Low Sampling Frequency Research Articles

Maximum power extraction and DC-Bus voltage regulation in grid-connected PV/BES system using modified incremental inductance with a novel inverter control

Low ripples and variations in the DC-Bus voltage in single-phase Photovoltaic/Battery Energy Storage (PV/BES) grid-connected systems may cause significant harmonics distortion, instability, and reduction in power factor. The use of short-life electrolytic capacitor on the DC-Bus is considered a standard way for reducing these ripples and variations because of its large capacitance but results in short lifetime of the inverter. Replacing large electrolytic capacitors with small film capacitors can extend the lifetime of a PV/BES grid-connected system because small film capacitors have longer lifetime than large electrolytic capacitors. These film capacitors have low capacitance, which causes severe oscillations in the output current, and voltage drop due to huge ripples on the DC-Bus voltage. In this research, the main goal is to eliminate the output current ripples and voltage fluctuations associated with employing film capacitors. First, a modified incremental conductance (MIC) technique is proposed for tracking the maximum power by controlling the duty ratio of the DC-DC boost converter. Second, for the first time, a simple and novel d-q current regulation technique, which employs flowchart decision logic, is used in the DC-Bus control system for both the PV power system and the state of charge (SOC) of the BES. In this case, the DC-Bus controller is characterized by a cost-effective implementation because of its low sampling frequency. Although the presented approaches are successful in eliminating voltage distortion and fluctuations, they have unacceptable dynamic performance. Therefore, to improve the dynamic performance, BES was used to maintain a reliable and stable harvest from PV modules for varying loads while also increasing the dynamic performance of the overall system. The proposed PV/BES grid-connected systems, which employs a small 10-µF bus capacitor, is simulated and connected to the grid (230 V, 50 Hz). The DC-Bus voltage overshoot, undershoot and the total harmonics distortion (THD) of the output current for the proposed MIC are (1 V), (2.5 V) and (less than 5%), respectively. The average time response under rising radiation to track the global peak for MIC, traditional incremental conductance and variable step size incremental conductance are 1.403 s, 1.501 s and 1.113 s respectively. The obtained findings demonstrated the efficacy and superiority of the proposed d-q current control and MIC technique.

Quality prediction and sensitivity analysis of fine chemical products based on support matrix machine

Real-time detection of product quality in the chemical production process is essential for quality control and process adjustment. At present, in the chemical production process, product quality is sometimes difficult to be tested online, which is mainly obtained through laboratory sampling and testing. Due to the low sampling frequency of products and the large proportion of qualified products, there are problems of small sample size and imbalance between positive and negative samples. In addition, the chemical production process is complex, and there is coupling relationship among process variables. The soft quality prediction model based on the multiclass support matrix machine (MSMM) combined with bagging-SMOTE(BAG-SMOTE) and a sensitivity analysis method based on the change of curve shape is proposed in this paper. First, the time series process data is intercepted over a period of time based on the product sampling time to accurately correspond to the quality data from the laboratory. Second, the MSMM is used to build quality prediction model, which using the structural information between the ranks of the matrix samples consisted of time series process data. Besides, the imbalance problem between positive and negative samples is effectively solved by BAG-SMOTE ensemble sampling method. Then, the ensemble classification method is applied to the Tennessee Eastman (TE) and a fine chemical process. The classification evaluation index G-mean are 93% and F-score are 92%, respectively. The superiority of the proposed ensemble classification method is verified. Finally, in order to analyze the key factors of quality fluctuation, a sensitivity analysis method based on curve shape change is proposed via integrated classification metamodel. The method can effectively find out the main factors affecting the quality fluctuation, and the results are consistent with the actual situation.

Preprocessing of PPG and EDA signals for detection of emotional and cognitive states via physiological signals

Presented in the current paper is a methodology for approaching the preprocessing of Photoplethysmography and Electrodermal activity for the detection of emotional and cognitive states in humans via physiological signals. Examined closely are the effects of downsampling and segmentation of the PPG, the segmentation and separation of the Skin Conductance Level (SCL), and Skin Conductance Response (SCR) components of the EDA signal with both median and low pass filters. The results from the research indicate that the most appropriate preprocessing with regard to emotions and cognitive load classification is segmentation of 2 minutes which is the recommended length for frequency analysis of heart rate variability. Recommended, furthermore, is the downsampling of the PPG to 64 Hz, which proved to be the lowest sampling frequency that doesn’t introduce errors in the systolic peak detection, neither does it drastically affect the length of the Inter Beat Intervals (IBIs). Proposed, as to the separation of the SCL component of the EDA, is the usage of median filter with window length of 75% of the sampling frequency, which introduces negligible artefacts, mainly at the start of the signal.

