On-line Stability Research Articles

A Semi-Supervised Learning Framework Based on EMD and SGAN for Online Stability Monitoring of DC Power Electronic Systems

A Semi-Supervised Learning Framework Based on EMD and SGAN for Online Stability Monitoring of DC Power Electronic Systems

Resolving the BWR Stability Paradox of Space-Dependent Decay Ratios

Noise signals obtained from local power range monitors and average power range monitors are routinely used for extracting stability information for boiling water reactors. The stability parameters of decay ratio (DR) and natural frequency are produced by signal processing algorithms. While theoretically a dynamical system like a reactor core composed of coherently coupled components possesses a unique DR, noise measurements from different detectors have been reported in several published works to produce different DRs, creating the impression that a DR is not unique at a given operating state but rather is space dependent. This paper is an attempt to reconcile theory with measurements and resolve the space-dependent DR paradox that was encountered afresh in the course of designing a new high-fidelity online stability monitor. As such, the issue of space dependence could not be overlooked as attributable to variability within the uncertainty of noise analysis algorithms.

A Comprehensive Stability Assessment System for EV DC Charging Station Based on Multitimescale Impedance Model

The existing all-in-one impedance-based stability analysis only qualitatively reveal the instability factors by the graphic Nyquist criterion while the stability margin is not considered. In this article, a design-oriented multiattribute and multidimensional based comprehensive assessment system (MAMD-CAS) is proposed to evaluate the stability quantitatively and guide the parameter design as well as ensure the desired dynamic response and stability margin. This MAMD-CAS mainly includes the loop-gain-based multi-timescale impedance estimation to analyze the low-frequency oscillation (LFO), phase margin (PM) calculation of different timescales and dynamic performance indicators as the time-domain quantitative expression of impedance estimation. In detail, the multi-timescale impedance model is established and its loop-gain representation is deduced to implement the impedance estimation. Combined with the known PM, the influence of the parameters on dynamic performance and stability margin can be comprehensively evaluated, then the conflict between dynamic performance and stability margin is explored. It is found that the voltage-loop parameters do not have the coupling problem between dynamic performance and stability margin while <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">J</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">vir</sub> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">svr</sub> present a conflicting effect between the inertia-loop LFO dynamic performance and stability margin and their interaction affects the severity of inertia-loop LFO. The appropriate control parameters can be thus selected for a good stability margin while suppressing the impedance peak. This MAMD-CAS analysis result is verified by simulation and experiment and this assessment system provides theoretical fundamental for the online stability monitor of multi-timescale power electronics system.

Robust Power System Stability Assessment Against Adversarial Machine Learning-Based Cyberattacks via Online Purification

The increasing complexity associated with renewable generation brings more challenges to power system stability assessment (SA). Data-driven approaches based on machine learning (ML) techniques for stability assessment have received significant research interest and shown their promising performance. However, ML-based models are recognized to be vulnerable to adversarial disturbances, where a slight perturbation to power system measurements could lead to unacceptable errors. To address this issue, this paper develops a novel lightweight mitigation strategy, i.e., robust online stability assessment (ROSA), to enhance the ML-based assessment model against both white-box and the black-box adversarial disturbances (i.e., purification) in the online implementation. The ROSA involves a supervised learning-based module for the primary stability assessment and a self-supervised learning-based module. The two modules are trained jointly with different objective (loss) functions and implemented in sequence. A suitable purification objective and various time-series data augmentation methods are designed for SA applications to tackle adversarial disturbances adaptively. Case studies are performed, and the comparative results have clearly illustrated the competitive, robust accuracy against various adversarial scenarios and verified the effectiveness of the proposed online purification strategy.

Comparing different stabilization strategies for reduced order modeling of viscoelastic fluid flow problems

This paper presents a computational framework for model order reduction of viscoelastic fluid flows, with a particular focus on the stabilization of the reduced order model (ROM) and the efficient approximation of non-linear terms appearing in the governing equations. We compare three different stabilization approaches, two of which are based on offline stabilization only and one that uses both offline and online stabilization. Two main techniques for the treatment of non-linear terms are examined and compared: the discrete empirical interpolation method, and a reduced quadrature method based on a sparse recovery technique. Numerical experiments are conducted on two benchmark flows such as the flow past a sphere and the 4:1 contraction flow problem. We show that significant speedups can be achieved using offline streamline upwind Petrov–Galerkin stabilization together with hyper-reduction. We also discuss the potential accuracy losses of the ROM that could result from mesh-related issues of the full-order model (FOM) and propose possible remedies.

