Power Characteristics Research Articles

Multi-Objective Coordinated Optimization Method of Active and Reactive Power Considering Power Characteristics of Renewable Energy Converters

In new power systems with a high proportion of renewable energy, optimization criteria based solely on economic efficiency or system stability may lead to a reduction in the static stability domain of the system or lead to long-term deviations from economic operation, thus reducing the overall applicability of such methods. This paper proposes a multi-objective active–reactive power coordinated optimization model that considers both economic efficiency and static stability indicators. The goal of the model is to minimize operating costs while optimizing static stability margins. It combines the reactive power support capabilities of converters and other reactive power compensation equipment to ensure safe and economical dispatch of the system. The proposed method is verified through a case study, which shows that this method can make full use of the potential reactive power regulation capability of the converter. At the same time, the economics and stability of the system are significantly improved by using this method. The overall improvement is about is 12.3%.

Selection of photovoltaic panels for floating systems: an analysis based on Entropy, CRITIC and TOPSIS

ABSTRACT In the context of sustainable energy use, multiple criteria are involved in the decision to select the best energy generation projects, as well as its installation location. However, despite the widespread use of decision-making techniques, there is a noticeable gap due to the lack of a systematic process for selecting the best projects for energy transition. This paper evaluates alternatives of photovoltaic panels for energy generation in floating systems and proposes a procedure to select the best project using a multiple-criteria decision analysis. The Entropy method was used to determine the weight of eight criteria, including cost, number of cells, efficiency, area, panel weight, and power characteristics, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was used to make the selection. A sensitivity analysis was conducted considering different weighing methods. Among the 20 photovoltaic panels analysed, the method proved to be effective in determining the most successful one for installation in floating systems. The chosen panel maintained the best performance in all scenarios tested. This paper provides a systematic approach for selecting the most suitable photovoltaic panel for floating energy systems, contributing to researchers to refine decision-making methodologies and practitioners to optimise project implementation in sustainable energy initiatives.

A novel horizontal self-tuning piezoelectric energy harvester based on a beam-slider system

Abstract With the rapid development of piezoelectric energy harvesting technologies, this approach is emerging as a promising power source for smart sensors. Despite this progress, a significant challenge remains in achieving a wider bandwidth while increasing the output power. In response to this challenge, this paper introduces a horizontal self-tuning beam-slider piezoelectric energy harvester (STBS-PEH). This innovative harvester employs a unilateral beam-slider mechanism to enable self-tuning of the energy harvesting by adjusting its resonant frequency to match the external excitation frequency. The electromechanical coupled dynamic model of this harvester has been established. Comprehensive simulations and experimental analysis have been conducted to demonstrate the wide bandwidth and high output power characteristics of the proposed harvester. Notably, the simulation results exhibit strong agreement with the experimental findings. The resonance response bandwidth of the STBS-PEH is particularly noteworthy, which is twice that of a conventional cantilever beam piezoelectric energy harvester. The root mean square (RMS) voltage and output power of the developed STBS-PEH can reach 23.54 V and 194.61 µW, respectively, at a frequency of 21 Hz, with an excitation amplitude of 0.5 g. These results demonstrate that the STBS-PEH is capable of powering low-power electronic devices in a broadband vibrational environment.

Numerical simulation of power and flow field characteristics of different spiral stirred reactors

Numerical simulation of power and flow field characteristics of different spiral stirred reactors

Research on power characteristics of high external power ratio power grid in market environment

Research on power characteristics of high external power ratio power grid in market environment

Electroencephalographic signatures of migraine in small prospective and large retrospective cohorts

Migraine is one of the most common neurological disorders in the US. Currently, the diagnosis and management of migraine are based primarily on subjective self-reported measures, which compromises the reliability of clinical diagnosis and the ability to robustly discern candidacy for available therapies and track treatment response. In this study, we used a computational pipeline for the automated, rapid, high-throughput, and objective analysis of encephalography (EEG) data at Cleveland Clinic to identify signatures that correlate with migraine. We performed two independent analyses, a prospective analysis (n = 62 subjects) and a retrospective age-matched analysis on a larger cohort (n = 734) obtained from the sleep registry at Cleveland Clinic. In the prospective analysis, no significant difference between migraine and control groups was detected in the mean power spectral density (PSD) of an all-electrodes montage in the frequency range of 1–32 Hz, whereas a significant PSD increase in single occipital electrodes was found at 12 Hz in migraine patients. We then trained machine learning models on the binary classification of migraine versus control using EEG power features, resulting in high accuracies (82–83%) with occipital electrodes’ power at 12 Hz ranking highest in the contribution to the model’s performance. Further retrospective analysis also showed a consistent increase in power from occipital electrodes at 12 and 13 Hz in migraine patients. These results demonstrate distinct and localized changes in brain activity measured by EEG that can potentially serve as biomarkers in the diagnosis and personalized therapy for individuals with migraine.

