Adaptive Power Research Articles

Adaptive solar power generation forecasting using enhanced neural network with weather modulation

Solar power generation forecasting plays a vital role in optimizing grid management and stability, particularly in renewable energy-integrated power systems. This research paper presents a comprehensive study on solar power generation forecasting, evaluating traditional and advanced machine learning methods, including ARIMA, Exponential Smoothing, Support Vector Regression, Random Forest, Gradient Boosting, and Physics-based Models. Moreover, we propose an innovative Enhanced Artificial Neural Network (ANN) model, which incorporates Weather Modulation and Leveraging Prior Forecasts to enhance prediction accuracy. The proposed model is evaluated using real-world solar power generation data, and the results demonstrate its superior performance compared to traditional methods and other machine learning approaches. The Enhanced ANN model achieves an impressive Root Mean Square Error (RMSE) of 0.116 and a Mean Absolute Percentage Error (MAPE) of 36.26%. The integration of Weather Modulation allows the model to adapt to changing weather conditions, ensuring reliable forecasts even during adverse scenarios. Leveraging Prior Forecasts enables the model to capture short-term trends, reducing forecasting errors arising from abrupt weather changes. The proposed Enhanced ANN model showcases its potential as a promising tool for precise and reliable solar power generation forecasting, contributing to the efficient integration of solar energy into the power grid and advancing sustainable energy practices.

Design and development of energy management system for solar electric vehicle with dynamic power split approach

ABSTRACT The integration of on-roof solar photovoltaic (PV) systems in electric vehicles (EVs) represents a groundbreaking advancement in the evolution of sustainable transportation. The objective of the research is to design and validate an integrated system for on-roof solar PV integration in EVs, optimising energy utilisation and reducing reliance on grid electricity. This paper explores the implementation of an Adaptive Maximum Power Point Tracking (MPPT) system, which maintains the output of the PV system at a constant level, leading to a significant improvement of approximately 10.20% in energy output compared to conventional MPPT systems. Additionally, a dynamic power split approach (DPAS) is employed to intelligently manage the allocation of power between the solar PV system and the vehicle battery, ensuring continuous fulfilment of energy demand while simultaneously recharging the battery. The proposed system reduces 65% of energy demand from grid and hence, reliance on grid. Furthermore, the dynamic power split approach enable substantial reductions in battery capacity requirements, with a potential decrease of 25–35% observed. These advancements not only enhance the performance and efficiency of EVs but also contribute to cost savings and the transition towards a cleaner and more sustainable transportation ecosystem.

A Comprehensive Review of Battery‐Integrated Energy Harvesting Systems

AbstractAt the forefront of technological advancement, the proliferation of portable and wearable electronics has necessitated the development of innovative power solutions. As these devices become increasingly indispensable in daily life, the demand for sustainable and adaptable power sources has intensified. This review focuses on integrated self‐charging power systems (SCPSs), which synergize energy storage systems, particularly through rechargeable batteries like lithium‐ion batteries, with energy harvesting from solar, mechanical, thermal, and chemical energy. These SCPSs extend operational times, reduce recharging frequency, and have the potential to develop self‐sufficient power systems. The study explores various approaches to optimize both individual components and the integrated power system for wearable and flexible electronics, covering SCPSs that combine multiple energy‐harvesting strategies. Special attention is given to design considerations, material advancements, and engineering challenges, alongside the latest research breakthroughs in energy harvesting and storage technology. The review concludes with an assessment of the prospects and challenges in the field of battery‐integrated energy harvesting systems, highlighting the need for advancements in energy density, power output, and safety to meet the demands of modern electronics.

