CN117150216A

CN117150216A - Regression analysis method and system for power data

Info

Publication number: CN117150216A
Application number: CN202311426197.0A
Authority: CN
Inventors: 王少林; 王茜; 牛诚东; 曹正; 谢枫; 周辛南; 韩硕辰; 张颖; 蒋英慧; 杨彩月; 胡蕊; 胡淏; 侯欣怡; 李威仪; 范秀云; 何佳美; 陈菲; 刘敦楠
Original assignee: Beijing Huadian Energy Internet Research Institute Co ltd; State Grid Corp of China SGCC; State Grid Jibei Electric Power Co Ltd
Current assignee: Beijing Huadian Energy Internet Research Institute Co ltd; State Grid Corp of China SGCC; State Grid Jibei Electric Power Co Ltd
Priority date: 2023-10-31
Filing date: 2023-10-31
Publication date: 2023-12-01
Anticipated expiration: 2043-10-31
Also published as: CN117150216B

Abstract

The invention relates to the technical field of power data regression analysis, in particular to a power data regression analysis method and system. The method comprises the following steps: basic data acquisition and power generation peak value extraction are carried out on the power plant, so that power generation peak value data are obtained; marking data of the power transmission nodes to obtain node line data; carrying out waveform energy analysis and penetration effectiveness definition on the node line data to obtain sound wave effective energy data; performing space offset evaluation on the node line data and performing space compensation to obtain effective compensation energy data; carrying out acoustic anomaly identification on the effective compensation energy data to obtain abnormal acoustic report data; carrying out regression analysis on the abnormal sound wave report data to obtain power abnormal period data; and carrying out fault positioning on the power transmission line and carrying out power transmission fault planning to obtain a power fault planning strategy. The invention realizes more accurate power failure analysis and plan formulation through regression analysis of the power data.

Description

Regression analysis method and system for power data

Technical Field

The invention relates to the technical field of power data regression analysis, in particular to a power data regression analysis method and system.

Background

Regression analysis of power data is a method used to study the relationships between various factors in a power system. The method can help predict the power demand, optimize the energy utilization and improve the operation of the power grid. However, the conventional regression analysis of power data cannot accurately determine the power transmission failure point and cannot dynamically schedule the power transmission line in time.

Disclosure of Invention

Based on the foregoing, it is necessary to provide a regression analysis method and system for power data, so as to solve at least one of the above-mentioned problems.

In order to achieve the above purpose, a power data regression analysis method and system, the method includes the following steps:

step S1: basic data acquisition is carried out on a power plant through a power system, so that power generation time series data are obtained; carrying out power generation peak value extraction according to the power generation time sequence data to obtain power generation peak value data;

step S2: marking data of the power transmission nodes to obtain node line data; waveform energy analysis is carried out on the node line data to obtain power transmission waveform energy data; carrying out penetration effectiveness definition on the transmission waveform energy data to obtain sound wave effective energy data;

step S3: performing spatial offset evaluation on the node line data to obtain a spatial offset coefficient; performing space compensation on the effective energy data of the sound waves according to the space offset coefficient to obtain effective compensation energy data; carrying out acoustic anomaly identification on the effective compensation energy data to obtain abnormal acoustic report data;

Step S4: carrying out regression analysis on the abnormal sound wave report data to obtain power abnormal period data; carrying out fault positioning on the power transmission line according to the power abnormal period data to obtain power transmission line fault data; and carrying out power transmission fault planning according to the power transmission line fault data to obtain a power fault planning strategy.