Delay-dependent stability of load frequency control with adjustable computation accuracy and complexity

Cyber–physical power systems (CPPS) utilize wide-area measurement system, where communication networks employed in the control loop introduce time delays inevitably. The existing studies focus on either computation accuracy or efficiency for investigating the delay-dependent stability of large-scale delayed CPPS. Whereas, the requirements for calculation accuracy and efficiency vary from the demand for online stability analysis and offline controller design. This paper investigates the demand-oriented stability of the delayed CPPS and takes load frequency control (LFC) systems as typical examples. Novel regulation schemes have been proposed with adjustable conservatism and computation complexity. Different from existing studies, this paper establishes regulated stability criteria by constructing variable Lyapunov–Krasovskii functionals and using compatible integral inequalities for estimations. Then, an algorithm is designed by introducing a threshold parameter to calculate accurate delay margins or to realize high computation efficiency flexibly. Case studies are complemented on the two-area LFC scheme and IEEE 39-bus system. It is illustrated that the proposed method can realize on-demand adjustments for real power systems, i.e., setting the threshold parameter to zero achieves almost accurate delay margins like the frequency-domain method, which guides the offline controller design with desired performance; less computation complexity is achieved with a relatively large threshold parameter, and thus, the proposed method is applicable for online stability analysis.

Physics-Informed Neural Network Based Online Impedance Identification of Voltage Source Converters

The wide integration of voltage source converters (VSCs) in power grids as the interface of renewables causes the converter-grid interaction stability challenge. The black-box impedance of VSCs identified at the converter terminal is the key to facilitate the study of converter-grid interaction stability. However, since the limited impedance data amount in online measurement, the existing impedance identification methods cannot accurately capture characteristics of the impedance model in various operating scenarios with the changing profiles of renewables and loads. In this article, a physics-informed neural network based impedance identification is proposed to fill this research gap. The physics knowledge of the VSC is used to compress the artificial neural network, which can reduce the calculation burden of online impedance identification. Meanwhile, the two-steps impedance identification is developed with the inspiration of the transfer learning theory to further increase the online impedance identification efficiency. This method can significantly reduce the required data amount used in online impedance identification for the online stability analysis with the changing operating points. The case studies confirm the effectiveness of the proposed method.

A stall diagnosis method based on entropy feature identification in axial compressors

AbstractA stall diagnosis method based on the entropy feature extraction algorithm is developed in axial compressors. The reliability of the proposed method is determined and a parametric sensitivity analysis is experimentally conducted for two different types of compressor stall diagnoses. A collection of time‐resolved pressure sensors is mounted circumferentially and along the chord direction to measure the dynamic pressure on the casing. Results show that the stall and prestall precursor embedded in the dynamic pressures are identified through nonlinear feature perturbation extraction using the entropy feature extraction algorithm. Further analysis demonstrates that the prestall precursor with the peak entropy value is related to the unsteady tip leakage flow for the spike‐type stall diagnosis. The modal wave inception with increasing amplitude is identified by the considerable increase of the entropy value. The flow field in the tip region indicates that the modal wave corresponds to the flow separation in the suction side of the rotor blade. The warning time is 100–300 rotor revolutions for both types of stall diagnoses, which is beneficial for stall control in different axial compressors. Moreover, a parametric study of the embedding dimension m, similar tolerance n, similar radius r, and data length N in the fuzzy entropy method is conducted to determine the optimal parameter setting for stall diagnosis. The stall warning based on the entropy feature extraction algorithm provides a new stall diagnosis approach in the axial compressor with different stall types. This stall warning can also be adopted as an online stability monitoring index when using the concept of active stall control.

Real-Time Digital Video Stabilization of Bioinspired Robotic Fish Using Estimation-and-Prediction Framework

The rhythmic movement of bioinspired robotic fish brings about undesirable visual jitter. Note that the unstable camera path of this kind of robot is characterized by obvious regularity and predictability, of which traditional stabilization methods have not made full advantage. This article proposes a novel estimation-and-prediction framework for real-time digital video stabilization of bioinspired robotic fish. First, based on the attitude information of an inertial measurement unit (IMU), a camera–IMU model is established, where the homography transformation with eight degrees of freedom (DOFs) is reduced to translation transformation with two DOFs. Second, traditional optical flow and gray projection methods as well as a novel translation estimation network are employed to estimate the translations between consecutive frames. Third, a lightweight long short-term memory (LSTM) network is constructed, allowing remarkable prediction and smoothing of the camera path. Finally, aquatic experiments under various scenarios are conducted on a manta-inspired robot, demonstrating the effectiveness of the proposed method. Specifically, compared with the state-of-the-art commercial offline stabilization software, our online stabilization algorithm achieves approximate visual stability and remarkably faster stabilization speed. The obtained results shed light on visual sensing and control applications of bioinspired underwater vehicles.

