5 results about "State space matrices" patented technology

The present application relates to the field of power system stability analysis and control, in particular to a kind of thermal mass energy storage converter broadband oscillation suppression parameter stable domain identification method, comprising the following steps: establish general electric-thermal coupling mechanism model, construct the dynamic differential equation describing the evolution of input electric power and energy storage medium temperature state of converter;Mechanical dynamics equation is constructed;Establish the small signal state space model of whole system, linearization is carried out at steady state operating point and the state space matrix of whole system is derived;Define practical small disturbance stability domain, set the real part decay rate threshold and minimum damping ratio threshold of whole system eigenvalue;Algebraic singularity constraint equation in high-dimensional parameter space is constructed;Solve algebraic singularity constraint equation, establish parameter stable domain.This method avoids the repeated iteration of traditional eigenvalue analysis, can accurately depict parameter boundary, effectively improve the frequency support capability of high proportion of new energy power grid and system robustness.

The invention discloses a power system fault detection method and device, equipment and a medium, and relates to the field of power fault detection, and the method comprises the steps: constructing a to-be-solved target state space based on the historical operation data of a target power system; performing low-rank decomposition on a data matrix constructed based on the historical operation data, solving the target state space to be solved according to an obtained decomposition result, and obtaining a target state space matrix of the target power system; determining a current system residual error based on a target Kalman filter constructed through the target state space matrix and the collected current input and output data, and splicing the current system residual error and a historical system residual error to obtain an augmented residual error vector; and calculating chi-square statistical magnitude based on the augmented residual vector, determining a target detection threshold according to a preset false alarm rate and the current degree of freedom, and if the chi-square statistical magnitude is continuously greater than the target detection threshold, performing power system fault alarm. Therefore, rapid and reliable power system fault detection can be realized.

This invention relates to the field of fault prediction technology, specifically to a method for predicting optical fiber network resource faults. The method includes the following steps: obtaining the arrival rate of optical cross-connection requests and the wavelength allocation signaling processing time of the optical network; substituting these into the controller system state space matrix to generate a block tridiagonal transfer rate array; iteratively solving the block tridiagonal transfer rate array to generate a signaling state steady-state probability vector. In this invention, the barcode length shortening rate under continuous time windows continues to be included in the paralysis risk quantification set along with the buffer over-limit probability assessment value. This preserves both the topology contraction trend and the signaling backlog, thus the output network resource fault early warning signal possesses both temporality, structural characteristics, and service stress resistance. It can distinguish between short-term disturbances and sustained instability before the fault manifests, improving the early warning lead time and risk location accuracy.

This invention relates to the field of diesel generator set condition monitoring and fault diagnosis technology, specifically disclosing a method for fault prediction and health management of ultra-high voltage high-power diesel generator sets. The method involves collecting multi-source sensor data to construct a high-dimensional state space matrix; mapping data points to a low-dimensional manifold space to form a manifold distribution; extracting the operating condition cycle reference manifold and calculating the geodesic distance to obtain the manifold deviation; decomposing the manifold deviation into operating condition change components along the operating condition cycle trajectory and fault degradation components perpendicular to the operating condition cycle trajectory, extracting the fault degradation components as fault feature indicators; comparing the fault feature indicators with an adaptive early warning threshold to generate early warning information and updating the operating condition cycle reference manifold. This invention decouples the essence of operating condition changes and fault degradation through manifold space decomposition, enabling robust extraction of early minor fault features under conditions of severe load fluctuations, achieving adaptive dynamic early warning, and significantly improving the accuracy and reliability of fault diagnosis under varying operating conditions.