Properties and Analysis of the Guard Interval in Infinite Impulse Response–Orthogonal Frequency Division Multiplexing Systems

Recently, an orthogonal frequency division multiplexing (OFDM) technique for the infinite impulse response (IIR) channel (IIR-OFDM) was proposed, which carries the dedicated guard interval to maintain the circular convolution of the received signal and channel coefficients. Therefore, the loop of an IIR channel can be converted to the frequency domain, and single-tap equalization can still be used to equalize loop interference, like classical OFDM. In this paper, we describe how to build the IIR system based on the channel with loops and derive the properties of the dedicated guard interval for a general multi-order IIR channel, which is different from the classical cyclic prefix (CP) obtained by replicating the samples at the tail end of the signal. In particular, we address two special models for first-order and delay IIR channels. It is demonstrated that the guard interval composition and power characteristics of the two special models are similar. Moreover, the complexity of the guard interval depends not only on the maximum delay of the loop, but also on the number of loops. Finally, we simulate the IIR-OFDM performance under different IIR channels.

Stability Improvement of Multimachine System Using Back-To-Back Converters

As the penetration level of inverter-based generators continues to increase, it becomes crucial to reduce power oscillations between inverters and synchronous generators (SG) to improve system stability. This paper uses a multimachine system as the research platform and proposes a grid separation method employing a multi-terminal back-to-back converter. The converter is designed to be placed at the point of common coupling (PCC) in the original multimachine system and can divide the original grid into multiple microgrid regions while retaining the ability to transmit power between different regions. Each microgrid or region contains a self-regulated power source such as grid-forming (GFM) inverter or synchronous generator, which controls key power characteristics such as frequency and voltage within its region. The back-to-back converter facilitates power transmission between different regions. The AC-DC-AC conversion process ensures isolation and simplification of control over each microgrid’s power characteristics. The performance of inverters with different control strategies in each microgrid has been investigated, and the characteristics of various inverters are compared and demonstrated. Additionally, it was found that the battery storage requirements in each inverter-based microgrid are reduced using this method. The results are verified through MATLAB/SIMULINK simulations.

Research progress on the development of myopia prediction models and their predictive performance

The incidence of myopia is continuously increasing worldwide, especially in China, where the prevention and control of myopia is of great significance. Accurate prediction of myopia and early intervention are of great importance for slowing down the development of myopia and reducing the social burden. This article reviews the research progress of myopia prediction models and their predictive performance in recent years. It includes models based on refractive power and ocular biological characteristics, such as those using parameters such as refractive power, axial length, and percentile curves for prediction; models combined with environmental factors, which explore the relationship between myopia in school-age children and the age of onset, parents' myopia, education, and various living environmental factors; models based on genetic factors, which predict by evaluating parents' myopia status and single nucleotide polymorphisms discovered in genome-wide association studies. In addition, the article also introduces the application of artificial intelligence in myopia prediction, such as machine learning and deep learning algorithms that can predict changes in axial length growth and refractive power. These research advances provide a reference for establishing more accurate myopia prediction models and help achieve precise and personalized myopia prevention and control.

