Noise In Measured Data Research Articles

Methods for addressing variable turbine operations during wind farm noise compliance monitoring

Post-construction wind farm noise measurements at dwelling locations are commonly required in Australia to verify compliance with noise limits. These are often required as soon as the wind farm begins operating. However, commissioning issues and electricity network restrictions are a common occurrence during initial stages of operation. As such, curtailed modes of operation and shutdowns are a regular occurrence, and large sections of a wind farm may only be operating intermittently. This presents challenges to obtaining noise measurement data that is representative of normal operation of the wind farm, and may ultimately limit the conclusions that are able to be reached from measurements during the early stages of operation. To address these challenges, it is necessary to identify and account for any periods when operational noise levels may be lower as a result of turbines being curtailed or shut down. Several methods have been developed for this purpose, and range from simple procedures that remove any data affected by curtailment or shutdown, through to more complex assessments of the magnitude and implications of the uncertainty introduced by variable wind turbine operations. These methods are presented and assessed in this paper with case studies of their application to operational projects.

A comparison of machine learning methods for recovering noisy and missing 4D flow MRI data.

Experimental blood flow measurement techniques are invaluable for a better understanding of cardiovascular disease formation, progression, and treatment. One of the emerging methods is time-resolved three-dimensional phase-contrast magnetic resonance imaging (4D flow MRI), which enables noninvasive time-dependent velocity measurements within large vessels. However, several limitations hinder the usability of 4D flow MRI and other experimental methods for quantitative hemodynamics analysis. These mainly include measurement noise, corrupt or missing data, low spatiotemporal resolution, and other artifacts. Traditional filtering is routinely applied for denoising experimental blood flow data without any detailed discussion on why it is preferred over other methods. In this study, filtering is compared to different singular value decomposition (SVD)-based machine learning and autoencoder-type deep learning methods for denoising and filling in missing data (imputation). An artificially corrupted and voxelized computational fluid dynamics (CFD) simulation as well as in vitro 4D flow MRI data are used to test the methods. SVD-based algorithms achieve excellent results for the idealized case but severely struggle when applied to in vitro data. The autoencoders are shown to be versatile and applicable to all investigated cases. For denoising, the in vitro 4D flow MRI data, the denoising autoencoder (DAE), and the Noise2Noise (N2N) autoencoder produced better reconstructions than filtering both qualitatively and quantitatively. Deep learning methods such as N2N can result in noise-free velocity fields even though they did not use clean data during training. This work presents one of the first comprehensive assessments and comparisons of various classical and modern machine-learning methods for enhancing corrupt cardiovascular flow data in diseased arteries for both synthetic and experimental test cases.

Assessing wayside traffic noise levels using long-term noise monitoring and available Department of Transportation traffic data - worst-case noise traffic conditions, effects of repaving, and methods for comparing traffic noise data sets

The availability of automated continuous traffic data from State Departments of Transportation (DOTs), such as the California Department of Transportation (Caltrans) Performance Measurement System (PeMS), provides increased opportunities for the analysis of highway noise. Traffic data that is free to access and downloadable via the internet can be matched with unattended long-term noise measurement data to provide large datasets for analysis, without requiring field staff to be continuously on site. A 2018 ICF/Caltrans paper documented how this approach was used on an operating freeway to determine the volume of traffic that results in the highest one-hour energy average traffic noise level. At the time of those measurements, the subject freeway had Portland cement concrete pavement. The freeway has since been repaved with an open-graded rubberized asphalt concrete pavement overlay. The current study uses new (2023) traffic noise measurements and PeMS data to investigate how worst-noise conditions have changed and the effect of the repaving on wayside noise levels. Changes in traffic patterns since 2018 required the authors to normalize the measurement data for comparison. Opportunities were identified to take advantage of the large measurement data sets to match before-and-after data, and those methods are also described in this paper.

