Signal-to-noise Ratio Performance Research Articles

Experimental evaluation of wearable LED strip and side-emitting fiber for optical camera communications systems

This paper presents an experimental evaluation of two types of light-emitting diode (LED)-based distributed transmitters, namely an LED strip and an LED-coupled side-emitting optical fiber, in both laboratory and wearable optical camera communication (OCC) systems. We study the system performance in terms of success of reception (SoR) with regard to the transmission distance. The best value of SoR is achieved when the camera is facing directly to the transmitter (T x ) from a close distance of 1 m. Additionally, we compare the power consumption, the signal-to-noise ratio performance (SNR) and all the obtained values under optimal conditions are better than the forward error correction (FEC) limit in OCC systems.

Performance Analysis of BER and SNR Performance in High-QAM Modulations Using Advancements in MIMO Systems

Wireless communications is one of the most active areas of technology development of our time. This development is being driven primarily by the transformation of what has been largely a medium for supporting voice telephony into a medium for supporting other services, such as the transmission of video, images, text, and data. Today, the widely deployed network type is based on a centralized approach that requires a network point of access, commonly called the Access Point (AP), to serves as a gateway between the mobile device and the Internet. The obtained results demonstrate that spatial diversity along with the power of STBC significantly improves the error performance in frequency selective wireless fading channels. The BER level is depend on the modulation type, SNR value and channel behavior. Modulation schemes that we have used in this paper are 4-QAM, 8-QAM, 16-QAM and 64-QAM which further improved using forward error correction codes (FEC). A comparison is made between the diversity gains of MIMO systems in terms of BER for high QAM modulation scheme. This work presents, a simulation toll MATLAB R2023a used for model implemented using Nakagami channel to study the performance analysis of Bit Error rate (BER) V/S Signal to Noise ratio (SNR). Keywords: AWGN, BER, Fading Channel, MIMO System, Nakagami, OFDM, QAM Modulation, SNR, STBC etc.

Evaluation of the performance of a 7-T 8 × 2 transceiver array.

The decoupled 8 × 2 transceiver array has been shown to achieve a mean B1 + of 11.7 uT with a coefficient of variation of ~11% over the intracranial brain volume for 7-T MR imaging. However, this array may be thought to give lower signal-to-noise ratio (SNR) and higher g-factors for parallel imaging compared with a radio frequency (RF) receive-only coil due to the latter's higher coil count and use of coil overlap to reduce the mutual impedance. Nonetheless, because the transceiver's highly decoupled design (pertinent for transmission) should also be constructive for reception, we measured the noise correlation, g-factors, and SNR for the decoupled transceiver in comparison with a commercial reference coil. We found that although the transceiver has half the number of receive elements in comparison with the reference coil (16 vs. 32), comparable g-factors and SNR over the head were obtained. From five subjects, the transceiver versus reference coil SNR was 65 ± 10 versus 67 ± 15. The mean noise correlation for all coil pairs was 10% ± 5% and 12% ± 9% (transceiver and reference coil, respectively). As changes in load impedance may alter the S parameters, we also examined the performance of the transceiver with tuned and matched (TM) versus untuned and unmatched (UTM) conditions on five subjects. We found that the noise correlation and SNR are robust to load variation; a noise correlation of 10% ± 5% and 10% ± 6% was determined with TM versus UTM conditions (SNRUTM/SNRTM = 0.97 ± 0.08). Finally, we demonstrate the performance of the array in human brain using T2-weighted turbo spin echo imaging, finding excellent SNR performance in both caudal and rostral brain regions.

Highly Discriminative Amplification of a Single Frequency Comb Line

AbstractThe optical power of a single line in optical frequency combs (OFCs) is inherently weak, so the extraction and amplification of a single OFC line is required for many high‐precision applications. In this work, a high gain, narrowband, and all‐polarization‐maintaining (PM) fiber Brillouin amplifier is introduced to address this issue. The use of PM fiber as the gain medium provides higher Brillouin gain and avoids polarization sensitivity to thermal and acoustic disturbance. The gain fiber is reduced to tens of meters with higher pump power, which is beneficial for the phase quality of the amplified comb line. Numerical simulations and experiments are performed to investigate the features of the Brillouin amplification process for an individual OFC line. Notably, direct generation of a Watt‐level single‐mode laser from an OFC is demonstrated, with a signal‐to‐noise‐ratio (SNR) of 47 dB and a side‐mode‐suppression‐ratio (SMSR) exceeding 60 dB, which represents the highest power, and best SNR and SMSR performance ever reported, to the best of the knowledge.