An occupancy grid ruler based highly robust displacement measurement method and its application in brake wear assessment

The displacement of a multi-plate brake actuator provides insights into degradation. However, the displacement signal in a low sampling frequency is difficult to be assessed due to noise and outliers. This paper proposes an occupancy grid ruler (OGR) based displacement measurement method that sequentially implements displacement signal denoising and measurement value outlier removal and smoothing. The displacement and duration measurement values are extracted from the signal denoised by generalized Moreau envelope total variation. Next, the measurement values are assessed by the OGR, which has an updating function based on a binary Bayesian filter and inverse sensor model, and a time window with a specified observation length. Finally, the mass center of the grids with occupancy belief over the threshold is defined as the measurement result. A numerical simulation and experiments verified the proposed method is suitable for a quasi-static measurement with a given accuracy requirement, featuring dynamic performance and robustness.

A fusion framework for lithium-ion batteries state of health estimation using compressed sensing and entropy weight method

Accurate estimation for state of health (SOH) is an important component of lithium-ion batteries (LIBs) health management system. A fusion framework for SOH estimation is proposed via using compressed sensing (CS) and entropy weight method (EWM). Firstly, incremental capacity curve (ICC) is extracted as health indicators (HIs), and CS technique is introduced to process the ICC to: (1) improve the sampling frequency; (2) reconstruct potential missing information caused by low sensor sampling frequency, and (3) eliminate noise interference. Then Gaussian process regression (GPR) is utilized to characterize the relationship between Pearson correlation analysis (PCA) based HIs and capacity, and discrete aging model (DAM) is further established for particle filter (PF) to realize the continuous estimation by taking the identified capacity of GPR functioned as observation. Finally, the capacities of GPR and DAM are fused via EWM for final capacity estimation. The experimental results based on open battery data sets from NASA demonstrate that proposed method has higher precision with the average error of 2.5%. In addition, lab experiments are further conducted with two standard 18650 batteries, and the experimental results indicate that the proposed strategy is capable to realize reliable estimation with the average error of 2.6%, which further illustrates the feasibility and applicability of the method

Fault Location Based on Comprehensive Grey Correlation Degree Analysis for Flexible DC Distribution Network

Flexible DC distribution networks have a strong capacity for new energy consumption and have received extensive attention from scholars in recent years, but fault location for DC distribution lines is a difficult task. To solve this problem, we propose using a comprehensive grey correlation degree analysis to analyze the similarity of aerial mode traveling differential current waveforms at the first and last ends of a DC line, thus achieving fault location by obtaining the optimal time shift. Additionally, we built a six-terminal flexible DC distribution network model in Matlab/Simulink for simulation and validation, showing that the method can complete the rapid and accurate location of all types of faults on a DC line, and that it possesses an anti-transition resistance capability, making it suitable and highly reliable for cases of low sampling frequency.

Persistence and long run co-movements across stock market prices

This paper investigates long memory, persistence and co-movements in the most representative stock markets from all over the world. We look at seven stock market indices from Europe, Asia and North America, first individually, by looking at the order of integration of the series from a fractional point of view and comparing different sampling periods (daily, weekly and monthly) for the time period 2009-2020. Then, co-movements across the series are examined by looking at the differences between them. The results indicate that all the individual series are highly persistent, with orders of integration close to 1 in most cases; evidence of a small degree of mean reversion is found in the two American indices (S&P500 and Dow Jones) and, generally, lower orders of integration are found at lower sampling frequencies. Focusing on the co-movements across the series, we observe a reduction in the degree of persistence in the one-by-one differential comparison of the series. Even though the differencing parameter is small compared with what we should have obtained under standard cointegration, this factor still shows long-memory as it ranges in the interval (0.5, 1) in the majority of cases; and appears to be greater when comparing markets from the same geographic region, showing evidence that the convergence process between the stocks is slower between markets of the same continents.

