20 results about "Vector group" patented technology

The invention relates to the technical field of electrical fault monitoring, in particular to an intelligent mobile substation electrical fault monitoring method, which comprises the following steps of: synchronously acquiring an electrical monitoring signal, a multi-dimensional environment noise signal and a dynamic working condition parameter of a mobile substation through a multi-source sensor; the method comprises the following steps: constructing an environmental noise floor vector group by adopting multi-scale spectrum mode decomposition, filtering an environmental noise interference component from an electrical monitoring signal through orthogonal projection filtering, and outputting a baseline correction signal; a working condition disturbance response field matrix is constructed based on time domain and frequency domain correlation analysis of dynamic working condition parameters, gradient sensitivity coefficients are calculated, and components strongly related to working condition disturbance and residual components weakly related to equipment faults are separated out; reconstructing the residual component into a pure fault feature vector; and finally, fault type diagnosis and risk early warning are carried out on the basis. The method effectively solves the problem of fault feature annihilation caused by noise pollution in a complex environment and the problem of false alarm and missing alarm caused by confusion of working condition disturbance and real fault signals.

The invention relates to the technical field of charging piles, and provides an electric energy metering method and device for a charging pile and a storage medium, and the method comprises the steps: carrying out the real-time data collection of a charging process of the charging pile, obtaining an instantaneous voltage and an instantaneous current, generating an energy density change trend, carrying out the continuous node energy efficiency ratio evaluation, obtaining a feature mapping vector group, and carrying out the calculation of the feature mapping vector group; and performing energy heteromorphic analysis on the feature mapping vector group to obtain an energy deviation index and a load response matching degree, and then calculating an electric energy metering value. The charging process of the charging pile is monitored in real time, and electric energy metering is optimized through fine mathematical analysis and an algorithm model, so that the defects of the traditional technology in coping with a complex charging mode and load fluctuation are overcome, and the defect that the analysis on the electric energy consumption process is lacked in the complex charging mode is overcome; abnormal fluctuation or load response in the charging process cannot be effectively recognized, and the accuracy of electric energy metering is poor.

The present invention relates to the technical field of electric fault monitoring, and specifically to an intelligent mobile substation electric fault monitoring method. The method synchronously collects electrical monitoring signals, multi-dimensional environmental noise signals and dynamic operating parameters of the mobile substation through multi-source sensors; adopts multi-scale spectral modal decomposition to construct an environmental noise basis vector group, filters out environmental noise interference components from the electrical monitoring signal through orthogonal projection filtering, and outputs a baseline correction signal; constructs an operating condition disturbance response field matrix based on time domain and frequency domain correlation analysis of dynamic operating condition parameters, calculates gradient sensitivity coefficients, separates components strongly correlated with operating condition disturbances and residual components weakly correlated with equipment faults; reconstructs the residual components into pure fault feature vectors; and finally performs fault type diagnosis and risk warning based on the pure fault feature vectors. The method effectively solves the problem of fault feature annihilation caused by noise pollution in complex environments, and the problem of false alarms and missed alarms caused by confusion between operating condition disturbances and real fault signals.

The invention relates to a non-intrusive three-level inverter phase current reconstruction method based on a fixed sampling moment, and belongs to the technical field of permanent magnet synchronous motor control. Aiming at the problems that the sampling time is not fixed, blind areas exist in a low modulation area and a sector boundary, and harmonic waves are increased due to intrusive compensation in current reconstruction of an existing three-level inverter, redundant small vectors at the starting moment and the center moment of a carrier are adopted as effective reconstruction vectors, and a single-sensor topology of a coupling phase current and a midpoint capacitance branch is designed; and a zero vector group is used for replacement in a low modulation region, so that non-intrusive fixed sampling is realized. The method covers all areas except an overmodulation area in a hexagonal space voltage vector contour, the reconstruction error is smaller than 5%, the bus utilization rate reaches 95%, a complex compensation algorithm is not needed, and harmonic and switching loss is reduced.

The invention discloses a permanent magnet electric roller performance test system based on a sensor, which relates to the technical field of electric roller performance test and comprises a data acquisition module, a feature extraction and modeling module, a state judgment module, an atlas matching analysis module and a health assessment and early warning module. Parameters such as current, voltage, temperature, rotating speed and vibration are collected through a multi-source real-time sensor, and a roller operation data matrix is constructed; extracting a multi-dimensional feature vector group and constructing a nonlinear coupling state mapping model; calculating an operation deviation index and matching the operation deviation index with a historical working condition map to generate a full-parameter dynamic working condition fingerprint map; and finally, according to an evaluation result and a deviation degree, outputting a roller health state grade and early warning information. The system can realize accurate identification and trend analysis of the running state of the roller under multiple working conditions, and has high reliability, intelligence and application and popularization value.

The invention discloses a load distribution dynamic balance control method and device for a high-voltage power grid, equipment, a medium and a product, and relates to the technical field of power system automation control, and the method comprises the steps: obtaining a capacity adjustment capability parameter set through an edge node load multi-frequency disturbance vector group and a center node frequency division-fault-tolerant adjustment matrix; constructing three-dimensional cross-node frequency band equilibrium tensor data, traversing the data, marking abnormal nodes, and distributing a push-back feedback weight; and obtaining a cross-band adaptive load balancing strategy table and controlling a power grid based on the back-push feedback weight of the abnormal node, the three-dimensional cross-node frequency band balancing tensor data and the edge node load multi-frequency disturbance vector group subjected to amplitude limiting smoothing processing. According to the invention, through frequency band disturbance differential response and dynamic weight distribution, unification of rapidness, accuracy and robustness of multi-frequency-band resource scheduling is realized, and the load balancing capability of a high-voltage power grid in a complex environment is remarkably improved.

The present invention discloses a method for modeling low-voltage distribution network equipment data by joint non-negative matrix decomposition, including storing and standardizing the type, operating environment, asset identification, and electrical characteristic data information of the primary equipment of distribution transformers, low-voltage junction boxes, and user meter boxes in the form of a matrix; using a joint non-negative matrix decomposition method to project all data sets into the same space, taking vectors with larger coefficients in the same basis vector in the space as heterogeneous vectors; forming a modal space for the heterogeneous vectors to characterize the key information of the equipment; and completing the rapid identification of distribution network equipment information and modeling of low-voltage distribution network equipment data information based on heterogeneous vector information. The present invention adopts a non-negative matrix decomposition method to achieve dimensionality reduction of low-voltage distribution network equipment data information, filter out information irrelevant to the equipment, and help save information processing resources and improve computing efficiency.

This invention relates to the field of power system fault detection technology, and discloses a method for rapid location of high-resistivity faults in distribution networks based on directional frequency scanning, including the following steps: S1, intercepting the zero-sequence current signal I collected from various monitoring points in the distribution network. 0j and bus zero-sequence voltage signal U0; S2, determine the fault occurrence time t f S3. Extract the first intrinsic mode function (IMF) and use it directly to reconstruct the original fault signal; S4. Project the IMF signal onto the reference orthogonal vector group respectively; S5. Use a clustering algorithm to distinguish the upstream and downstream of the fault point, and use the clustering results to locate the fault area. The method of constructing a virtual reference direction using orthogonal vector groups eliminates the need to install voltage transformers at all measurement points. Furthermore, its distributed parallel computing mode enables rapid local processing of fault data without uploading complete data to the main station, thereby reducing hardware investment and maintenance costs, decreasing dependence on physical hardware, and significantly improving fault detection speed.

The invention relates to the technical field of cable circulation monitoring, in particular to a high-voltage cable circulation monitoring method and system. The method comprises the following steps: determining a load step triggering moment and a corresponding low-load current fundamental wave vector group and a corresponding high-load current fundamental wave vector group; constructing an induced voltage deviation objective function based on the difference of the two vector groups, and iteratively determining an optimal mutual inductance coefficient vector; performing induced voltage reconstruction on the high-load current fundamental wave vector group by using the optimal mutual inductance coefficient vector, and determining a sheath loop impedance parameter based on a vector relationship between the reconstructed voltage and three-phase sheath loop current data; determining a voltage model calculation deviation degree based on the difference between the reconstruction voltage and the inversion impedance voltage drop; and the operation state of the high-voltage cable is determined. According to the method, the deviation degree and the sheath loop impedance parameter representing the conductivity of electrical connection are calculated by combining the voltage model representing the integrity of the circuit topology, and an inherent structure fault and a real resistive fault are effectively distinguished.

The invention provides an electric power diagram working condition analysis method based on dynamic clustering, and belongs to the technical field of oil pumping unit working condition diagnosis. The method comprises the following steps: acquiring actually measured electric power diagram data of the oil pumping unit, preprocessing the actually measured electric power diagram data, and drawing an electric power diagram according to the preprocessed electric power diagram data; respectively carrying out time domain analysis and frequency domain analysis on the electric power diagram, and extracting a time domain feature vector S and a frequency domain feature vector P from the electric power diagram; and combining the time domain feature vector S and the frequency domain feature vector P to obtain a working condition identification feature vector, carrying out dynamic clustering on the working condition identification feature vector by using a preset dynamic clustering method to obtain a diagnosis result of the working condition of the oil pumping unit, and constructing an electrical work diagram working condition identification model based on the preset dynamic clustering method. According to the method, the frequency domain analysis and the time domain analysis of the electrical work diagram are combined, the feature extraction method is enriched, the working condition recognition accuracy is improved, the technical bottleneck of electrical parameter working condition recognition is broken through, the engineering practicability is high, and the system research and development cost is low.

The method for energised conductor phase identification based on synchronous measurements using network model is performed with a system comprising a field device, a reference device and a server unit connectable to said reference and field devices, wherein the server is provided with a network model comprising information about connectivity, locations of network nodes, busbars, transformer units, field rotation, transformer vector groups and information about other phase shifting devices present in the network. In comparison to known methods, no measurements files are needed, but the identification of conductor phase is performed by matching the field device measurements to the selected reference device measurements, followed by application of the existing knowledge about network model (identification of transformers, field rotation, and connectivity in the network) and calculation of the phase shift from installed reference devices to all or selected busbars in the network model by a software installed and running either on the server, and / or the field device and / or the reference device.

The application discloses a direct torque control method and system considering DC bus voltage imbalance, and relates to the field of motor control. The method comprises the following steps: determining the distribution law of voltage vectors in the fundamental wave alpha-beta subplane and the harmonic x-y subplane when the DC bus voltage is unbalanced; determining three groups of virtual vector groups according to the distribution law and a set condition; the set condition is that the distribution of three basic voltage vectors in the harmonic x-y subplane is greater than 180 degrees, and the three basic voltage vectors are composed of two large vectors and one medium vector in the fundamental wave alpha-beta subplane; obtaining the optimal virtual vector based on the three groups of virtual vector groups according to the DC bus voltage ratio, the sector K value, the system torque and the flux demand; sorting the switches in two two-level three-phase voltage source inverters according to the action time of the three basic vectors in a control period, obtaining the optimized switch sorting, and then controlling the voltage source inverters. The application can efficiently operate under the DC bus voltage imbalance.

The invention provides a PCBA electrical performance detection method, device and equipment and a storage medium, and the method comprises the steps: collecting multi-channel time sequence electrical signals of a plurality of test nodes on a PCBA, and carrying out the synchronous processing of the signals, and generating a signal sequence tensor; performing multi-scale feature extraction on the tensor, and constructing a channel-level embedded vector group; mapping the embedded vectors to corresponding nodes of a PCBA circuit diagram structure to generate a circuit diagram node set; performing adjacent node feature deviation analysis based on node state representation extracted by a graph neural network in combination with an original signal to generate an abnormal score value vector; meanwhile, a label-free representation model is constructed; and finally, based on the score value and the representation model, performing electrical performance scoring and state judgment on the newly acquired signal. The method can be used for global electrical performance detection of a complex circuit board, solves the problem of missing detection caused by inconsideration of overall structure perception and local anomaly recognition, and improves the accuracy of electrical performance detection.

An impedance online inversion method for a power distribution line, comprising: on the basis of a section to be measured, selecting a measurement node of a main line and a measurement node of a branch line (S1, S2); a main line voltage square matrix, a main line current column vector, and a main line admittance column vector forming a main line voltage matrix equation used for inversion calculation of main line admittance, and further determining the admittance of a main line section to be measured (S3, S4, S5, S6, S7); a branch line voltage square matrix, a branch line current column vector, and a branch line admittance column vector forming a branch line voltage matrix equation used for inversion calculation of admittance of the branch line, and further determining admittance of all branch line sections to be measured (S8, S9, S10, S11, S12); and determining the line impedance of all the sections to be measured by combining the admittance of all the main line sections to be measured and the admittance of all the branch line sections to be measured (S13). In the impedance online inversion method, accurate line impedance can be calculated and inverted online and in real time, and accurate line parameters and topological information can be provided for real-time setting of a power grid protection constant value and real-time power flow calculations. In addition, applications such as power grid planning, scheduling, operation control, source network load storage interaction, and the like, are supported.

The present application relates to the technical field of multiphase motor system shaft current suppression, and particularly relates to a multiphase motor shaft current suppression method, system and storage medium. The method first performs system modeling, analyzes zero sequence current according to modeling, evaluates the influence of different vector combinations on shaft current peak value, then synthesizes target voltage vector according to duty cycle, reduces the contribution of low-order harmonics to shaft current, and smooths current waveform, effectively suppresses the peak value and fluctuation of shaft current, dynamically calculates dead time compensation according to the switching state and direction of each phase, and ensures that the influence of dead time on shaft current is minimized. From the aspects of vector selection, harmonic suppression, dead time compensation and other multidimensional collaborative optimization, the peak value of shaft current is reduced from the root cause under single power driving conditions, low-order harmonics and operating loss of the motor are effectively controlled, and the reliability, stability and service life of the multiphase open-winding motor driving system are comprehensively improved.

The invention relates to the technical field of power system fault detection, and discloses a power distribution network high-resistance fault rapid positioning method based on directional frequency scanning, and the method comprises the following steps: S1, intercepting a zero-sequence current signal I0j and a bus zero-sequence voltage signal U0 collected by each monitoring point of a power distribution network; s2, determining a fault occurrence moment tf; s3, extracting a first intrinsic mode function to be directly used for reconstructing an original fault signal; s4, projecting the first intrinsic mode function signals to a reference orthogonal vector group; and S5, distinguishing the upstream and the downstream of the fault point by using a clustering algorithm, and positioning a fault area by using a clustering result. According to the method for constructing the virtual reference direction through the orthogonal vector group, voltage transformers do not need to be installed at all measuring points, in addition, local rapid processing of fault data can be achieved through a distributed parallel computing mode, complete data do not need to be uploaded to a master station, therefore, hardware investment and maintenance cost are reduced, dependence on physical hardware is reduced, and the fault detection efficiency is improved. And meanwhile, the fault detection speed is greatly improved.

The present invention relates to the technical field of dual inverters, and specifically to a dual inverter virtual vector modulation method, which utilizes redundant small vectors and a middle vector to construct a virtual middle vector, combines them into a basic vector group, and synthesizes a reference vector; determines the position of the reference vector, selects adjacent non-zero voltage vectors and calculates the action time; calculates the action time of the zero vector, and synthesizes the final modulation signal according to the action time of each vector; determines the switching state of each basic vector, and distributes the corresponding switching actions according to the action sequence and time. The present invention improves the DC bus voltage utilization rate of the floating capacitor dual inverter with a voltage ratio of 1:0.5, expands the motor speed regulation range, enhances competitive advantages, can reduce capacitor voltage ripple, reduce the required capacitance value, reduce the volume and mass of the drive system, improve power density and reduce costs, and has significant advantages. It greatly improves the capacitor voltage balancing speed, copes with complex working conditions, and improves system stability and reliability.

The invention discloses a self-calibration method, device, equipment, medium and product for an electric power meter, and relates to the field of calibration, and the method comprises the steps: obtaining a current power supply phase vector group in a set sampling period of an edge node according to the collected current power supply phase vector of each voltage channel in the electric power meter; vector difference offset processing is carried out on the current power supply phase vector set and the edge synchronization reference phase, and the comprehensive phase offset of the current period is obtained; after a power supply abnormal interruption event is detected, a zero point error estimation value is obtained according to the power supply phase vector extreme value set before power failure and the initial power supply phase vector set after power on; the zero error estimated value and the comprehensive phase offset are jointly input into the calibration correction function, the local correction factor set is updated, and a self-adaptive calibration parameter set is formed. The method is suitable for a remote deployment scene in a complex environment, and the long-term operation stability and the self-recovery capacity of the electric power meter are remarkably improved.

The application discloses a rail surface damage identification method and system based on improved discrete entropy, and equipment. The method comprises the following steps: adopting a normal cumulative distribution function to perform standardization processing on an original rail profile data sequence; generating a vector group based on the standardized sequence; corresponding each vector in the vector group with each discrete mode, and calculating the weighted probability distribution of each discrete mode according to the complexity of each vector; calculating the improved discrete entropy entropy value by adopting Rényi entropy according to the weighted probability distribution; obtaining the entropy value distribution range of each type of damage; and identifying the rail surface damage based on the improved discrete entropy entropy value and the entropy value distribution range. The application can automatically and high-precisely realize the extraction of various damage characteristics, mine valuable information in the damage data, and provide decision-making help and information support for railway staff.