CN114626641A - Transformer power failure prediction system based on data processing - Google Patents
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Abstract
The invention relates to the technical field of fault early warning, in particular to a transformer power fault prediction system based on data processing, which comprises a voltage transformation power management and control platform, wherein a voltage transformation frequency noise processing unit, a power operation calculation unit and a safety early warning unit are arranged in the voltage transformation power management and control platform, and the voltage transformation power management and control platform generates a frequency noise signal and transmits the frequency noise signal to the voltage transformation frequency noise processing unit. The time consumed by early warning analysis is saved, and the working efficiency is improved.
Description
Technical Field
The invention relates to the technical field of fault early warning, in particular to a transformer power fault prediction system based on data processing.
Background
The transformer is a device for changing alternating voltage by utilizing the principle of electromagnetic induction, and main components are a primary coil, a secondary coil and an iron core; the main functions are as follows: voltage transformation, current transformation, impedance transformation, isolation, voltage stabilization, and the like;
at present, transformers obtain extensive application along with social development, however, in the use of transformers, the trouble of transformers often appears, thereby influence normal work efficiency, common fault detection is that corresponding technical staff monitors the transformers through a plurality of check out test set, then judge the reason of damage according to technical staff's experience, and often just can discover the part damage after damaging, consume manpower resources, and reduce work efficiency, can't carry out the analysis to the running condition of transformers according to a monitoring platform, thereby early warning is carried out according to state analysis's data in advance, increase the security of transformer operation, improve work efficiency.
Disclosure of Invention
The invention aims to provide a transformer power failure prediction system based on data processing, which carries out numerical conversion on various operation data of a transformer by acquiring the data of the operation state of the transformer, expresses various data states of the transformer by a display value, increases the intuition of the operation state of the transformer, is convenient to extract evaluation values related to the operation state of the transformer, analyzes the influence values of various data of the transformer at different time points by carrying out data analysis and calculation on the power operation of the transformer, calculates the evaluation conversion value of the power operation according to the influence values, carries out safety numerical calculation according to the evaluation conversion value of the power operation and the operation state data, judges the actual condition of the transformer, carries out early warning on the transformer, and increases the stability of the transformer when in use, the time consumed by early warning analysis is saved, and the working efficiency is improved.
The purpose of the invention can be realized by the following technical scheme:
a transformer power failure prediction system based on data processing comprises a transformation power management and control platform, wherein a transformation frequency noise processing unit, a power operation calculation unit and a safety early warning unit are arranged in the transformation power management and control platform;
the variable voltage power control platform generates a frequency noise signal and transmits the frequency noise signal to the variable voltage frequency noise processing unit, and the variable voltage frequency noise processing unit processes data of the state of the transformer, so that whether the running state of the transformer is abnormal or not is judged, and a vibration state value and a noise state value are calculated;
the transformer power management and control platform generates an electric power operation signal and transmits the electric power operation signal to the electric power operation calculation unit, and the electric power operation calculation unit acquires and processes the electric power operation of the transformer, so that the electric power of the transformer is subjected to numerical value conversion, and an electric power operation evaluation conversion value is calculated;
the transformer electric power management and control platform generates an early warning signal and transmits the early warning signal to the safety early warning unit, the safety early warning unit performs numerical calculation on the state of the transformer and the operation electric power change, the safety state of the transformer is judged according to the numerical calculation result, and the transformer electric power management and control platform generates a transformer safety signal, a transformer early warning signal and a damage warning signal and displays the transformer electric power management and control platform.