Artificial Neural Network-Based Pole-Tracking Method for Online Stabilization Control of Grid-Tied VSC

To cope with the weak grid stability issue of grid-tied voltage source converters (VSCs), this article proposes an artificial neural network (ANN) based approach for online stabilization control of the grid-tied VSC with the pole-tracking feature. First, an ANN is adopted to establish the mapping between the control parameters and the closed-loop poles of the grid-VSC system, serving as a computationally light model surrogate that is favorable for real-time control applications. Then, an online parameter search algorithm enabling simultaneous tuning of multiple controllers and parameters is developed, by which the system's poles under a new grid condition can be pulled to the reference ones, i.e., achieving the pole-tracking-based stabilization control of this article. Finally, the efficacy of the proposed method along with its stabilization effect is verified by MATLAB simulations and experimental results.

An iterative learning control with learnable band extension for the nanopositioning stage

PurposeDue to the advantages of fast response, high positioning precision and large stiffness, the piezoelectric-actuated nanopositioning stage is widely used in the micro/nanomachining fields. However, due to the inherent nonlinear hysteresis of the piezoelectric-actuator, the positioning accuracy of nanopositioning stage is greatly degraded. Besides, the nanopositioning stage is always performed with repetitive trajectories as the reference signals in applications, which makes the hysteresis behavior periodic. To this end, an adaptive resonance suppression iterative learning control (ARS-ILC) is proposed to address the hysteresis effect. With this effort, the positioning accuracy of the nanopositioning stage is improved.Design/methodology/approachThe hysteresis behavior is identified by the Prandtl–Ishlinskii model. By establishing a convergence function, it is demonstrated that the learnable band of ILC is restricted by the lightly damping resonance of nanopositioning stage. Then, an adaptive notch filter (ANF) with constrained poles and zeros is adopted to suppress the resonant peak. Finally, online stability supervision (OSS) is used to ensure that the estimated frequency converges to the resonant frequency.FindingsA series of experiments were carried out in the nanopositioning stage, and the results validated that the OSS is available to ensure the convergence of the ANF. Furthermore, the learnable band was extended via ARS-ILC; thus, the hysteresis behavior of nanopositioning stage has been canceled.Originality/valueDue to high accuracy and easy implementation, the ARS-ILC can be used in not only nanopositioning stage control but other fabrication process control with repetitive motion.

Adaptive Multi-Parameter-Tuning for Online Stabilization Control of Grid-Tied VSC: An Artificial Neural Network-Based Method

Voltage Source Converter (VSC) for grid integration of renewable energies are prone to have small-signal stability issues when connected to weak AC grids. Such stability issues largely arise from the lack of VSC control adaptivity to the varying grid condition (e.g., grid impedance). To address this issue, this letter presents an adaptive multi-parameter tuning method using the Artificial Neural Network. Innovative aspect of the proposal lies in that it enables the VSC to simultaneously tune multiple controller parameters online, which brings about a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pole-tracking</i> -based stabilization control feature for the VSC. Experimental results demonstrate that the proposed method can effectively and adaptively stabilize the VSC when the grid impedance is varied.

Synchrophasor-Based Online Transient Stability Assessment Using Regression Models

An online post-fault transient stability assessment method is proposed in this study using synchrophasor or PMU measurements. Initially, a post-fault multimachine system is converted into a suitable one machine infinite bus (OMIB) system using the single machine equivalent (SIME) method. Thus, the - trajectory obtained through the OMIB system enabled a normalized transient stability index to be calculated offline. By using synchrophasor measurements before and during the fault as inputs, the regression model can be trained offline to predict the normalized stability margins. Following a fault, the synchrophasor measurements are used as input to this trained model for online stability margin prediction. If the predicted margin is negative, then the post-fault power system is indicated to be unstable. Alternatively, positive values for the predicted margin identify the system as stable. The proposed assessment method is verified using the New England (NE) 39 bus test system. The results obtained are then compared with offline simulations.