According to the invention, the basic data acquisition is carried out on the power plant through the power system, the system can acquire the data of the power plant in real time, including voltage, current and power information, which is helpful for monitoring the operation state of the power plant, ensuring the normal operation of the power plant, obtaining the power generation time sequence data means that you can acquire detailed historical records about power generation, which is very important for analyzing, planning and optimizing the operation of the power plant, extracting the power generation peak data is helpful for determining the maximum power output of the power plant in a certain time period, which is very important for the load management and the power market transaction of the power system, because the peak time period is usually associated with high electricity price, the load of the power system can be planned better through the power generation peak data, so that the demand in peak time can be met, which is helpful for avoiding the shortage or waste of power, and improving the efficiency of power transmission; the data labeling of the power transmission nodes means that accurate line data is established for each node, the established node line data is the basis of topology analysis of the power system, the structure and the connection mode of the power network can be known, the accurate node data can be used for quickly identifying fault positions in the power system, abnormal fluctuation or harmonic waves in the power system can be detected through waveform energy analysis, the abnormal fluctuation or harmonic waves can possibly indicate potential problems or faults, the transmission waveform energy data is defined in a penetrating effectiveness manner, and the sound wave effective energy data can help to identify the potential faults in the power system and help to take maintenance measures as soon as possible; the distribution condition of the sound wave in different parts of the power system can be identified through knowing the spatial offset coefficient, the sound source or abnormality can be positioned, the spatial offset coefficient can be used for evaluating the attenuation degree of the sound wave signal, the propagation distance of the sound wave in the power system can be estimated, the accuracy and consistency of effective energy data of the sound wave can be improved through compensating the spatial offset, the sound wave signal can be ensured to have similar amplitude in different positions through compensating, the abnormal sound wave identification is easier, the abnormal sound wave report data can provide detailed information, and the quick positioning and the problem solving of an operation and maintenance team can be facilitated; the periodic trend in the abnormal acoustic data can be identified through regression analysis, which is helpful for detecting potential power system problems, such as equipment aging, load change or other periodic interference, and after the power abnormal periodic data is obtained, the future power abnormal trend can be predicted, so that planning maintenance and resource allocation are facilitated, the power abnormal periodic data can be used for positioning faults or positions of problems on the power transmission line, so that maintenance time can be shortened, power failure time can be reduced, power supply reliability can be improved, and positioning faults is also helpful for identifying the properties of the problems, such as line short circuit, insulation damage or other fault types; according to the power transmission line fault data, a detailed power fault processing strategy can be formulated. These strategies include maintenance procedures, equipment replacement planning, personnel scheduling, helping to quickly restore power supply, and when planning fault plans, optimizing resource allocation, ensuring that the required personnel and equipment are available when needed. Therefore, the power data regression analysis method and the power data regression analysis system are improved processing of the traditional power data regression analysis method, so that the problems that the traditional power data regression analysis method cannot accurately judge the power transmission fault point and cannot dynamically schedule the power transmission line in time are solved, the accuracy of judging the power transmission fault point is improved, and meanwhile, the power transmission line can be dynamically scheduled in time.

Preferably, step S1 comprises the steps of:

step S11: acquiring basic data of a power plant through a power system to obtain basic data of the power plant;

step S12: classifying the power generation mode of the basic data of the power plant to obtain power generation mode data of the power plant;

step S13: carrying out time sequence analysis on the power generation mode data of the power plant to obtain power generation time sequence data;

step S14: and carrying out power generation peak value extraction according to the power generation time sequence data to obtain power generation peak value data.

By collecting basic data of the power plant, the invention can ensure that comprehensive and accurate information including the scale, equipment and position of the power plant is obtained, which is helpful for establishing a complete profile of the power plant; classifying the power generation modes of the power plant can enable the data to be more readable and manageable, which is helpful for comparing and analyzing different types of power generation modes; by time series analysis, the change of the power generation performance of the power plant with time can be tracked, which helps identify potential performance problems or trends, such as efficiency degradation or load fluctuation; the extraction of the power generation peak data is helpful to determine the highest performance level of the power plant, the power generation peak data can also be used for load management, the power plant is helped to meet challenges when high load demands, and stable power supply is ensured.

Preferably, step S2 comprises the steps of:

step S21: marking data of the power transmission nodes to obtain power transmission node data;

step S22: carrying out power transmission line data extraction processing according to the power transmission node data to obtain node line data;

step S23: acquiring power transmission acoustic signals of the node line data according to the power generation peak value data to obtain power transmission acoustic signals;

step S24: performing frequency domain conversion on the power transmission acoustic signals to generate power transmission acoustic signal data;

step S25: waveform energy analysis is carried out on the power transmission acoustic wave signal data to obtain power transmission waveform energy data;

step S26: and carrying out penetration effectiveness definition on the transmission waveform energy data to obtain sound wave effective energy data.

According to the invention, accurate line data is established for each node by marking the data of the power transmission nodes, and the established node line data is the basis of topology analysis of the power system, so that the structure and the connection mode of the power network can be known, and the accurate node data can be used for rapidly identifying the fault position in the power system; by collecting the sound wave signals, sound abnormality in the power transmission system, such as equipment fault and discharge, is facilitated to find problems early, sound data can be converted into frequency distribution data by frequency domain conversion of the sound wave signals, frequency components in the sound wave signals are facilitated to be analyzed, and events or faults with specific frequencies are recognized; analyzing waveform energy of the acoustic signals can be used for diagnosing health states of the transmission line and equipment, abnormal waveform energy can indicate problems, identifying acoustic effective energy data is helpful for filtering irrelevant or noise data, accuracy of subsequent analysis is improved, the acoustic effective energy data contains information related to faults of the transmission line or equipment, and the system is helpful for real-time monitoring and potential problem identification.

Preferably, step S25 comprises the steps of:

step S251: frequency decomposition is carried out on the power transmission sound wave signal data to obtain power transmission sound wave frequency data;

step S252: marking the time point of the power transmission sound wave frequency data to obtain sound wave time point data;

step S253: constructing a waveform density matrix for the power transmission acoustic frequency data to obtain waveform density matrix data;

step S254: performing matrix decomposition on the waveform density matrix data to obtain waveform density characteristic data;

step S255: carrying out acoustic wave fluctuation difference calculation on the acoustic wave time point data according to the acoustic wave time point data to obtain acoustic wave fluctuation difference data;

step S256: and carrying out waveform energy analysis and calculation on the acoustic wave fluctuation difference data and the waveform density characteristic data by utilizing an acoustic wave energy algorithm to obtain power transmission waveform energy data.

