Harmonic Detection Research Articles

A new approach for harmonic detection based on eliminating oscillatory coupling effects in microgrids

AbstractThe primary goal of grid‐connected microgrids is to control the active and reactive power, which is reachable by the inner current control loop in the control structure of power converters. However, when facing unbalanced conditions, the inner current control loop implemented in the dq frame does not function properly. In such conditions, the popular current control loop malfunctions since there is an oscillatory coupling between harmonic components. Therefore, in this study, a new harmonic detector based on decoupled double synchronous reference frame within the current control loop is proposed in which the oscillatory coupling between harmonic components is eliminated, and the overall performance of the power converter control system is significantly improved. The performance of the precisely developed mathematical models is verified by Matlab simulations, and the simulation results confirm the accuracy and proper operation of the proposed strategy.

Fast object imaging and classification based on circular harmonic Fourier moment detection.

Limited by the number of illumination fields and the speed of a spatial light modulator, single-pixel imaging (SPI) cannot realize real-time imaging and fast classification of an object. In this paper, we proposed the circular harmonic Fourier single-pixel imaging (CHF-SPI) for the first time to realize fast imaging and classification of objects. The light field distribution satisfies the circular harmonic Fourier formula, and the light intensity values of the single-pixel detector are equivalent to the circular harmonic Fourier moments. Then the target can be reconstructed under low sampling ratio by inverse transformation. Through simulation and experimental verification, clear imaging can be performed at a sampling ratio of 0.9%. In addition, circular harmonic Fourier moments are used to construct multi-distortion invariant to classify objects with rotation and scale change. The scale change multiples of objects can be calculated and the objects can be classified by using 10 light fields. It is of great significance to classify objects quickly without imaging.

Research on improved Harmonic current detection method based on SOGI-PLL instantaneous reactive Power

In view of the traditional instantaneous reactive power detection has low anti-harmonic interference ability, poor real-time performance, and insufficient accuracy when the power grid frequency fluctuates, this paper improved the detection link. The working principle of single-phase harmonic detection is analyzed, and the improved second-order generalized integrator is added to make the phase-locked loop detect the voltage phase accurately and efficiently. Based on the comparison and compensation simulation results of the active compensation device, the quality of grid-connected current of the inverter is improved effectively, and the precision of phase locking is increased.

Voltage Detection-Based Selective Harmonic Current Compensation Strategies for Photovoltaic Inverters

Voltage and current detection-based harmonic current compensation (VDB-HCC and CDB-HCC, respectively) strategies allow the use of photovoltaic (PV) inverters to enhance the grid power quality. CDB-HCC strategies require converter hardware retrofit by inserting an extra current sensor to measure load or downstream grid currents. On the other hand, VDB-HCC strategies are straightforward solutions employing only embedded measurements used for protection, control, and synchronization purposes. This paper provides a comprehensive comparison of three VDB-HCC strategies: VDB-HCC based on a single current loop (SCL); VDB-HCC based on dual parallel current control loops (DCL); and VDB-HCC based on parallel voltage and current control loops (VCL). The comparison includes the requirement of harmonic detection algorithms, PV array maximum power point tracking (MPPT) performance, stability over grid impedance variations and inverter power rating, steady-state harmonic compensation, enabling transient response, and computational burden. Experimental results using a 1.5-kW commercial PV inverter are conducted. Based on distributed HCC applications by PV systems, VCL has shown the best performance in terms of voltage distortion reduction under weak grids.

Sensitive detection of electric field-induced second harmonic signals.

We demonstrate sensitive electric field measurements by coherent homodyne amplification of the electric field induced second harmonic generation (E-FISH) technique. In the process of E-FISH, an applied electric field breaks the centrosymmetry of an otherwise homogeneous medium, in turn promoting the generation of the second harmonic frequency of an incident field. Due to weak third-order hyperpolarizability and the requirement of an applied field to break the symmetry, the E-FISH technique has been mainly used to study high fields, also requiring a strong optical field and sensitive detection. Here we superimpose the E-FISH signal with an auxiliary beam, also termed a local oscillator (LO), at double the incident frequency. Coherent superposition of the LO and the E-FISH output (LOE-FISH) allows for a homodyne amplification of the otherwise weak nonlinear signal. We have demonstrated an increase of signal-to-noise by a factor of seven, which results in a measurement time reduction of a factor of 49. This technique, LOE-FISH, has a number of advantages: detection with intensified detectors is not required. Furthermore, instead of millijoule pulsed lasers, we can work with microjoule pulsed lasers, which allows measuring at repetition rates of megahertz and opens single shot and real-time capability. The LOE-FISH technique increases in sensitivity at lower electric field values. Our work is a demonstration of the principle. Already with our first results from the demonstration, one can see the high potential of LOE-FISH.