Comparative Study of Incremental Capacity Curve Determination Methods for Lithium-Ion Batteries Considering the Real-World Situation

The incremental capacity analysis (ICA) method is a promising method in battery state of health (SOH) estimation studies. The incremental capacity (IC) curve determination is one of the critical parts of the ICA method. However, the uncertain and incomplete charging conditions of real-world electric vehicles (EVs) significantly limit the IC curve determination. This article provides a thorough analysis of the practicality and limitations of four IC curve determination methods based on the datasets collected from real-world operating EVs with a comprehensive comparison scheme. The Lorentz function fitting method is improved by the differential evolution algorithm, breaking the limitation of fixed parameter boundary constraints. A novel PCHIP method is further proposed to determine the IC curve, with higher robustness to realistic uncertain and incomplete charging conditions. The proposed method is validated by real-world data from 40 EVs with low sampling frequency. The results show the extracted features from the IC curves, determined by the proposed method have a stronger correlation with the SOH, allowing the accurate SOH estimation with a 2% error. With less computational resources and sampling frequency requirements, this method shows great potential for the realistic battery management system implementation.

A pilot directional protection based on low frequency voltage variable quantity for LCC-HVDC transmission lines

In this paper, a pilot directional protection based on low frequency voltage variable quantities for LCC-HVDC transmission lines is proposed. Based on the regularities of low frequency voltage distribution along the HVDC transmission line, the low frequency voltage variable quantities of the positive pole and negative pole are defined. Then, the theory of low frequency voltage variable quantities based directional protection is presented. The low frequency voltage variable quantity variation characteristics of the relay device location point and calculation point under different fault conditions are analyzed. If a forward fault occurs, the low frequency voltage variable quantity of the calculation point would be larger than that of relay device location point. If a reverse fault occurs, the low frequency voltage variable quantity of the calculation point will be less than that of relay device location point. Next, the criteria of the low frequency voltage variable quantity based pilot directional protection is proposed. Finally, an LCC-HVDC transmission system is modelled for simulation. Simulation results reveal that the presented pilot directional protection has obvious physical significance with simple calculation and is adaptive to lower sampling frequency. Besides, the presented algorithm is independent on the HVDC transmission line boundary and invulnerable to strong noise and high fault resistance.

On the effect of sampling frequency on the electricity theft detection performance

Electricity theft is a common problem in many countries and affects the revenue of the electricity industry significantly. Recently, machine and deep learning techniques are being used widely to detect thieves by analysing the consumption patterns. While the prediction accuracy of these methods depends on the number and quality of the existing samples used for training models, the majority of previous research work focussed on data with high sampling frequencies, for example, data from smart grids. However, based on technological and operational limitations, the rate of metre reading is too low in many countries, which can affect detection performance. To investigate the effect of sampling frequency on the performance of detection methods, we designed a processing framework to evaluate different classification algorithms on versions of a challenging dataset obtained by down-sampling the original data at various rates. We used different features from time, frequency and time-frequency domains and their combinations in our investigations. Experimental results show that the performance of the models decreases with a small slope up to the sampling frequency of one data per week. The performance degradation rate is significant for data with lower sampling frequencies. We also proposed a bagging network that could improve the overall detection performance at different sampling frequencies.

A three component model for superdiffusive motion effectively describes migration of eukaryotic cells moving freely or under a directional stimulus.

Although the simple diffusion model can effectively describe the movement of eukaryotic cells on a culture surface observed at relatively low sampling frequency, at higher sampling rates more complex models are often necessary to better fit the experimental data. Currently available models can describe motion paths by involving additional parameters, such as linearity or directional persistence in time. However sometimes difficulties arise as it is not easy to effectively evaluate persistence in presence of a directional bias. Here we present a procedure which helps solve this problem, based on a model which describes displacement as the vectorial sum of three components: diffusion, persistence and directional bias. The described model has been tested by analysing the migratory behaviour of simulated cell populations and used to analyse a collection of experimental datasets, obtained by observing cell cultures in time lapse microscopy. Overall, the method produces a good description of migration behaviour as it appears to capture the expected increase in the directional bias in presence of wound without a large concomitant increase in the persistence module, allowing it to remain as a physically meaningful quantity in the presence of a directional stimulus.

Low-Cost Wireless Structural Health Monitoring of Bridges.