Further, the voltage-variable frequency noise processing unit performs state data acquisition and analysis operation on the transformer according to the frequency noise signal, and the specific operation process of the state data acquisition and analysis operation is as follows:
collecting model data, seismic frequency data, time data and noise data;
extracting corresponding time data, seismic frequency data and noise data according to model data, performing mean value calculation on a plurality of corresponding seismic frequency data according to different time data, calculating a seismic frequency mean value, performing difference value calculation on the seismic frequency mean value and the plurality of seismic frequency data, calculating a plurality of seismic frequency difference values, performing difference value calculation on the plurality of seismic frequency difference values and a seismic frequency threshold value respectively, calculating a plurality of seismic frequency threshold difference values, marking the seismic frequency threshold difference values with positive and negative values, marking abnormal seismic frequency threshold difference values larger than or equal to zero as abnormal frequency data, marking seismic frequency threshold difference values smaller than zero as normal values, identifying the frequency of abnormal values and marking the abnormal frequency data, identifying the number of the plurality of seismic frequency data and marking the abnormal frequency data as the seismic frequency data, performing proportion calculation on the abnormal frequency data and the seismic frequency data, and calculating an abnormal seismic frequency proportion;
carrying out mean value calculation on a plurality of seismic frequency difference values to calculate a seismic frequency average difference value, carrying out difference value calculation on the plurality of seismic frequency difference values and the seismic frequency average difference value to calculate a plurality of seismic frequency offset difference values, carrying out mean value calculation on the plurality of seismic frequency offset difference values to calculate a seismic frequency offset mean value;
processing the noise data according to a calculation method of the abnormal seismic frequency ratio and the average value of the frequency deviation of the seismic frequency, and calculating the abnormal noise ratio and the average value of the noise deviation;
extracting a seismic frequency deviation mean value, a noise deviation mean value, a seismic frequency abnormity ratio and a noise abnormity ratio, performing transformer state abnormity processing according to the seismic frequency deviation mean value, the noise deviation mean value, the seismic frequency abnormity ratio and the noise abnormity ratio, and calculating a vibration state value and a noise state value;
the model data is expressed as the model of each frequency converter, the vibration frequency data is expressed as the vibration frequency of the frequency converter when the frequency converter operates at each time point, the time data is expressed as the time of the frequency converter when the frequency converter operates, and the noise data is expressed as the sound size emitted by the frequency converter when the frequency converter operates.
Further, the method for calculating the noise abnormal ratio and the noise deviation mean value specifically comprises the following steps:
calculating the mean value of a plurality of corresponding noise data according to different time data, calculating the difference value between the noise mean value and the noise data, calculating a plurality of noise difference values, calculating the difference value between the noise difference values and a noise threshold value respectively, calculating a plurality of noise threshold difference values, marking the noise threshold difference values as positive values and negative values, marking the noise threshold difference values which are more than or equal to zero as abnormal values, marking the noise threshold difference values which are less than zero as positive values, identifying the occurrence times of the abnormal values and marking the abnormal values as abnormal data, identifying the number of the noise data and marking the noise data as the noise data, calculating the proportion between the abnormal data and the noise data, and calculating the abnormal noise proportion;
the noise deviation calculation method comprises the steps of carrying out mean value calculation on a plurality of noise difference values, calculating noise average difference values, carrying out difference value calculation on the noise difference values and the noise average difference values, calculating a plurality of noise deviation values, carrying out mean value calculation on the noise deviation values, and calculating noise deviation mean values.
Further, the electric power operation calculation unit performs operation safety processing operation on the operation of the transformer electric power according to the electric power operation signal, and the specific operation process of the operation safety processing operation is as follows:
acquiring heat production data, wind power data, environment temperature data, temperature change data and humidity data;
the method comprises the steps that heat production data are expressed as heat production size of equipment when a transformer operates, the heat production data are marked as CRi, the value of i is a positive integer, the wind power data are expressed as wind power size of the surrounding environment when the transformer operates, the wind power data are marked as FLi, the value of i is a positive integer, the environment temperature data are expressed as the environment temperature size of the surrounding environment when the transformer operates, the ring temperature data are marked as HWi, the value of i is a positive integer, the temperature change data are expressed as temperature change of the transformer during operation, the temperature change data are marked as BWi, the value of i is a positive integer, the humidity data are expressed as humidity of the transformer during operation, the humidity data are marked as SDi, and the value of i is a positive integer;
extracting corresponding heat production data, wind power data, environment temperature data, temperature change data and humidity data according to the model data and the corresponding time data, carrying out mean value calculation on the environment temperature data at a plurality of time points according to the time data, calculating an environment temperature mean value, carrying out difference value calculation on the environment temperature mean value and the plurality of environment temperature data, calculating a plurality of environment temperature difference values, comparing the environment temperature difference values with an environment temperature safety threshold value, when the environment temperature difference values are greater than or equal to the environment temperature safety threshold value, extracting the time points and the environment temperature difference values corresponding to the corresponding environment temperature difference values, and calibrating the time points and the environment temperature difference values corresponding to the corresponding environment temperature difference values as environment temperature influence time points and influence environment temperature values respectively;
according to the method for calculating the ring temperature influence time point and the ring temperature influence value, heat production data, wind power data, temperature change data and humidity data are processed, and the heat production influence time point, the influence heat production value, the wind power influence time point, the influence wind power value, the temperature change influence time point, the influence temperature change value, the influence humidity influence time point and the influence humidity value are calculated;
extracting a ring temperature influence time point, an influence ring temperature value, a heat production influence time point, an influence heat production value, a wind power influence time point, an influence wind power value, a temperature change influence time point, an influence temperature change value, a humidity influence time point and an influence humidity value, performing time point matching calculation on the ring temperature influence time point, the heat production influence time point, the wind power influence time point, the temperature change influence time point and the humidity influence time point, and calculating an electric power operation evaluation conversion value.