Ultra-sensitive terahertz introduced harmonic emission towards terahertz detection

The sixth-generation (6G) technology has garnered significant interest from researchers due to its improved spectral and energy efficiency. However, a major challenge lies in the strong attenuation of terahertz waves in the air, necessitating the development of ultra-sensitive detectors. In recent years, there has been gradual progress in the development of harmonic signal detection methods, offering a new solution for terahertz wave detection with a low detection threshold and high sensitivity. In our study, we proposed a highly sensitive detection approach involving a 1040 nm femtosecond laser and broadband terahertz waves incident on SnSe2 nanosheets with a few layers, obtained through mechanical exfoliation. Utilizing confocal microscopy, the distinct harmonic wave can be observed, with the intensity of the harmonic signal varying based on the polarization angle difference between the laser and terahertz waves. Our estimation suggests that this detection sensitivity could be around ten times higher than that of existing detectors. This approach, which involves THz-induced harmonic generation without the need for high voltage or intense THz/laser exposure, holds immense potential for weak signal detection applications and carries significant importance for the advancement of 6G technology.

Sensor Fusion for Power Line Sensitive Monitoring and Load State Estimation.

This paper deals with a specific approach to fault detection in transformer systems using the extended Kalman filter (EKF). Specific faults are investigated in power lines where a transformer is connected and only the primary electrical quantities, input voltage, and current are measured. Faults can occur in either the primary or secondary winding of the transformer. Two EKFs are proposed for fault detection. The first EKF estimates the voltage, current, and electrical load resistance of the secondary winding using measurements of the primary winding. The model of the transformer used is known as mutual inductance. For a short circuit in the secondary winding, the observer generates a signal indicating a fault. The second EKF is designed for harmonic detection and estimates the amplitude and frequency of the primary winding voltage. This contribution focuses on mathematical methods useful for galvanic decoupled soft sensing and fault detection. Moreover, the contribution emphasizes how EKF observers play a key role in the context of sensor fusion, which is characterized by merging multiple lines of information in an accurate conceptualization of data and their reconciliation with the measurements. Simulations demonstrate the efficiency of the fault detection using EKF observers.

Viscoelastic testing: Critical appraisal of new methodologies and current literature.

United States Food and Drug Administration (FDA)-approved viscoelastic testing (VET) methodologies have significantly changed in the last 10 years, with the availability of cartridge-based VET. Some of these cartridge-based methodologies use harmonic resonance-based clot detection. While VET has always allowed for the evaluation of real-time clot formation, cartridge-based VET provides increased ease of use as well as greater portability and robustness of results in out-of-laboratory environments. Here we review the use of VET in a variety of clinical contexts, including cardiac surgery, trauma, liver transplant, obstetrics, and hypercoagulable states such as COVID-19. As of now, high quality randomized trial evidence for new generation VET (TEG 6s, HemoSonics Quantra, ROTEM sigma) is limited. Nevertheless, the use of VET-guided transfusion algorithms appears to result in reduced blood usage without worsening of patient outcomes. Future work comparing the new generation VET instruments and continuing to validate clinically important cut-offs will help move the field of point-of-care coagulation monitoring forward and increase the quality of transfusion management in bleeding patients.

Inverter Controller with Synthetic Inertia and Adaptive Harmonic Damping Based on Fourier Linear Combiners

This paper proposes an inverter control strategy that combines virtual synchronous generator control with harmonic detection based on Fourier linear combiners with adaptive gain. In the proposed strategy, the virtual synchronous generator control calculates the current in the fundamental frequency component, responsible for the control inertial characteristic, and harmonic voltage detector based on Fourier linear combiners selectively detects the harmonic voltages to be damped. The 5th harmonic order adaptive gain is calculated from voltage and current measurements on a bus of interest in the system. Real-time simulation results were developed in a hardware-in-the-loop test-bench to show that the proposed control can simultaneously mitigate the grid voltage and current harmonics and reduce the peaks of frequency oscillations after transients.