The invention can more clearly analyze different frequency components in sound by carrying out frequency decomposition on the power transmission sound wave signal data, can detect abnormal or fault signals in a specific frequency range through the frequency decomposition, is helpful for identifying problems, can be used for determining the occurrence time of sound wave events at a marked time point, is helpful for analyzing the time mode and the correlation of the events, can provide shape information on sound wave waveforms and is helpful for analyzing the morphological characteristics of the sound, can be used for identifying sound wave modes or characteristics, such as periodic vibration or abnormal noise, can be used for extracting waveform density characteristic data, and can contain information related to faults or abnormal; the acoustic wave differential data provides data about the wave nature of the acoustic wave signal, abnormal wave differences may indicate instability or change, sudden changes or trends in the acoustic wave signal may be detected by calculating the wave differential, helping to find anomalies, waveform energy analysis may extract energy characteristics of the acoustic wave signal, which helps to identify the intensity and changes in sound, and by analyzing waveform energy, sudden changes in energy may be detected, helping to find problems or anomalies early.

Preferably, the acoustic wave energy algorithm in step S256 is as follows:

in (1) the->Representing the value of the acoustic wave energy,representing the difference in acoustic wave,>representing the acoustic velocity coefficient, +.>Representing a value per unit time, ">Representing the sound wave amplitude +.>Representing the acoustic frequency coefficient, < >>Representing the volume value of the space>Representing the sound propagation reference speed value,/->Representing error correction values for the acoustic wave energy algorithm.

The invention constructs an acoustic wave energy algorithm which is determined by the influence on the amplitude, the propagation speed, the frequency, the time range and the space range of the acoustic wave, and the output result of the acoustic wave energy algorithm can be controlled by adjusting the parameters so as to meet specific requirements or optimize the calculation of acoustic wave energy. The algorithm fully considers the difference value of the acoustic wave fluctuationA larger difference in fluctuation increases the energy value of the sound wave because a larger amplitude means a stronger vibration of the sound wave; acoustic wave velocity coefficientA larger velocity coefficient results in a faster acoustic wave propagation velocity, thereby increasing the distance that the acoustic wave propagates per unit time, and thus increasing the acoustic wave energy value; value per unit time->The unit time value represents a time range in which the acoustic energy is calculated, and a longer time range may contain more acoustic vibration cycles, thereby increasing the accumulation of acoustic energy; acoustic wave amplitude- >A larger amplitude increases the energy value of the sound wave, since a larger amplitude means a stronger sound wave vibration; acoustic wave frequency coefficient->Higher frequency coefficients increase the frequency of the sound wave, thereby increasing the sound wave energy value; spatial volume value->A larger spatial volume value means that the sound wave propagates over a larger area, thereby increasing the energy value of the sound wave; sound propagation reference speed value +.>The sound propagation reference speed value represents the speed of sound waves propagating in a specific medium, and a larger propagation reference speed value can lead to a faster sound wave propagation speed, and increase the distance of the sound waves propagating in unit time, thereby increasing the sound wave energy value; error correction value of acoustic wave energy algorithm>The error correction value is used for correcting errors possibly existing in the algorithm, and the algorithm can more accurately estimate the energy value of the sound wave by adjusting the error correction value.

Preferably, step S26 includes the steps of:

step S261: performing diffusion path positioning on the power transmission waveform energy data to obtain energy diffusion path data;

step S262: carrying out sound wave absorption detection on the material of the power transmission line to obtain material absorption sound wave data;

step S263: carrying out line internal gas transmission density calculation on the energy diffusion path data to obtain internal gas density data;

Step S264: and according to the material absorption sound wave data and the internal gas density data, carrying out penetration effectiveness definition on the transmission waveform energy data to obtain sound wave effective energy data.

According to the invention, the diffusion path of the acoustic wave energy can be determined by positioning the diffusion path of the energy data of the transmission waveform, so that an acoustic wave influence range area is obtained, the acoustic wave absorption detection can be used for evaluating the acoustic wave absorption performance of the material of the transmission line, the poor absorption performance possibly indicates that the material is aged or damaged and needs maintenance or replacement, and meanwhile, the influence factors of the material on the acoustic wave can be represented, so that the acoustic wave data can be accurately collected; the calculation of the internal gas density data helps to understand the distribution and density of the gas inside the transmission line, which is important for predicting faults such as arc flashovers, as they generally involve the participation of gas, while the internal gas will attenuate the transmission of acoustic wave energy, and based on the acoustic wave energy, material absorption and gas density data, this step can determine whether the acoustic wave can effectively penetrate the transmission line, which helps to identify obstructions or obstructions to the transmission of the acoustic wave signal in the system.