Further, the specific process of performing the time point matching calculation is as follows:
when any two time points of the environment temperature influence time point, the heat production influence time point, the wind power influence time point, the temperature change influence time point and the humidity influence time point are not superposed, judging that no abnormal time point is superposed, and generating a superposition-free signal;
when one or more groups of two time points among the environment temperature influence time point, the heat production influence time point, the wind power influence time point, the temperature change influence time point and the humidity influence time point coincide, a primary coincidence signal is generated;
when one or more groups of three time points among the environment temperature influence time point, the heat production influence time point, the wind power influence time point, the temperature change influence time point and the humidity influence time point coincide, generating a secondary coincidence signal;
when one or more groups of four time points among the environment temperature influence time point, the heat production influence time point, the wind power influence time point, the temperature change influence time point and the humidity influence time point are superposed, generating a three-level superposed signal;
when one or more groups of five time points among the environment temperature influence time point, the heat production influence time point, the wind power influence time point, the temperature change influence time point and the humidity influence time point coincide, generating a four-level coincidence signal;
marking a non-coincidence signal, a first-level coincidence signal, a second-level coincidence signal, a third-level coincidence signal and a fourth-level coincidence signal as identification numbers CHa, wherein a =1, 2, 3.. 5, and carrying out influence assignment on the non-coincidence signal, the first-level coincidence signal, the second-level coincidence signal, the third-level coincidence signal and the fourth-level coincidence signal, wherein the coincidence influence value is MINa, and a =1, 2, 3.. 5;
marking the electric power operation evaluation conversion value as
Marking the value of the ring temperature of influence
Marking the heat production affected value as
Marking the impact wind power value as
Will influenceTemperature value marking
Marking the impact humidity value as
Sequentially marking evaluation conversion weight coefficients influencing the ring temperature value, the heat production value, the wind force value, the temperature change value and the humidity value as e1-e5, and marking the superposition influence value as MINA;
calculating an equation according to the electric power operation evaluation conversion value:
calculating the electric power operation evaluation conversion value
。
Further, the safety early warning unit carries out equipment early warning operation to the running state and the running power of transformer according to early warning signal, and the concrete operation process of equipment early warning operation is:
extracting a vibration state value and a noise state value, calculating a difference value and summing the vibration state value and the noise state value, calculating a state difference value and a state total value, and unifying steel amount before calculation;
comparing the state difference value with a state threshold value, judging that the difference between the two states is small when the state difference value is smaller than the state threshold value, generating a same signal, and judging that the difference between the two states is large when the state difference value is larger than or equal to the state threshold value, generating a different signal;
comparing the total state value with a safety threshold, judging that the difference between the two states is small when the total state value is less than or equal to the safety threshold, generating a two-identity signal, and judging that the difference between the two states is large when the total state value is greater than the safety threshold, and generating a two-difference signal;
and assigning the same signal and the different signal as follows: x1 and X2, assigning the diplomatic and the heterodiplomatic signals to R1 and R2, labeling X1 and X2 as Xc, c =1, 2, labeling R1 and R2 as Rv, v =1, 2;
converting the power operation evaluation into a value
And Xc and Rv are substituted into the calculation:
pj is expressed as a voltage transformation calculation value;
comparing the transformation calculation value with a safety range threshold value, judging the safety of the transformer when the transformation calculation value is smaller than the minimum value of the safety range threshold value, generating a transformation safety signal, judging the potential safety hazard of the transformer when the transformation calculation value belongs to the safety range threshold value, generating a transformation early warning signal, and judging the damage of the transformer when the transformation calculation value is larger than the maximum value of the safety range threshold value, and generating a damage warning signal;
and displaying the transformation safety signal, the transformation early warning signal and the damage warning signal on the transformation electric power management and control platform, and sending out a prompt.
The invention has the following beneficial effects:
the invention carries out numerical conversion on various operation data of the transformer by acquiring the data of the operation state of the transformer, expresses various data states of the transformer by a display value, increases the intuition of the operation state of the transformer, is convenient for extracting the evaluation value related to the operation state of the transformer, carries out data analysis and calculation on the power operation of the transformer, thereby analyzing the influence values of various data of the transformer at different time points, calculating the evaluation conversion value of the power operation according to the influence values, performing safety value calculation according to the evaluation conversion value of the power operation and the operation state data, and judging the actual condition of the transformer, early warning is carried out on the transformer in advance, so that the stability of the transformer in use is improved, the time consumed by early warning analysis is saved, and the working efficiency is improved.