Highly Sensitive Detection of Microstructure Variation Using a Thickness Resonant Transducer and Pulse-Echo Third Harmonic Generation.

In nonlinear ultrasound testing, the relative nonlinear parameter is conveniently measured as a sensitive means of detecting and imaging overall variation of microstructures and damages. Compared to the quadratic nonlinear parameter (β'), the cubic nonlinear parameter (γ'), calculated as the third harmonic amplitude divided by the cube of the fundamental amplitude, has generally a higher value, providing better sensitivity in nonlinear parameter mapping. Since the third harmonic amplitude is about two orders of magnitude lower than the fundamental amplitude, efficient excitation and highly sensitive reception of third harmonic is very important. In this paper, we explore an odd harmonic thickness resonant transducer that meets the requirements for pulse-echo third harmonic generation (THG) measurements. We also address the problem of source nonlinearity that may be present in the measured amplitude of the third harmonic and propose a method to properly correct it. First, we measure γ' for a series of aluminum specimens using the through-transmission method to observe the behavior of γ' as a function of specimen thickness and input voltage, and examine the effects of various corrections such as attenuation, diffraction and source nonlinearity. Next, we apply the odd harmonic resonant transducer to pulse-echo THG measurements of precipitation heat-treated specimens. It is shown that such transducer is very effective in generation and detection of fundamental and third harmonics under finite amplitude toneburst excitation. The highly sensitive detectability of γ' are presented as a function of aging time, and the sensitivity of γ' is compared with that of β' and β'2.

A Harmonic and Interharmonic Detection Method for Power Systems Based on Enhanced SVD and the Prony Algorithm

To address the problem of harmonic pollution in power systems, a harmonic and interharmonic detection method based on the adaptive order and the dominant factor algorithms is proposed. The proposed method greatly improves the accuracy and precision of harmonic detection, overcoming the notorious problem of high sensitivity to noise of the traditional Prony algorithm that often leads to unsatisfactory detection results. In the proposed method, the “adaptive order determination” algorithm is first used to determine the optimal order of Singular Value Decomposition (SVD) denoising, resulting in a more accurate distinction between signal and noise components. Then, signal reconstruction is carried out to effectively remove noise components to enhance the denoising ability of SVD. This mitigates the Prony algorithm’s high sensitivity to noise and greatly reduces the amplitude of false components in the fitting results. Finally, the dominant factor algorithm is applied to accurately screen out the non-false components in Prony’s fitting results. Simulation results show that the proposed method can effectively reduce signal noise in different noise environments with noise intensities ranging from 5 dB to 30 dB, achieving an average signal-to-noise ratio improvement of around 20 dB. Meanwhile, the identification and screening results of harmonic and interharmonic components in the signal are accurate and reliable, with detection errors in amplitude, frequency, and phase at around 0.5%, 0.01%, and 0.5%, respectively. Overall, the proposed method is well suited for detecting harmonics and interharmonics in power systems under various noise environments.

Cost-effective detection system of cross-site scripting attacks using hybrid learning approach

Cross-Site Scripting (XSS) attacks inject malicious code payloads into web application logs, triggering stored cross-site scripting execution when accessing the view-logs interface. The destruction produced by the XSS injection susceptibilities is especially significant since the attacker can steal sensitive data such as the stored user's cookies and tokens or control the host remotely by using remote code execution of XSS. For example, if an attacker manages to obtain the cookies of the website administrator, the whole website can be taken over. In this paper, we develop and evaluate the performance of a machine-learning-based XSS detection system for website applications. Particularly, we investigate using three supervised machine learning: optimizable k-nearest neighbours, optimizable naïve bays, and hybrid (ensemble) learning of decision trees. To validate the system's efficacy, we employed the XSS-Attacks-2019 dataset consisting of modern real-world traffic-subjected types of classes normal (benign) or anomaly (XSS attack). To verify the performance evaluation, we have used several conventional metrics, including the confusion matrix analysis, the detection accuracy, the detection precision, the detection sensitivity, the harmonic detection means, and the detection time. The experimental results demonstrated the predominance of the hybrid learning-based XSS detection system. The best performance indicators peaked at 99.8% (accuracy, precision, and sensitivity) with a very short detection time of 103.1 μSec. Conclusively, the proposed hybrid model outpaced several recent XSS-attacks detection systems in the same study area.