Comparison of FBMC and OFDM With Adaptive Bit and Power Loading for CD-Constrained IM/DD Transmission Over 50-km SSMF

Comparison of FBMC and OFDM With Adaptive Bit and Power Loading for CD-Constrained IM/DD Transmission Over 50-km SSMF

Signal Envelope-Based Adaptive Power Swing Blocking

This research paper proposes detection of faults during power swings by using a peak envelope-based method to prevent incorrect distance relay operation. Distance relays should not operate during power swings except to remove a correctly detected fault event. In this study, distinction between faults and power swings is achieved by correctly observing fault occurrence based on dynamic thresholds generated by peak envelopes. The method utilizes a DC offset current in determining the fault inception and fifth harmonic voltage to assess the fault clearance. If the current surpasses the calculated threshold due to fault existence, the relay is allowed to operate. Re-blocking the relay after fault clearance is achieved in the case of a higher value of the voltage than the threshold value. Current and voltage data obtained from two different test systems simulated in PSCAD for various scenarios have been used in the proposed algorithm developed in MATLAB. In order to show the effectiveness of the proposed method, the results on the second test system have also been compared to the results of selected methods in the literature. It has been perceived that the developed method has a high accuracy and provides an appropriate time range for relay and breaker operation.

Optimizing Energy-Efficient Communication Protocols for IoT Devices in Smart Cities Using Narrowband IoT and LTE-M Technology

In the context of optimizing energy-efficient communication protocols for IoT devices in smart cities, this research addresses the pressing need for sustainable and reliable solutions. The research provides a comprehensive understanding of the strategies that contribute to minimizing energy consumption in smart city IoT deployments, with a specific focus on Narrowband IoT (NB-IoT) technology. Employing a structured framework and an integrated system architecture, incorporating LTE-M technology and an application server, the research leverages various optimization strategies. These include Adaptive Power Control, Duty Cycling, Data Aggregation, Protocol Optimization, and Network Topology Optimization. Methodologically, the study adopts a quantitative approach, utilizing metrics such as duty cycle percentages and energy consumption reductions. Data is collected through simulations and experiments, and the analysis involves assessing the impact of each optimization strategy on energy efficiency. Key findings demonstrate significant reductions in energy consumption, ranging from 15% to 25%, depending on the specific strategy employed. Adaptive Power Control and Network Topology Optimization emerge as particularly effective approaches. The implications of these findings underscore the importance of adopting tailored optimization strategies in smart city IoT deployments to achieve both energy efficiency and reliability. The study discusses the broader significance of these results and acknowledges potential limitations, paving the way for future research endeavors in the realm of sustainable and resource-conserving smart cities.

Fault Prediction and Awareness for Power Distribution in Grid Connected RES Using Hybrid Machine Learning

This study addresses defects in electrical power systems, focusing on short circuits that can disrupt normal operation. The method emphasis is on hybrid microgrid systems connected to the grid, crucial for efficient power management. The novel method proposes an adaptive electricity management technique for fault scenarios in grid-tied conversions, minimizing sensor requirements. Power flow involves the hybrid energy storage system (HESS), renewable energy sources (RES), the electrical grid, and loads through a parallel hybrid RES arrangement. Semiconductor switch failures are common vulnerabilities, impacting grid functionality. The novel electricity management technique considers converter failures, renewable energy availability, and HESS state of charge restrictions. Power quality goals such as harmonic reduction, reactive power support, and a power factor of one are addressed. The proposed method was evaluated using MATLAB–based Simulink, showcasing its performance. The approach employs an Adaptive Power Management Strategy (APMS) in a hybrid renewable energy system, integrating wind and solar sources with battery and supercapacitor storage. A Hybrid Machine Learning-based Active Power Management System (HML-APMS) optimizes energy production, storage, and distribution for stability, reliability, and efficiency in the power utility system. The study underscores the significance of technology integration in achieving sustainable, reliable, and cost-effective power solutions.

Transmitter Adaptation and Wireless Power Control for Capsule Endoscopy

Transmitter Adaptation and Wireless Power Control for Capsule Endoscopy

Adaptive Virtual Inertial Control and Virtual Droop Control Coordinated Control Strategy for Hybrid Energy Storage Taking into Account State of Charge Optimization

For energy-storage-assisting conventional units to participate in the primary frequency regulation of a power system, firstly, based on the frequency regulation mechanism of virtual inertial control (VIC) and virtual droop control (VDC) of energy storage, we analyze the effect of the action timing of energy storage on the frequency deviation of the grid under two control methods and put forward a reasonable combination of the two control methods; on this basis, we also put forward hybrid energy storage adaptive VIC and VDC based on the demand of VIC and VDC on the power and capacity of energy storage. On this basis, based on the demand of VIC and VDC on the power and capacity of energy storage, a hybrid energy storage adaptive VIC and VDC coordinated control strategy based on supercapacitor–lithium batteries is proposed, whereby a high-power storage supercapacitor responds to inertial control signals to rapidly suppress a drop in frequency, and the high-capacity lithium battery responds to droop control signals to perform long-time droop control. The high-capacity lithium battery responds to the sagging control signal and is used to perform a long-time sagging power response; finally, in order to avoid the state of charge (SOC) of energy storage falling into a low/high working condition and losing the subsequent frequency regulation ability, an adaptive power control strategy of energy storage based on the improved logistic function is proposed. The simulation results show that under typical load disturbance, the SOC level of the proposed strategy increases by 19.17% and 30.16%, respectively, compared with that of the single-lithium strategy and no energy storage, and the SOC level of the supercapacitor and lithium battery increases by 29.4% and 2.1%, respectively, compared with that of logistic optimization.

Investigator choice of standard therapy versus sequential novel therapy arms in the treatment of relapsed follicular lymphoma (REFRACT): study protocol for a multi-centre, open-label, randomised, phase II platform trial

BackgroundRelapsed or refractory follicular lymphoma (rrFL) is an incurable disease associated with shorter remissions and survival after each line of standard therapy. Many promising novel, chemotherapy-free therapies are in development, but few are licensed as their role in current treatment pathways is poorly defined.MethodsThe REFRACT trial is an investigator-initiated, UK National Cancer Research Institute, open-label, multi-centre, randomised phase II platform trial aimed at accelerating clinical development of novel therapies by addressing evidence gaps. The first of the three sequential novel therapy arms is epcoritamab plus lenalidomide, to be compared with investigator choice standard therapy (ICT). Patients aged 18 years or older with biopsy proven relapsed or refractory CD20 positive, grade 1-3a follicular lymphoma and assessable disease by PET-CT are eligible. The primary outcome is complete metabolic response by PET-CT at 24 weeks using the Deauville 5-point scale and Lugano 2014 criteria. Secondary outcomes include overall metabolic response, progression-free survival, overall survival, duration of response, and quality of life assessed by EQ-5D-5 L and FACT-Lym. The trial employs an innovative Bayesian design with a target sample size of 284 patients: 95 in the ICT arm and 189 in the novel therapy arms.DiscussionWhilst there are many promising novel drugs in early clinical development for rrFL, understanding the relative efficacy and safety of these agents, and their place in modern treatment pathways, is limited by a lack of randomised trials and dearth of published outcomes for standard regimens to act as historic controls. Therefore, the aim of REFRACT is to provide an efficient platform to evaluate novel agents against standard therapies for rrFL. The adaptive Bayesian power prior methodology design will minimise patient numbers and accelerate trial delivery.Trial registrationClinicalTrials.gov: NCT05848765; 08-May-2023.EudraCT2022-000677-75; 10-Feb-2022.

