essential to cognition that likely underlie TBI-associated learning and memory impairments, and elucidate potential targets for future neuromodulation therapies.

The challenges of regulatory pluralism

Countries with small and/or less-resourced regulatory authorities that operate outside of a larger medical product regulatory system face a regulatory strategy dilemma. These countries may rely on foreign well-resourced regulators by recognising the regulatory decisions of large systems and following suit (regulatory reliance); alternatively, such countries may extend formal decision recognition to regulators in multiple other jurisdictions with similar oversight and public health goals, following a system which we call regulatory pluralism. In this policy comment, we discuss three potential limitations to regulatory pluralism: (i) regulatory escape, in which manufacturers exploit regulatory variation and choose the lowest regulatory threshold for their product; (ii) increased fragmentation and complexity for countries adopting this approach, which may, in turn, lead to inconsistent processes; and (iii) loss of international bargaining power in developing regulatory policies. We argue that regulatory pluralism has important long-term implications, which may not be readily apparent to policy makers opting for such an approach. We advocate for the long-term value of an alternative approach relying on greater collaboration between regulatory authorities, which may relieve administrative pressures on countries with small or less-resourced regulatory authorities, regardless of whether countries pursue a strategy of domestic regulation or regulatory pluralism.

Power Semiconductor Junction Temperature and Lifetime Estimations: A Review

The lifetime of power electronic systems is the focus of both the academic and industrial worlds. Today, compact systems present high switching frequency and power dissipation density, causing high junction temperatures and strong thermal fluctuations that affect their performance and lifetime. This paper is a review of the existing techniques for the electro-thermal modelling of Mosfet and IGBT devices regarding lifetime estimation. The advantages and disadvantages of the methodologies used to achieve lifetime prediction are discussed, and their benefits are highlighted. All the factors required to predict power electronic device lifetime, including Mosfet and IGBT electrical models, the computation of power losses, thermal models, temperature measurement and management, lifetime models, mission profiles, cycle counting, and damage accumulation, are described and compared.

Solving power system economic emission dispatch problem under complex constraints via dimension differential learn butterfly optimization algorithm with FDC-based

The economic emission dispatch (EED) aims to minimize the fuel and pollutant emission costs of generator units under various complex constraints. Optimizing the EED problem is of crucial importance for alleviating the current energy and environmental pressures. In this work, nearly all known complex constraints in the EED problem, including the valve-point effect, transmission line power loss, prohibited operating zones, and ramp-rate limits, are taken into account, and an enhanced version of butterfly optimization algorithm (FDCDLBOA) is proposed to solve it. First, a new adaptive fragrance is employed to optimize the instability caused by target differences and improve the convergence performance. Second, the proposed dimension differential learning strategy evolves the position of individuals with the help of superior dimensional information in the population, and this extensive learning exchange can balance global and local search, maintain diversity, and get rid of local optima. Third, the Fitness-Distance-Constraint (FDC) guide selection method is employed for the first time to handle the complex constraints of EED problems, enhancing the ability of individuals to bypass the infeasible search areas. After evaluating the proposed FDCDLBOA on CEC 2022 test suite, it is applied to solve 8 EED cases, encompassing small-, medium- and large-scale systems. Notably, the 280-generator case is the first large-scale test to exceed 200 generators. Compared with 9 representative algorithms, FDCDLBOA performs outstandingly in terms of robustness, improvement index (IF), mean constraint violation (MV), feasibility rate (FR) and Quade multiple comparison, among which IF, MV, FR, and Quade are all employed for evaluating the EED problem for the first time. The presented results confirm that the proposed method effectively enhances the robustness of high-quality solutions and the ability to handle complex constraints, demonstrating strong competitiveness and potential in solving the EED problem.

Multi-heterogeneous interfaces boost dielectric polarization in PBA-derived Co/MnO@NC ternary composites for electromagnetic wave absorption

The elaborate construction of multi-component composites has been deemed as a promising strategy to enhance dielectric polarization response capability in the preparation of highly efficient microwave absorbers. However, the rational design and integration of homogeneous composites with diverse components continues to pose a great challenge. Herein, we propose a straightforward self-assembly-carbonization strategy for fabricating Co/MnO@NC ternary composites derived from CoMn-Prussian Blue Analogous precursors, featuring enriched heterogeneous interfaces and balanced magneto-electric coupling synergy. The ternary composites effectively introduce diverse dissipation mechanisms, including interfacial polarization behavior originated from the constructed heterogeneous interfaces, dipole polarization relaxation triggered by atomic defects, and optimized magnetic loss ability from well-dispersed metallic Co species. Benefiting from these advantages, the Co/MnO@NC composites demonstrate superior electromagnetic wave absorption performances, with a minimum reflection loss value of −61.37 dB at the thickness of 3.5 mm and effective frequency absorption bandwidth of 6.32 GHz, covering the full Ku-band. Furthermore, power loss density and radar cross-section simulations validate that the Co/MnO@NC ternary composites possess exceptional microwave signal attenuation abilities, with a maximum radar cross-section reduction value of up to 27.98 dBm2 at 0°. Such outstanding absorption properties exceed those of most currently reported ternary composites and exhibit significant potential to replace conventional ferromagnetic-based absorbers. This work offers a straightforward strategy to fabricate ternary composites with excellent electromagnetic wave attenuation properties and sheds novel insights into the dissipation mechanisms.

The Effect of Partial Shadings on the Output Power of the Photovoltaic Modules Connected with Different Current and Voltage Characteristics

A mismatch in the output power of photovoltaic (PV) modules in a PV array can occur due to partial shading or a module replacement. Substitution of a module in a PV array with a new one might lead to different current and voltage characteristics between the new and existing modules and result in power losses. The amount of power loss might be increased more if the PV array experiences partial shading. For this reason, this study aims to investigate the effect of partial shadings on the power output of photovoltaic arrays with different current and voltage characteristics. The PV array under test consists of 25 units of solar modules with a total cross-tied (TCT) configuration. There are five shading conditions applied to the test PV array, i.e., the short narrow (ShN), the short wide (ShW), the long narrow (LnN), the long wide (LnW), and the diagonal. A magic square method is applied to reduce the power loss when the PV modules experience partial shading conditions. The results show that the power loss due to partial shadings, either on all identical modules or partially identical, is the same. The most significant power loss occurs in the long comprehensive shading scenario, where 80% of the modules experience shading, which is 41.30%.

Network reconfiguration and integration of distributed energy resources in distribution network by novel optimization techniques

Nowadays, electrical load demand in radial distribution networks (RDN) is continuously growing, therefore RDNs are facing some serious challenges like voltage violation and line losses. Since modern RDNs have reconfiguration capabilities, optimal network reconfiguration is preferred over the costly construction of new lines and cables. In addition, the optimal integration of distributed energy resources (DER) helps to decrease above mentioned network challenges. This paper presents an improved version of the radiality maintenance algorithm (IRMA) to solve the optimal reconfiguration problem using a meta-heuristics algorithm. It also proposes two different schemes of algorithms by the blending of a Genetic algorithm (GA) and Teaching learning-based optimization (TLBO) to solve the optimal DER integration problem, where blending enhances the search space of each scheme which helps to find global minima in less iterations and time, while existing algorithms get stuck in local minima with same number of searching agents. The proposed problems are solved by minimizing active power loss by the single objective function and the sum of active power loss, reactive power loss, and voltage deviation index by multi-objective function using the weighted sum method where all objectives are equally dealt with, and their sum is used as single fitness function for the optimization algorithm. Four case studies are simulated using different DER technologies to improve each objective function. The efficiency of the proposed IRMA and two newly developed schemes of optimization algorithms, named GA-TLBO and TLBO-GA, are tested on two IEEE benchmark RDN of 33 and 69 buses. The results validate the efficiency of proposed algorithms that remarkably minimize a single objective from 210.9800 kW in the base case to 58.8768 kW, whereas existing algorithms like GWO and HHO were able to only reduce it to 72.7861 kW and 72.900 kW for IEEE 33 bus RDN, respectively. Similarly, for IEEE 69 bus system, single objective is reduced from base case of 224.9917 kW to 36.2543 kW, while existing algorithms like QOTLBO and QOSIMBO were only able to reduce it to 71.6250 kW and 71 kW, respectively. Furthermore, in multi-objective functions, reactive power losses and voltage deviation are also significantly improved from their base values. After that, the results of improved algorithms are compared with those of existing algorithms in the literature review for a comprehensive evaluation, which proves that proposed algorithm schemes are much more efficient and stable for the proposed problem as well as for standard benchmark optimization functions.

Hercules: Enabling Atomic Durability for Persistent Memory with Transient Persistence Domain

Persistent memory (pmem) products bring the persistence domain up to the memory level. Intel recently introduced the eADR feature that guarantees to flush data buffered in CPU cache to pmem on a power outage, thereby making the CPU cache a transient persistence domain . Researchers have explored how to enable the atomic durability for applications’ in-pmem data. In this article, we exploit the eADR-supported CPU cache to do so. A modified cache line, until written back to pmem, is a natural redo log copy of the in-pmem data. However, a write-back due to cache replacement or eADR on a crash overwrites the original copy. We accordingly developed Hercules, a hardware logging design for the transaction-level atomic durability, with supportive components installed in CPU cache, memory controller (MC), and pmem. When a transaction commits, Hercules commits on-chip its data staying in cache lines. For cache lines evicted before the commit, Hercules asks the MC to redirect and persist them into in-pmem log entries and commits them off-chip upon committing the transaction. Hercules lazily conducts pmem writes only for cache replacements at runtime. On a crash, Hercules saves metadata and data for active transactions into pmem for recovery. Experiments show that, by using CPU cache for both buffering and logging, Hercules yields much higher throughput and incurs significantly fewer pmem writes than state-of-the-art designs.

Hurricanes make headlines, but chronic utility failure drives energy (in)security in the U.S. Virgin Islands

Natural disasters and extreme events expose significant challenges to electricity infrastructure. We explore concepts of energy security and energy resilience in the aftermath of Hurricanes Maria & Irma in the U.S. Caribbean in 2017. We look at household consequences of regular and storm related power outages for communities, and investigate the motivations for household adoption of solar energy in the wake of a natural disaster, using surveys and interviews in the U.S. Virgin Islands. Results indicate that the storm-related utility failure was widely acknowledged as catastrophic by Islanders, severely limiting the ability for people to work, purchase and store food, and live healthy and productive lives – with lasting consequences. We also find that residents are just as impacted by chronic utility failure as they were disaster-related outages. In the wake of perceived failure from the local utility to provide energy security or energy resilience, households are making decision to invest in self-provided systems to meet these needs by purchasing generators and solar PV. Yet, provision of solar PV follows systemic and racialized patterns of access. We suggest that the practical ways in which utilities make decisions about resilience are outdated, and call for a new paradigm of energy security.

Integrating hydrogen into a hybrid system to meet a laboratory's electricity demand

Providing quality electricity to improve the living conditions of rural and urban populations in Africa, particularly in Cameroon, represents a challenging but achievable task. Indeed, Cameroon has the second largest hydrographic network in Central Africa, and thanks to this network, over 90% of electricity comes from aging hydroelectric dams that cause permanent power outages. The desire to take advantage of other benefits that nature has given this country has directed this work towards renewable energies. Thanks to the HOMER Pro software, a renewable energy hybrid system with hydrogen gas serving as storage and energy vector was adopted. Photovoltaic panels and wind turbines are proposed as primary energy sources for the permanent, low-cost supply of the IRAD laboratory at the University of Maroua. The software proposes three systems, one with the wind turbine as the only primary source, and considered as the most optimal. It has an energy cost equal to $0.0633, an NPC of $20330, and an initial capital of $8500. The other two systems are hybrids with a renewable fraction equal to 100%, and a load distribution strategy that is the charge cycle (CC) like the first system. The first less expensive hybrid system has an energy cost and an NPC respectively of $0.0778 and $24986. As for the second hybrid system, it has an NPC and an energy cost respectively of $26195 and $0.0778. Hydrogen fully plays its role as an energy vector and storage in all three systems. A sensitivity analysis was conducted in this work to ensure that the system proposed for the laboratory can also be adapted in the 9 other regions of the country. It appears that the values of wind resources, solar radiation, and temperature have a significant impact on the energy cost. The cities of Yaoundé and Ebolowa, displaying respective wind speeds of 2.07 m/s and 1.76 m/s, are recognized for having the lowest wind speeds in the country, which hinders the overall energy production and profitability of the system. The hybrid system suggested for these cities proves to be expensive, with energy costs exceeding the standard electricity price set by the energy distribution and marketing organization in the country.