Nowadays, low-cost accelerometers are getting more attention from civil engineers to make Structural Health Monitoring (SHM) applications affordable and applicable to a broader range of structures. The present accelerometers based on Arduino or Raspberry Pi technologies in the literature share some of the following drawbacks: (1) high Noise Density (ND), (2) low sampling frequency, (3) not having the Internet’s timestamp with microsecond resolution, (4) not being used in experimental eigenfrequency analysis of a flexible and a less-flexible bridge, and (5) synchronization issues. To solve these problems, a new low-cost triaxial accelerometer based on Arduino technology is presented in this work (Low-cost Adaptable Reliable Accelerometer—LARA). Laboratory test results show that LARA has a ND of 51 µg/√Hz, and a frequency sampling speed of 333 Hz. In addition, LARA has been applied to the eigenfrequency analysis of a short-span footbridge and its results are compared with those of a high-precision commercial sensor.

Close-proximity, conservative extrapolation of load spectra

PurposeThe purpose of this paper is to present the author’s method of conservative load spectrum (LS) derivation and close-proximity LS extrapolation applying a correction for measurement uncertainty caused by too low sampling frequency or signal noise, which may affect the load histories collected during the flying session and cause some recorded load increments to be lower than the actual values.Design/methodology/approachHaving in mind that the recorded load signal is burdened with some measurement error, a conservative approach was applied during qualification of the recorded values into 32 discrete load-level intervals and derivation of 32 × 32 half-cycle arrays. A part of each cell value of the half-cycle array was dispersed into the neighboring cells placed above by using a random number generator. It resulted in an increase in the number of load increments, which were one or two intervals higher than those resulting from direct data processing. Such an array was termed a conservative clone of the actual LS. The close-proximity approximation consisted of multiplication of the LSs clones and their aggregation. This way, the LS for extended time of operation was obtained. The whole process was conducted in the MS Excel environment.FindingsFatigue life calculated for a chosen element of aircraft structure using conservative LS is about 20%–60% lower than for the actual LS (depending on the applied value of dispersion coefficients used in the procedure of LSs clones generation). It means that such a result gives a bigger safety margin when operational life of the aircraft is estimated or when the fatigue test for an extended operational period is programed based on a limited quantity of data from a flying session.Originality/valueThis paper presents a proposal for a novel, conservative approach to fatigue life estimation based on the short-term LS derived from the load signal recorded during the flying session.

Results of a worldwide external quality assessment of cfDNA testing in lung Cancer

BackgroundCirculating cell free DNA (cfDNA) testing of plasma for EGFR somatic variants in lung cancer patients is being widely implemented and with any new service, external quality assessment (EQA) is required to ensure patient safety. An international consortium, International Quality Network for Pathology (IQNPath), has delivered a second round of assessment to measure the accuracy of cfDNA testing for lung cancer and the interpretation of the results.MethodsA collaboration of five EQA provider organisations, all members of IQNPath, have delivered the assessment during 2018–19 to a total of 264 laboratories from 45 countries. Bespoke plasma reference material containing a range of EGFR mutations at varying allelic frequencies were supplied to laboratories for testing and reporting according to routine procedures. The genotyping accuracy and clinical reporting was reviewed against standardised criteria and feedback was provided to participants.ResultsThe overall genotyping error rate in the EQA was found to be 11.1%. Low allelic frequency samples were the most challenging and were not detected by some testing methods, resulting in critical genotyping errors. This was reflected in higher false negative rates for samples with variant allele frequencies (VAF) rates less than 1.5% compared to higher frequencies. A sample with two different EGFR mutations gave inconsistent detection of both mutations. However, for one sample, where two variants were present at a VAF of less than 1% then both mutations were correctly detected in 145/263 laboratories. Reports often did not address the risk that tumour DNA may have not been tested and limitations of the methodologies provided by participants were insufficient. This was reflected in the average interpretation score for the EQA being 1.49 out of a maximum of 2.ConclusionsThe variability in the standard of genotyping and reporting highlighted the need for EQA and educational guidance in this field to ensure the delivery of high-quality clinical services where testing of cfDNA is the only option for clinical management.

Xenon LFP Analysis Platform Is a Novel Graphical User Interface for Analysis of Local Field Potential From Large-Scale MEA Recordings.