Preferably, step S3 comprises the steps of:

Step S31: performing effective energy sampling on the effective energy data of the sound waves to obtain effective sampled energy data;

step S32: acquiring space data of the node line data to obtain the space data of the node line;

step S33: carrying out space offset evaluation on the node line space data to obtain a space offset coefficient;

step S34: performing spatial compensation on the effective sampling energy data according to the spatial offset coefficient to obtain effective compensation energy data;

step S35: and carrying out acoustic anomaly identification on the effective compensation energy data in the transmission process by using a preset acoustic fault identification model to obtain abnormal acoustic report data.

The invention can extract key sound wave energy information by sampling the sound wave effective energy data, reduce redundant data, is beneficial to improving the processing efficiency of the data, collects the space data of the node line data and collects the surrounding environment of the node line, wherein the space data of the node line can be obtained by including air flow rate and air pressure; the space offset evaluation is helpful for knowing the relevance between the sound wave data and the node line data, can identify the space offset condition between the sound wave source and the node line, performs space compensation on the effective sampling energy data according to the space offset coefficient, can correct the data, ensures that the sound wave data accords with the actual position and relation of the node line, analyzes the sound wave data subjected to space compensation by utilizing a preset sound wave fault identification model, can detect sound wave abnormality, and positions line fault points.

Preferably, step S33 includes the steps of:

step S331: detecting the external air flow rate of the node line space data to obtain air flow rate data;

step S332: calculating the fluid pressure of the air flow velocity data to obtain fluid pressure data;

step S333: carrying out gas circulation intensity assessment according to the air flow velocity data and the fluid pressure data to obtain gas circulation intensity data;

step S334: and carrying out space deviation evaluation on the gas circulation intensity data to obtain a space deviation coefficient.

According to the invention, the external air flow rate detection is carried out on the node line space data, so that the transmission of sound waves is influenced, a large flow rate can isolate a plurality of sound waves, and the pressure level of fluid (usually air) in the environment can be known by calculating the fluid pressure, so that the method is helpful for analyzing the atmospheric pressure change factors possibly influencing a power transmission system; from the external air flow rate and fluid pressure data, the intensity of the gas circulation can be evaluated, which is important for understanding the flow characteristics of the gas around the node line and the potential temperature, humidity and pressure gradients, and the spatial offset evaluation helps to relate the gas circulation intensity data to the position and topology of the node line, which helps to determine the variation and distribution of the gas circulation in different parts and the relationship to the node line.

Preferably, step S4 comprises the steps of:

step S41: carrying out power transmission anomaly tracing on the anomaly acoustic wave report data to obtain power anomaly tracing data;

step S42: carrying out regression analysis on the power abnormality tracing data to obtain power abnormality period data;

step S43: carrying out fault positioning on the power transmission line according to the power abnormal period data to obtain power transmission line fault data;

step S44: carrying out optimal scheduling of the power transmission line on the node line data according to the power transmission line fault data to obtain line fault scheduling data;

step S45: performing fault repair on the power transmission line fault data by using a preset power line fault repair manual, and recording to obtain fault repair data;

step S46: and carrying out power transmission fault planning according to the fault repair data and the line fault scheduling data to obtain a power fault planning strategy.

The invention can determine the source of the abnormal situation in the power transmission system through analyzing the abnormal sound wave report data, which is helpful for rapidly identifying the problem and taking further action; regression analysis may help determine the periodicity or trend of the abnormal situation, which may be used to predict future possible abnormal situations, taking precautions; according to the power anomaly period data, faults on the power transmission line can be more accurately positioned, so that the fault positioning time is shortened, maintenance resources can be more effectively distributed, the maintenance efficiency is improved by optimizing and scheduling according to the power transmission line fault data, the faults on the power transmission line can be quickly repaired by adopting a preset power line fault repair manual, and a power fault planning strategy is formulated according to the fault repair data and the line fault scheduling data, so that possible future fault conditions can be better handled, and the system risk is reduced.

Preferably, the invention provides a regression analysis method and a regression analysis system for electric power data:

the power generation peak value acquisition module is used for acquiring basic data of a power plant through a power system to obtain power generation time sequence data; carrying out power generation peak value extraction according to the power generation time sequence data to obtain power generation peak value data;

the transmission and electroacoustic capacity definition module is used for marking data of transmission nodes to obtain node line data; waveform energy analysis is carried out on the node line data to obtain power transmission waveform energy data; carrying out penetration effectiveness definition on the transmission waveform energy data to obtain sound wave effective energy data;

the abnormal sound wave identification module is used for carrying out space deviation evaluation on the node line data to obtain a space deviation coefficient; performing space compensation on the effective energy data of the sound waves according to the space offset coefficient to obtain effective compensation energy data; carrying out acoustic anomaly identification on the effective compensation energy data to obtain abnormal acoustic report data;

the power failure plan making module is used for carrying out regression analysis on the abnormal sound wave report data to obtain power abnormal period data; carrying out fault positioning on the power transmission line according to the power abnormal period data to obtain power transmission line fault data; and carrying out power transmission fault planning according to the power transmission line fault data to obtain a power fault planning strategy.