Admittance matrix estimation of radial distribution grids using power and voltage magnitude measurements

Accurate knowledge of the admittance matrix of distribution grids is essential for grid operation and control as more renewable energy sources and electric vehicles are integrated into distribution grids. Due to limited observability, measurement noise, topology assumptions, and partial data availability, the exact calculation of the admittance matrix of distribution grids is challenging. In this paper, we present a method for estimating the admittance matrix of radial distribution grids. The proposed method uses low-cost measurement devices that record ten-minute-based measurements of voltage magnitudes, active power, and reactive power at a single end of the lines or transformers instead of second-based phasor measurements. The impedance of lines or transformers is then estimated using a statistical optimization subject to the distribution flow (DistFlow) model. The effectiveness of proposed method is demonstrated by simulation, laboratory testing, and field measurements taken from a distribution grid in Switzerland.

Research on noise reduction methods for indoor substations based on resonant sound absorption

Aiming to reduce the impact of substation noise on the lives of surrounding residents, improving the low and middle frequencies acoustic performance of traditional resistive mufflers in indoor substations is significant. The paper is based on the resonance sound absorption mechanism, and for the first time applies multi-frequency resonance noise reduction technology to the research field of indoor substation noise control, proposing a multi-frequency resonant muffling structure. The acoustic performance of the proposed muffler unit is investigated through theoretical and numerical analysis. The measured noise data of the indoor substation is used as sound excitation in the numerical analysis. The results show that at frequencies of 100 Hz, 200 Hz, 300 Hz, 400 Hz, and 500 Hz, the noise in indoor substations is significantly reduced by the multi-frequency resonant muffling structure. The paper provides references for the application of multi-frequency resonant mufflers in the field of noise control of indoor substations.

Short-term voltage instability prediction using pre-identified voltage templates and machine learning classifiers

System operators use different procedures to detect disturbances which can lead to short-term voltage instability. These schemes are designed using the domain knowledge and generally specific for the corresponding system. The design procedure of such schemes is heuristic and cannot be directly applied to any other power system. This paper proposes a data-driven approach to predict short-term voltage stability using set of post-disturbance voltage templates and a machine learning-based classifier. In this scheme, a set of voltage templates corresponding to stable/unstable events is identified for selected buses using a suitably generated training data set. The proximity of real-time voltage trajectory to each of the template is computed and input to a trained machine learning classifier to predict the short-term voltage stability status. The proposed approach is validated using IEEE Nordic test system. The investigations show that this method provides accurate short-term voltage stability predictions. Furthermore, impact of adverse phenomena such as measurement noise and missing data on the proposed method is analyzed to confirm the robustness of this scheme in particle environments. © 2017 Elsevier Inc. All rights reserved.

Distribution line parameters estimation framework with correlated injections using smart meter measurements

Accurate estimation of power system line parameters (resistance and reactance) is vital for operational and planning studies in power distribution systems. However, obtaining these estimates is challenging due to temperature and aging dependencies, limited measurements, correlated injections from renewable sources, and change/addition in network elements in due course of time. Most of the existing research for line parameter estimation (LPE) uses regression-based approaches, which are highly susceptible to noise in measurement data. In addition, these methods are unable to converge in the case of an ill-conditioned matrix, which often occurs in the active distribution system due to correlated injections. Furthermore, existing work requires μPMUs at each node of the distribution network, which may increase the overall cost. To address these challenges, this paper presents a data-driven error-in-variable model in the optimization-based framework for LPE in low and medium-voltage networks using smart-meter data. The proposed LPE method is specifically designed to handle correlated power injections and noise in measurement data. An equidistant loss sampling approach is also introduced for injection sample selection, reducing sample complexity. Simulation results on a real 6-Bus and IEEE 33-Bus system depict the ability of the proposed method to obtain line parameters with correlated and erroneous injections.