Differential quartz-enhanced photoacoustic spectroscopy

In this paper, a differential quartz-enhanced photoacoustic spectroscopy (D-QEPAS) sensor is reported. The differential photoacoustic cell (PAC) was used to generate the photoacoustic effect. Two quartz tuning forks (QTFs) with a quality factor (Q) up to 10 000 were used as the acoustic wave transducers. The signal of D-QEPAS sensor was doubly enhanced by the differential characteristic of differential PAC and resonant response of QTF. The background noise was suppressed based on the differential principle. With the help of the finite element method, the acoustic field characteristics were simulated and calculated. Wavelength modulation spectroscopy technique and second harmonic (2f) detection technique were applied to detect photoacoustic signal. Trace acetylene (C2H2) gas detection was performed to verify the D-QEPAS sensor performance. The 2f signal amplitude of differential mode was 116.03 μV, which had a 1.65 times improvement compared with the 2f signal amplitudes of QTF1. When the integration time was 334 s, the minimum detection limit of D-QEPAS sensor was about 496.7 ppb. The reported D-QEPAS provides a development and idea for the widely reported QEPAS technique.

Torque Ripple Reduction of PMSM Based on Modified DBN-DNN Surrogate Model

Due to the nonlinearity and dead time of the inverter, a great number of harmonic components is introduced into the motor input current, causing the torque ripple of the motor. The paper aims to suppress the torque ripple of a low-speed and high-torque permanent magnet synchronous motor (PMSM) with a two-step stator voltage optimization method, including a high precision harmonic voltage injection (HPHVC) method and a deep belief network (DBN)-deep neural network (DNN) voltage prediction model. Firstly, a closed-loop harmonic current detection system with Type-II Chebyshev filter is adopted to enhance the harmonic current extraction accuracy. Secondly, a harmonic voltage algorithm is designed considering the dq-axis current coupling relationship, which improves the accuracy of harmonic voltage calculation. Thirdly, a dq-axis voltage prediction model is constructed using DBN-DNN algorithm as the surrogate model, which further reduces the current harmonic components caused by the harmonic injection structure and the PI control algorithm, reduces the torque pulsation of the motor, and boosts the dynamic performance of the motor. Finally, the experimental platform is constructed by embedding the proposed strategy into the field oriented control system for reducing the torque pulsation. The experimental results verify the proposed strategy.

Rough signal processing of AC power intelligent sensor under the background of smart grid

In the rough signal processing of AC intelligent sensor, the effective value and initial phase of voltage/current determine the test accuracy. To improve the harmonic detection and compensation performance of the existing APF and promote the improvement of power grid power quality. The direct positioning method is used as the comparison method, and the error LMS method is proposed to obtain and test the voltage and current signals of intelligent sensors. The simulation results of error LMS method show that the accuracy of voltage RMS and initial phase value calculated by method 1 increases with the increase of the number of sampling points, while the accuracy of voltage RMS of method 2 and method 3 does not change significantly. The results of correlation analysis method show that the test accuracy of the proposed method is 1/2–1/3 of the direct definition method when the amplitude of interference noise signal is 5%, 10% and 15%. Compared with the direct definition method, the rough signal processing technology has lower sampling amount and higher test accuracy, which helps to simplify the system and save the overhead cost.

Laser Linewidth Analysis and Filtering/Fitting Algorithms for Improved TDLAS-Based Optical Gas Sensor.

Tunable Diode Laser Absorption Spectroscopy (TDLAS) has been widely applied in in situ and real-time monitoring of trace gas concentrations. In this paper, an advanced TDLAS-based optical gas sensing system with laser linewidth analysis and filtering/fitting algorithms is proposed and experimentally demonstrated. The linewidth of the laser pulse spectrum is innovatively considered and analyzed in the harmonic detection of the TDLAS model. The adaptive Variational Mode Decomposition-Savitzky Golay (VMD-SG) filtering algorithm is developed to process the raw data and could significantly eliminate the background noise variance by about 31% and signal jitters by about 12.5%. Furthermore, the Radial Basis Function (RBF) neural network is also incorporated and applied to improve the fitting accuracy of the gas sensor. Compared with traditional linear fitting or least squares method (LSM), the RBF neural network brings along the enhanced fitting accuracy within a large dynamic range, achieving an absolute error of below 50 ppmv (about 0.6%) for the maximum 8000 ppmv methane. The proposed technique in this paper is universal and compatible with TDLAS-based gas sensors without hardware modification, allowing direct improvement and optimization for current optical gas sensors.