The method has the advantages that basic data acquisition is carried out on the power plant through the power system, the system can acquire data of the power plant in real time, including voltage, current and power information, which is helpful for monitoring the operation state of the power plant, ensuring the normal operation of the power plant, obtaining the power generation time sequence data means that you can acquire detailed historical records about power generation, which is very important for analyzing, planning and optimizing the operation of the power plant, extracting power generation peak data is helpful for determining the maximum power output of the power plant in a certain time period, which is very important for load management and power market transaction of the power system, because the peak time period is usually associated with high electricity price, the load of the power system can be planned better through the power generation peak data, so as to meet the requirements of peak time, which is helpful for avoiding power shortage or waste, and improving the efficiency of power transmission; the data labeling of the power transmission nodes means that accurate line data is established for each node, the established node line data is the basis of topology analysis of the power system, the structure and the connection mode of the power network can be known, the accurate node data can be used for quickly identifying fault positions in the power system, abnormal fluctuation or harmonic waves in the power system can be detected through waveform energy analysis, the abnormal fluctuation or harmonic waves can possibly indicate potential problems or faults, the transmission waveform energy data is defined in a penetrating effectiveness manner, and the sound wave effective energy data can help to identify the potential faults in the power system and help to take maintenance measures as soon as possible; the distribution condition of the sound wave in different parts of the power system can be identified through knowing the spatial offset coefficient, the sound source or abnormality can be positioned, the spatial offset coefficient can be used for evaluating the attenuation degree of the sound wave signal, the propagation distance of the sound wave in the power system can be estimated, the accuracy and consistency of effective energy data of the sound wave can be improved through compensating the spatial offset, the sound wave signal can be ensured to have similar amplitude in different positions through compensating, the abnormal sound wave identification is easier, the abnormal sound wave report data can provide detailed information, and the quick positioning and the problem solving of an operation and maintenance team can be facilitated; the periodic trend in the abnormal acoustic data can be identified through regression analysis, which is helpful for detecting potential power system problems, such as equipment aging, load change or other periodic interference, and after the power abnormal periodic data is obtained, the future power abnormal trend can be predicted, so that planning maintenance and resource allocation are facilitated, the power abnormal periodic data can be used for positioning faults or positions of problems on the power transmission line, so that maintenance time can be shortened, power failure time can be reduced, power supply reliability can be improved, and positioning faults is also helpful for identifying the properties of the problems, such as line short circuit, insulation damage or other fault types; according to the power transmission line fault data, a detailed power fault processing strategy can be formulated. These strategies include maintenance procedures, equipment replacement planning, personnel scheduling, helping to quickly restore power supply, and when planning fault plans, optimizing resource allocation, ensuring that the required personnel and equipment are available when needed. Therefore, the power data regression analysis method and the power data regression analysis system are improved processing of the traditional power data regression analysis method, so that the problems that the traditional power data regression analysis method cannot accurately judge the power transmission fault point and cannot dynamically schedule the power transmission line in time are solved, the accuracy of judging the power transmission fault point is improved, and meanwhile, the power transmission line can be dynamically scheduled in time.

Drawings

FIG. 1 is a schematic flow chart of a regression analysis method and system for power data;

FIG. 2 is a flowchart illustrating the detailed implementation of step S2 in FIG. 1;

FIG. 3 is a detailed flowchart illustrating the implementation of step S25 in FIG. 2;

FIG. 4 is a flowchart illustrating the detailed implementation of step S26 in FIG. 2;

the achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.

Detailed Description

The following is a clear and complete description of the technical method of the present patent in conjunction with the accompanying drawings, and it is evident that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.

Furthermore, the drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. The functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor methods and/or microcontroller methods.

It will be understood that, although the terms "first," "second," etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of example embodiments. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.

In order to achieve the above objective, please refer to fig. 1 to 4, a power data regression analysis method and system, the method includes the following steps:

In the embodiment of the present invention, as described with reference to fig. 1, a step flow diagram of a power data regression analysis method and system according to the present invention is provided, and in this example, the power data regression analysis method and system includes the following steps:

in the embodiment of the invention, basic data including current, voltage, frequency and time series data are acquired from a power system of a power plant, and the time series data are processed to extract power generation peak data, which can be realized by searching the maximum value in the time series.

in the embodiment of the invention, a sensor or data acquisition equipment is installed at a power transmission node, the acquired data is subjected to time stamp marking so as to ensure the time relevance of the data, the marked data is associated with each power transmission node, and node line data is established; preprocessing the data of each node, including denoising, filtering, etc., to improve the data quality, converting the time domain data into frequency domain data using suitable mathematical and signal processing tools, such as fourier transforms or wavelet transforms, calculating the waveform energy of each node, formulating suitable criteria or thresholds to define the effective energy range of the sound wave, which criteria can be determined based on previous experience, data distribution, or domain knowledge, validity defining the waveform energy data of each node, and screening out data points that fall within the effective energy range.