Accurate Contour Error Estimation-based robust contour control for dual-linear-motor-driven gantry stages

This article presents a novel adaptive contour control scheme for dual-linear-motors-driven gantry stages (DLMDGSs) with time-varying model parameters and disturbances, aiming to realize high-precision contour control. Specifically, a novel adaptive contour controller based on a coupled model is proposed, incorporating powerful projection adaption laws, to achieve the synchronization of multi-axis motors, regression of adaptive parameters, and high-precision contour performance. Additionally, a contour error estimation (CEE) method with four-order convergence rate is designed to ensure estimation accuracy, enhance estimation robustness, and reduce estimation time. Moreover, considering the periodicity of common control contour tasks, an iterative learning control (ILC) reference trajectory compensation structure is adopted to further improve the contour control effect. Finally, the stability and convergence of the closed-loop system are rigorously proven, and comparative experiments are conducted on a DLMDGS platform to demonstrate the superiority of the proposed control strategy.

Cooperative NOMA in cognitive radio networks: A study on imperfect CSI, SIC and hardware impairments over Nakagami-m fading channel

This paper proposes a cooperative uplink–downlink non orthogonal multiple access (NOMA) in an overlay cognitive radio network (CR). We examine the impact of imperfect channel state information (impCSI), imperfect successive interference cancellation (impSIC) and residual hardware impairments (RHI) on the cooperative cognitive radio network over Nakagami-m fading channels. We derive the exact outage probabilities of primary user (PU) and multicast secondary users (SUs) under perfect/imperfect conditions for fixed and adaptive power allocations. We show that the use of multiple relays enhances the diversity order, but with fixed power allocation in perfect/imperfect case and adaptive power allocation in imperfect case both suffer from an error floor, which reduces the diversity order to zero. On the other hand, the adaptive power allocation for the perfect case achieves full diversity gain. We validate the analytical results with Monte Carlo simulations and demonstrate the diversity gains of the considered schemes.

An Improved Interference Mitigation Scheme for Femtocell Networks

Femtocell networks have gained significant attention in recent years due to their ability to improve indoor wireless coverage and capacity. However, they are susceptible to interference from neighboring cells, which significantly degrade their performance and quality of service to the end-user. The study introduced an adaptive power control technique that causes the power level of the femtocell base station to adjust dynamically to reduce the interference caused by adjacent femtocell networks, thereby adjusting the system bandwidth, carrier frequency, path loss and femtocell Base Station (BS) transmission power based on the request made by the users. The interference mitigation scheme was optimized by tuning the parameters of the power control algorithm, such as the transmit power levels and the interference thresholds. The algorithm was simulated with MATLAB simulation tools. Throughput, Signal to Interference Noise Ratio (SINR), coverage enhancement and user satisfaction were used as measurement metrics for the model using descriptive statistics. The results showed that the average performance obtained for the throughput increased by 25.88% from 3.40 X 1010 (bps) to 4.28 X 1010 (bps), and the SINR increased by 35.29% from 1.70 X 109 dB to 2.30 X109 dB for the benchmarked power control and improved power control techniques respectively. Coverage enhancement and user satisfaction were nearly 0, which indicates that the technique was of average performance. This study concluded that the new scheme enhances wider coverage to reach more users and mitigate interference while maintaining a higher level of femtocell user satisfaction.

Adaptive compound power quality disturbance detection via OMD and improved networks for renewable energy systems

In the evolving landscape of power systems, the integration of various renewable energy resources (RERs) introduces complex challenges, particularly in maintaining power quality, which are paramount for system stability. To address this issue, an adaptive power quality disturbance (PQD) detection framework is implemented in this paper. First, the optimal mode decomposition (OMD) is developed to decompose the compound PQDs into sub-ingredients to make them more visible based on the optimal energy ratio. Subsequently, we propose an improved attention convolutional neural network (IACNN), an advanced neural network architecture that leverages an enhanced attention mechanism to expedite the identification of PQDs. Importantly, the sub-ingredients can be strengthened based on the established PQD detection framework. Finally, a series of experiments are conducted under different noise levels and various types of PQDs. The results demonstrate that the proposed framework has profound detection effectivity with about 99.2% accuracy under the simulation condition of 20 dB noise level. In addition, the experimental verification analysis proves a satisfactory real-time performance. This underscores the potential of the proposed framework as a significant advancement in the realm of power quality management, offering a robust solution to the challenges posed by the integration of RERs into modern power systems.

Multi-Gb/s visible light communication based on AlGaInP amber micro-LED.

Light-emitting diodes (LEDs), pivotal for solid-state illumination (SSL) and highly regarded as potential candidates in visible light communication (VLC) systems, have garnered significant interest as a solution to alleviate the congested radio frequency spectrum in next-generation communications. Addressing the challenge of extremely limited bandwidth due to the low response of phosphor in conventional illumination, our research focuses on an AlGaInP-based amber LED. This LED represents a promising avenue for phosphor-free, high-speed VLC applications when used in conjunction with the prevalent blue LED technology based on nitride materials. The fabricated AlGaInP amber LED, with a mesa diameter of 100 µm2, has undergone comprehensive optoelectronic property and transmission performance characterization. We have successfully demonstrated a proof-of-concept for VLC using the amber LED, achieving a data transmission rate of 2.94 Gb/s that complies with the forward-error-correction (FEC) standard of 3.8 × 10-3, utilizing adaptive bit and power loading with discrete multitone (BPL-DMT) modulation.

Genomic scan of endangered moroccan endemic trees (Argania spinosa L. Skeels) along a latitudinal gradient: Exploring gene-environment correlations

Understanding the adaptive mechanism of trees to climate change and effective management of genetic resources necessitate a comprehensive knowledge of the relationship between environmental factors and their adaptive power. This study focuses on the endangered Moroccan endemic tree, Argania spinosa L., known for its remarkable adaptive power in the Mediterranean climate and heavily impacted by climate change. Through genomic scans along a latitudinal gradient, we aimed to identify genomic regions associated with convergent or divergent selection by searching the genome for outlier loci demonstrating high population differentiation. Variation of dominant markers (Amplified Fragment Length Polymorphism) was correlated with variation of environmental factors to discern signals of natural selection in 13 natural populations of Argania spinosa L. along a latitudinal transect.Genotyping of 130 individuals with 477 AFLP markers revealed six outlier loci exhibiting elevated population differentiation values within argan forests. The BayeScan v2.1 software was used to detect outlier loci, and the (MLM) method strongly associated these loci with at least five environmental factors, particularly bioclimatic variables. Notably, all outlier loci exhibited significant correlations with Soil Moisture, with additional associations with Temperature, Precipitation, Landforms, and Soil Type, influencing the local adaptation of Argania spinosa L.The outlier AFLP loci may not be the direct targets of natural selection, but locate closely to genes that are potential involved in local adaptation. Consequently, further investigations are imperative to validate these candidate loci. This research underscores the significance of considering bioclimatic variables, especially Soil Moisture, in understanding the local adaptation of Argania spinosa L. amid climate change in Morocco.

Wind turbine emulator using three-phase IM controlled through an adaptive reactive power estimator fed by soft-VSI topology

PurposeThe purpose of this paper aims to design a robust wind turbine emulator (WTE) based on a three-phase induction motor (3PIM).Design/methodology/approachThe 3PIM is driven by a soft voltage source inverter (VSI) controlled by a specific space vector modulation. By adjusting the appropriate vector sequence selection, the desired VSI output voltage allows a real wind turbine speed emulation in the laboratory, taking into account the wind profile, static and dynamic behaviors and parametric variations for theoretical and then experimental analysis. A Mexican hat profile and a sinusoidal profile are therefore used as the wind speed system input to highlight the electrical, mechanical and electromagnetic system response.FindingsThe simulation results, based on relative error data, show that the proposed reactive power control method effectively estimates the flux and the rotor time constant, thus ensuring an accurate trajectory tracking of the wind speed for the wind emulation application.Originality/valueThe proposed architecture achieves its results through the use of mathematical theory and WTE topology combine with an online adaptive estimator and Lyapunov stability adaptation control methods. These approaches are particularly relevant for low-cost or low-power alternative current (AC) motor drives in the field of renewable energy emulation. It has the advantage of eliminating the need for expensive and unreliable position transducers, thereby increasing the emulator drive life. A comparative analysis was also carried out to highlight the online adaptive estimator fast response time and accuracy.