Time-Adaptive Transient Stability Assessment Based on the Gating Spatiotemporal Graph Neural Network and Gated Recurrent Unit

With the continuous expansion of the UHV AC/DC interconnection scale, online, high-precision, and fast transient stability assessment (TSA) is very important for the safe operation of power grids. In this study, a transient stability assessment method based on the gating spatiotemporal graph neural network (GSTGNN) is proposed. A time-adaptive method is used to improve the accuracy and speed of transient stability assessment. First, in order to reduce the impact of dynamic topology on TSA after fault removal, GSTGNN is used to extract and fuse the key features of topology and attribute information of adjacent nodes to learn the spatial data correlation and improve the evaluation accuracy. Then, the extracted features are input into the gated recurrent unit (GRU) to learn the correlation of data at each time. Fast and accurate evaluation results are output from the stability threshold. At the same time, in order to avoid the influence of the quality of training samples, an improved weighted cross entropy loss function with the K-nearest neighbor (KNN) idea is used to deal with the unbalanced training samples. Through the analysis of an example, it is proved from the data visualization that the TSA method can effectively improve the assessment accuracy and shorten the assessment time.

Online stability boundary drifting prediction in milling process: An incremental learning approach

The machining stability is an essential prerequisite for achieving required dimensional accuracy and high-quality surface of the machined parts. The stability boundary will drift with the changing machining dynamic characteristics due to tool wear, cutting temperature increase, etc. The online milling stability prediction is of great significance for improving machining efficiency and surface quality. In this article, we focus on the online monitoring of the position and shape of the stability boundary while milling titanium alloy wherein severe tool wear occurs. The machine learning binary classification algorithm is introduced to update the stability boundary of the machining process. The difficulties lie in: (a) identifying changes of the stability boundary with high sensitivity, (b) providing a high generalization capability to accommodate different characteristics of chatter state, (c) reducing the repetitive learning during the model updating and improving real-time performance. To tackle these challenges, a label-propagation-comparison-based incremental sequential minimal optimization (LPC-ISMO) model is proposed for stability boundary drift prediction. During the tool wear process, the stability domain change is treated as concept drift. The stability boundary updating is considered as a decision boundary training from support vector machine (SVM). The LPC-ISMO method is used to identify whether the stability boundary needs to be updated first, and if needed then update the boundary in the variation directions of the stability domain. Results show that the accuracy of the proposed approach is above 85%, which indicates that we provide a new method for online prediction of stability domain.

From classroom training to e-learning: a journey through the quality of learning life of nurse students in post-graduate education - A longitudinal qualitative study.

Background and aim:recent studies regarding COVID-19 experiences of nursing students highlighted the effect of the transition from face-to-face to online education, rather than the complexity of the overall quality of educational life. This study aim investigating of how the students perceive the quality of educational life in the forced online training, searching for any shift of meanings concerning the students learning experience, from the first phase of the sudden transition to online and the online stabilization phase.Methods:a longitudinal qualitative study, carried during two moments of the online teaching activity forced by COVID-19, the first one in May-June 2020 and the second six months later in January -February 2021. A convenience sample of 24 students attending post-graduate courses for health professions recruited at University of Parma, answered in-depth interviews, videotaped, verbatim transcribed and analyzed using the Braun and Clarke model.Results:five themes emerged from meaning shift of data collection: reactions to change in educational life; factors hindering the perception of the quality of educational life; strategies to facilitate communication and lack of the classroom; factors favoring a new quality of educational life; adaptation strategies to the new educational life.Conclusions:participants perceive advantages of online teaching, on quality of their educational life. The issue of how to create opportunities for internship period remains open. Further research to understand online internship and exploring what extent it is essential to propose it in face-to-face modality. (www.actabiomedica.it)

Sparse Signal Reconstruction on Fixed and Adaptive Supervised Dictionary Learning for Transient Stability Assessment

Fixed and adaptive supervised dictionary learning (SDL) is proposed in this paper for wide-area stability assessment. Single and hybrid fixed structures are developed based on impulse dictionary (ID), discrete Haar transform (DHT), discrete cosine transform (DCT), discrete sine transform (DST), and discrete wavelet transform (DWT) for sparse features extraction and online transient stability prediction. The fixed structures performance is compared with that obtained from transient K-singular value decomposition (TK-SVD) implemented while adding a stability status term to the optimization problem. Stable and unstable dictionary learning are designed based on datasets recorded by simulating thousands of contingencies with varying faults, load, and generator switching on the IEEE 68-bus test system. This separate supervised learning of stable and unstable scenarios allows determining root mean square error (RMSE), useful for online stability status assessment of new scenarios. With respect to the RMSE performance metric in signal reconstruction-based stability prediction, the present analysis demonstrates that [DWT], [DHT|DWT] and [DST|DHT|DCT] are better stability descriptors compared to K-SVD, [DHT], [DCT], [DCT|DWT], [DHT|DCT], [ID|DCT|DST], and [DWT|DHT|DCT] on test datasets. However, the K-SVD approach is faster to execute in both off-line training and real-time playback while yielding satisfactory accuracy in transient stability prediction (i.e., 7.5-cycles decision window after fault-clearing).