Boosting SNR of cascaded FBGs in a sapphire fiber through a rapid heat treatment.

This Letter reports the performance of femtosecond (fs) laser-written distributed fiber Bragg gratings (FBGs) under high-temperature conditions up to 1600°C and explores the impact of rapid heat treatment on signal-to-noise ratio (SNR) enhancement. FBGs are essential for reliable optical sensing in extreme temperature environments. Comprehensive tests demonstrate the remarkable performance and resilience of FBGs at temperatures up to 1600°C, confirming their suitability for deployment in such conditions. The study also reveals significant fringe visibility improvements of up to ∼10 dB on a 1-m-long sapphire optical fiber through rapid heat treatment, representing a first-time achievement to the best of our knowledge. These enhancements are vital for improving the SNR and overall performance of optical fiber systems in extreme temperatures. Furthermore, the research attains long-term stability for the cascaded FBGs over a 24-hr period at 1600°C. This research expands our understanding of the FBG behavior in high-temperature environments and opens avenues for developing robust optical fiber systems for energy, aerospace, oil and gas, and high-temperature distributed sensing applications.

Noise analysis in direct detection and coherent detection phase-sensitive optical time-domain reflectometry systems.

This study compares noise and signal-to-noise ratio (SNR) in direct detection and coherent detection fiber-based distributed acoustic sensing (DAS) systems. Both detection schemes employ the dynamic analysis of Rayleigh-backscattered light in phase-sensitive optical time-domain reflectometry (ΦOTDR) systems. Through theoretical and experimental analysis, it is determined that for photodetection filters with a sufficiently narrow bandwidth, the SNR performance of both detection schemes is comparable. However, for filters with poor selectivity, coherent detection was found to exhibit superior performance. These findings provide crucial guidelines for the design of high-performance time-domain DAS systems.

Active Learning-Aided CNN-Based Entropy-Tunable Automatic Modulation Identification for Rate-Flexible Coherent Optical System

Flexible rate and real-time link monitoring are important tasks in the development of software-defined elastic optical networks (EONs). The tunable spectral efficiency characteristic of probabilistic constellation shaping (PCS) naturally provides a possibility to dynamically regulate the rate for future optical communication systems. In this work, we firstly propose an active learning-aided entropy-tunable automatic modulation identification (AL-aided ET-AMI) scheme based on convolution neural network (CNN) model for a PCS-based coherent optical system. An AL-based neural network allows monitoring of the link rate and signal-to-noise ratio (SNR) with tuning entropy or optical power fluctuation. The proposed ALaided ET-AMI scheme is demonstrated over a 350 ∼ 550-Gbps line rate 10-km dual-polarized coherent optical communication system at entropies from 3.5 to 5.5. When the entropy tuning step is 0.1, corresponding to a rate tuning step of 5 Gbps at 50 Gbaud, the recognition accuracy can reach 98% with data aggregation (DA). When the fluctuation of SNR is 1 dB, the recognition rate can reach 87% at an entropy of 4.5 over 400 samples. The verifications show that our proposed AL-aided ET-AMI solution can monitor the rate and SNR performance of PCS-based high-speed rateflexible optical links well. The solution provides a new perspective and tool for future optical systems and network monitoring.

Bayesian Direction of Arrival Estimation with Prior Knowledge from Target Tracker

The performance of traditional direction of arrival (DOA) estimation methods always deteriorates at a low signal-to-noise ratio (SNR) or without sufficient observations. This paper investigates the Bayesian DOA estimation problem aided by the prior knowledge from the target tracker. The Bayesian Cramér–Rao lower bounds (CRLB) and the expected CRLB are first derived to evaluate the theoretical performance of Bayesian DOA estimation. Based on the maximum a posterior (MAP) estimator in the Bayesian framework, two methods are proposed. One is a two-step grid search method for a single target DOA case. The other is a gradient-based iterative solution for multiple targets DOA case, which extends the traditional Newton method by incorporating the prior knowledge. We also propose a minimum mean square error (MMSE) estimator using a Monte Carlo method, which requires trading off accuracy against computational complexity. By comparing with the maximum likelihood (ML) estimators and the MUSIC algorithm, the proposed three Bayesian estimators improve the DOA estimation performance in low SNR or with limited snapshots. Moreover, the performance is not affected by the correlation between sources.

