Power Efficiency Research Articles

The power control and efficiency optimization strategy of dynamic wireless charging system for multiple electric vehicles

Purpose This study aims to solve the problem that both the speed and the required driving power of electric vehicles (EVs) will change during the dynamic wireless charging (DWC) process, making it difficult to charge EVs with a constant power considering the overall efficiency of DWC system, the numbers of EVs and the power supply capacity. Therefore, this paper proposes the power control and efficiency optimization strategies for multiple EVs. Design/methodology/approach The wireless power charging system for multiple loads with a structure of double-sided LCC compensation topology is established. The expressions of optimal transmission efficiency and optimal equivalent impedance are derived. Taking the Tesla Model 3 as an example, a method to determine the number of EVs allowed by one transmitter coil and the overall charging power is proposed considering EV speed, power supply capacity, safe braking distance and overall efficiency. Then, the power control strategy, which can adapt to the changes of EV speed and the efficiency optimization strategy under different numbers of EVs are proposed. Findings In this paper, a method to determine the numbers of EVs allowed by one transmitter coil and the overall charging power is proposed considering EVs speed, power supply capacity, safe braking distance and overall efficiency. The accuracy of the charging power is good enough and the overall efficiency reaches a maximum of 91.79% when the load resistance changes from 5Ω to 20Ω. Originality/value In this paper, the power control and efficiency optimization strategy of DWC system for multiple EVs are proposed. Specifically, a method of designing the number of EVs and charging power allowed by one transmitter coil considering the factors of EV speed, power supply capacity, safe braking distance and overall efficiency is designed. The overall efficiency of the experiment reaches a maximum of 91.79% after adopting the optimization strategy.

Informer model with season-aware block for efficient long-term power time series forecasting

With the development of electricity spot markets, accurate electricity load forecasting enables power generation companies to supply the right amount of electricity, greatly avoiding power waste. As a result, time series forecasting in the field of power can bring great benefits. Previously, the Informer model successfully introduced the Transformer into long time series forecasting(LTSF) by proposing the ProbSparse self-attention mechanism, which solved the inherent problem of high memory complexity in self-attention. Recent research has further demonstrated the potential of the self-attention for mining complex dependencies. However, the limited amount of historical data has become one of the main challenges in applying deep learning techniques to power LSTF tasks. Previous researches often add a large number of time covariates to provide more information. In this paper, to address this issue, (i) we design a simple but effective Season-aware Block to enhance the model's ability to mine artificial prior information in temporal covariates; (ii) we conduct experiments using the provincial power data of Zhejiang Province, China, from 2019 to 2022, and our model outperforms other models, achieving a 19 percent MSE relative improvement, which can help power generation companies reduce costs and further reduce prices to benefit consumers; (iii) we conduct ablation experiments to assess the efficacy of the Season-aware Block in extracting temporal periodic features. Furthermore, we elucidate the underlying reasons for the effectiveness of both the self-attention mechanism and the Season-aware Block through visualization experiments through visualization techniques.

A Taxonomy of Low-Power Techniques in Wearable Medical Devices for Healthcare Applications

Chronic diseases are the most prevalent and non-communicable health crisis globally. Most chronic disease patients require continuous physiological monitoring, using wearable technology for timely treatment, precise illness detection, and preventive healthcare. Nonetheless, efficient power management is required for such resource-constrained wearable devices. This work aims to analyze low-power techniques (LPTs) in wearable medical devices using a data-driven approach and identify novel approaches promising higher power savings. Through an intensive literature analysis, we identify the most relevant LPTs for minimizing power consumption in wearable devices for physiological monitoring while recognizing the barriers to adopting these techniques. As a result, a novel taxonomy based on the common characteristics of the LPTs is proposed, along with strategies for the combination of LPTs. Through our analysis, we propose possible enhancements in using LPTs and suggest mechanisms for the medical device industry to facilitate their adoption. Overall, our proposed strategies guide the use of LPTs on wearable medical devices toward continuous physiological monitoring.