Training at asymptote stabilizes motor memories by reducing intracortical excitation

Learning similar motor skills in close succession is limited by interference, a phenomenon that takes place early after acquisition when motor memories are unstable. Interference can be bidirectional, as the first memory can be disrupted by the second (retrograde interference), or the second memory can be disrupted by the first (anterograde interference). The heightened plastic state of primary motor cortex after learning is thought to underlie interference, as unstable motor memories compete for neural resources. While time-dependent consolidation processes reduce interference, the passage of time (~6 h) required for memory stabilization limits our capacity to learn multiple motor skills at once. Here, we demonstrate in humans that prolonged training at asymptote of an initial motor skill reduces both retrograde and anterograde interference when a second motor skill is acquired in close succession. Neurophysiological assessments via transcranial magnetic stimulation reflect this online stabilization process. Specifically, excitatory neurotransmission in primary motor cortex increased after short training and decreased after prolonged training at performance asymptote. Of note, this reduction in intracortical excitation after prolonged training was proportional to better skill retention the following day. Importantly, these neurophysiological effects were not observed after motor practice without learning or after a temporal delay. Together, these findings indicate that prolonged training at asymptote improves the capacity to learn multiple motor skills in close succession, and that downregulation of excitatory neurotransmission in primary motor cortex may be a marker of online motor memory stabilization.

Impact of converter interfaced generators on power system long-term voltage stability monitoring and control

This paper investigates the impact of power-electronic Converter Interfaced Generators (CIG) connected at the distribution network on bulk power system long-term voltage stability. In particular, the CIG effect on instability onset, on-line stability monitoring and protection against voltage collapse is examined using an IEEE Test System properly modified to include significant CIG penetration. Several simulations are executed to evaluate the CIG impact on instability onset and determine the performance of a voltage instability monitoring method, as well as of three protection scheme variants. Different CIG connection topologies, as well as reactive control modes are examined. Conclusions drawn show significant impact of CIGs and demonstrate the need for specific countermeasures design that account for CIG interaction with traditional protection measures.

Online Stability Investigations of Platinum Electrodes in Nonaqueous Media

In the process of developing novel electrocatalysts, the focus mainly lies on finding highly active and selective materials. The stability of electrodes is often underestimated, even though a poor stability is inextricably related to economic losses. Fundamental understanding of electrode dissolution processes in nonaqueous media is not only crucial for applications like nonaqueous battery systems, sensors, capacitors but also for organic electrosynthesis. The reasons for electrode degradation are among others anodic dissolution, cathodic dissolution or cathodic corrosion. Enhanced knowledge of dissolution mechanisms, especially understanding the influence of factors like composition or structure of electrodes, electrolytes and temperature, is important to optimize the operation conditions of reactors, and with that limit electrode degradation and increase the lifetime of devices and improve processes.Coupling electrochemical measurements with an inductively coupled plasma mass spectrometer (ICP-MS), allows online stability studies and gives valuable insights into degradation mechanisms of different electrodes and catalyst materials, which are relevant in various applications. We have developed an electroanalytical flow cell (EFC) coupled to an ICP-MS, which is suitable for the use in aggressive nonaqueous electrolytes. Furthermore, the EFC is operated in an argon filled glovebox, which allows otherwise unattainably strict control of experimental conditions, like the water content of electrolytes [1,2].In order to demonstrate the capabilities of this novel method, dissolution of platinum as an ideal model system will be depicted first: this metal has excellent electrocatalytic activity and somewhat falsely supposed inertness. It is widely used in various fields of applications and its behavior in aqueous media is fairly well known. New aspects of its stability in different nonaqueous electrolytes are unveiled together with dependences on the chemical properties of the solvents and the conducting salts. While the dissolution behavior of completely water-free protic and aprotic electrolytes is very similar, the dissolution behavior changes markedly upon temperature variation or in presence of trace amounts of water.Different platinum dissolution profiles in several nonaqueous electrolytes will be discussed and dissolution mechanisms will be proposed, based on studies with changing water content. The dissolution of this electrode material has been followed during a running electrosynthesis. The correlation of possible reaction intermediates with the stability will be illustrated for a Kolbe electrolysis.Literature:[1] J. Ranninger, et al., Electrochem. Comm., 2020, 114 (106702), [2] J. Ranninger, et al., J. Electrochem. Soc., 2020, 167 (121507)