Adaptive PI + VPI Harmonic Current Compensation Strategy under Weak Grid Conditions

With the increase of the penetration rate of renewable energy power generation in the power system, the power grid gradually tends to be weak. Under the scenario of nonlinear load connecting to weak power grid, the grid-side voltage will produce unbalance, distortion and frequency deviation due to the influence of large internal impedance, insufficient inertia, three-phase current unbalance and harmonics of weak power grid. The operation of active power filters (APF) under above conditions can lead to significant degradation in harmonic compensation performance. An adaptive proportional integral (PI) + vector PI (VPI) harmonic compensation strategy for a three-phase APF is proposed for use in complex conditions of weak grid. The strategy consists of an adaptive notch filter based synchronous phase-locked loop (ANF-PLL) and an adaptive PI + VPI current control method. In this strategy, a series of adaptive lattice notch filters are introduced to improve the accuracy of grid phase and frequency estimation under the condition of voltage unbalance, distortion and a large range of frequency deviation. Then the phase and frequency information are used in the Park transformation of harmonic detection and current compensation control and the update of PI + VPI current control law parameters. A VPI current tracking controller with adaptive resonant frequency is constructed, which can improve the gain of the resonant frequency point and ensure that the APF can maintain the best compensation performance under the above weak grid conditions. Finally, the simulation and experimental results indicate that the ANF-PLL shows a better phase and frequency estimation performance under complex conditions of weak grid than the conventional and notch filter based methods, and the harmonic compensation strategy presented in this paper achieves a significant performance improvement under complex conditions of weak grid compared with Fixed VPI/SRF-PLL strategy and adaptive VPI/NF-PLL strategy.

The effect of laser repetition rate in second harmonic generation imaging and intensity detection

Here we revealed that lasers with lower repetition rates (5 kHz and 50 kHz) from an amplifier were more efficient in SHG imaging than a laser with high repetition rate (80 MHz) from an oscillator, demonstrating that peak power played a more important role than the repetition rate for high SHG yield. Although lasers with relatively high peak power damaged the cancer cells used in the imaging at a relatively lower laser power, better signal/noise ratio was achieved before the occurrence of the adverse damage. It was also revealed that photobleaching was avoided with the lower repetition rate lasers. Furthermore, the three lasers were applied in analyzing the scattered SHG intensity from colloidal BaTiO3 nanoparticles using photon counting method. All tests with the three lasers reached satisfactory results. Importantly, the lasers with low repetition rates showed great potential in the detection of weak nonlinear scattering.

Harmonics and intermodulation products-based fuzzy logic (HIPBFL) algorithm for vital sign frequency estimation using a UWB radar

The ultra-wide band (UWB) radar can achieve non-contact measurement of human vital sign including respiration and heartbeat. This can be applied in the fields of healthcare, security and search-rescue mission. At present, most studies adopt the relationship between harmonics and fundamental frequency to estimate the frequency of vital sign. In this work, the harmonics and intermodulation products-based fuzzy logic (HIPBFL) algorithm for vital sign frequency estimation is proposed. For the first time, to the best of our knowledge, intermodulation products information is considered to locate possible heart rates. Then a fuzzy logic system is designed to select the optimal heart rate from them. 120 s measurement experiment on human beings was carried out at the hospital. Compared with the commonly used chirp Z-transform and moving target indicator (CZT-MTI), the harmonic multiple loop detection (HMLD) and the variational mode decomposition (VMD), the proposed HIPBFL algorithm obviously improves the estimation accuracy of heart rate. This work paves a way for the investigation of non-contact vital signs monitoring via UWB radars.

Recognition of Whistle-like Signals Based on FM Harmonic Characteristics

While human hearing can easily identify whistle-like sounds made by dolphins and whales, manually identifying them from the increasingly variety and quantity of underwater acoustic signals (such as other marine life sounds and underwater acoustic communication signals) is undoubtedly laborious. To solve this question, a recognition method based on the frequency modulated feature and harmonic characteristic is analyzed in this paper. A harmonic energy analysis algorithm is proposed and is compared to a harmonic detection method. The experimental results prove that the proposed method could identify the whistle-like sounds with an accuracy over 90% and the traditional one could only reach 50%. The recognition method may be applied to the investigation of marine biological habits and the monitoring of marine ecological environment, and can also potentially be further applied to the detection of bionic communication signal.