CN-122026599-A - Intelligent power distribution system monitoring method and monitoring device based on FTU

CN122026599ACN 122026599 ACN122026599 ACN 122026599ACN-122026599-A

Abstract

The application relates to the technical field of intelligent monitoring of power distribution systems, and discloses an intelligent power distribution system monitoring method and device based on an FTU. The method comprises the steps of processing current signal data obtained by the FTU equipment to obtain amplitude information and phase information, determining correlation characteristics of amplitude attenuation and propagation distance, extracting spectrum distribution change data based on the correlation characteristics, performing multi-scale decomposition, judging coupling relation between spectrum distribution change and amplitude attenuation and dynamic mapping relation between phase offset data and propagation distance, generating multi-dimensional characteristic data based on the correlation characteristics, the coupling relation and the dynamic mapping relation, inputting the multi-dimensional characteristic data into a joint distribution model, performing calibration processing on the joint distribution model to obtain an optimized distance relation model, and collecting dynamic fluctuation data of the current signal by the FTU equipment, inputting the dynamic fluctuation data into the distance relation model, and obtaining accurate positioning results of fault positions. The application improves the positioning accuracy of the fault position of the power distribution system.

Inventors

LIU LINGYUN
ZHANG QISHUN
ZHANG LONGBIAO
SUN ZHIYIN
WANG SHUCHAO
SUN XUE

Assignees

中宝电气有限公司

Dates

Publication Date: 20260512
Application Date: 20260112

Claims (10)

1. An intelligent power distribution system monitoring method based on an FTU, wherein a plurality of FTU devices are disposed at different positions in a power distribution system, and are used for acquiring current signal data when the power distribution system fails, the method comprising: S1, performing time domain and frequency domain separation processing on the current signal data to obtain amplitude information and phase information, analyzing an amplitude attenuation rule based on the amplitude information, and determining the correlation characteristic of the amplitude attenuation and a propagation distance, wherein the propagation distance is the line distance between the current signal data transmitted from a fault position to FTU equipment; S2, extracting spectrum distribution change data based on the association features, performing multi-scale decomposition on the spectrum distribution change data, judging a coupling relation between spectrum distribution change and amplitude attenuation, and analyzing phase shift influence based on the phase information and the coupling relation to obtain a dynamic mapping relation between phase shift data and propagation distance; Step S3, generating multi-dimensional characteristic data based on the association characteristics, the coupling relation and the dynamic mapping relation, establishing a joint distribution model, determining the comprehensive relation between the multi-dimensional characteristic data and the propagation distance, acquiring line condition data and environment adaptation data of a power distribution system, and performing calibration processing on the joint distribution model to obtain a distance relation model adapting to a complex scene; and S4, the FTU equipment acquires dynamic fluctuation data of the real-time current signal, inputs the dynamic fluctuation data into the distance relation model, and acquires an accurate positioning result of the fault position by combining background data of complex scene analysis.
2. The method of claim 1, wherein determining the correlation characteristic of the amplitude attenuation and the propagation distance in step S1 comprises: Step S11, setting fixed time length, dividing the amplitude information in the target time length into a plurality of target windows, calculating an average value of the amplitude in each target window, and comparing the average value of the amplitude to obtain a first change curve representing amplitude attenuation; step S12, identifying an inflection point of the first change curve, and performing time domain signal decomposition on the first change curve by adopting empirical mode decomposition based on the inflection point to obtain a plurality of intrinsic mode functions; and S13, calculating the energy spectrum of each function according to each eigenmode function, obtaining the attenuation slope of each section of curve, fitting the attenuation slope with a preset propagation distance parameter, and determining the correlation characteristic.
3. The method according to claim 2, wherein determining the coupling relation between the spectral distribution variation and the amplitude attenuation in step S2 comprises: step S211, converting the spectrum distribution change data into a frequency domain representation by adopting a frequency domain transformation method; Step S212, performing multi-scale decomposition on the converted spectrum distribution change data to obtain a multi-scale decomposition result comprising a low-frequency part and a high-frequency part; Step S213, calculating the energy ratio of the spectrum distribution change data under each scale based on the multi-scale decomposition result, and carrying out correlation analysis with the correlation characteristic to obtain a correlation coefficient; Step S214, if the correlation coefficient is larger than a preset first threshold value, judging that the strong coupling relation exists between the spectrum distribution change data and the amplitude attenuation, otherwise, the strong coupling relation exists.
4. The method according to claim 1, wherein obtaining the dynamic mapping relationship between the phase offset data and the propagation distance in the step S2 includes: Step S221, acquiring a phase value of each time point of the current signal data based on the phase information, and generating phase value sequence data; Step S222, comparing the phase value sequence data with the coupling relation, and identifying influencing factors causing phase shift, wherein the influencing factors comprise signal attenuation intensity and frequency component interference; Step S223, calculating the phase difference between adjacent times in the phase value sequence data, obtaining phase offset data, obtaining historical propagation distances corresponding to the phase offset data at each time point, generating paired data, and constructing a correlation data set between the phase offset data and the propagation distances based on the paired data; step S224, a regression model is established, the associated data set is input into the regression model, and a dynamic mapping relation between the phase offset and the propagation distance is obtained; and S225, analyzing the space-time difference of the current signal data acquired by the plurality of FTU devices, and optimizing the dynamic mapping relation based on the space-time difference.
5. The method of claim 4, wherein step S225 comprises: The method comprises the steps of carrying out unified reference alignment on current signal data acquired by a plurality of FTU devices through a time synchronization mechanism, and acquiring corrected signal acquisition time delay data; According to the signal acquisition time delay data, calculating time sequence deviation values among the FTU devices by adopting a high-precision clock protocol, and adjusting clocks of the FTU devices in real time based on the time sequence deviation values to obtain consistent time sequence signals; Extracting key characteristic values in the phase offset data by combining a multi-characteristic coupling analysis framework aiming at the consistency time sequence signals, wherein the multi-characteristic coupling analysis framework integrates a plurality of signal characteristics by linearly combining amplitude attenuation, spectrum distribution change data and phase information so as to comprehensively analyze the signal propagation process; and judging whether the dynamic mapping relation between the key characteristic value and the propagation distance has deviation, and optimizing the dynamic mapping result under the condition of yes.
6. The method of claim 5, wherein optimizing the dynamic mapping result comprises: comparing the dynamic mapping relation with a preset association curve and calculating deviation degree, wherein the association curve is a standard relation curve between phase deviation and propagation distance; if the deviation degree is larger than a preset standard threshold value, fitting the phase offset data by using a least square method; and determining a correction range of the phase offset data based on the fitting result, setting a mapping function based on a linear interpolation method, inputting the correction range into the mapping function, and generating the optimized distribution of the dynamic mapping result.
7. A method according to claim 3, wherein determining the integrated relationship between the multi-dimensional feature data and the propagation distance in step S3 comprises: Taking spectrum distribution change data with strong coupling relation and amplitude attenuation as first input characteristics; performing Fourier transform on the dynamic mapping result of the phase offset data and the propagation distance, and converting the time domain signal into a frequency domain signal to obtain a second input characteristic; Splicing the first input features and the second input features to form multi-dimensional feature data, performing dimension reduction processing on the multi-dimensional feature data by adopting principal component analysis, and extracting key features; establishing a joint distribution model based on a Gaussian mixture model, and fitting the relation between the key features and the propagation distance; And verifying the fitting degree of the model by calculating a likelihood function value, and outputting the comprehensive relation between the key features and the propagation distance by the joint distribution model if the fitting degree is larger than a preset threshold value.
8. The method according to claim 1, wherein the calibrating the joint distribution model in step S3 includes: The line condition data of the power distribution system comprises line length, conductor type and insulating material parameters, the environment adaptation data comprises real-time monitoring values of temperature, humidity and wind speed, and the line impedance coefficient and the environment interference coefficient are calculated based on the line condition data and the environment adaptation data; and inputting the line impedance coefficient and the environment interference coefficient into a joint distribution model, ensuring that the model receives dynamic characteristics related to amplitude attenuation and phase deviation, carrying out parameter calibration on the joint distribution model based on the dynamic characteristics, and adjusting the adaptability of the model to a complex power distribution environment to obtain a distance relation model adapting to the complex scene.
9. The method according to claim 1, wherein the step S4 comprises: If the dynamic fluctuation data of the current signal exceeds a preset threshold range, real-time adjustment is carried out on the model parameters of the distance relation model by adopting a least square method, the updated parameters are re-integrated into the model, and a preliminary estimated value of the fault position is calculated according to the adjusted distance relation model; Obtaining background data in a complex scene, wherein the background data comprises line topography and weather records, inputting the background data into the distance relation model, adjusting the preliminary estimated value through a gradient descent method, and performing multi-round iterative optimization on the preliminary estimated value to obtain an accurate positioning result of a fault position matched with an actual line condition.
10. An intelligent power distribution system monitoring device based on FTU for implementing an intelligent power distribution system monitoring method based on FTU according to any one of claims 1 to 9, wherein the monitoring device comprises: The separation module is used for carrying out time domain and frequency domain separation processing on the current signal data to obtain amplitude information and phase information, analyzing an amplitude attenuation rule based on the amplitude information, and determining the correlation characteristic of the amplitude attenuation and the propagation distance, wherein the propagation distance is the line distance between the current signal data transmitted from a fault position to the FTU equipment; The mapping module is used for extracting spectrum distribution change data based on the association features, carrying out multi-scale decomposition on the spectrum distribution change data, judging the coupling relation between spectrum distribution change and amplitude attenuation, analyzing phase shift influence based on the phase information and the coupling relation, and obtaining a dynamic mapping relation between phase shift data and propagation distance; the preliminary judgment module generates multidimensional feature data based on the association features, the coupling relations and the dynamic mapping relations, establishes a joint distribution model, determines the comprehensive relation between the multidimensional feature data and the propagation distance, acquires line condition data and environment adaptation data of a power distribution system, and performs calibration processing on the joint distribution model to obtain a distance relation model adapting to a complex scene; And the optimization module is used for acquiring dynamic fluctuation data of the real-time current signal by the FTU equipment, inputting the dynamic fluctuation data into the distance relation model, and acquiring an accurate positioning result of the fault position by combining background data of complex scene analysis.

Description

Intelligent power distribution system monitoring method and monitoring device based on FTU Technical Field The application relates to the technical field of intelligent monitoring of power distribution systems, in particular to an intelligent power distribution system monitoring method and device based on an FTU. Background With the rapid development of socioeconomic and the acceleration of the urban process, the power demand is continuously increasing, and the stability and reliability of the power distribution system become the key of power supply. The traditional power distribution system monitoring method relies on manual inspection and periodical maintenance, so that the efficiency is low, and real-time monitoring and fault early warning of power distribution equipment are difficult to realize. In addition, with the increasing complexity of the distribution network, the conventional monitoring means cannot meet the intelligent and automatic requirements of the modern distribution system. The intelligent power distribution system realizes real-time monitoring, remote control and intelligent analysis of power distribution equipment by integrating advanced information technology, communication technology and automation technology. The feeder terminal Unit (FTU, feeder Terminal Unit) is used as key equipment in the intelligent power distribution system, is responsible for collecting operation data of a power distribution line, such as voltage, current, power and the like, and has remote communication and control functions. The method for monitoring the FTU provides a basis for the intellectualization of the power distribution system, but has the following problems and challenges that 1, the existing FTU monitoring method is not comprehensive in data acquisition, only partial key parameters are often concerned, other factors which possibly affect the stability of the power distribution system, such as ambient temperature, humidity and the like are ignored, 2, the data processing capacity is insufficient, the data acquired by the FTU is huge, the traditional data processing method is difficult to realize rapid analysis and effective utilization of the data, 3, the fault early warning is inaccurate, due to lack of deep analysis and learning of historical data, the existing FTU monitoring method has the problems of false alarm and missing report in the aspect of fault early warning, 4, the remote control is inflexible, the existing FTU monitoring system is often used for adopting a preset control strategy in the aspect of remote control, the capability of dynamically adjusting the control parameters according to real-time data is lacked, 5, the system integration is not high, the existing FTU monitoring system is often independently operated, and is difficult to realize effective integration with other intelligent power distribution equipment or systems. Aiming at the problems, the invention provides an intelligent power distribution system monitoring method based on an FTU, which aims to realize comprehensive, accurate and real-time monitoring and management of a power distribution system by improving key technologies such as data acquisition, data processing, fault early warning, remote control and the like. Disclosure of Invention In order to solve the technical problems, the application provides an intelligent power distribution system monitoring method and device based on an FTU, which are used for improving the monitoring precision of fault positions of the power distribution system. In a first aspect, the present application provides an intelligent power distribution system monitoring method based on FTU, where a plurality of FTU devices are set at different positions in a power distribution system, and the method is used to obtain current signal data when the power distribution system fails, where the method includes: S1, performing time domain and frequency domain separation processing on the current signal data to obtain amplitude information and phase information, analyzing an amplitude attenuation rule based on the amplitude information, and determining the correlation characteristic of the amplitude attenuation and a propagation distance, wherein the propagation distance is the line distance between the current signal data transmitted from a fault position to FTU equipment; S2, extracting spectrum distribution change data based on the association features, performing multi-scale decomposition on the spectrum distribution change data, judging a coupling relation between spectrum distribution change and amplitude attenuation, and analyzing phase shift influence based on the phase information and the coupling relation to obtain a dynamic mapping relation between phase shift data and propagation distance; Step S3, generating multi-dimensional characteristic data based on the association characteristics, the coupling relation and the dynamic mapping relation, establishing a joint distribution model, de