Intrinsic time decomposition based differential protection with adaptive threshold for UPFC compensated transmission line

In the presence of nonlinear response created by power electronics-based compensators, reliable fault detection and classification by distance protection relays is a major concern. The unified power flow controller (UPFC) has a dynamic characteristics that causes stability and protection issues. A intrinsic time decomposition (ITD) based strategy is proposed for addressing this issue. A differential energy based detection index computed using ITD and adaptive thresholding technique is employed such that unerring fault detection is achieved wherein faulty phases of a UPFC compensated transmission line are well pointed out. Various fault and non-fault cases considering critical power system conditions are analysed for power systems with varying configurations modelled using EMTDC/PSCAD. A comparison of the current detection method to recently proposed techniques reveals the benefits and feasibility of the presented detection strategy, which has been proved to be accurate and efficient.

Research on compound fault diagnosis of rolling bearing based on intrinsic time scale decomposition and information fusion

In view of the difficulty to effectively extract compound faults of rolling bearing from aero-engine and precisely identify their types, the paper has proposed a method integrating signal separation algorithm and information fusion. Firstly, the method decomposes the vibration acceleration signals collected by sensors from different positions at the same moment based on intrinsic time scale decomposition algorithm. Secondly, cross correlation analysis is given to the proper rotation component (PRC) of the same layer, which are obtained after decomposition and correspond to the sensors from different positions and cross-correlation function is introduced to embody information fusion. Thirdly, signals are reconstructed according to cross-correlation function of each PRC. Finally, based on the frequency spectrum of reconstructed signal, extract the characteristics of rolling bearing and identify the type of faults under different sensor combinations and multiple compound fault types. The result shows, the proposed method can effectively extract the characteristics of compound faults of bearing and precisely identify the type of faults under different sensor combinations and multiple compound fault types of rolling bearing.

A new method to identify rotor–stator rubbing positions based on intrinsic time scale decomposition

To effectively identify the rotor–stator rubbing positions in aero-engine, the paper has proposed the combination of intrinsic time-scale decomposition (ITD) and classification algorithm. Regarding that with larger noise component in proper rotation component (PRC) signals after ITD, it will be more difficult to extract the characteristic information of rubbing faults, the PRC correspondings to the largest noise was eliminated. Meanwhile, signals were reconstructed based on residual proper rotation components, and positions of rubbing faults were identified according to the reconstructed signal. As rubbing extent and other factors cannot be completely the same in each rubbing, energy of reconstructed signal has been normalized to reduce the difference. Normalized energy indexes were inputted into classification algorithm as feature vectors to identify the positions of rubbing faults. To identify the superiority of approach, a comparison has been made between the proposed approach and the method of directly extracting normalized energy indexes of acceleration signals. The result of comparison shows that the two methods both work well in the identification rate of training and test samples; as for the identification rate for an unknown sample, the proposed method is superior to the other, with identification rate increasing by 17% and 9.4%.

Faults Detection and Classification under Variable Condition Using Intrinsic Time - Scale Decomposition and Neural Network

Misalignment and unbalance are a common fault occurring in the rotor system. A new approach for detecting misalignment and unbalance problems combining the intrinsic time - scale decomposition (ITD), the root mean square (RMS) and perceptron multilayer network (MLP) is proposed in this paper. Vibration signals of normal condition, misalignment horizontal, misalignment vertical and unbalance with different level are collected under different speed. ITD, nonlinear analysis of signals, was applied to decompose the vibration signals into 8 proper rotation components. The RMS values of 8 components are calculated and using as features vector. Last, the perceptron multilayer network was used for fault identification and classification. The proposed approach accurately classified and detection of unbalance and misalignment; the average accuracy achieved is 97.99%.

Symplectic Quantization II: Dynamics of Space–Time Quantum Fluctuations and the Cosmological Constant

The symplectic quantization scheme proposed for matter scalar fields in the companion paper (Gradenigo and Livi, arXiv:2101.02125, 2021) is generalized here to the case of space–time quantum fluctuations. That is, we present a new formalism to frame the quantum gravity problem. Inspired by the stochastic quantization approach to gravity, symplectic quantization considers an explicit dependence of the metric tensor $$g_{\mu \nu }$$ g μ ν on an additional time variable, named intrinsic time at variance with the coordinate time of relativity, from which it is different. The physical meaning of intrinsic time, which is truly a parameter and not a coordinate, is to label the sequence of $$g_{\mu \nu }$$ g μ ν quantum fluctuations at a given point of the four-dimensional space–time continuum. For this reason symplectic quantization necessarily incorporates a new degree of freedom, the derivative $${\dot{g}}_{\mu \nu }$$ g ˙ μ ν of the metric field with respect to intrinsic time, corresponding to the conjugated momentum $$\pi _{\mu \nu }$$ π μ ν . Our proposal is to describe the quantum fluctuations of gravity by means of a symplectic dynamics generated by a generalized action functional $${\mathcal {A}}[g_{\mu \nu },\pi _{\mu \nu }] = {\mathcal {K}}[g_{\mu \nu },\pi _{\mu \nu }] - S[g_{\mu \nu }]$$ A [ g μ ν , π μ ν ] = K [ g μ ν , π μ ν ] - S [ g μ ν ] , playing formally the role of a Hamilton function, where $$S[g_{\mu \nu }]$$ S [ g μ ν ] is the standard Einstein–Hilbert action while $${\mathcal {K}}[g_{\mu \nu },\pi _{\mu \nu }]$$ K [ g μ ν , π μ ν ] is a new term including the kinetic degrees of freedom of the field. Such an action allows us to define an ensemble for the quantum fluctuations of $$g_{\mu \nu }$$ g μ ν analogous to the microcanonical one in statistical mechanics, with the only difference that in the present case one has conservation of the generalized action $${\mathcal {A}}[g_{\mu \nu },\pi _{\mu \nu }]$$ A [ g μ ν , π μ ν ] and not of energy. Since the Einstein–Hilbert action $$S[g_{\mu \nu }]$$ S [ g μ ν ] plays the role of a potential term in the new pseudo-Hamiltonian formalism, it can fluctuate along the symplectic action-preserving dynamics. These fluctuations are the quantum fluctuations of $$g_{\mu \nu }$$ g μ ν . Finally, we show how the standard path-integral approach to gravity can be obtained as an approximation of the symplectic quantization approach. By doing so we explain how the integration over the conjugated momentum field $$\pi _{\mu \nu }$$ π μ ν gives rise to a cosmological constant term in the path-integral approach.