Current Signature Research Articles

A Current Differential Protection Scheme for Distribution Networks with Inverter-Interfaced Distributed Generators Considering Delay Behaviors of Sequence Component Extractors

The high-level proliferation of inverter-interfaced distributed generators (IIDGs) in modern distribution networks (DNs) has changed system topologies and fault current signatures, which compromises the protective relays in DNs. Investigating IIDG fault behaviors-based protection scheme will benefit the grid’s safety and stability. This paper proposes a novel current differential protection (CDP) scheme that considers the delay behaviors of positive- and negative-sequence component extractors for IIDGs in DNs. A frequency-domain analytical model of the fault current for a grid-connected IIDG with the PQ control strategy and a low-voltage ride-through (LVRT) capability is investigated. The dynamic behavior of the IIDGs considering the sequence-component extractor based on the Pade approximation is presented, where the T/4 delay extractor of the IIDGs causes a two-stage behavior in the fault transient process. It is found that a 5 ms error between the measured and actual values after the fault will affect the transient characteristics of the IIDGs. The transient current generated by the IIDGs during grid faults contains a large number of low-order harmonic components within the range of 0–200 Hz, which is significantly different to the current provided by the power grid. Therefore, the proposed CDP scheme uses protective relays at both terminals to obtain the required transient electric quantity using the Prony method. By constructing the frequency-characteristics ratio (FCR) and the exchanging FCR between two terminal relays, the developed protection criteria are implemented. The accuracy of the fault analysis method, whose maximum computational error is below 0.1%, and the feasibility of the proposed protection scheme are demonstrated by using a 10 kV DN in a PSCAD/EMTDC simulation, which can be applied to various fault conditions and traditional DNs without IIDGs.

Explainable offline automatic signature verifier to support forensic handwriting examiners

Signature verification is a critical task in many applications, including forensic science, legal judgments, and financial markets. However, current signature verification systems are often difficult to explain, which can limit their acceptance in these applications. In this paper, we propose a novel explainable offline automatic signature verifier (ASV) to support forensic handwriting examiners. Our ASV is based on a universal background model (UBM) constructed from offline signature images. It allows us to assign a questioned signature to the UBM and to a reference set of known signatures using simple distance measures. This makes it possible to explain the verifier’s decision in a way that is understandable to non-experts. We evaluated our ASV on publicly available databases and found that it achieves competitive performance with state-of-the-art ASVs, even when challenging 1 versus 1 comparisons are considered. Our results demonstrate that it is possible to develop an explainable ASV that is also competitive in terms of performance. We believe that our ASV has the potential to improve the acceptance of signature verification in critical applications such as forensic science and legal judgments.

Wear Detection of Extruder Elements Based on Current Signature by Means of a Continuous Wavelet Transform

Assessing the wear of components in a single-screw extruder and its condition during the process is difficult. In this context, wavelet analysis was used to investigate the wear condition of extruder elements, which yielded data on current waveforms obtained from 1 kHz frequency converters. To date, no tests of this type have been conducted on single-screw food extruders, which further emphasizes the relevance of the research undertaken by the authors. Experimental tests have been conducted to verify the hypothesis that it is possible to assess the level of wear of the working elements of an extruder by monitoring the variations in the frequencies on the current spectrum using wavelet analysis tools. The root mean square (RMS) values of the current were compared for two configurations of the working elements of the device, i.e., new and used. Observation of the frequency variations of the current spectrum values using wavelet analysis tools can provide valuable information on the technical condition of the working elements of an industrial extruder. Therefore, they can indicate the need for prompt replacement of friction elements in order to improve the efficiency and performance of the machine.

Identity-based proxy blind signature scheme on lattice in high-speed network environment

The methods of network attacks are becoming more and more diverse In high-speed networks, and protecting private data is a key issue for users’ security. Proxy blind signature technology combines proxy and blind signature technology to achieve signature transitivity and message confidentiality. However, current signature schemes will not be secure in quantum environments. Meanwhile, the certificate storage of the existing scheme is relatively complicated. Therefore, to avoid the need for certificates and resist quantum attacks, this paper proposes an anti-quantum identity-based proxy blind signature (ID-Proxy-BS) scheme on lattice. This ID-Proxy-BS scheme improves the efficiency of signature through Gaussian distribution and sampling techniques. Security analysis shows that the scheme satisfies correctness, efficiency, blindness, and unforgeability.