In the embodiment of the invention, the data of each node is subjected to spatial offset evaluation to determine whether the signal is spatially offset in the propagation process, which involves the establishment of a signal propagation model and the distance and position information between the nodes; calculating a spatial offset coefficient for each node, the coefficient describing the change in acoustic wave intensity of the signal during propagation; using the spatial offset coefficients calculated in the spatial offset estimation, the acoustic wave effective energy data is spatially compensated, which typically involves shifting or stretching the data to correct it to the correct position, resulting in effective compensated energy data that has been corrected to the correct position, ready for subsequent acoustic wave anomaly identification, and an anomaly identification algorithm is applied to the processed data to identify and flag an anomalous acoustic wave event.

In the embodiment of the invention, the abnormal sound wave report data is prepared, the data comprises the time stamp, the position information and the characteristic data of the abnormal sound wave event, regression analysis is carried out to determine the periodic mode of the abnormal sound wave event, the method can be completed by using methods such as time sequence analysis, frequency spectrum analysis, regression model and the like, and the power abnormal period data is obtained, wherein the data describe the periodic characteristics such as frequency and amplitude of the abnormal sound wave event; using the power anomaly cycle data to perform positioning analysis on each part of the power transmission line, which can include predicting the position of an abnormal acoustic event on the power transmission line by using a power system model to obtain power transmission line fault data, including fault position, possible reasons and fault type information; and based on the transmission line fault data, a power fault plan is formulated. The protocol may include operational steps to cope with different types of faults, emergency maintenance procedures, personnel scheduling plans.

Preferably, step S1 comprises the steps of:

In the embodiment of the invention, the range, the frequency and the target of data acquisition are determined. This may include selection of power plants involved in the power system, time periods for data acquisition, determining data sources, requiring interfacing with monitoring systems, sensors or databases of the power plants to obtain data in real time or periodically; defining different power generation modes, such as coal, natural gas, wind energy and solar energy, classifying power plants according to the power generation modes based on basic data of the power plants, and generating power generation mode data of the power plants; the time series data of each power generation mode is analyzed using a suitable time series analysis tool and algorithm, such as ARIMA, seasonal analysis, trend analysis, and the power generation peak data is extracted from the time series data using a suitable algorithm, such as data aggregation, statistical method, or threshold detection.

Preferably, step S2 comprises the steps of:

As an example of the present invention, referring to fig. 2, the step S2 in this example includes:

in the embodiment of the invention, the power transmission node to be marked is determined, and the power transmission node comprises the geographical position and the electrical characteristic information of the node.

in the embodiment of the invention, the data of the power transmission lines related to the nodes are extracted based on the marked data of the power transmission nodes, wherein the data comprise the length, the material, the current load, the paving route and the height information of the paving route.

in the embodiment of the invention, the acoustic wave sensor is arranged on the marked power transmission node and line and used for collecting acoustic wave signal data, and the acoustic wave signal data generated by the acoustic wave sensor are collected and comprise the amplitude, frequency and time information of the acoustic wave.

in the embodiment of the invention, the acquired sound wave signals are preprocessed, such as denoising and filtering, so as to improve the signal quality, and the time-domain sound wave signals are converted into frequency domain representation by using Fourier transformation or other frequency domain conversion technologies, so that the frequency spectrum data of the sound wave signals are obtained.

in an embodiment of the invention, the energy of each frequency component is calculated based on the data of the frequency domain representation. This may be achieved by operating on the amplitude of the spectral data.

In the embodiment of the invention, the energy of which frequency components have a penetrating effect, i.e. can be transmitted to a longer distance. This typically involves analysis of acoustic wave characteristics associated with a particular line material and length, and based on the definition of penetration effectiveness, effective acoustic wave energy data is extracted that reflects acoustic wave characteristics associated with the transmission line, such as vibration, resonance.