Toward Airframe Noise Reduction for Passenger Aircraft

Airframe noise is one of the main noise sources of the latest passenger aircraft during the approach phase. To demonstrate noise reduction technology based on unsteady Computational Fluid Dynamics (CFD) and wind tunnel tests, JAXA conducted flight tests using the JAXA Experimental Aircraft "Hisho" in 2016 and 2017 in cooperation with industries in Japan. The noise data obtained from flyover noise source measurements successfully showed consistent characteristics with the noise reduction designs for the flaps and the main landing gear. The noise measurement data from the flight test 2017 were used to validate the wind tunnel tests and CFD used in the design phase. Subsequently, the research activities proceeded towards the research and development targeting regional jet aircraft, aiming for practical application in passenger aircraft. This paper overviews these research activities toward developing airframe noise reduction technology for passenger aircraft.

Background noise model of spaceborne photon-counting lidars over oceans and aerosol optical depth retrieval from ICESat-2 noise data

The ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) photon-counting lidar is a revolutionary active remote sensing device and is extremely sensitive to both laser signal and solar background induced noise photons. The weak laser signals, such as aerosol and water subsurface backscattered laser photons, may be buried in noise in the daytime. A noise model for spaceborne photon-counting lidars is of vital importance in understanding and evaluating the performance of a lidar system, as well as in data processing and applications. From the reciprocity perspective, the main noise source for photon-counting lidars is solar background radiation, which in turn is the signal source of ocean color sensors. In the absence of clouds, it is possible to estimate the aerosol information using background noise data of ICESat-2 if the background noise is accurately modeled. In this study, a background noise model of photon-counting lidars over oceans is proposed that carefully considers the contributions of gas molecules, aerosol, water surface, subsurface, and detector dark noise. As the background rate from clouds are much larger than the sum of other factors, in this study, we only consider the cloudless atmosphere by using a cloud screening process. By inputting the system and environmental parameters, the theoretical predictions of noise level in six study areas have an average MAPE of ∼5% compared with ICESat-2 measured noise data. The results also indicate that without clouds, the variation of aerosols dominates the variation of ICESat-2 total background noise in open ocean areas. Then, based on this noise model, a method to estimate aerosol optical depths (AODs) over oceans is proposed solely on the ICESat-2 product including background noise rate and environmental data. Validation against MODIS (Moderate Resolution Imaging Spectroradiometer) daily AODs indicates that the average MAPE is <20% and the average correlation coefficient is 0.95 in six study areas. This method may provide a new AOD retrieval way over oceans in the daytime, when it is difficult to directly calculate AODs using lidar backscattered signal under strong background noise. In addition, the background noise of the ICESat-2 photon-counting lidar in its six channels (or pixels) has the potential to be regarded as the signal of an “ocean color camera” with only a single narrow green band. Based on this consideration, future works may be conducted to combine ocean color remote sensing with ICESat-2 photon-counting lidar.

Estimation of ambient marine noise levels in New South Wales

Marine and offshore infrastructure is increasingly becoming more prominent in NSW resulting in a requirement to investigate the potential behavioral and physiological impacts to marine fauna during construction and operational phases. Typically, detailed modeling is undertaken to compare the predicted underwater noise levels against noise thresholds with limited consideration to the existing ambient noise levels specific to the site. Vessel movements are known to be the primary contributor to ambient underwater noise levels from anthropogenic sources whereas prevailing noise is generally dominated by natural sources such as wind, waves, precipitation, and seismic activity. Underwater noise monitoring of baseline conditions is not always feasible and can also be cost-prohibitive to undertake. Additionally, there is currently limited publicly available underwater noise measurement data along the eastern coastline of NSW, specifically in shallow waters at depths less than 50 m. This study includes simple procedures to allow for preliminary estimates of the key contributors to the marine noise environment in the context of the Eastern coastline of NSW, with the aim of producing maps estimating existing marine noise levels for common descriptors such as SPL(rms) and SEL(24hour).