Differentiating EDRs From the Background Magnetopause Current Sheet: A Statistical Study

AbstractThe solar wind is a continuous outflow of charged particles from the Sun's atmosphere into the solar system. At Earth, the solar wind's outward pressure is balanced by the Earth's magnetic field in a boundary layer known as the magnetopause. Plasma density and temperature differences across the boundary layer generate the Chapman‐Ferraro current which supports the magnetopause. Along the dayside magnetopause, magnetic reconnection can occur in electron diffusion regions (EDRs) embedded into the larger ion diffusion regions (IDRs). These diffusion regions form when opposing magnetic field lines in the solar wind and Earth's magnetic field merge, releasing magnetic energy into the surrounding plasma. While previous studies have given us a general understanding of the structure of the diffusion regions, we still do not have a good grasp of how they are statistically differentiated from the non‐diffusion region magnetopause. By investigating 251 magnetopause crossings from NASA's Magnetospheric Multiscale (MMS) Mission, we demonstrate that EDR magnetopause crossings show current densities an order of magnitude higher than regular magnetopause crossings—crossings that either passed through the reconnection exhausts or through the non‐reconnecting magnetopause, providing a baseline for the magnetopause current sheet under a wide range of driving conditions. Significant current signatures parallel to the local magnetic field in EDR crossings are also identified, which is in contrast to the dominantly perpendicular current found in the regular magnetopause. Additionally, we show that the ion velocity along the magnetopause is highly correlated with a crossing's location, indicating the presence of magnetosheath flows inside the magnetopause.

Analysis Optimization and Comparison to Detect Failures in the Squirrel-Cage Rotor using High-Level Wavelets

The methods and tools used for signal analysis extracted from the induction motors, such as the motor current signature analysis (MCSA) used for data collection on a non-invasive basis, the multi-resolution analysis (MRA) and discrete wavelet transform (DWT), are efficient tools for the signal analysis at different levels or resolutions, these tools have been applied together to improve detection of failures in the rotor of induction motors in condition of no-load. This work focuses on the study of rotor cage end ring, in a condition with lower-load or no-load where uncertainty predominates, this area of study is complicated to analyze correctly with conventional methods, but in these circumstances, the analysis using TDW has better performance. The article presents an alternative way of detecting failures in three phases induction motors in no-load state method with an optimized method and a comparison between results of the analysis with two different levels of the high-order Wavelet Daubechies, studying and evaluating its performance for the detection of broken ring, all this supported with a specific signal pre-processing and post-processing to improve the results of detection in incipient faults.

Acoustic signature of strong deep-water currents in the vicinity of the New England Seamounts

This paper presents some initial findings from the New England Seamount Acoustics (NESMA) Pilot experiment, focusing on the data obtained in April—June 2023 with three moored autonomous noise recorders (MANRs). Two MANRs were each equipped with a tilt current meter (TCM). Unexpectedly strong near-bottom currents were observed at depths of 2500m and 4443m, with the flow speeds reaching 110 cm/s and 80 cm/s, respectively. At 2500 m, recorded current speeds exceeded 20 cm/s approximately 5% of the time throughout the entire eight-week measurement period. Collocated ambient sound measurements revealed episodic large increases in the low-frequency noise intensity. A strong correlation is found between the noise intensity in the 2–20 Hz band and the measured current speed. As expected for flow noise, the intensity and the characteristic frequency of the power spectrum peaks increase with the increasing current speed. Observations of the flow noise by the MANR not equipped with a TCM indicate the presence and timing of strong current events and allow for an estimate of the current speeds at that location. The possible origins of the strong near-bottom currents and the flow noise effect on operation of deep-water acoustic systems will be discussed.

Comprehensive prognostic signatures in thyroid cancer: A summarized review for molecular signatures construction strategies

Thyroid carcinoma (TC) is one of the most common endocrine carcinomas with an increasing rate of morbidity in recent decades. With a high risk of relapse and metastasis occurring in TC patients, it is essential to identify potential prognostic signatures for TC patients. Here, through a comprehensive review, we summarized 45 prognostic signatures for TC patients and concluded three main strategies for signature establishment after an extensive investigation. In particular, these signatures were classified according to different construction strategies, and the verification methods were summarized. Besides, we found that 18 key genes were overrepresented in reported signatures. This review provides a comprehensive understanding, systematic summary, and integrated analysis of current prognostic signatures of TC, which may help researchers to further understand cancer progression, construct prognostic signatures of TC, and guide future clinical treatment.