Drawings
The invention will be further described with reference to the accompanying drawings.
FIG. 1 is a system block diagram of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the present invention is a transformer power failure prediction system based on data processing, including a transformer power management and control platform, a transformer frequency noise processing unit, a power operation calculation unit, and a safety pre-warning unit;
the variable voltage frequency noise processing unit, the electric power operation calculating unit and the safety early warning unit are all arranged in the variable voltage electric power management and control platform and are in communication connection with one another;
the variable voltage electric power management and control platform generates a frequency noise signal and transmits the frequency noise signal to the variable voltage frequency noise processing unit, the variable voltage frequency noise processing unit acquires state data and analyzes the transformer according to the frequency noise signal, and the specific operation process of the state data acquisition and the analysis operation is as follows:
collecting the model of each frequency converter and calibrating the model as model data, collecting the vibration frequency of the frequency converter when the frequency converter operates at each time point and calibrating the vibration frequency as vibration frequency data, marking the vibration frequency data as ZDi, and marking the value of i as a positive integer, collecting the time of the frequency converter when the frequency converter operates and calibrating the time data as time data, marking the time data as SJi, and marking the value of i as a positive integer, collecting the sound emitted by the frequency converter when the frequency converter operates and calibrating the sound data as noise data, marking the noise data as ZYi, and marking the value of i as a positive integer;
extracting corresponding time data, seismic data and noise data according to the model data, and bringing the corresponding seismic data into an average value calculation formula according to different time data:
and the value of n is a positive integer, and the seismic frequency mean value is calculated
Performing difference calculation on the seismic frequency mean value and a plurality of seismic frequency data, calculating a plurality of seismic frequency difference values, performing difference calculation on the plurality of seismic frequency difference values and a seismic frequency threshold value respectively, calculating a plurality of seismic frequency threshold difference values, performing positive and negative value marking on the seismic frequency threshold difference values, calibrating the seismic frequency threshold difference value larger than or equal to zero as an abnormal value, calibrating the seismic frequency threshold difference value smaller than zero as a normal value, identifying the frequency of the abnormal value and marking the abnormal value as abnormal data, identifying the number of the plurality of seismic frequency data and marking the abnormal data as the seismic frequency data, performing proportion calculation on the abnormal data and the seismic frequency data, and calculating the seismic frequency abnormal proportion value;
carrying out mean value calculation on a plurality of seismic frequency difference values to calculate a seismic frequency average difference value, carrying out difference value calculation on the plurality of seismic frequency difference values and the seismic frequency average difference value to calculate a plurality of seismic frequency offset difference values, carrying out mean value calculation on the plurality of seismic frequency offset difference values to calculate a seismic frequency offset mean value;
and (3) according to different time data, bringing a plurality of corresponding noise data into a mean value calculation formula:
calculating the mean value of the noise
And the value of n is a positive integer, the noise mean value and a plurality of noise data are subjected to difference calculation to calculate a plurality of noise difference values, the noise difference values are respectively subjected to difference calculation with a noise threshold value to calculate a plurality of noise threshold difference values, the noise threshold difference values are subjected to positive and negative value marking, the noise threshold difference value larger than or equal to zero is marked as an abnormal sound value, the noise threshold difference value smaller than zero is marked as a positive sound value, the frequency of occurrence of the abnormal sound value is identified and marked as abnormal sound data, the number of the noise data is identified and marked as noise data, and the abnormal sound data is marked as abnormal sound dataCarrying out proportion calculation with the noise data, and calculating the noise abnormal proportion value;
carrying out mean value calculation on the plurality of noise difference values, calculating a noise average difference value, carrying out difference value calculation on the plurality of noise difference values and the noise average difference value, calculating a plurality of noise deviation values, carrying out mean value calculation on the plurality of noise deviation values, and calculating a noise deviation mean value;
extracting a seismic frequency deviation mean value, a noise deviation mean value, a seismic frequency abnormity ratio and a noise abnormity ratio, and performing transformer state abnormity processing according to the seismic frequency deviation mean value, the noise deviation mean value, the seismic frequency abnormity ratio and the noise abnormity ratio, wherein the specific process of the transformer state abnormity processing is as follows:
and (3) bringing the seismic frequency deviation difference mean value and the seismic frequency anomaly ratio value into a calculation formula: the vibration state value = vibration frequency anomaly ratio value +/-vibration frequency deviation is a vibration weight coefficient;
and (3) substituting the noise deviation mean value and the noise abnormal ratio into a calculation formula: noise state value = noise deviation mean value noise weight coefficient ± noise anomaly ratio;
the transformer electric power management and control platform generates an electric operation signal and transmits the electric operation signal to the electric operation computing unit, the electric operation computing unit performs operation safety processing operation on the operation of transformer electric power according to the electric operation signal, and the specific operation process of the operation safety processing operation is as follows:
collecting heat production of equipment during the operation of a transformer and calibrating the heat production as heat production data, marking the heat production data as CRi, wherein the value of i is a positive integer, collecting wind power of the surrounding environment during the operation of the transformer and calibrating the wind power as wind power data, marking the wind power data as FLi, the value of i is a positive integer, collecting the temperature of the surrounding environment during the operation of the transformer and calibrating the temperature to be environment temperature data, marking the environment temperature data as HWi, the value of i is a positive integer, collecting the temperature change of the transformer during the operation and calibrating the temperature change data as variable temperature data, marking the variable temperature data as BWi, wherein the value of i is a positive integer, collecting the humidity of the transformer during the operation and calibrating the humidity data as SDi, and the value of i is a positive integer;
extracting corresponding heat production data, wind power data, environment temperature data, temperature change data and humidity data according to the model data and the corresponding time data, carrying out mean value calculation on the environment temperature data at a plurality of time points according to the time data, calculating an environment temperature mean value, carrying out difference value calculation on the environment temperature mean value and the plurality of environment temperature data, calculating a plurality of environment temperature difference values, comparing the environment temperature difference values with an environment temperature safety threshold value, when the environment temperature difference values are greater than or equal to the environment temperature safety threshold value, extracting the time points and the environment temperature difference values corresponding to the corresponding environment temperature difference values, and calibrating the time points and the environment temperature difference values corresponding to the corresponding environment temperature difference values as environment temperature influence time points and influence environment temperature values respectively;
according to the time data, performing mean calculation on the heat production data at a plurality of time points, calculating a heat production mean value, performing difference calculation on the heat production mean value and the heat production data, calculating a plurality of heat production difference values, comparing the heat production difference values with a heat production safety threshold value, when the heat production difference values are greater than or equal to the heat production safety threshold value, extracting the time points and the heat production difference values corresponding to the corresponding heat production difference values, and calibrating the time points and the heat production difference values corresponding to the corresponding heat production difference values as heat production influence time points and influence heat production values respectively;
calculating the mean value of the wind power data according to the time data, calculating the mean value of the wind power, calculating the difference value of the mean value of the wind power and the wind power data, calculating a plurality of wind power difference values, comparing the wind power difference values with a wind power safety threshold value, extracting the time point and the wind power difference value corresponding to the corresponding wind power difference value when the wind power difference value is greater than or equal to the wind power safety threshold value, and calibrating the time point and the wind power difference value corresponding to the corresponding wind power difference value as a wind power influence time point and an influence wind power value respectively;
calculating the mean value of the variable temperature data at a plurality of time points according to the time data, calculating the variable temperature mean value, calculating the difference value of the variable temperature mean value and the variable temperature data, calculating a plurality of variable temperature difference values, comparing the variable temperature difference values with a variable temperature safety threshold value, when the variable temperature difference values are greater than or equal to the variable temperature safety threshold value, extracting the time points and the variable temperature difference values corresponding to the corresponding variable temperature difference values, and calibrating the time points and the variable temperature difference values corresponding to the corresponding variable temperature difference values into variable temperature influence time points and variable temperature influence values respectively;
calculating the average value of the humidity data at a plurality of time points according to the time data, calculating the average value of the humidity, calculating the difference value of the average value of the humidity and the humidity data, calculating a plurality of humidity difference values, comparing the humidity difference values with a humidity safety threshold, extracting the time point and the humidity difference value corresponding to the corresponding humidity difference value when the humidity difference value is greater than or equal to the humidity safety threshold, and calibrating the time point and the humidity difference value corresponding to the corresponding humidity difference value as a humidity influence time point and an influence humidity value respectively; all the threshold values are preset values;
extracting a ring temperature influence time point, an influence ring temperature value, a heat production influence time point, an influence heat production value, a wind power influence time point, an influence wind power value, a temperature change influence time point, an influence temperature change value, a humidity influence time point and an influence humidity value, and performing time point matching on the ring temperature influence time point, the heat production influence time point, the wind power influence time point, the temperature change influence time point and the humidity influence time point:
when any two time points of the environment temperature influence time point, the heat production influence time point, the wind power influence time point, the temperature change influence time point and the humidity influence time point are not superposed, judging that no abnormal time point is superposed, and generating a superposition-free signal;
when one or more groups of two time points among the environment temperature influence time point, the heat production influence time point, the wind power influence time point, the temperature change influence time point and the humidity influence time point coincide, a primary coincidence signal is generated;