High-density multi-electrode array (HD-MEA) has enabled neuronal measurements at high spatial resolution to record local field potentials (LFP), extracellular action potentials, and network-wide extracellular recording on an extended spatial scale. While we have advanced recording systems with over 4,000 electrodes capable of recording data at over 20 kHz, it still presents computational challenges to handle, process, extract, and view information from these large recordings. We have created a computational method, and an open-source toolkit built in Python, rendered on a web browser using Plotly’s Dash for extracting and viewing the data and creating interactive visualization. In addition to extracting and viewing entire or small chunks of data sampled at lower or higher frequencies, respectively, it provides a framework to collect user inputs, analyze channel groups, generate raster plots, view quick summary measures for LFP activity, detect and isolate noise channels, and generate plots and visualization in both time and frequency domain. Incorporated into our Graphical User Interface (GUI), we also created a novel seizure detection method, which can be used to detect the onset of seizures in all or a selected group of channels and provide the following measures of seizures: distance, duration, and propagation across the region of interest. We demonstrate the utility of this toolkit, using datasets collected from an HD-MEA device comprising of 4,096 recording electrodes. For the current analysis, we demonstrate the toolkit and methods with a low sampling frequency dataset (300 Hz) and a group of approximately 400 channels. Using this toolkit, we present novel data demonstrating increased seizure propagation speed from brain slices of Scn1aHet mice compared to littermate controls. While there have been advances in HD-MEA recording systems with high spatial and temporal resolution, limited tools are available for researchers to view and process these big datasets. We now provide a user-friendly toolkit to analyze LFP activity obtained from large-scale MEA recordings with translatable applications to EEG recordings and demonstrate the utility of this new graphic user interface with novel biological findings.

The Sleep Well Baby project: an automated real-time sleep-wake state prediction algorithm in preterm infants.

Study ObjectivesSleep is an important driver of early brain development. However, sleep is often disturbed in preterm infants admitted to the neonatal intensive care unit (NICU). We aimed to develop an automated algorithm based on routinely measured vital parameters to classify sleep–wake states of preterm infants in real-time at the bedside.MethodsIn this study, sleep–wake state observations were obtained in 1-minute epochs using a behavioral scale developed in-house while vital signs were recorded simultaneously. Three types of vital parameter data, namely, heart rate, respiratory rate, and oxygen saturation, were collected at a low-frequency sampling rate of 0.4 Hz. A supervised machine learning workflow was used to train a classifier to predict sleep–wake states. Independent training (n = 37) and validation datasets were validation n = 9) datasets were used. Finally, a setup was designed for real-time implementation at the bedside.ResultsThe macro-averaged area-under-the-receiver-operator-characteristic (AUROC) of the automated sleep staging algorithm ranged between 0.69 and 0.82 for the training data, and 0.61 and 0.78 for the validation data. The algorithm provided the most accurate prediction for wake states (AUROC = 0.80). These findings were well validated on an independent sample (AUROC = 0.77).ConclusionsWith this study, to the best of our knowledge, a reliable, nonobtrusive, and real-time sleep staging algorithm was developed for the first time for preterm infants. Deploying this algorithm in the NICU environment may assist and adapt bedside clinical work based on infants’ sleep–wake states, potentially promoting the early brain development and well-being of preterm infants.

Predicting the Feed Intake of Cattle Based on Jaw Movement Using a Triaxial Accelerometer

The use of an accelerometer is considered as a promising method for the automatic measurement of the feeding behavior or feed intake of cattle, with great significance in facilitating daily management. To address further need for commercial use, an efficient classification algorithm at a low sample frequency is needed to reduce the amount of recorded data to increase the battery life of the monitoring device, and a high-precision model needs to be developed to predict feed intake on the basis of feeding behavior. Accelerograms for the jaw movement and feed intake of 13 mid-lactating cows were collected during feeding with a sampling frequency of 1 Hz at three different positions: the nasolabial levator muscle (P1), the right masseter muscle (P2), and the left lower lip muscle (P3). A behavior identification framework was developed to recognize jaw movements including ingesting, chewing and ingesting–chewing through extreme gradient boosting (XGB) integrated with the hidden Markov model solved by the Viterbi algorithm (HMM–Viterbi). Fourteen machine learning models were established and compared in order to predict feed intake rate through the accelerometer signals of recognized jaw movement activities. The developed behavior identification framework could effectively recognize different jaw movement activities with a precision of 99% at a window size of 10 s. The measured feed intake rate was 190 ± 89 g/min and could be predicted efficiently using the extra trees regressor (ETR), whose R2, RMSE, and NME were 0.97, 0.36 and 0.05, respectively. The three investigated monitoring sites may have affected the accuracy of feed intake prediction, but not behavior identification. P1 was recommended as the proper monitoring site, and the results of this study provide a reference for the further development of a wearable device equipped with accelerometers to measure feeding behavior and to predict feed intake.