Translocation of linearized full-length proteins through an engineered nanopore under opposing electrophoretic force.

Nanopores have recently been used to identify and fingerprint proteins. However, because proteins, unlike DNA, do not have a uniform charge, the electrophoretic force cannot in general be used to translocate or linearize them. Here we show that the introduction of sets of charges in the lumen of the CytK nanopore spaced by ~1 nm creates an electroosmotic flow that induces the unidirectional transport of unstructured natural polypeptides against a strong electrophoretic force. Molecular dynamics simulations indicate that this electroosmotic-dominated force has a strength of ~20 pN at -100 mV, which is similar to the electric force on single-stranded DNA. Unfolded polypeptides produce current signatures as they traverse the nanopore, which may be used to identify proteins. This approach can be used to translocate and stretch proteins for enzymatic and non-enzymatic protein identification and sequencing.

Extracting Broken-Rotor-Bar Fault Signature of Varying-Speed Induction Motors

An effective way to detect broken-bar faults of squirrel-cage induction motors is to extract the characteristic frequency component in the stator current as a fault signature, or so-called motor current signature analysis (MCSA). However, for inverter-fed motor drive systems, the motor is typically operating under varying-speed, varying-load, and noisy environments, which makes the fault signature extraction a very challenging problem. In this paper, we propose a sparsity-driven and graph-based method to extract the fault signature effectively, where the fault signature is modeled as a sparse component in the frequency domain for each short-time window measurement while gradually changing from window to window in the time-domain. Compared to the conventional short-time Fourier transform-based method, our method is more robust to noise and varying speed operations. Experiments are carried out to demonstrate the effectiveness of the proposed method.

Fault Detection of Rotor Bars in Inverter-Fed Induction Motors Based on Current Signature Analysis Technique

Induction motors, which are one key piece of equipment for power plants, waterworks facilities, and factories, must be maintained appropriately for reliable operation. A motor current signature analysis (MCSA) technique, which monitors and detects problems in motors and diagnoses devices, has already been marketed by some companies. Recently, applications of inverter-fed motors have increased for greater energy conservation. However, a fault-detection method in inverter-fed motors has been inadequately studied despite the risk of misdetection due to inverter noise. This paper shows our results that detected a broken rotor bar in an inverter-fed motor based on a MCSA technique. An abnormal motor with a broken rotor bar and a normal motor are driven by an inverter. The current supplied to both motors is measured and the frequency spectra results are compared. In the measurements, the inverter’s drive frequency is varied from 120 to 10 Hz in 10 Hz increments. In each drive frequency, the slip is varied in a range of 0-5% by adjusting the load connected to the motor. The results of comparing the current spectra show significant reinforcement of the signal intensities in abnormal motors. Some signals are reinforced by both inverter noise and a component that originated in the broken bar. The superposition that may lead to a misdiagnosis in inverter-fed motors is avoided by identifying the normal spectrum shapes of the target equipment in advance.

Robust Inter-Turn Short-Circuit Fault Detection in PMSGs With Respect to the Bandwidths of Current and Voltage Controllers

Reliable inter-turn short-circuit (ITSC) fault detection is essential for permanent magnet synchronous generators (PMSGs). The harmonics in machine currents and voltages are affected by the bandwidths of dual-loop controllers, which may lead to the failure of ITSC fault detection based on the current or voltage signature analysis. In this paper, a robust method for ITSC fault detection is proposed to deal with this problem. First, the mathematical model of PMSG with ITSC fault is established. Then, the behaviors of the second order harmonics in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dq</i> -axis currents, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dq</i> -axis voltages and dc-link voltage with respect to the bandwidths of current and voltage controllers are investigated. After that, a reliable detection method based on two fault detection indicators and adaptive to the bandwidth changes of dual-loop controllers is proposed. The first indicator is simple and easy when only the bandwidth of current controller changes, and the other one weights and sums the second harmonics in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</i> -axis current, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</i> -axis voltage and dc-link voltage to ensure the robustness and improve the accuracy of ITSC detection when both the bandwidths of current and voltage controllers change. Finally, the effectiveness of proposed method is validated by experiments.