Modeling and investigation on the performance enhancement of hovering UAV-based FSO relay optical wireless communication systems under pointing errors and atmospheric turbulence effects

This paper investigates and enhances unmanned aerial vehicle (UAV) relay-assisted free-space optics (FSO) optical wireless communication (OWC) systems under the effects of pointing errors (PEs) and atmospheric turbulences (ATs). The incorporation of UAVs as buffer-aided moving relays in the conventional FSO (CFSO) relay-assisted systems is proposed for enhancing the performance of PEs through AT. Using M-PSK (phase shift keying) and M-QAM (quadrature amplitude modulation), the impact of PEs on transmission quality is evaluated in this work. We evaluate and optimize the symbol error rate, outage probability (OP), and signal-to-noise ratio (SNR) for the UAV-to-ground station-based FSO communications systems. The spatial diversity-based relay-assisted CFSO systems can enhance the performance of the UAV-UAV FSO links. In this paper, a new FSO (NFSO) channel model for the hovering UAV-FSO OWC fluctuations under the PEs, AT effects, jitter, deviation, receiving an error, and wind resistance effects are established. To improve the performance of hovering UAV-based FSO relay OWC systems. We reduce the influence of UAV-FSO OWC fluctuations under PEs and AT effects. By receiving incoherent signals at various locations, the spatial diversity-based relay-assisted NFSO systems can significantly increase the system's redundancy and enhance connection stability. Numerical results show that to achieve a bit-error-rate (BER) of le 10^{ - 5}, the required SNR is ≥ 23 dB when the wind variance of the UAVs sigma_{alpha }^{2} increases from 0 to 7 mrad with FSO link distance L = 2000 m. The required SNR is ≥ 25 dB when the wind variance sigma_{alpha }^{2} is 1 mrad at an OP of 10^{ - 6}. To obtain an average BER of 10^{ - 6}, the SNR should be 16.23 dB, 17.64 dB, and 21.45 dB when sigma_{alpha }^{2} is 0 mrad, 1 mrad, and 2 mrad, respectively. Using 8-PSK modulation without PEs requires 23.5 dB at BER of 10^{ - 8} while 16-QAM without PEs requires 26.5 dB to maintain the same BER of 10^{ - 8}. Compared with 16-QAM without PEs, the SNR gain of 8-PSK without PEs is 3 dB. The results show the relay-assisted UAV-FSO system with five stationary relays can achieve BER 10^{ - 8} at 25 dB SNR in the ideal case and 10^{ - 5} at 27 dB SNR with AT and PE at FSO length 1000 m. The results show the relay UAV-FSO system outperforms the CFSO at the BER and SNR performance. The effects of UAV’FSO s fluctuation increase when the UAV-FSO link length, {text{L}}_{{{text{fso}}}} increases. The results of the weak turbulence achieve better SER compared with MT and ST. The obtained results show that decreasing {text{L}}_{{{text{fso}}}} can compensate for the effects of UAV-FSO link fluctuation on the proposed system. Finally, we investigated the CFSO relay-assisted UAV-FSO system with aided NFSO-UAVs spatial diversity-based relay-based on NFSO OWC and revealed the benefits of the resulting hybrid architecture.

Compartment-based reconstruction of 3D acquisition-weighted 31 P cardiac magnetic resonance spectroscopic imaging at 7 T: A reproducibility study.

Even at 7 T, cardiac 31 P magnetic resonance spectroscopic imaging (MRSI) is fundamentally limited by low signal-to-noise ratio (SNR), leading to long scan times and poor temporal and spatial resolutions. Compartment-based reconstruction algorithms such as magnetic resonance spectroscopy with linear algebraic modeling (SLAM) and spectral localization by imaging (SLIM) may improve SNR or reduce scan time without changes to acquisition. Here, we compare the repeatability and SNR performance of these compartment-based methods, applied to three different acquisition schemes at 7 T. Twelve healthy volunteers were scanned twice. Each scan session consisted of a 6.5-min 3D acquisition-weighted (AW) cardiac 31 P phase encode-based MRSI acquisition and two 6.5-min truncated k-space acquisitions with increased averaging (4 × 4 × 4 central k-space phase encodes and fractional SLAM [fSLAM] optimized k-space phase encodes). Spectra were reconstructed using (i) AW Fourier reconstruction; (ii) AW SLAM; (iii) AW SLIM; (iv) 4 × 4 × 4 SLAM; (v) 4 × 4 × 4 SLIM; and (vi) fSLAM acquisition-reconstruction combinations. The phosphocreatine-to-adenosine triphosphate (PCr/ATP) ratio, the PCr SNR, and spatial response functions were computed, in addition to coefficients of reproducibility and variability. Using the compartment-based reconstruction algorithms with the AW 31 P acquisition resulted in a significant increase in SNR compared with previously published Fourier-based MRSI reconstruction methods while maintaining the measured PCr/ATP ratio and improving interscan reproducibility. The alternative acquisition strategies with truncated k-space performed no better than the common AW approach. Compartment-based spectroscopy approaches provide an attractive reconstruction method for cardiac 31 P spectroscopy at 7 T, improving reproducibility and SNR without the need for a dedicated k-space sampling strategy.

Fast GNSS acquisition algorithm based on SFFT with high noise immunity

The Global Navigation Satellite System(GNSS) has been widely used in various fields. To achieve positioning, the receiver must first lock the satellite signal. This is a complicated and expensive process that consumes a lot of resources of the receiver. For this reason, this paper proposes a new fast acquisition algorithm with High Signal-to-noise ratio(SNR) performance based on sparse fast Fourier transform(HSFFT). The algorithm first replaces the IFFT process of the traditional parallel code phase capture algorithm with inverse sparse fast Fourier transform (ISFFT) with better computing performance, and then uses linear search combined with code phase discrimination to replace the positioning loop and the estimation loop with poor noise immunity in ISFFT. Theoretical analysis and simulation results show that, compared with the existing SFFT parallel code phase capture algorithm, the calculation amount of this algorithm is reduced by 19%, and the SNR performance is improved by about 5dB. Compared with the classic FFT parallel code phase capture algorithm, the calculation amount of the algorithm in this paper is reduced by 43%, and when the capture probability is greater than 95%, the SNR performance of the two is approximately the same.

Microwave photonic radar system with improved SNR performance utilizing optical resonant amplification and random Fourier coefficient waveforms.

A microwave photonic (MWP) radar system with improved signal-to-noise ratio (SNR) performance is proposed and experimentally demonstrated. By improving the SNR of echoes through properly designed radar waveforms and resonant amplification in the optical domain, the proposed radar system can detect and image weak targets that were previously hidden in noise. Echoes with a common low-level SNR obtain high optical gain and the in-band noise is suppressed during resonant amplification. The designed radar waveforms, based on random Fourier coefficients, reduce the effect of optical nonlinearity while providing reconfigurable waveform performance parameters for different scenarios. A series of experiments are developed to verify the feasibility of the SNR improvement of the proposed system. Experimental results show a maximum SNR improvement of 3.6 dB with an optical gain of 28.6 dB for the proposed waveforms over a wide input SNR range. From a comparison with linear frequency modulated signals in microwave imaging of rotating targets, significant quality enhancement is observed. The results confirm the ability of the proposed system to improve SNR performance of MWP radars and its great application potential in SNR-sensitive scenarios.

Actuation Mechanisms Used in MRI-Compatible Robotic Surgeries: A Review

There has been increasing use of robotic surgery over the years, as it has evolved as an improvement over minimally invasive surgery (MIS), where a surgeon can use a tele-manipulator to operate on a patient. Magnetic resonance imaging (MRI) has become the leading form of image acquisition due to its ability to produce high resolution images during robotically assisted MIS. However, the MRI scanner places conditions on the robots that allow only certain compatible actuation methods used in the robotic system to negate large interference that hinders the image quality. The current review focused on the four main MRI-compatible actuation mechanisms: hydraulic, pneumatic, piezoelectric, and shape memory alloy. This review mainly discussed signal-to-noise ratio (SNR) reduction, performance, and limitations from the recent publications on MRI-compatible robotic surgeries. Favorable MRI compatibility with low SNR reduction, performance, and simple implementation was observed to be the most important characteristics of a proper actuation mechanism for MRI robotic surgeries. After reviewing each approach, it was concluded that shape memory alloy, despite having a form of limitation, demonstrated to be more favorable compared to other actuation methods because of factors such as low cost, negligible SNR reduction, and high-power output for medical interventions

Multi-scale interactive network in the application of DAS seismic data processing