Disturbance observer-based finite-time adaptive neural control scheme of DFIG-wind turbine

This paper introduces a novel disturbance observer-based finite-time adaptive neural control approach to optimize wind power conversion in a doubly fed induction generator-based wind turbines (DFIG-WT). This control strategy offers appealing features including rapid finite-time convergence, both transient and steady-state performance enhancements, and robustness against external disturbances and inherent model uncertainties. The control strategy integrates the neural networks estimation capability with the interesting proprieties of the finite-time control method to achieve efficient wind power conversion. Closed-loop finite-time stability is conducted using the finite-time Lyapunov stability concept of nonlinear systems. The developed control strategy’s effectiveness is confirmed through numerical simulation.

Electromagnetic Signal Analysis for Electrical Fault Diagnosis in Synchronous Generators

This study investigates the enhancement of fault diagnostics in synchronous generators by incorporating electromagnetic signal analysis with conventional diagnostic methodologies. The critical role of synchronous generators in maintaining power system stability and efficiency in industrial and power plant environments is underscored. Finite element modeling (FEM) is employed to simulate various fault conditions, such as stator and rotor winding faults. This study proposes a method that integrates stator current and stray magnetic flux analysis to identify five distinct types of short-circuit faults in stator and rotor windings, thereby enhancing the diagnostic capabilities for electrical faults in synchronous generators. This approach successfully identifies these electrical faults using non-invasive methods, offering a cost-effective solution that enhances fault detection. These findings are based on simulation results and serve as a preliminary stage for further validation through experimental studies. This integration is crucial for the development of efficient diagnostic systems that are capable of adapting to complex fault patterns, reducing human intervention, and streamlining maintenance operations, thus improving the reliability of synchronous generators globally.

Enhancing thermodynamic performance with an advanced combined power and refrigeration cycle with dual LNG cold energy utilization

Utilizing waste heat to drive thermodynamic systems is imperative for improving energy efficiency, thereby improving sustainability. A combined cooling and power systems (CCP) utilizes heat from a temperature source to deliver both power and cooling. However, CCP systems utilizing LNG cold energy suffers from low second law efficiency due to significant temperature differences. To address this, an “Advanced Power and Cooling with LNG Utilization (ACPLU)” system is proposed, integrating a cascaded transcritical carbon dioxide (TCO2)-LNG cycle with an Organic Rankine cycle (ORC) for improved power generation and an absorption refrigeration system (ARS) for simultaneous cooling. This study evaluates the second law efficiency, net work output, and exergy destruction performance through a sensitivity analysis, optimizing variables such as heat source temperature, superheater temperature difference, ORC and CO2 turbine inlet and condenser pressures, evaporator temperature, and pinch point temperatures of heat exchangers and generator. Compared to previous studies on CCP systems, the ACPLU shows a superior performance, with a second law efficiency of 27.3 % and a net work output of 11.76 MW. Cyclopentane as an ORC working fluid resulted in the highest second law efficiency of 29.06 % and net work output of 12.27 MW. Parametric analysis suggested that heat source temperature significantly impacts net power output. The exergy analysis concluded that a high-pressure ratio and good thermal match between the heat exchangers enhance overall performance. Utilizing artificial neural network (ANN) to produce a multiple-input-multiple-output (MIMO) objective function and performing multi-objective optimization (MOO) using genetic algorithm (GA), an improved second law efficiency and net power output by 28.11 % and 14.16 MW respectively, with pentane as the working fluid, is demonstrated. An average cost rate of 9.121 $/GJ was observed through a thermo-economic analysis. The ACPLU system is promising for medium temperature waste heat recovery, such as, pharmaceutical manufacturing plants.

A novel FVF-based GHz-range biquad in a 28 nm CMOS FD-SOI technology

Inductor-less CMOS filters with bandwidth exceeding several GHz are required in high-speed data converter applications. This paper introduces two complementary biquad filters, one N-based and the other P-based, utilizing the well-established flipped voltage follower (FVF) stage. These filters exhibit more than 7 GHz cut-off frequency and a low power consumption of 0.54 mW/pole for the N-type biquad, and 0.3 mW/pole for the P-type one, demonstrating impressive figures-of-merit (FOMs) even considering bandwidth and dynamic range. The implementation of these biquads in the STMicroelectronics FD-SOI 28-nm CMOS process, along with extensive simulations, ensures stable performance under process, supply voltage and temperature (PVT) variations and mismatches, as confirmed by post-layout simulations. Notably, the area occupied by each biquad is merely 246 μm2 for N-type biquad and 193 μm2 for P-type, marking one of the smallest footprints in the existing literature. The achieved figures-of-merit are noteworthy, showcasing excellent power efficiency, minimal area occupation, and commendable dynamic range.

Virtual coupling control of photovoltaic-energy storage power generation system for efficient power support

The key to achieving efficient and rapid frequency support and suppression of power oscillations in power grids, especially with increased penetration of new energy sources, lies in accurately assessing the inertia and damping requirements of the photovoltaic energy storage system and establishing a controllable coupling relationship between the virtual synchronous generator and the main network. In this paper, the inertia and damping requirements of the photovoltaic energy storage system are estimated using frequency safety warnings and power oscillation suppression constraints. Furthermore, the oscillation characteristics of the power system, which include photovoltaic and energy storage in the presence of periodic load disturbances, are analyzed. Based on this analysis, a coupled virtual synchronous controller for energy storage is proposed. This controller employs a forced oscillation suppression technique through natural frequency shifting, and establishes a controllable power coupling relationship between the photovoltaic energy storage system and the main network to achieve the desired frequency shift. Finally, a simulation system incorporating conventional generators and a photovoltaic energy storage system controlled with the proposed strategy is built to test the frequency support and power oscillation suppression behaviors of the grid-connected power generation system.

A Dual control strategy for improved power quality in grid-tied off-board bidirectional electric vehicle charger

Electric vehicle (EV) chargers face significant challenges in maintaining grid power quality (PQ) and ensuring efficient power management during grid-to-vehicle (G2V) and vehicle-to-grid (V2G) operations. Additionally, they must seamlessly switch to vehicle-to-load (V2L) mode during grid disturbances to provide uninterrupted power supply (UPS) for domestic loads. This article explores a dual control approach incorporating adaptive model predictive direct power control (AMP-DPC) on the rectifier side and adaptive direct power control (ADPC) on the dual active bridge (DAB) side to address these challenges. The AMP-DPC employs a control strategy referred to the second-order generalized integrator (SOGI) which estimate synchronizing voltage templates specifically designed for single-phase systems. The proposed optimization control aims to mitigate the cost function value by selecting appropriate switching modes. The optimized function is independent of the weighting factor, expressing the optimal modulation function across various weighting factors without additional design or selection. The current reference used by the charger is designed to ensure that the power factor remains at closer unity throughout G2V and V2G modes. The proposed control algorithm ensures the grid current remains low in harmonics during G2V, V2G, and V2L modes of operation. The experimental validation of the proposed control strategy is performed in the laboratory on a 0.5 kW off-board electric vehicle charger (EVC) prototype under various conditions to demonstrate its effectiveness in compliance with the IEEE 519–2022 standard.

Dual polarization for efficient III-nitride-based deep ultraviolet micro-LEDs

The deep ultraviolet (DUV) micro-light emitting diode (μLED) has serious electron leakage and low hole injection efficiency. Meanwhile, with the decrease in the size of the LED chip, the plasma-assisted dry etching process will cause damage to the side wall of the mesa, which will form a carrier leakage channel and produce non-radiative recombination. All of these will reduce the photoelectric performance of μLED. To this end, this study introduces polarized bulk charges into the hole supply layer (p-HSL) and the electron supply layer (n-ESL) respectively (dual-polarized structure) of the DUV μLED at an emission wavelength of 279 nm to enhance the binding of carriers and increase the injection efficiency of carriers. This is because the polarization-induced bulk charge can shield the polarized sheet charge on the interface and reduce the polarization electric field. The reduced polarization electric field in p-HSL can increase the effective barrier height of the conduction band in the p-type region and reduce the effective barrier height of the valence band. The decrease in the polarized electric field of n-HSL can reduce the thermal velocity of electrons, thereby enhancing the electron injection efficiency, reducing the Shockley–Read–Hall (SRH) recombination, and increasing the effective barrier height of the valence band. The study results indicate that the electron concentration and hole concentration of a μLED with dual polarization were increased by 77.93% and 93.6%, respectively. The optical power and maximum external quantum efficiency of μLED reached 31.04 W/cm2 and 2.91% respectively, and the efficiency droop is only 2.06% at 120 A/cm2. These results provide a new approach to solving the problem of insufficient carrier injection and SRH recombination in high-performance DUV μLEDs.

Efficient sparse spiking auto-encoder for reconstruction, denoising and classification

Auto-encoders are capable of performing input reconstruction, denoising, and classification through an encoder-decoder structure. Spiking Auto-Encoders (SAEs) can utilize asynchronous sparse spikes to improve power efficiency and processing latency on neuromorphic hardware. In our work, we propose an efficient SAE trained using only Spike-Timing-Dependant Plasticity (STDP) learning. Our auto-encoder uses the Time-To-First-Spike (TTFS) encoding scheme and needs to update all synaptic weights only once per input, promoting both training and inference efficiency due to the extreme sparsity. We showcase robust reconstruction performance on the Modified National Institute of Standards and Technology (MNIST) and Fashion-MNIST datasets with significantly fewer spikes compared to state-of-the-art SAEs by 1–3 orders of magnitude. Moreover, we achieve robust noise reduction results on the MNIST dataset. When the same noisy inputs are used for classification, accuracy degradation is reduced by 30%–80% compared to prior works. It also exhibits classification accuracies comparable to previous STDP-based classifiers, while remaining competitive with other backpropagation-based spiking classifiers that require global learning through gradients and significantly more spikes for encoding and classification of MNIST/Fashion-MNIST inputs. The presented results demonstrate a promising pathway towards building efficient sparse spiking auto-encoders with local learning, making them highly suited for hardware integration.

Performance assessment of VSC-based HVDC system in asynchronous grid interconnection: Offline and real-time validation of control design with symmetric optimum PI tuning

Asynchronous interconnection is essential for integrating AC networks operating at different frequencies, typically 50 Hz and 60 Hz. This need arises from distributed power generation methods, including offshore renewable sources and diverse regional grid configurations. Advanced strategies are required to overcome these frequency differences and ensure uninterrupted power transfer.High-Voltage Direct Current (HVDC) transmission systems facilitate efficient power exchange, enhancing grid reliability and stability. This study focuses on optimizing the Proportional-plus-Integral (PI) controller parameters within a 20 MVA Voltage Source Converters (VSC)-based HVDC system to enable asynchronous interconnection between offshore and onshore AC networks. The offshore VSC regulates active and reactive power, while the onshore VSC controls DC voltage and reactive power. A vector control approach with symmetric optimum PI tuning is proposed for a comprehensive performance assessment of the VSC-based HVDC transmission system.The effectiveness of the tuned PI controller parameters is evaluated through four test cases using MATLAB/Simulink for offline simulation and Typhoon HIL604 for real-time validation. These cases involve abrupt changes in reference active and reactive power for the offshore VSC; and in reference reactive power and DC voltage for the onshore VSC. Results demonstrate rapid and satisfactory dynamic performance across all test cases, as evidenced by offline simulations and real-time validation.The validation highlights the effectiveness of the proposed control design with symmetric optimum PI tuning, confirming its ability to enhance the overall performance of the HVDC transmission system for efficient asynchronous interconnection.