when one or more groups of three time points among the environment temperature influence time point, the heat production influence time point, the wind power influence time point, the temperature change influence time point and the humidity influence time point coincide, generating a secondary coincidence signal;
when one or more groups of four time points among the environment temperature influence time point, the heat production influence time point, the wind power influence time point, the temperature change influence time point and the humidity influence time point are superposed, generating a three-level superposed signal;
when one or more groups of five time points among the environment temperature influence time point, the heat production influence time point, the wind power influence time point, the temperature change influence time point and the humidity influence time point coincide, generating a four-level coincidence signal;
marking a non-coincidence signal, a first-level coincidence signal, a second-level coincidence signal, a third-level coincidence signal and a fourth-level coincidence signal as identification numbers CHa, wherein a =1, 2, 3.. 5, and carrying out influence assignment on the non-coincidence signal, the first-level coincidence signal, the second-level coincidence signal, the third-level coincidence signal and the fourth-level coincidence signal, wherein the coincidence influence value is MINa, and a =1, 2, 3.. 5;
calculating an equation according to the electric power operation evaluation conversion value:
wherein, the first and the second end of the pipe are connected with each other,
expressed as the electric power operation evaluation conversion value,
expressed as the value affecting the ring temperature,
expressed as affecting the heat production value,
as indicated to affect the value of the wind force,
expressed as the effect on the temperature change value,
expressing as an influence humidity value, respectively expressing e1-e5 as an evaluation conversion weight coefficient of an influence ring temperature value, an influence calorific value, an influence wind power value, an influence temperature change value and an influence humidity value, expressing MINA as a coincidence influence value, and taking values of f1-f5 and n1-n5 as positive integers;
the transformer power management and control platform generates an early warning signal and transmits the early warning signal to the safety early warning unit, the safety early warning unit performs equipment early warning operation on the running state and running power of the transformer according to the early warning signal, and the specific operation process of the equipment early warning operation is as follows:
extracting a vibration state value and a noise state value, calculating a difference value and a summation of the vibration state value and the noise state value, calculating a state difference value and a state total value, and unifying steel quantity before calculation;
comparing the state difference value with a state threshold value, judging that the difference between the two states is small when the state difference value is smaller than the state threshold value, generating a same signal, and judging that the difference between the two states is large when the state difference value is larger than or equal to the state threshold value, generating a different signal;
comparing the total state value with a safety threshold, judging that the difference between the two states is small when the total state value is less than or equal to the safety threshold, generating a two-identity signal, and judging that the difference between the two states is large when the total state value is greater than the safety threshold, and generating a two-difference signal;
and assigning the same signal and the different signal as follows: x1 and X2, assigning the diplomatic and the heterodiplomatic signals to R1 and R2, labeling X1 and X2 as Xc, c =1, 2, labeling R1 and R2 as Rv, v =1, 2;
converting the power operation evaluation into a value
And Xc and Rv are substituted into the calculation:
pj is expressed as a calculation value of voltage transformation;
comparing the transformation calculation value with a safety range threshold value, judging the safety of the transformer when the transformation calculation value is smaller than the minimum value of the safety range threshold value, generating a transformation safety signal, judging the potential safety hazard of the transformer when the transformation calculation value belongs to the safety range threshold value, generating a transformation early warning signal, judging the damage of the transformer when the transformation calculation value is larger than the maximum value of the safety range threshold value, and generating a damage warning signal;
and displaying the transformation safety signal, the transformation early warning signal and the damage warning signal on the transformation electric power management and control platform, and sending out a prompt.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (6)
1. The utility model provides a transformer power failure prediction system based on data processing, includes vary voltage electric power management and control platform, its characterized in that: a variable voltage frequency noise processing unit, an electric power operation calculation unit and a safety early warning unit are arranged in the variable voltage electric power control platform;
the variable voltage power control platform generates a frequency noise signal and transmits the frequency noise signal to the variable voltage frequency noise processing unit, and the variable voltage frequency noise processing unit processes data of the state of the transformer, so that whether the running state of the transformer is abnormal or not is judged, and a vibration state value and a noise state value are calculated;
the transformer power management and control platform generates an electric power operation signal and transmits the electric power operation signal to the electric power operation calculation unit, and the electric power operation calculation unit acquires and processes the electric power operation of the transformer, so that the electric power of the transformer is subjected to numerical value conversion, and an electric power operation evaluation conversion value is calculated;
the transformer electric power management and control platform generates an early warning signal and transmits the early warning signal to the safety early warning unit, the safety early warning unit performs numerical calculation on the state of the transformer and the operation electric power change, the safety state of the transformer is judged according to the numerical calculation result, and the transformer electric power management and control platform generates a transformer safety signal, a transformer early warning signal and a damage warning signal and displays the transformer electric power management and control platform.
2. The transformer power failure prediction system based on data processing according to claim 1, wherein the transformer frequency-noise processing unit performs a status data collection and analysis operation on the transformer according to the frequency-noise signal, and the status data collection and analysis operation includes:
collecting model data, seismic frequency data, time data and noise data;
extracting corresponding time data, seismic frequency data and noise data according to model data, performing mean value calculation on a plurality of corresponding seismic frequency data according to different time data, calculating a seismic frequency mean value, performing difference value calculation on the seismic frequency mean value and the plurality of seismic frequency data, calculating a plurality of seismic frequency difference values, performing difference value calculation on the plurality of seismic frequency difference values and a seismic frequency threshold value respectively, calculating a plurality of seismic frequency threshold difference values, marking the seismic frequency threshold difference values with positive and negative values, marking abnormal seismic frequency threshold difference values larger than or equal to zero as abnormal frequency data, marking seismic frequency threshold difference values smaller than zero as normal values, identifying the frequency of abnormal values and marking the abnormal frequency data, identifying the number of the plurality of seismic frequency data and marking the abnormal frequency data as the seismic frequency data, performing proportion calculation on the abnormal frequency data and the seismic frequency data, and calculating an abnormal seismic frequency proportion;
carrying out mean value calculation on a plurality of seismic frequency difference values to calculate a seismic frequency average difference value, carrying out difference value calculation on the plurality of seismic frequency difference values and the seismic frequency average difference value to calculate a plurality of seismic frequency offset difference values, carrying out mean value calculation on the plurality of seismic frequency offset difference values to calculate a seismic frequency offset mean value;
processing the noise data according to a calculation method of the abnormal seismic frequency ratio and the average value of the frequency deviation of the seismic frequency, and calculating the abnormal noise ratio and the average value of the noise deviation;
extracting a seismic frequency deviation mean value, a noise deviation mean value, a seismic frequency abnormity ratio and a noise abnormity ratio, performing transformer state abnormity processing according to the seismic frequency deviation mean value, the noise deviation mean value, the seismic frequency abnormity ratio and the noise abnormity ratio, and calculating a vibration state value and a noise state value;
the model data is expressed as the model of each frequency converter, the vibration frequency data is expressed as the vibration frequency of the frequency converter when the frequency converter operates at each time point, the time data is expressed as the time of the frequency converter when the frequency converter operates, and the noise data is expressed as the sound size emitted by the frequency converter when the frequency converter operates.
3. The transformer power failure prediction system based on data processing as claimed in claim 2, wherein the method for calculating the noise abnormal ratio and the noise deviation mean value specifically comprises:
calculating the mean value of a plurality of corresponding noise data according to different time data, calculating the difference value between the noise mean value and the noise data, calculating a plurality of noise difference values, calculating the difference value between the noise difference values and a noise threshold value respectively, calculating a plurality of noise threshold difference values, marking the noise threshold difference values as positive values and negative values, marking the noise threshold difference values which are more than or equal to zero as abnormal values, marking the noise threshold difference values which are less than zero as positive values, identifying the occurrence times of the abnormal values and marking the abnormal values as abnormal data, identifying the number of the noise data and marking the noise data as the noise data, calculating the proportion between the abnormal data and the noise data, and calculating the abnormal noise proportion;
the noise deviation calculation method comprises the steps of carrying out mean value calculation on a plurality of noise difference values, calculating noise average difference values, carrying out difference value calculation on the noise difference values and the noise average difference values, calculating a plurality of noise deviation values, carrying out mean value calculation on the noise deviation values, and calculating noise deviation mean values.