Experimental and simulation investigation for rotor bar fault diagnosis in closed-loop induction motors drives

This research presents a comparative analysis of two broken rotor bar (BRB) fault identification techniques for closed-loop induction motors (IMs). Both motor current signal analysis and Hilbert transform (HT) rely on spectrum analysis by means of fast fourier transform (FFT). Both approaches have shown their ability to identify BRBs under varying loads. In contrast, the HT is deemed more efficient than the motor current signature analysis (MCSA) approach when the motor is working without load. To maintain a high-performance speed control and to compensate for BRBs effect on the mechanical speed, the approach of control used is direct torque control (DTC). Utilizing a real-time implementation in MATLAB/Simulink with the real-time interface (RTI) based on the dSPACE 1104 board, the efficacy of the two techniques was evaluated.

Experimental and simulation investigation for rotor bar fault diagnosis in closed-loop induction motors drives

This research presents a comparative analysis of two broken rotor bar (BRB) fault identification techniques for closed-loop induction motors (IMs). Both motor current signal analysis and Hilbert transform (HT) rely on spectrum analysis by means of fast fourier transform (FFT). Both approaches have shown their ability to identify BRBs under varying loads. In contrast, the HT is deemed more efficient than the motor current signature analysis (MCSA) approach when the motor is working without load. To maintain a high-performance speed control and to compensate for BRBs effect on the mechanical speed, the approach of control used is direct torque control (DTC). Utilizing a real-time implementation in MATLAB/Simulink with the real-time interface (RTI) based on the dSPACE 1104 board, the efficacy of the two techniques was evaluated.

Inverter-Fed Motor Drive System: A Systematic Analysis of Condition Monitoring and Practical Diagnostic Techniques

Due to their efficiency and control capabilities, induction motors fed with inverters have become prevalent in various industrial applications. However, ensuring the reliable operation of the motor and diagnosing faults on time are crucial for preventing unexpected failures and minimizing downtime. This paper systematically analyzes condition monitoring and practical diagnostic techniques for inverter-fed motor drive systems. This study encompasses a thorough evaluation of different methods used for condition monitoring and diagnostics of induction motors, with the most crucial faults in their stator, rotor, bearings, eccentricity, shaft currents, and partial discharges. It also includes an assessment of their applicability. The presented analysis includes a focus on the challenges associated with inverter-fed systems, such as high-frequency harmonics, common-mode voltages causing the bearing currents, and high voltage gradients (dv/dt) due to fast switching frequency, which can impact the motor operation, as well as its faults analysis. Furthermore, this research explores the usefulness and efficiency of various available diagnostic methods, such as motor current signature analysis and other useful analyses using advanced signal processing techniques. This study aims to present findings that provide valuable insights for developing comprehensive condition monitoring strategies, and practical diagnostic techniques that enable proactive maintenance, enhanced system performance, and improved operational reliability of inverter-fed motor drive systems.

A Novel Mode Un-Mixing Approach in Variational Mode Decomposition for Fault Detection in Wound Rotor Induction Machines

Condition monitoring of induction machines (IMs) with the aim of increasing the machine’s lifetime, improving the efficiency and reducing the maintenance cost is necessary and inevitable. Among different types of methods presented for mechanical and electrical fault tracing in induction machines, stator current signature analysis has attracted great attention in recent decades. This popularity is mainly due to the non-invasive nature of this technique. A non-recursive method named variational mode decomposition (VMD) is used for the decomposition of any signal into several intrinsic mode functions (IMFs). This technique can be employed for detection of faulty components in a current signature. However, mode mixing of extracted IMFs makes the mechanical and electrical fault detection of IMs complicated, especially in the case where fault indices emerge close to the supply frequency. To achieve this, we rectify the signal of stator current prior to applying VMD. The main advantage of the presented approach is allowing the fault indices to be properly demodulated from the main frequency to avoid mode mixing phenomenon. The method shows that the dominant frequencies of the current signal can be isolated in each IMFs, appropriately. The proposed strategy is validated to detect the rotor asymmetric fault (RAF) in a wound rotor induction machine (WRIM), in both transient and steady-state conditions.