Distributed acoustic sensing (DAS) is regarded as a novel acquisition technology for seismic data. Compared with conventional electrical geophones, DAS has a series of obvious advantages including low-cost, high spatial resolution, good coverage, and strong resistance to the harsh environment. Noise attenuation is an essential step in seismic data processing. However, there are two main difficulties faced by the denoising task of DAS seismic data. On the one hand, some background noise in DAS seismic data, such as optical low-frequency noise, horizontal noise, and fading noise, is unique and not presented in the conventional seismic data; on the other hand, the signal-to-noise ratio (SNR) of DAS seismic data is relatively low. Recently, a convolutional neural network (CNN) has shown superior denoising performance compared to the traditional method. To follow this promising trend, we propose a multi-scale interactive convolutional neural network (MSI-Net) and apply it to denoise the challenging DAS seismic data. Different from most of the existing CNN architecture used in seismic data denoising, the MSI-Net considers both coarse-scale and fine-scale features by improving the inherent serial convolution to multi-scale parallel convolution, which is beneficial to recover detailed information. Moreover, we utilize some connections to achieve the information interaction between different scales, which promotes the flow of information and enables the network to extract more informative multi-scale features from the DAS seismic data. Moreover, both synthetic and real examples demonstrate that the proposed MSI-Net can effectively attenuate a variety of unique DAS background noise and also completely recover the weak signals. Compared with conventional CNN architecture, MSI-Net exhibits better performance in global SNR and local details.

Performance Evaluation of Space Receive Diversity Techniques for Massive MIMO System

This paper presents performance evaluation of massive MIMO system in terms of signal to noise ratio (SNR) and system capacity with respect to increasing number of receive antennas at the base station for a wireless communication between a single transmit antenna of user terminal per time. The analysis performed in MATLAB simulation environment revealed that for M = 128, 256, and 512, the SNR improvement in dB achieved using selective combining (SC) was 7.31, 7.85, and 8.34 respectively, whereas for equal gain combining (EGC) and maximal ratio combining (MRC) the SNRs performance in dB were 19.79 and 20.83, 22.95 and 24, and 25.96 dB, and 27 respectively. Generally, the outcomes of simulations have proven that optimizing the number of receive antenna based on selection by changing the number of antennas at base station (BS) can provide improved SNR and system capacity.

Optimizing Optical Attocells Positioning of Indoor Visible Light Communication System

Visible light communication (VLC), which is a prominent emerging solution that complements the radio frequency (RF) technology, exhibits the potential to meet the demands of fifth-generation (5G) and beyond technologies. The random movement of mobile terminals in the indoor environment is a challenge in the VLC system. The model of optical attocells has a critical role in the uniform distribution and the quality of communication links in terms of received power and signal-to-noise ratio (SNR). As such, the optical attocells positions were optimized in this study with a developed try and error (TE) algorithm. The optimized optical attocells were examined and compared with previous models. This novel approach had successfully increased minimum received power from −1.29 to −0.225 dBm, along with enhanced SNR performance by 2.06 dB. The bit error rate (BER) was reduced to and by utilizing OOK-NRZ and BPSK modulation techniques, respectively. The optimized attocells positions displayed better uniform distribution, as both received power and SNR performances improved by 0.45 and 0.026, respectively. As the results of the proposed model are optimal, it is suitable for standard office and room model applications.

Time Domain IRCI-Free Pulse Compression for OQAM-OFDM Radar System

In this article, a novel majorization-minimization (MM)-based time domain pulse compression method (MM-based TDPCM) is proposed for offset quadrature amplitude modulation-based orthogonal frequency division multiplexing (OQAM-OFDM) radar system. We first propose a TDPCM by constructing a general linear model from the time domain with the estimated weights of the OQAM-OFDM signal and solving the model with a whitening operation to complete the pulse compression. The proposed TDPCM greatly reduces the required number of subcarriers for the given range cells and achieves inter range cell interference (IRCI)-free pulse compression. Furthermore, the MM-based TDPCM is proposed to maximize the signal-to-noise ratio (SNR) of the TDPCM. By theoretically analyzing the SNR performance of the TDPCM, we develop an optimization model to design the transmitted weights of the OQAM-OFDM signal. Then, the MM algorithm is proposed to solve the optimization model, realizing the maximum SNR output. Finally, numerical simulation results verify the superior performance of the proposed MM-based TDPCM.

Rapid 3D breath-hold MR cholangiopancreatography using deep learning-constrained compressed sensing reconstruction.

