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Conservation voltage reduction (CVR) is a potentially effective and efficient technique for inertia synthesis and frequency support in modern grids comprising power electronics (PE)-based components, aiming to improve dynamic stability. However, due to the complexities of PE-based grids, implementing the CVR methods cannot be performed using traditional techniques as in conventional power systems. Further, quantifying the CVR impacts in modern grids, while focusing on dynamic time scales, is critical, consequently making the traditional methods deficient. This is an important issue as CVR utilization/quantification depends on grid conditions and CVR applications. Considering these concerns, this work offers a thorough analysis of CVR applications, implementation, and quantification strategies, including data-driven AI-based methods in PE-based modern grids. To assess the CVR applications from a new perspective, aiming to choose the proper implementation and quantification techniques, they are divided into categories depending on various time scales. CVR implementation methods are categorized into techniques applied to PE-based grids and islanded microgrids (MGs) where different control systems are adopted. Additionally, to address the evaluation issues in modern grids, CVR quantification techniques, including machine learning- and deep learning-based techniques and online perturbation-based methods are evaluated and divided based on the CVR application. Concerns with the further utilizing and measuring of CVR impacts in modern power systems are discussed in the future trends section, where new research areas are suggested.

The transmission end of large-scale wind power generation bases faces challenges such as high AC-DC coupling strength, low system inertia, and weak voltage support capabilities. Deploying distributed synchronous condensers (SCs) within and around wind farms can effectively provide transient reactive power support, enhance grid system inertia at the transmission end, and improve dynamic frequency support capabilities. However, the high investment and maintenance costs of SCs hinder their large-scale deployment, necessitating the investigation of optimal SC configuration strategies at critical nodes in the transmission grid. Initially, a node inertia model was developed to identify weaknesses in dynamic frequency support, and a critical inertia constraint based on node frequency stability was proposed. Subsequently, a multi-timescale reactive power response model was formulated to quantify the impact on short-circuit ratio improvement and transient overvoltage suppression. Finally, a two-stage optimal configuration strategy for distributed SCs at the transmission end was proposed, considering dynamic frequency support and transient voltage stability. In the first stage, the optimal SC configuration aimed to maximize system inertia improvement per unit investment to meet dynamic frequency support requirements. In the second stage, the configuration results from the first stage were adjusted by incorporating constraints for enhancing the multiple renewable short-circuit ratio (MRSCR) and suppressing transient overvoltage. The proposed model was validated using the feeder grid of a large energy base in western China. The results demonstrate that the optimal configuration scheme effectively suppressed transient overvoltage at the generator end and significantly enhanced the system’s dynamic frequency support strength.

This paper demonstrates the use of a novel virtual synchronous generator (VSG) to provide dynamic frequency support in an autonomous photovoltaic (PV)–diesel hybrid microgrid with an energy storage system (ESS). Due to the lack of enough rotating machines, PV fluctuation might give rise to unacceptable frequency excursions in the microgrid. The VSG entails controlling the voltage-source inverter (VSI) to emulate a virtual inertial and a virtual damping via power injection from/to the ESS. The effect of the VSG on the frequency is investigated. The virtual inertia decreases the maximum frequency deviation (MFD) and the rate of change of frequency (RoCoF), but in exchange for raising the virtual inertia, the system is more oscillating. Meanwhile, raising the virtual damping brings reductions in the amplitude of the oscillations of frequency. However, the dynamic frequency support provided by them is lagging behind. In this regard, an improved VSG based on the differential feedforward of the diesel generator set (DGS) output current is proposed to further mitigate the MFD and the RoCoF. Simulations and experimental results from an autonomous microgrid consisted of a 400 kW DGS, and a 100 kVA VSG are provided to validate the discussion.

This paper investigates virtual inertia control of doubly fed induction generator (DFIG)-based wind turbines to provide dynamic frequency support in the event of abrupt power change. The model and control scheme of the DFIG is analysed. The relationships among the virtual inertia, the rotor speed and the network frequency variation are then investigated. The \"hidden\" kinetic energy that can be released to contribute to the grid inertia by means of shifting the operating point from the maximum power tracking curve to a virtual inertia control curve is investigated. The virtual inertia control strategy based on shifting power tracking curves of the DFIG is proposed and the calculation method for determining these virtual inertia control curves is presented. A three-machine system with 20 percent of wind penetration is used to validate the proposed control strategy. Simulation results show that by the proposed control strategy, DFIG based wind farms have the capability of providing dynamic frequency support to frequency deviation, and thus improving the dynamic frequency performance of the grid with high wind power penetration. (6 pages)

Multi-scale dispersion entropy (MDE 1D ) is an effective nonlinear dynamic tool to characterize the complexity of time series and has been extensively applied to mechanical fault diagnosis. However, with the increase of scale factor, the values of MDE 1D often fluctuate largely, resulting in poor stability. Besides, it only extracts the complexity information from the time domain of vibration signal, while the complexity information in the frequency domain is ignored. To enhance the stability of MDE 1D and extract the complexity characteristics from the time–frequency domain of vibration signal, this paper first develops a two-dimensional multi-scale reverse dispersion entropy (MRDE 2D ), inspired by the MDE 1D and two-dimensional multi-scale dispersion entropy (MDE 2D ) through introducing the “distance information from white noise”. Then a two-dimensional multi-scale time–frequency reverse dispersion entropy (MTFRDE 2D ) combined with time–frequency analysis is proposed. After that, considering that the length of the coarse-grained sequence used in the multi-scale coarse-grained process of MTFRDE 2D will become shorter and shorter with the increase of scale factor, resulting in a loss of potentially useful information, the two-dimensional composite multi-scale time–frequency reverse dispersion entropy (CMTFRDE 2D ) is proposed through using the composite coarse-grained process. The effectiveness and advantages of CMTFRDE 2D algorithm are demonstrated by analyzing different kinds of noise signals. Following that, a new rolling bearing fault diagnosis method is proposed based on the CMTFRDE 2D for feature extraction and gravitational search algorithm optimized support vector machine for mode identification. The proposed fault diagnosis method is employed on two rolling bearing test data sets and also compared with the existing MTFRDE 2D ,- MRDE 2D ,- MDE 2D ,- and MDE 1D -based fault diagnosis methods. The analysis results reveal that the proposed fault diagnosis method can successfully extract the fault information from rolling bearing vibration signals in time–frequency domain and can accurately identify different fault locations and severities of rolling bearings with certain advantages.

The dynamic behavior of the large-pitch screw during turning affects the stability of the cutting process, which in turn impacts the machining quality of the large-pitch screw. The large-pitch screw turning system among the machine tool, cutting tool, and the workpiece is taken as the present research object, and the frequency response function modeling of the large-pitch screw turning process system is carried out. The concept of generalized modal field and generalized stiffness field of large-pitch screw turning process system is introduced. Considering the dynamic change of the whole process system with the change of tool position, the dynamic characteristic information of the processing system is obtained and analyzed and ultimately reflects the inherent properties of the large-pitch screw turning process system and the ability to resist deformation. The cutting stability prediction model based on support vector machines (SVM) is established, and the average prediction error is 5.04%. The artificial bee colony algorithm is used to optimize the cutting parameters, and finally, the optimization method of large-pitch thread cutting stability based on SVM is proposed. This method can reduce the cutting vibration and effectively improve the cutting stability.

A fault diagnosis method of nonlinear analog circuits is proposed that combines the generalized frequency response function (GFRF) and the simplified least squares support vector machine (LSSVM). In this study, the harmonic signal is used as an input to estimate the GFRFs. To improve the estimation accuracy, the GFRFs of an analog circuit are solved directly using time-domain data. The Fourier transform of the time-domain data is avoided. After obtaining the fault features, a multi-fault classifier is designed based on the LSSVM. In order to improve the training speed and reduces storage, a simplified LSSVM model is used to construct the classifier, and the conjugate gradient algorithm is used for training. The fault diagnosis simulation experiment is conducted on a biquad filter circuit to verify the proposed method. The experimental results show that the proposed method has high diagnostic accuracy and short training time.

In this study, analytical solutions are developed for the super-critical vibration of fluid-conveying pipes with elastic supports. By accounting for the geometric nonlinearity, the nonlinear buckling problem is solved and the equilibrium configurations are obtained. Using a coordinate transform, the dynamic model of a super-critical pipe is obtained for the vibrations around the non-trivial equilibrium configuration. The present analytical solutions of interest for the equilibrium configurations, the natural frequencies, the mode shape, and the transient response are obtained by combining the Laplace transform method with the complex mode method. The Laplace transform method is applied to formulate a steady-state Green’s function solution. Using the complex mode methodology, vibration characteristics of the super-critical pipe around the equilibrium configurations are presented. The present analytical solutions can be readily reduced to classical boundary conditions by setting end-spring parameters to zero or infinity. Numerical simulations are conducted to understand the impact mechanisms of the elastic support parameters on the free and forced vibration characteristics. This work offers important guidance for optimization and design of super-critical fluid-conveying pipes with elastic supports for engineering applications.

In view of the shortcomings of traditional hydraulic support system, such as poor mobility and limited range of adjustment, a new configuration with double parallelogram structure is proposed. Combining the kinematic diagram of the mechanism of the new support system and the relevant parameters of the balance moment, using the kinematic vector closed-loop modeling method and D’Alembert’s principle, the relationship equation between the generalized coordinates of the hydraulic support and the geometric position parameters is obtained, and the analysis model with plural-freedom is established. Dynamic response characteristics and natural frequencies of column hydraulic cylinder and balance hydraulic cylinder are analyzed. The results demonstrate that the natural frequencies of the improved balance hydraulic cylinder and column hydraulic cylinder have been greatly improved, and the safety performance of the hydraulic support has been greatly increased.

This article develops a new method to mitigate chatter vibrations in the thin-wall milling of the structures with half-opened side walls through designing a supplementary device, which can provide double-side support to the weakly rigid positions between cutter and workpiece. It aims to improve the stiffness and damping responses of the side walls without the need to consider the limitation of the workpiece’s geometrical configuration. That is, it is suitable for both the flat and the curved shapes. The typical structural characteristic of the device lies in that as the cutting continues, the supporting positions can be easily adjusted up and down along the axial direction to meet the instantaneous chatter mitigation requirement. Dynamic models of both the curved thin-wall milling process and the workpiece with the supplementary support device are derived by integrating the milling mechanics with the receptance coupling substructure analysis. The in-process modal parameters of the workpiece with the support device, i.e., natural frequency and modal shape, are calculated by comprehensively considering the removal of material and the influence of the supporting position change. Finally, a method that combines the derived models with the dynamic response of the spindle-tool system is used to predict the stability lobe diagrams (SLDs), with which chatter vibrations can be well avoided by reasonably selecting the cutting parameters. A series of thin-wall milling experiments are carried out on typical curved plates to validate the effectiveness of the designed support device together with the proposed methods for mitigating chatter vibrations.