Preferably, step S25 comprises the steps of:

As an example of the present invention, referring to fig. 3, the step S25 in this example includes:

in the embodiment of the invention, the acquired sound wave signal data is used for converting the sound wave signal data in the time domain into the frequency domain representation by using Fast Fourier Transform (FFT) or other frequency domain analysis technology, so that the frequency spectrum data of sound waves are generated, and the amplitudes and phases of different frequency components are displayed.

in the embodiment of the invention, in the power transmission sound wave frequency data, an event or abnormality in sound waves is detected by using a proper algorithm and a threshold value, and the time point of the frequency abnormality is marked, so that the time point data of the sound wave event is created.

in the embodiment of the invention, the sound wave frequency data is divided into overlapped time windows, and a sliding window technology is generally used, wherein each window contains the sound wave frequency data in a period of time; within each window, the density of frequency components is calculated, using techniques such as histogram or kernel density estimation, which will generate waveform density data for each window, constructing the waveform density data within each window into a matrix, where each column represents a time window and each row represents a frequency component.

in the embodiment of the invention, a matrix decomposition technique is used to decompose the waveform density matrix into a set of waveform density eigenvectors. These feature vectors capture the relationship and importance between the frequency components.

in the embodiment of the invention, the acoustic wave time point data is used for calculating the acoustic wave fluctuation difference of each event. This may be accomplished by calculating the amplitude differences, frequency differences, or other acoustic signature differences of the events.

In the embodiment of the invention, waveform energy analysis and calculation are performed based on the acoustic wave motion difference data and the waveform density characteristic data, which can include calculation of information such as energy level, frequency distribution and the like of acoustic wave events.

Preferably, the acoustic wave energy algorithm in step S256 is as follows:in (1) the->Representing the sound wave energy value, +.>Representing the difference in acoustic wave,>representing the acoustic velocity coefficient, +.>Representing a value per unit time, ">Representing the sound wave amplitude +.>Representing the acoustic frequency coefficient, < >>Representing the volume value of the space>Representing the sound propagation reference speed value,/->Representing error correction values for the acoustic wave energy algorithm.

The invention constructs an acoustic wave energy algorithm which is determined by the influence on the amplitude, the propagation speed, the frequency, the time range and the space range of the acoustic wave, and the output result of the acoustic wave energy algorithm can be controlled by adjusting the parameters so as to meet specific requirements or optimize the calculation of acoustic wave energy. The algorithm fully considers the difference value of the acoustic wave fluctuationA larger difference in fluctuation increases the energy value of the sound wave because a larger amplitude means a stronger vibration of the sound wave; acoustic wave velocity coefficientA larger velocity coefficient results in a faster acoustic wave propagation velocity, thereby increasing the distance that the acoustic wave propagates per unit time, and thus increasing the acoustic wave energy value; value per unit time->The unit time value represents a time range in which the acoustic energy is calculated, and a longer time range may contain more acoustic vibration cycles, thereby increasing the accumulation of acoustic energy; acoustic wave amplitude- >A larger amplitude increases the energy value of the sound wave, since a larger amplitude means a stronger sound wave vibration; acoustic wave frequency coefficient->Higher frequency coefficients increase the frequency of the sound wave, thereby increasing the sound wave energy value; spatial volume value->A larger spatial volume value means soundThe wave propagates over a larger area, increasing the energy value of the acoustic wave; sound propagation reference speed value +.>The sound propagation reference speed value represents the speed of sound waves propagating in a specific medium, and a larger propagation reference speed value can lead to a faster sound wave propagation speed, and increase the distance of the sound waves propagating in unit time, thereby increasing the sound wave energy value; error correction value of acoustic wave energy algorithm>The error correction value is used for correcting errors possibly existing in the algorithm, and the algorithm can more accurately estimate the energy value of the sound wave by adjusting the error correction value.

Preferably, step S26 includes the steps of:

As an example of the present invention, referring to fig. 4, the step S26 in this example includes:

in the embodiment of the invention, the acquired transmission waveform energy data is used, an acoustic propagation model or other suitable methods are used for analyzing the propagation paths of energy in a transmission line or related structures, which can include reflection, refraction, scattering and other phenomena of sound waves, so as to determine the propagation paths of the sound waves in the system, the propagation paths of the sound wave energy are determined according to the analysis result of the diffusion paths, and the path information is recorded so as to generate the energy diffusion path data.

in the embodiment of the invention, the acoustic wave sensor or other appropriate equipment is used for detecting the acoustic wave absorption of the power transmission line and related materials thereof, which can comprise detecting the acoustic wave absorption characteristics of components such as a tower, an insulator, a wire and the like, and recording the detected acoustic wave absorption data, including absorption rate and frequency response information.

in the embodiment of the invention, the gas transmission density in the power transmission line is calculated by using a corresponding gas physical model and a transmission equation, which may involve measurement and consideration of temperature, pressure and humidity factors, and the calculated internal gas density data, typically the spatial distribution data along the line, is recorded.

In the embodiment of the invention, the material is used for absorbing sound wave data and internal gas density data, and the propagation attenuation of sound waves in the power transmission line is calculated. This may be based on the acoustic wave frequency, distance, and propagation path factors, and based on the results of the propagation attenuation calculations, determine whether the acoustic wave energy is strong enough to be detected or penetrated at a particular location, thereby determining the effective energy of the acoustic wave, which involves setting appropriate thresholds or criteria.