Project Management Strategies for Developing an Arduino-based Weather & Heat Index Monitoring System

This research explores an Arduino-based prototype for monitoring heat index outdoors. Employing a hybrid project management approach with Agile principles, the project addressed iterative development and collaboration within a six-person team. The successful prototype collects temperature and humidity data, calculates heat index, and transmits data wirelessly. We discuss how the methodology facilitated iterative development, communication, and adaptability. This research offers a cost-effective approach to environmental monitoring using Arduino and valuable insights for managing Arduino prototyping projects, especially in team settings. We propose future work to improve sensor accuracy, data security, power efficiency, and data visualization capabilities.

Organizational Politics: How Internal Power Struggles Undermine Employee Productivity and Efficiency

This study examines the detrimental effects of organizational politics on employee performance and workplace dynamics. Through a qualitative case study approach and theoretical framework analysis, I investigate how power conflicts and political maneuvering within organizations can create a negative work environment, akin to a "cold war zone." My findings suggest that organizational politics can significantly decrease employee efficiency, stifle creativity, and hinder productivity. The paper provides insights into the mechanisms by which political dynamics can lead to a toxic work environment and offers implications for managers and organizations seeking to mitigate these effects and foster a more positive and productive workplace culture.

Low-high-low doped Ga2O3 Schottky barrier IMPATT diodes on various crystal orientations for terahertz applications

This article investigates low-high-low doped Ga2O3 Schottky barrier IMPATT diodes based on [100], [010], and [001] crystal orientations to promote oscillation power at the low-frequency band of terahertz regime. Simulation results demonstrate a conversion power of 4.88 MW/cm2 along [100] at its optimum frequency of 100 GHz, increasing by 0.73 times and 2.2 times compared to the [100] at 150 GHz and the [001] at 100 GHz, and exhibit the highest conversion efficiency of 6.83 % along [010] as indicated by the DC-RF conversion ability promoting 37 % and 30 % than that of [100] and [001] orientations, respectively. When considering a parasitic resistance of 1 × 10−5 Ω cm2, the optimal frequencies of along three orientations decrease to 90 GHz, 127 GHz, and 91 GHz, correspondingly, yielding the peak output power and total efficiency decrease by 0.3–0.4 times compared to those of the conversion characteristics.

Advanced particle swarm optimization for efficient and fast global maximum power point tracking under partial shading conditions

Partial shading (PS) is a common issue in photovoltaic systems (PVs), and it can significantly reduce the system's output power. This paper presents the advanced particle swarm optimization (APSO) algorithm. APSO is designed to alleviate the challenges posed by PS in PVs in from where of effectiveness and stability speed so that it works to achieve and maintain the global maximum power point (GMPP) under PS conditions. It leverages persistent variables to store and track system states and iterations; it also includes checks to ensure that the duty cycle remains within specified bounds facilitating more effective optimization. Additionally, APSO optimizes solar panel duty cycles and velocities to converge toward an optimal solution to improve overall power generation efficiency and settling time. The results evaluation involves testing the performance of photovoltaic panels under three different shading scenarios and comparative analysis against recent Heuristic-optimization-based GMPP techniques, this study and comparative analyses demonstrate APSO's effectiveness and superiority in terms of high efficiency that reaches 99.85% and fast settling time of GMPP at less than 0.01 second across all test cases. APSO presents a promising solution for maximizing PV power output in the presence of partial shading.

Design and development of electric radiant heaters for local heating inside the cabin of electric vehicles

The transition towards electric vehicles (EVs) represents a crucial step in mitigating global warming and reducing greenhouse gas emissions. However, the energy efficiency of EVs, particularly in terms of cabin thermal management, remains a significant challenge, especially under extreme climatic conditions. This manuscript addresses the challenge by focusing on the design and optimization of electric radiant (ER) heaters for enhancing the thermal management of EV cabins. Through an in-depth investigation involving experimental analysis and computational modeling, this study explores various materials in the design of the ER heater, aiming to maximize radiant heating effect while ensuring energy conservation. Key findings reveal that the choice of materials for the ER heater’s components significantly impacts its thermal performance. Specifically, a combination of rigid polyurethane (PU) foam as insulation and expanded polypropylene (EPP) 30 for the base structure was identified as the optimal design to achieve a maximum radiant power efficiency of about 47.3%. This improved radiant power efficiency outperformed the baseline design by more than 25%.