Skeletal muscle adaptations to high-intensity, low-volume concurrent resistance and interval training in recreationally active men and women.

This study compared the structural and cellular skeletal muscle factors underpinning adaptations in maximal strength, power, aerobic capacity, and lean body mass to a 12-week concurrent resistance and interval training program in men and women. Recreationally active women and men completed three training sessions per week consisting of high-intensity, low-volume resistance training followed by interval training performed using a variety upper and lower body exercises representative of military occupational tasks. Pre- and post-training vastus lateralis muscle biopsies were analyzed for changes in muscle fiber type, cross-sectional area, capillarization, and mitochondrial biogenesis marker content. Changes in maximal strength, aerobic capacity, and lean body mass (LBM) were also assessed. Training elicited hypertrophy of type I (12.9%; p = 0.016) and type IIa (12.7%; p = 0.007) muscle fibers in men only. In both sexes, training decreased type IIx fiber expression (1.9%; p = 0.046) and increased total PGC-1α (29.7%, p < 0.001) and citrate synthase (11.0%; p < 0.014) content, but had no effect on COX IV content or muscle capillarization. In both sexes, training increased maximal strength and LBM but not aerobic capacity. The concurrent training program was effective at increasing strength and LBM but not at improving aerobic capacity or skeletal muscle adaptations underpinning aerobic performance.

Adaptive Frequency Droop Feedback Control-Based Power Tracking Operation of a DFIG for Temporary Frequency Regulation

Frequency regulation scheme of doubly-fed induction generators (DFIGs) are implemented to improve the frequency stability achieving by rotor speed control. However, the coupling dynamics between the maximum power point tracking operation control loop and frequency regulation control loop would impair the frequency regulation capability. To eliminate the adverse influences, firstly, analytical derivation for coupling output power dynamics is represented when adopting frequency regulation scheme of the DFIG. Then, an adaptive frequency droop feedback control based on power tracking operation strategy, which is a decoupling control scheme for frequency regulation, is proposed to respond the dynamic grid frequency without reserving power. The frequency deviation-based droop control with dynamics gains is used to calculate the coefficient of power tracking operation of the DFIG. The system stability with various power tracking operation of the DFIG is analyzed, in addition, the stable boundary is used to limit the setting of coefficient of power tracking operation. Simulations are carried based on an EMTP-RV simulator. It is found that the suggested adaptive frequency droop feedback control based on power tracking strategy can provide dual frequency regulation capability and reduce the maximum frequency excursion under various scenarios.

Research on Mine Wide Voltage Adaptive Power Supply

To solve the problem of multiple voltage levels and wide range of variations of underground power supply in domestic coal mines, this paper putting forward four wide power supply of adaptive design options with wide input voltage (AC 80-850 V), including phase shifted rectification based on fully controlled bridge, direct voltage adjustment based on AC power supply, voltage adjustment based on AC power supply rectification followed by chopping control, and a power supply voltage adjustment based on magnetic coupling transformer and thyristor automatic switching. After experimental comparison, the preferred scheme for automatic switching between magnetic coupling transformer and thyristors is preferred. The main circuit of this scheme adopts magnetic coupling transformer and thyristor control method, and the control circuit adopts a three way drive circuit based on MCU control, while adopting the strategy of instantaneous voltage tracking control. This scheme achieves stable operation of the output DC power supply voltage at 24V when the input AC power supply voltage varies from 80 to 850 V. The experimental results showed programme was stable and reliable, and it’s worth applying and has practical significance.