To compare the image quality of three-dimensional breath-hold magnetic resonance cholangiopancreatography with deep learning-based compressed sensing reconstruction (3D DL-CS-MRCP) to those of 3D breath-hold MRCP with compressed sensing (3D CS-MRCP), 3D breath-hold MRCP with gradient and spin-echo (3D GRASE-MRCP) and conventional 2D single-shot breath-hold MRCP (2D MRCP). In total, 102 consecutive patients who underwent MRCP at 3.0 T, including 2D MRCP, 3D GRASE-MRCP, 3D CS-MRCP, and 3D DL-CS-MRCP, were prospectively included. Two radiologists independently analyzed the overall image quality, background suppression, artifacts, and visualization of pancreaticobiliary ducts using a five-point scale. The signal-to-noise ratio (SNR) of the common bile duct (CBD), contrast-to-noise ratio (CNR) of the CBD and liver, and contrast ratio between the periductal tissue and CBD were measured. The Friedman test was performed to compare the four protocols. 3D DL-CS-MRCP resulted in improved SNR and CNR values compared with those in the other three protocols, and better contrast ratio compared with that in 3D CS-MRCP and 3D GRASE-MRCP (all, p < 0.05). Qualitative image analysis showed that 3D DL-CS-MRCP had better performance for second-level intrahepatic ducts and distal main pancreatic ducts compared with 3D CS-MRCP (all, p < 0.05). Compared with 2D MRCP, 3D DL-CS-MRCP demonstrated better performance for the second-order left intrahepatic duct but was inferior in assessing the main pancreatic duct (all, p < 0.05). Moreover, the image quality was significantly higher in 3D DL-CS-MRCP than in 3D GRASE-MRCP. 3D DL-CS-MRCP has superior performance compared with that of 3D CS-MRCP or 3D GRASE-MRCP. Deep learning reconstruction also provides a comparable image quality but with inferior main pancreatic duct compared with that revealed by 2D MRCP. • 3D breath-hold MRCP with deep learning reconstruction (3D DL-CS-MRCP) demonstrated improved image quality compared with that of 3D MRCP with compressed sensing or GRASE. • Compared with 2D MRCP, 3D DL-CS-MRCP had superior performance in SNR and CNR, better visualization of the left second-level intrahepatic bile ducts, and comparable overall image quality, but an inferior main pancreatic duct.

Restoration of Lung Sound Signals Using a Hybrid Wavelet-Based Approach

A unique and ideal integration of wavelet-based total variation (WATV) and empirical Wiener denoising method is proposed in this article to significantly enhance the signal-to-noise ratio (SNR) while preserving the characteristics of a lung sound signal. While individual wavelet-based denoising filters based on a single basis function have been employed in the past, the outcome has been unsatisfactory because only significant (signal) wavelet coefficients are considered for denoising analysis. The new wavelet-based empirical Wiener (WATV-Wiener) hybrid technique, proposed here, takes into account both significant and insignificant (noise) wavelet coefficients of the noisy signal. An intensive analysis of selecting and fine-tuning the WATV-Wiener filter parameters is presented here through the simulation studies. The WATV-Wiener filter applied here onto different 1-D lung sound signals of different noise levels has led to an optimal root mean square error (RMSE) compared with seven other state-of-the-art filters reported in the literature. The optimal parameters achieved through our simulation studies led to a 3–20-dB improvement in SNR, and the average SNR was improved by 4–30 dB in our experiment. We also observed that the WATV-Wiener filter is less sensitive to the variation of SNR values of the input signal. Furthermore, the WATV-Wiener filter obtains similar SNR performance between continuous piecewise signal (wheeze) and noncontinuous piecewise signal (crackle) in both simulation and experimental studies.

Disturbance extracted methods for auxiliary power quality monitor-based voltage sag localization in distribution network

It is of great significance to extract the voltage sag disturbances with high signal-to-noise ratio (SNR) for safety, environmental and economic interests, which can be applied to locate the voltage sag source using the widely deployed auxiliary power quality monitor (αPQM) in distribution network. Two classical disturbance extraction methods (DEM) and the respective advantages, disadvantages and applicable scenarios are summarized. To overcome their shortcomings at the requirements for fault duration and sampling frequency caused by the small storage space of αPQM, three new DEMs with higher SNR are proposed based on the sine wave reconstruction. The extracted disturbances are analyzed in terms of SNR using theoretical signals. Compared with the classical DEMs, the new ones show better SNR performance at sampling frequency, disturbance duration and residual voltage, as well as harmonics and noise. Besides, the test results using the simulation data of the IEEE 34 nodes distribution network show that they outperform classical DEMs in adapting to voltage sag localization algorithms. The sampling frequency of αPQM is recommended to be an integer multiple of the system frequency. In order to adapt αPQM and more DEMs to locate voltage sag, the longest possible steady-state waveforms are suggested to be stored in the case of redundant storage space.