Machine learning-based EEG analysis for enhanced schizophrenia classification and diagnosis

Abstract. Schizophrenia can have a significant impact on patients' lives, studies, and work. Patients may experience delusions, hallucinations, disorganized thinking, and abnormal behavior, among other symptoms. Therefore, research related to schizophrenia is of great importance. This paper proposes the design of a classifier based on machine learning for diagnosing schizophrenia. The classifier extracts N100 features, P300 features, and power features across different frequency bands from both time-domain and frequency-domain characteristics from Electroencephalography (EEG) signals. Given the small dataset, a Support Vector Machine (SVM) machine learning algorithm was chosen to process and classify the selected features. To address the limitations of SVM in handling high-dimensional data and nonlinear problems, this study introduces a comprehensive improvement method based on Bayesian optimization, Recursive Feature Elimination (RFE), and data augmentation. Bayesian optimization was used to find the best combination of hyperparameters for the model, thereby improving its performance; RFE was employed to assess feature importance and remove the least important features, enhancing the model's training efficiency and generalization capability. Additionally, data augmentation was included to increase the sample size and introduce diversity, thereby improving the model's robustness. The study found that these methods effectively improved classification accuracy and generalization ability.

Using Deep Reinforcement Learning (DRL) for minimizing power consumption in Video-on-Demand (VoD) storage systems

As video streaming services such as Netflix become popular, resolving the problem of high power consumption arising from both large data size and high bandwidth in video storage systems has become important. However, because various factors, such as the power characteristics of heterogeneous storage devices, variable workloads, and disk array models, influence storage power consumption, reducing power consumption with deterministic policies is ineffective. To address this, we present a new deep reinforcement learning (DRL)-based file placement algorithm for replication-based video storage systems, which aims to minimize overall storage power consumption. We first model the video storage system with time-varying streaming workloads as the DRL environment, in which the agent aims to find power-efficient file placement. We then propose a proximal policy optimization (PPO) algorithm, consisting of (1) an action space that determines the placement of each file; (2) an observation space that allows the agent to learn a power-efficient placement based on the current I/O bandwidth utilization; (3) a reward model that assigns a greater penalty for increased power consumption for each action; and (4) an action masking model that supports effective learning by preventing agents from selecting unnecessary actions. Extensive simulations were performed to evaluate the proposed scheme under various solid-state disk (SSD) models and replication configurations. Results show that our scheme reduces storage power consumption by 5% to 25.8% (average 12%) compared to existing benchmark methods known to be effective for file placement.

Machine learning for forecasting initial seizure onset in neonatal hypoxic-ischemic encephalopathy.

This study was undertaken to develop a machine learning (ML) model to forecast initial seizure onset in neonatal hypoxic-ischemic encephalopathy (HIE) utilizing clinical and quantitative electroencephalogram (QEEG) features. We developed a gradient boosting ML model (Neo-GB) that utilizes clinical features and QEEG to forecast time-dependent seizure risk. Clinical variables included cord blood gas values, Apgar scores, gestational age at birth, postmenstrual age (PMA), postnatal age, and birth weight. QEEG features included statistical moments, spectral power, and recurrence quantification analysis (RQA) features. We trained and evaluated Neo-GB on a University of California, San Francisco (UCSF) neonatal HIE dataset, augmenting training with publicly available neonatal electroencephalogram (EEG) datasets from Cork University and Helsinki University Hospitals. We assessed the performance of Neo-GB at providing dynamic and static forecasts with diagnostic performance metrics and incident/dynamic area under the receiver operating characteristic curve (iAUC) analyses. Model explanations were performed to assess contributions of QEEG features and channels to model predictions. The UCSF dataset included 60 neonates with HIE (30 with seizures). In subject-level static forecasting at 30 min after EEG initiation, baseline Neo-GB without time-dependent features had an area under the receiver operating characteristic curve (AUROC) of .76 and Neo-GB with time-dependent features had an AUROC of .89. In time-dependent evaluation of the initial seizure onset within a 24-h seizure occurrence period, dynamic forecast with Neo-GB demonstrated median iAUC = .79 (interquartile range [IQR] .75-.82) and concordance index (C-index) = .82, whereas baseline static forecast at 30 min demonstrated median iAUC = .75 (IQR .72-.76) and C-index = .69. Model explanation analysis revealed that spectral power, PMA, RQA, and cord blood gas values made the strongest contributions in driving Neo-GB predictions. Within the most influential EEG channels, as the preictal period advanced toward eventual seizure, there was an upward trend in broadband spectral power. This study demonstrates an ML model that combines QEEG with clinical features to forecast time-dependent risk of initial seizure onset in neonatal HIE. Spectral power evolution is an early EEG marker of seizure risk in neonatal HIE.