Preferably, step S3 comprises the steps of:

In the embodiment of the invention, the effective energy data of the sound wave is sampled by adopting a proper sampling method and sampling rate, which involves selecting a sampling time window or frequency range, recording the effective energy data obtained by sampling for subsequent processing and analysis, and acquiring the node data of the power transmission line by using a sensor or other data acquisition equipment, wherein the node data may comprise node position, temperature, humidity and current related data; the node line spatial data is analyzed to evaluate the spatial offset condition of the line. This may include distance between nodes, relative position information, calculating spatial offset coefficients for subsequent spatial compensation based on the results of the evaluation, using the spatial offset coefficients to spatially compensate the sampled effective energy data, which may involve data interpolation, weighting or other correction methods to correct for spatial offset effects in acoustic wave propagation; the compensated effective energy data is recorded, which will more accurately reflect the energy changes of the acoustic wave at different node locations, and a pre-built acoustic fault recognition model is used, possibly based on machine learning algorithms, statistical methods or expert knowledge, for recognizing acoustic wave abnormal patterns, such as fault sounds, abnormal vibrations, the compensated effective energy data is input into the acoustic fault recognition model, which will analyze the data and detect if abnormal acoustic wave patterns are present, and if the model detects abnormal acoustic wave patterns, abnormal acoustic wave report data is generated, including the location, type and severity of the anomalies.

Preferably, step S33 includes the steps of:

In the embodiment of the invention, a wind speed measuring device, such as an anemometer or a wind speed sensor, is arranged at a proper position near a node line, the wind speed measuring device measures external air flow velocity data in real time, the data are usually recorded in the form of wind speed (meters per second) or wind speed vector, the acquired external air flow velocity data need to be recorded and stored for later analysis, the external air flow velocity data are converted into fluid pressure data by using a basic fluid mechanics principle, and a gas circulation model is established based on the existing external air flow velocity data and the fluid pressure data. This may be a model based on physical equations or an empirical model, and the calculated gas circulation intensity data is analyzed to evaluate the spatial offset of the line, which may include circulation differences between nodes, the effect of air flow rate changes on circulation, and calculating a spatial offset coefficient for subsequent applications, such as spatially compensating or analyzing the gas circulation intensity data.

Preferably, step S4 comprises the steps of:

In an embodiment of the present invention, abnormal sound wave report data is collected, which may include sound frequency, vibration data, time stamp information, and location information of the abnormal event, associating the abnormal sound wave with a possible power transmission event, this may involve acoustic feature extraction and pattern recognition, generating power anomaly traceability data, including type of anomaly event, location, timestamp information, regression analysis is carried out on the power anomaly traceability data, data related to the periodicity of the anomaly event is fitted, power anomaly periodicity data is extracted from the regression analysis, the data may include cycle, amplitude information of the anomaly event, using the power anomaly cycle data in combination with topology and other related information of the transmission line to locate the location of the fault event on the transmission line, generating transmission line fault data including fault type, location, time stamp information, integrating transmission line fault data and node line data including line topology, load information, considering the status, load demand, fault condition, etc. of the transmission line, generating an optimal line scheduling scheme, to minimize the impact of faults on power transmission, generate line fault scheduling data, including scheduling plans, switching strategies, using preset power line fault repair manuals, according to the fault type and position, executing corresponding repair measures, recording the executed repair operations including repair personnel and repair time information, analyzing the power fault data based on the fault repair data and the line fault dispatching data, the characteristics and the trend of the fault event are known, and a power fault planning strategy is formulated according to the data analysis result, wherein the power fault planning strategy comprises steps of coping with different types of faults and a resource allocation strategy.

The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.

The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. The regression analysis method of the electric power data is characterized by comprising the following steps of:

2. The power data regression analysis method according to claim 1, characterized in that step S1 comprises the steps of:

3. The power data regression analysis method according to claim 2, characterized in that step S2 comprises the steps of:

4. The power data regression analysis method according to claim 3, characterized in that step S25 comprises the steps of:

5. The power data regression analysis method of claim 4 wherein the sonic energy algorithm of step S256 is as follows:in (1) the->Representing the sound wave energy value, +.>Representing the difference in acoustic wave,>representing the acoustic velocity coefficient, +.>Representing a value per unit time, ">Representing the sound wave amplitude +.>Representing the acoustic frequency coefficient, < >>Representing the volume value of the space>Representing the sound propagation reference speed value,/- >Representing errors in acoustic energy algorithmsAnd (5) correcting the difference correction value.

6. The power data regression analysis method of claim 4, wherein step S26 comprises the steps of:

7. The power data regression analysis method according to claim 6, characterized in that step S3 comprises the steps of:

8. The power data regression analysis method according to claim 7, characterized in that step S33 comprises the steps of:

9. The power data regression analysis method of claim 7, wherein step S4 comprises the steps of:

10. A power data regression analysis system for performing the power data regression analysis method according to claim 1, the power data regression analysis system: