IMPROVED COVARIANCE DRIVEN BLIND SUBSPACE IDENTIFICATION METHOD

An improved covariance driven subspace identification method is presented to identify the weakly excited modes. In this method, the traditional Hankel matrix is replaced by a reformed one to enhance the identifiability of weak characteristics. The robustness of eigenparameter estimation to noise contamination is reinforced by the improved Hankel matrix, in combination with component energy index （CEI） which indicates the vibration intensity of signal components, an alternative stabilization diagram is adopted to effectively separate spurious and physical modes. Simulation of a vibration system of multiple-degree-of-freedom and experiment of a frame structure subject to wind excitation are presented to demonstrate the improvement of the proposed blind method. The performance of this blind method is assessed in terms of its capability in extracting the weak modes as well as the accuracy of estimated parameters. The results have shown that the proposed blind method gives a better estimation of the weak modes from response signals of small signal to noise ratio （SNR）and gives a reliable separation of spurious and physical estimates.

Using Array Methods to Identify and Process Weak Signals Generated by the Brightlight(Ⅰ) 50t Explosion

Waveforms generated by the 50t explosion of project Brightlight （ I ） were recorded by HILR array. Using array techniques, the author performed identification, f-k analysis, velocity spectrum analysis, etc. of the weak signals. The analysis results show that the signal-to-noise ratio after beamforming was obviously enhanced, and the signal could be clearly shown. The energy from this explosion was mainly concentrated in the 1 -8Hz range from f-k analysis. The velocity spectrum gave clear positions of event phases, which could not be seen in the original weak signals. The maximum energy distribution obtained by the Beaman method is close to the theoretical value in the azimuth-slowness domain.

Evaluating the Vulnerability of Integrated Electricity-heat-gas Systems Based on the High-dimensional Random Matrix Theory

Faced with the tight coupling of multi energy sources,the interaction between different energy supply systems makes it difficult for integrated energy systems(IES)to identify weak nodes.Based on the analysis of the data generated by the actual operation of IES,this paper proposes a weak node identification method based on random matrix theory(RMT).First,establish a unified power flow model for IES.Secondly.introduce RMT and the characteristics of weak nodes,without considering the detailed physical model of the system,using historical data and real-time data to construct the random matrix.Thirdly,the two limit spectrum distribution functions(Marchenko-Pastur law and ring law)are used to qualitatively analyze the system’s operating status,calculate linear eigenvalue statistics such as mean spectral radius(MSR),and establish the weak node identification model based on entropy theory.Finally,the simulation of IES verifies the effectiveness of the proposed method and provides a new approach for the identification of weak nodes in IES.