Multi‐operating characteristic analysis and loss optimization of novel large capacity high‐speed permanent magnet generator

AbstractRecently, the development trend of large capacity generator has gradually shifted from electrical excitation generator to permanent magnet generator (PMG). As a result, the large capacity high‐speed permanent magnet generator (HSPMG) with high power density and high efficiency have received widespread attention. This paper aims to analyse the loss optimization of novel large capacity HSPMG under multi‐operating conditions. In terms of the parameter requirements, a 2.5 MW and 7000 rpm HSPMG is designed. To verify the accuracy of simulation model and the rationality of generator design, the output characteristics of large capacity HSPMG which has been combined with prototype experimental data are calculated under both no‐load and load conditions. Based on the load power characteristics of large capacity HSPMG, the optimization range of the magnetization angle under multi‐operating conditions is explored. By analysing the no‐load back electromotive force (EMF) and loss characteristics of large capacity HSPMG, the optimal magnetization angle that corresponds to the minimum loss under three conditions is obtained. The correctness of the analysis and optimization method is verified by comparing the loss distribution and electromagnetic results before and after the optimization. The research results provide a reference for the multi‐operating characteristics analysis and loss optimization of large capacity HSPMG.

Displacing the Constitution: Modernity, Sovereignty and Crisis in Interwar Romania

Abstract By a legal historical analysis of the context of the interwar period, it seeks to uncover the ideological infrastructure subjacent to the constitutional project of the interwar period, placing Romanian constitutional thought and practice in a proper international context. By tracing the changes within the practice of emergency and the shifting status of sovereign power within the years following the institution of the Constitution of 1923, it moves towards examining the affirmation of authoritarian features of unbound state power in the Constitution of 1938, the first dictatorial constitution in Romanian modernity.

Optimal Configuration Model for Large Capacity Synchronous Condenser Considering Transient Voltage Stability in Multiple UHV DC Receiving End Grids

In a multi-fed DC environment, the UHV DC recipient grid faces significant challenges related to DC phase shift failure and voltage instability due to the high AC/DC coupling strength and low system inertia level. While the new large-capacity synchronous condensers (SCs) can provide effective transient reactive power support, the associated investment and operation costs are high. Therefore, it is valuable to investigate the optimization of SC configuration at key nodes in the recipient grid in a scientific and rational manner. This study begins by qualitatively and quantitatively analyzing the dynamic characteristics of DC reactive power and induction motors under AC faults. The sub-transient and transient reactive power output model is established to describe the SC output characteristics, elucidating the coupling relationship between the SC’s reactive power output and the DC reactive power demand at different time scales. Subsequently, a critical stabilized voltage index for dynamic loads is defined, and the SC’s reactive power compensation target is quantitatively calculated across different time scales, revealing the impact of transient changes in DC reactive power on the transient voltage stability of the multi-fed DC environment with dynamic load integration. Finally, an optimal configuration model for the large-capacity SC is proposed under the critical stability constraint of dynamic loads to maximize the SC’s reactive power support capability at the lowest economic cost. The proposed model is validated in a multi-fed DC area, demonstrating that the optimal configuration scheme effectively addresses issues related to DC phase shift failures and voltage instability resulting from AC bus voltage drops.

Parametric study of combustion characteristics of diesel/methanol dual fuel engine using global sensitivity analysis and multi-objective optimization

The pursuit of clean and efficient internal combustion engine power necessitates the substitution of fossil fuels with methanol and the adoption of advanced combustion strategies. This study examines the combustion and emission characteristics of methanol/diesel dual-fuel compression ignition engines. To understand the intricate interplay between critical parameters and engine performance, the global sensitivity analysis (GSA) method is employed, utilizing Sobol sensitivity as the quantitative metric. The investigated parameters include methanol substitution rate, diesel injection parameters, and five thermodynamics parameters concerning in-cylinder conditions. A validated computational fluid dynamics (CFD) model, coupled with a chemical kinetic mechanism, is developed to predict the combustion behavior of the dual-fuel engine accurately. This predictive capability forms the basis for utilizing the tree-based process optimization (TPOT) algorithm. By implementing TPOT, a significant reduction in the mean square error (MSE) of the predicted characteristics model is achieved, averaging a decrease of 73.59 %. Subsequently, a multi-objective optimization is conducted, exploring the impact of varying target weights on the optimal outcomes. Results show the initial pressure significantly impacts the engine's economy, power and emission characteristics, and NOx formation should be carefully considered in the combustion optimization of dual-fuel engines.

Model selection to achieve reproducible associations between resting state EEG features and autism

A concern in the field of autism electroencephalography (EEG) biomarker discovery is their lack of reproducibility. In the present study, we considered the problem of learning reproducible associations between multiple features of resting state (RS) neural activity and autism, using EEG data collected during a RS paradigm from 36 to 96 month-old children diagnosed with autism (N = 224) and neurotypical children (N = 69). Specifically, EEG spectral power and functional connectivity features were used as inputs to a regularized generalized linear model trained to predict diagnostic group (autism versus neurotypical). To evaluate our model, we proposed a procedure that quantified both the predictive generalization and reproducibility of learned associations produced by the model. When prioritizing both model predictive performance and reproducibility of associations, a highly reproducible profile of associations emerged. This profile revealed a distinct pattern of increased gamma power and connectivity in occipital and posterior midline regions associated with an autism diagnosis. Conversely, model selection based on predictive performance alone resulted in non-robust associations. Finally, we built a custom machine learning model that further empirically improved robustness of learned associations. Our results highlight the need for model selection criteria that maximize the scientific utility provided by reproducibility instead of predictive performance.

Study on power characteristics of wind turbine on the Loess Plateau terrain based on wind tunnel experiment

In order to investigate the impact of the flow field on wind turbine power in northwest China’s Loess Plateau, a wind tunnel experiment was conducted using a self-designed model that simulated the actual terrain. By comparing experiments utilizing an ideal mountain model under varying incoming wind speeds, it was revealed that the Loess Plateau terrain resulted in an increased inflow wind speed for the wind turbine. This higher inflow wind speed led to an approximate 5% increase in output power but also caused about a 6.1% rise in power fluctuations. Through analysis of the power spectrum of the output, it was discovered that turbulence below the decoupling frequency strongly modulates the output power of the wind turbine. This study also provided insights into spatial scale ranges where incoming flow turbulence structures can modulate output power, ranging from approximately 0.3 times to 5 times the diameter of rotor blades.

Comparison of aerodynamics of vertical-axis wind turbine with single and combine Darrieus and Savonius rotors

Vertical-axis wind turbines (VAWT) with Darrieus and Savonius rotors were studied based on the developed specialized package of computational aerodynamics. The flow structure around the Savonius and Darrieus rotors was numerically reconstructed, considering the mutual influence. From this reconstruction, the main stages of the vortex structure formation during the flow of the turbulent wind around the rotors were identified. Qualitative and quantitative assessments of the influence of the Savonius rotor on the total aerodynamic and energy characteristics of a VAWT were carried out. It is shown that the main contribution to the torque of the VAWT is due to the Darrieus rotor, mainly in the windward section of the trajectory. The Savonius rotor accounts for only a few percent of the total torque produced by the installation. The interaction of the Darrieus rotor blades with macrovortices from the Savonius rotor in the leeward part of the trajectory leads to a sharp drop in the torque coefficient. In the absence of a Savonius rotor, the vortices separated from supported tower are much smaller both in size and intensity. Therefore, their interaction with the blades of the Darrieus rotor does not lead to a significant change in the aerodynamic characteristics. A power characteristics comparison of two VAWTs showed that the torque coefficient is higher for wind turbines with only single Darrieus rotor. The developed approaches and techniques make it possible to reproduce real aerodynamic processes of flow most reliably around bodies of arbitrary shape and calculate their aerodynamic characteristics.

CFD simulation of power characteristics and flow field distribution of different spiral stirring paddles

Stirred reactors are widely used in various industries, and the stirring paddle structure has a significant effect on its power consumption. Therefore, in this study, different spiral stirring paddles were investigated. The effects of the ratio of paddle length to leads (Ls/S) design values and number of blades on the power characteristics and internal flow field of the reactor are discussed in detail, and the correlation equation of power number (Np) concerning Re and Ls/S values is fitted. It was found that the Np of stirring paddles increased and then decreased as the Ls/S value increased, and the effect of the number of blades on the Np gradually reduced. When the Ls/S value is equal to 0.6, the high-speed region of the flow field is the largest and the mixing effect is the best. The conclusions obtained can provide a reference for the energy-saving optimal design of spiral stirring paddles.