Investigating and Improving the Efficiency of Space-time Codes in Visible light Communication Systems based on a Multi-input-multi-output Channel Model

Background: Visible light communication (VLC) is a new communication method for transmitting information through semiconductor lighting devices. One of the applicable methods to improve the spectral efficiency of the system is the multi-input-multiple-output (MIMO) structure. Also, carrierless amplitude-phase (CAP) modulation is a high-dimensional modulation technique that can be used to improve the data transmission rate in modern communication systems. Methods: In this paper, carrierless amplitude-phase method is introduced in indoor optical communication systems to improve the performance of multi-input multi-output (MIMO) structures. The CAP modulation is considered a suitable method due to its low complexity and is introduced and studied in two structures with one carrier or CAP and multiple carriers or m-CAP. Results: In this study, the target system is simulated in m-CAP structures and the improvement is measured in the MIMO structure using this modulation. The results show that by using this method, the PAPR of the system was reduced compared to other research with the MIMO-OFDM structure. In addition, by not using FFT blocks, the computational and processing complexity was also reduced. Conclusion: It has been shown that the use of m-CAP increases the data transfer rate and improves the spectral efficiency of the system. In addition to CAP, spatial modulation schemes can also help improve the spectral efficiency and power efficiency of the system. Moreover, the space-time code is combined with m-CAP modulation and it is shown that this idea improves the efficiency of the MIMO system.

Resource allocation for device-to-device communications underlaying uplink cellular networks

Underlying cellular networks, device-to-device (D2D) communications are a practical network technology that can increase power efficiency and spectrum usage for close-proximity wireless services and applications. However, D2D link interference, when sharing resources with cellular users (CUs), poses a major challenge in such distribution situations. In this research, we primarily utilize wireless channel data that exhibits slowly shifting large-scale fading to conduct spectrum sharing and power allocation. The overall ergodic capacity of all cellular user equipment (CUE) links is initially considered as the optimization target in order to maximize the overall throughput of CUE links while ensuring the reliability of each D2D link. Then, the expansion of the minimum ergodic capacity is measured to ensure a more consistent capacity performance across all CUE links. We utilized algorithms that are resilient to channel fluctuations and produce optimal resource allocation. We use MATLAB, and the computer simulation validates its intended performance.

Power efficiency improvement in reactive power dispatch under load uncertainty

Nowadays, there is a significant rise in electricity demand, posing challenges for power grid operators due to inaccurate forecasting, leading to excessive power losses and voltage instability. This paper addresses these issues by focusing on solving optimal reactive power dispatch (ORPD) while considering load demand uncertainty. The main objective of solving ORPD is to reduce power losses by adjusting generator voltage ratings, transformer tap ratio, and shunt capacitors' reactive power. Monte Carlo simulation (MCS) is employed to generate load scenarios using the normal probability density function, while a reduction-based technique is implemented to decrease the number of those scenarios. The improved gray wolf optimization (I-GWO) algorithm is introduced for the first time to address the stochastic ORPD problem. Experimentation is conducted on an IEEE-30 bus system when results are contrasted with conventional gray wolf optimization (GWO) and five other algorithms as stated in the literature. The I-GWO algorithm's performance is assessed with and without considering load demand uncertainty. Through Friedman's statistical tests, a significant decrease of 20.96% in active power losses and 63.06% in the summation of expected power losses is observed. The I-GWO algorithm's results on the ORPD problem demonstrate its effectiveness and robustness.