Methods for addressing variable turbine operations during wind farm noise compliance monitoring

Post-construction wind farm noise measurements at dwelling locations are commonly required in Australia to verify compliance with regulatory and contractual noise limits. These measurements are often required as soon as the wind farm has begun operating. However, commissioning issues and electricity network restrictions are a common occurrence during the initial stages of operation. Large wind farm projects are also commonly commissioned in stages. As a result, curtailed modes of operation and shutdowns are a regular occurrence, and large sections of a wind farm may only be operating intermittently. This presents challenges to obtaining noise measurement data that is representative of normal operation of the wind farm and may ultimately limit the conclusions that are able to be reached from measurements during the early stages of operation. To address these challenges, it is necessary to identify and account for any periods when operational noise levels may be lower as a result of turbines being curtailed or shut down. Several methods have been developed for this purpose, and range from simple procedures that remove any data affected by curtailment or shutdown, through to more complex assessments of the magnitude and implications of the uncertainty introduced by variable wind turbine operations. These methods are presented and assessed in this paper with case studies of their application to operational projects.

Development of machine learning techniques in corrosion inhibition evaluation of 5-methyl-1 H-benzotriazole on N80 steel in acidic media

In this study, three machine learning models of data analysis based on multiple linear regression (MLR), multiple nonlinear regression (MNR), and artificial neural network (ANN) has been proposed to model the behavior of 5methyl-1 H-benzotriazole (m-BTA) inhibitor on N80 steel in acidic media at different temperatures and inhibitor concentration. Based on experimental observation, the DC trend removal by Polynomial detrending revealed a suitable trend between electrochemical noise (ECN) measurement data and the results obtained from electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization test at m-Factor equals to 3, 4, and 5. The inhibition efficiency (η) of 91% was achieved by using 0.001 M of m-BTA inhibitor. The corrosion rate will increase by increasing the temperature for the blank sample. Still, the m-BTA resulted in corrosion protection for N80 at high temperatures, and ECN resistance (Rn) increases with increased m-BTA concentration. The experimental tests revealed that temperature and m-factor are significant for corrosion inhibition of m-BTA; also, inhibitor concentration is an essential factor. Therefore, these three parameters were chosen as inputs for the Rn of the inhibitor. Removing unnecessary data from the original dataset causes relevant results compatible with experimental observations. The results of the reduced data set showed that R2 for the test of whole and reduced MLR model is 0.41 and 0.69, respectively, which shows that the reduced model is more suitable for modeling the training data than the whole model. The MNR model presents better accuracy than the MLR model, demonstrating that the relation between inputs and output does not follow a linear dependency. Several topologies of the ANN model were constructed, and the number of neurons was changed in one hidden layer. Finally, five neurons in the hidden layer with the Levenberg–Marquardt backpropagation (LMBP) algorithm were selected to develop the optimum ANN model, which has R2 and RMSE values of 0.99 and 17.41.

A new base of wind turbine noise measurement data and its application for a systematic validation of sound propagation models

Abstract. Extensive measurements in the area of wind turbines were performed in order to validate a sound propagation model which is based on the Crank–Nicolson parabolic equation method. The measurements were carried out over a flat grass-covered landscape and under various environmental conditions. During the measurements, meteorological and wind turbine performance data were acquired and acoustical data sets were recorded at distances of 178, 535 and 845 m from the wind turbine. By processing and analysing the measurement data, validation cases and input parameters for the sound propagation model were derived. The validation includes five groups that are characterised by different sound propagation directions, i.e. downwind, crosswind and upwind conditions in varying strength. In strong upwind situations, the sound pressure levels at larger distances are overestimated because turbulence is not considered in the modelling. In the other directions, the model reproduces the measured sound propagation losses well in the overall sound pressure level and in the third octave band spectra. As in the recorded measurements, frequency-dependent maxima and minima are identified, and losses generally increase with increasing distance and frequency. The agreement between measured and modelled sound propagation losses decreases with distance. The data sets used in the validation are freely accessible for further research.

A Time-Domain Signal Processing Algorithm for Data-Driven Drive-by Inspection Methods: An Experimental Study

Constructional material deterioration and member damage can cause changes in the dynamic characteristics of bridge structures, and such changes can be tracked in the responses of passing vehicles via the vehicle-bridge interaction (VBI). Though data-driven methods have shown promising results in damage inspection for drive-by methods, there is still much room for improvement in their performance. Given this background, this paper proposes a novel time-domain signal processing algorithm for the raw vehicle acceleration data of data-driven drive-by inspection methods. To achieve the best data processing performance, an optimizing strategy is designed to automatically search for the optimal parameters, tuning the algorithm. The proposed method intentionally overcomes the difficulties in the application of drive-by methods, such as measurement noise, speed variance, and enormous data volumes. Meanwhile, the use of this method can greatly improve the accuracy and efficiency of Machine Learning (ML) models in vehicle-based damage detection. It consists of a filtering process to denoise the data, a pooling process to reduce data redundancy, and an optimizing procedure to maximize algorithm performance. A dataset is obtained to validate the proposed algorithm through laboratory experiments with a scale truck model and a steel beam. The results show that, compared to using raw data, the present algorithm can increase the average accuracy by 12.2-15.0%, and the average efficiency by 35.7-96.7% for different damaged cases and ML models. Additionally, the functions of filtering and pooling operations, the influence of window function parameters, as well as the performance of different sensor locations, are also investigated in the paper. The goal is to present a signal processing algorithm for data-driven drive-by inspection methods to improve their detection performance of bridge damage caused by material deterioration or structural change.

Data-driven sparse identification of galloping model of iced quad bundle conductors

The galloping of iced conductors is a serious threat to the safe operation of power systems. Establishing an accurate galloping model of iced conductors has always been a difficult point in galloping research. Therefore, the sparse identification of nonlinear dynamics (SINDy) algorithm is used to identify the galloping model from noise measurement data. A theoretical model of galloping of iced quad bundle conductors is established. Meanwhile, the algorithm is used to identify the simulated data of the theoretical model. The parameter identification ability of the algorithm under noisy velocity measurement is analyzed. An excellent denoising method was selected for data preprocessing, and then the model identification effect of the algorithm after data preprocessing is studied. Besides, the accuracy of the prediction model based on this algorithm and the support vector regression (SVR) prediction model under different training data lengths are compared. The results show that the model identified by the SINDy algorithm in the noise measurement data after data preprocessing has high accuracy and robustness. Moreover, the amount of data used is small. The model identified by this algorithm plays an important role in the rapid investigation, prediction and early warning of galloping phenomena.

On the Influence of Cavitation Volume Variations on Propeller Broadband Noise

Low-frequency broadband shipping noise causes a growing concern for marine fauna together with the demand for noise reduction. Detailed analyses of the cavitation behaviour in the time domain serve as a prerequisite for steps toward quieter propellers. Underwater noise measurement data from the propeller of a full-scale container vessel are used for the analysis of the cavitation volume behaviour. Sequencing methods, polynomial models, and stochastic evaluation methods feeding Monte Carlo simulations of unsteady sheet cavitation are applied to identify the cause of an often observed, yet unexplained similarity in shipping noise spectra. Characteristic features of the volume evolution are identified to enhance the understanding of noise generation by sheet cavitation.

An analytical model for electronic noise in a cryogenic bolometer detector readout circuit

This paper presents an analytical model to quantify the measured noise in a cryogenic bolometer readout circuit. The model includes the contributions from the bias resistors and sensor resistors, voltage and current noise of amplifier, and cable capacitance. The model parameters are empirically estimated using frequency domain analysis of the measured noise data of indigenously developed Neutron Transmutation Doped (NTD) Ge sensors. The model is shown to describe noise data for NTD Ge sensorsover a wide range of resistances corresponding to temperatures in the range 20–70 mK.Relative contributions of different components are discussed and it is shown that the contribution to the overall noise from the differential amplifier at 300 K is the dominant source. It is observed that the amplifier flicker noise is significantly lower than that specified in the amplifier datasheet. The present study also indicates that a desirable value of resistance of NTD sensor (R NTD) from noise considerations is ≲ 1 GΩ at ∼ 20 mK.

Thermo-Electrical state and demand response potential estimation for power systems with building thermostats

Heating and cooling are major resources of Demand Response (DR) to enhance the flexibility and reliability of power grids. In order to maximize their potential, it is necessary to reliably keep track of their operating states in real-time operation. This paper presents a comprehensive state estimation framework for power systems with building thermostats, with a tractable thermodynamic building model and integration of multi-source information from weather, power grid, building systems. Based on the thermodynamic model and state of the buildings, the building temperature trajectories in the next few hours can be accurately predicted, such that the DR potential of the building can be precisely estimated. To jointly estimate the state variables of power grids and state variables in building thermostats with different time scales of dynamics, a holistic estimation framework is developed based on the partial equivalence between the Weighted Least Squares (WLS) estimation problem and the correction stage of the Iterative Extended-Kalman Filter (IEKF). Simulation results show that the proposed framework can accurately track the thermo-electrical states of the system and estimate the DR potential in the presence of measurement noise and bad data.

Impact of ATC speed instructions on fuel consumption and noise exposure: an assessment of real operations in Zurich

Approach operations at busy airports are louder and less fuel efficient than they technically could be. Carrying out a safe approach under fluctuating wind and weather conditions while following individual air traffic control (ATC) instructions imposes a significant workload on the flight crew, especially with the limited systems support and information availability on the flight deck today. The SESAR exploratory research project DYNCAT (Dynamic Configuration Adjustment in the TMA) aims to highlight the impact of current approach operations on the environment based on all relevant data sources (on-board operational data, ATC commands, noise measurement data, surrounding traffic and weather information) allowed to evaluate individual approach operations in their full context, exemplarily for the Airbus A320 at Zurich airport. The evaluations performed are a unique opportunity to analyse the impact of ATC instructions on fuel consumption and noise exposure during real operations. The results showed that speed instructions, especially when given early during the transition phase, lead to high fuel consumption, due to the long-time flight in low speed levels correlated to earlier usage of Flaps configurations. A mean difference of almost 50 kg in fuel burned is observed when comparing the flights with speed restrictions to those without. On the other hand, speed restrictions provide a well-defined airspeed guidance for the pilots during transition and final approach, which leads to lower usage of speed brakes and also to lower speed levels for the landing gear extension. The less speed restrictions were given to the pilots by ATC, the higher was the usage of speed brakes during the final approach, and also the landing gear deployment was performed at higher speed levels, which ceteris paribus contributes significantly to the noise exposure and noise footprint. Based on these evaluations, requirements for a novel flight management system (FMS) function, which is set to be developed in the next steps of the project, were derived to support pilots and controllers through extended information exchange, thus increasing predictability of the lateral and vertical flight profiles for both sides. A central component is a novel airborne energy management assistance system including a configuration management functionality, to be implemented through an extension of the FMS capabilities.

Proper Orthogonal Decomposition Method for the Prediction of Fan Broadband Interaction Noise

The turbulent wake generated by a rotor interacting with outlet guide vanes (OGVs) is one of the dominant broadband noise sources in a turbofan engine. An accurate representation of the rotor wake turbulence is therefore important for the reliable prediction of the rotor–OGV interaction noise. This paper presents a turbulence synthesis method based on a spectral proper orthogonal decomposition (POD) representation of the turbulent wake that aims to reproduce the desired velocity cross-spectrum along the OGV leading edges where noise is emitted due to turbulence–OGV interaction. The method is first developed in the frequency domain based on a superposition of vortical modes with the appropriate amplitudes. Fourier modes and POD modes are proposed to represent the two-point velocity spectrum. The POD modes will be shown to be highly efficient in reconstructing the flowfield near the tip region where a large-scale coherent structure is present. An extension of the POD synthetic turbulence method to the time domain is also presented by means of a white noise filtering technique to allow the generation of time-varying velocity signals with the desired cross-spectral characteristics. The results show that both the one- and two-point statistics can be closely reproduced. The proposed frequency-domain POD synthetic turbulence method for fan broadband noise prediction for a realistic fan–OGV configuration is illustrated by the use of a frequency-domain linearized Navier–Stokes solver to predict the sound power radiation due to each vortical mode. Overall sound power levels at a number of discrete frequencies are predicted and compared against measured noise data. Agreement is found to be within the uncertainty of the noise sound power measurement.