Electrical Signature Analysis for Open-Circuit Faults Detection of Inverter With Various Disturbances in Distribution Grid

This paper proposes an electrical signature analysis method for open-circuit faults (OCFs) detection of inverter with various disturbances in distribution grid. According to the fault mechanism, the fundamental value, rated harmonics and direct current component of three-phase currents are used as fault electrical signatures. The signatures are estimated by unscented Kalman filter (UKF) and recognized by extreme learning machine (ELM) for fault detection. Both OCF of single switch and OCFs of multiple switches are tested with consideration of direct disturbances such as load change, overcurrent and bias current, and indirect disturbances such as grid frequency variation, background harmonics and unbalanced voltage dip. The simulations and experiments show that the OCF detection of the new method is still accurate even with these disturbances, and reveal that only the signatures of the faulty phase current are immune to the disturbances while the ones of unfaulty phases are not. The robustness when facing the various disturbances and all explainable detection results make the new method suitable and effective for OCF of inverter detection in complicated distribution grid environment.

Asymmetrical Fault Detection in Induction Motors Through Elimination of Load Torque Oscillations Effects in the Slight Speed Variations and Steady-State Conditions

Recently, motor current signature analysis (MCSA) has been widely considered for fault detections due to its simplicity, availability, and cost-effectiveness. The rotor asymmetry fault (RAF) is the most common fault that occurs in induction machines. It is extremely challenging to detect RAF indices occurred at the vicinity of the supply frequency. Moreover, load torque oscillations (LTOs) <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">which</b> appear as a sinusoidal phase modulation of the supply frequency, exhibit similar indications as the RAF, especially in gearbox-based electromechanical systems. Since LTOs lead to false alarm of RAF, separation of LTOs indices from RAF characteristic components is inevitable. In this paper, normalized frequency domain energy operator (NFDEO) is evaluated for demodulation and separation of RAF and LTOs in steady-state and transient conditions. Moreover, the proposed method is tested in both with-and-without speed control strategies. In this regard, the effects of RAF in the presence of LTOs investigated through both synthetic signals and experimental data in steady-state and transient conditions with slight variation in rotor speed. The obtained results show that NFDEO along with Winger-Ville can effectively detect RAF in the presence of LTOs even with speed control strategy.

V-belt fault detection using Extended Park Vector Approach (EPVA) in centrifugal fan driven by an induction motor

Monitoring is a crucial part of an overall process of controlling and supervising systems, as it aims to detect, locate and diagnose faults that may affect their operational safety, performance, and reliability. One of our primary objectives is to develop an effective monitoring tool for transmission systems that use electrical measurements, particularly stator currents, which are already available in machine control devices. In this paper, we propose a dynamic model for a V-belt transmission system to demonstrate the impact of faults on the driving induction motor. Subsequently, we discuss two diagnostic techniques based on spectral analysis of the stator currents: Motor Current Signature Analysis (MCSA), and the Extended Park Vector Approach (EPVA). To evaluate the effectiveness of these techniques, we introduce a side-cut-off fault by 5 cm on the belt. The results of both theoretical and experimental studies revealed that the EPVA exhibits a greater richness in characteristic frequencies associated with belt faults compared to the MCSA method. In addition, EPVA demonstrates higher sensitivity to V-belt defects as evidenced by a large difference in frequency amplitude between the normal and the defect states.

A Novel High Impedance Fault Detection Strategy for Microgrid Based on Differential Energy Signal of Current Signatures and Entropy Estimation

The non-linearly deterministic fault current characteristics with low current magnitude of high impedance fault (HIF) have thrown an inevitable challenge to the microgrid protection system. Moreover, differentiating HIF from non-fault events is also a challenging task under different operating scenarios of the microgrid. In order to address this, a simple and fast protection algorithm, based on Empirical Mode Decomposition (EMD) in conjugation with Teager-Kaiser Energy Operator (TKEO) is proposed in this article. This scheme process the current signals retrieved from both ends of the line through EMD, and subsequently, the current signal energy is extracted by TKEO. The incidence of a fault is affirmed by measuring the energy difference signal and estimation of entropy. By employing the second layer algorithm using entropy analysis, the non-fault disturbances, such as capacitor and load switching, temporary overloading, and DG outage, have been effectively distinguished from HIFs. The proposed technique is tested for grid-tied and autonomous microgrid operation modes with several HIF and non-fault cases. It is inferred that the proposed technique can detect and differentiate HIFs from other system perturbations seamlessly in less time. Compared to other methods, this scheme has procured excellent accuracy and reliability, and is also not affected by the dynamic current behavior of the microgrid. The PSCAD-EMTDC software is used to build and simulate the test system, and the MATLAB programming interface is utilized to develop and test the suggested method.