A generalized heterogeneous autoregressive model using market information

This paper introduces a novel class of volatility forecasting models that incorporate market realized (co)variances and semi(co)variances within the framework of a heterogeneous autoregressive (HAR) model. Our empirical analysis shows statistically and economically significant forecasting gains. For our most parsimonious market-HAR specification, stock volatility forecasting is improved by 9.80% points. Using a mixed sampling frequency market-HAR variant with low (high) sampling frequency for the stock (market) improves forecasting by a further 6.90% points. Our paper also develops noise-robust estimators to facilitate the use of realized semi(co)variances at high sampling frequencies.

A Simplified Space Vector Pulse Width Modulation Algorithm of a High-Speed Permanent Magnet Synchronous Machine Drive for a Flywheel Energy Storage System

A space vector pulse width modulation (SVPWM) algorithm is an important part of the permanent magnet synchronous machine (PMSM) drive to achieve direct current (DC) to alternating current (AC) conversion. The execution of the conventional SVPWM algorithm is a complex process which will limit the sampling frequency of the high-speed PMSM drive. Low sampling frequency will cause high current total harmonic distortion (THD) and eddy current loss. To increase the sampling frequency, this paper proposes a novel simplified SVPWM algorithm. The proposed SVPWM algorithm turns the vector composition problem of the conventional SVPWM algorithm into an optimization problem of the dwell time of the basic vector. The proposed SVPWM algorithm has an optimal vector dwell time (OVDT). The dwell time of the basic vector can be directly calculated by solving the optimization problem. The proposed SVPWM algorithm does not need sector identification compared to the conventional algorithm. The experiments of the proposed SVPWM algorithm are performed in a high-speed PMSM drive of a flywheel energy storage system (FESS). Compared to the conventional SVPWM algorithm, the execution time of the proposed SVPWM algorithm is reduced by 38%. By using the proposed SVPWM algorithm, the sampling frequency can be increased from 33 kHz to 40 kHz. With the higher sampling frequency, the current THD is reduced by 25.6%. The effectiveness of the proposed simplified SVPWM algorithm is verified experimentally.

LAI-NOS: An automatic network observation system for leaf area index based on hemispherical photography

The leaf area index (LAI) is an important indicator reflecting the growth status of vegetation and is widely used in agriculture, ecology, climate change, and other fields. The shortcomings of the currently available methods for manually measuring LAI include labor-intensive, low sampling frequency, and asynchronous data collection. Focusing on these issues, a LAI sensor based on hemispherical photogrammetry and an automatic network observation system (LAI-NOS) for LAI were developed, which consists of four parts: LAI sensor, sensor node, sink node, and online data management system. The LAI sensor measures LAI values based on hemispherical photography. The sensor node is responsible for controlling the sensor and obtaining the data measured by the LAI sensor. The sink node is responsible for local networking and communication with the remote server. Data storage, data management, data display, and sampling frequency are managed by the online data management system. Comparative studies with LAI-2200C and satellite products were also conducted in this study. The comparative study with LAI-2200C showed that the LAI measurements of different vegetation types from both sources were highly significantly correlated whether based on Pearson regression or Passing & Bablok regression. A preliminary study comparing LAI-NOS measurements with Sentinel-2 inversion LAI and MODIS LAI products (MOD15A2H) showed (1) all LAI-NOS nodes measurements agreed very well with Sentinel-2 inversion LAI in the experimental period (average R2=0.94, RMSE=0.41); (2) the possible overestimate of Sentinel-2 inversion LAI was found in the middle stage of wheat (jointing-anthesis); (3) MOD15A2H and LAI-NOS measurements showed similar crop growth trends in long-term observations.