4. The transformer power failure prediction system based on data processing according to claim 3, wherein the power operation calculation unit performs operation safety processing operation on the operation of the transformer power according to the power operation signal, and the specific operation process of the operation safety processing operation is as follows:
acquiring heat production data, wind power data, environment temperature data, temperature change data and humidity data;
the method comprises the steps that heat production data are expressed as heat production size of equipment when a transformer operates, the heat production data are marked as CRi, the value of i is a positive integer, the wind power data are expressed as wind power size of the surrounding environment when the transformer operates, the wind power data are marked as FLi, the value of i is a positive integer, the environment temperature data are expressed as the environment temperature size of the surrounding environment when the transformer operates, the ring temperature data are marked as HWi, the value of i is a positive integer, the temperature change data are expressed as temperature change of the transformer during operation, the temperature change data are marked as BWi, the value of i is a positive integer, the humidity data are expressed as humidity of the transformer during operation, the humidity data are marked as SDi, and the value of i is a positive integer;
extracting corresponding heat production data, wind power data, environment temperature data, temperature change data and humidity data according to the model data and the corresponding time data, carrying out mean value calculation on the environment temperature data at a plurality of time points according to the time data, calculating an environment temperature mean value, carrying out difference value calculation on the environment temperature mean value and the plurality of environment temperature data, calculating a plurality of environment temperature difference values, comparing the environment temperature difference values with an environment temperature safety threshold value, when the environment temperature difference values are greater than or equal to the environment temperature safety threshold value, extracting the time points and the environment temperature difference values corresponding to the corresponding environment temperature difference values, and calibrating the time points and the environment temperature difference values corresponding to the corresponding environment temperature difference values as environment temperature influence time points and influence environment temperature values respectively;
according to the method for calculating the ring temperature influence time point and the ring temperature influence value, heat production data, wind power data, temperature change data and humidity data are processed, and the heat production influence time point, the influence heat production value, the wind power influence time point, the influence wind power value, the temperature change influence time point, the influence temperature change value, the influence humidity influence time point and the influence humidity value are calculated;
extracting a ring temperature influence time point, an influence ring temperature value, a heat production influence time point, an influence heat production value, a wind power influence time point, an influence wind power value, a temperature change influence time point, an influence temperature change value, a humidity influence time point and an influence humidity value, performing time point matching calculation on the ring temperature influence time point, the heat production influence time point, the wind power influence time point, the temperature change influence time point and the humidity influence time point, and calculating an electric power operation evaluation conversion value.
5. The transformer power failure prediction system based on data processing as claimed in claim 4, wherein the specific process of performing the time point matching calculation is as follows:
when any two time points of the environment temperature influence time point, the heat production influence time point, the wind power influence time point, the temperature change influence time point and the humidity influence time point are not superposed, judging that no abnormal time point is superposed, and generating a superposition-free signal;
when one or more groups of two time points among the environment temperature influence time point, the heat production influence time point, the wind power influence time point, the temperature change influence time point and the humidity influence time point coincide, a primary coincidence signal is generated;
when one or more groups of three time points among the environment temperature influence time point, the heat production influence time point, the wind power influence time point, the temperature change influence time point and the humidity influence time point coincide, generating a secondary coincidence signal;
when one or more groups of four time points among the environment temperature influence time point, the heat production influence time point, the wind power influence time point, the temperature change influence time point and the humidity influence time point are superposed, generating a three-level superposed signal;
when one or more groups of five time points among the environment temperature influence time point, the heat production influence time point, the wind power influence time point, the temperature change influence time point and the humidity influence time point coincide, generating a four-level coincidence signal;
marking a non-coincidence signal, a first-level coincidence signal, a second-level coincidence signal, a third-level coincidence signal and a fourth-level coincidence signal as identification numbers CHa, wherein a =1, 2, 3.. 5, and carrying out influence assignment on the non-coincidence signal, the first-level coincidence signal, the second-level coincidence signal, the third-level coincidence signal and the fourth-level coincidence signal, wherein the coincidence influence value is MINa, and a =1, 2, 3.. 5;
marking the electric power operation evaluation conversion value as
Marking the value of the ring temperature of influence
Marking the heat production affected value as
Marking the impact wind power value as
Marking the temperature change affecting values as
Marking the impact humidity value as
Sequentially marking evaluation conversion weight coefficients influencing the ring temperature value, the heat production value, the wind force value, the temperature change value and the humidity value as e1-e5, and marking the superposition influence value as MINA;
calculating an equation according to the electric power operation evaluation conversion value:
calculating the electric power operation evaluation conversion value
。
6. The transformer power failure prediction system based on data processing of claim 5, wherein the safety pre-warning unit performs an equipment pre-warning operation on the running state and the running power of the transformer according to the pre-warning signal, and the specific operation process of the equipment pre-warning operation is as follows:
extracting a vibration state value and a noise state value, calculating a difference value and summing the vibration state value and the noise state value, calculating a state difference value and a state total value, and unifying steel amount before calculation;
comparing the state difference value with a state threshold value, judging that the difference between the two states is small when the state difference value is smaller than the state threshold value, generating a same signal, and judging that the difference between the two states is large when the state difference value is larger than or equal to the state threshold value, generating a different signal;
comparing the total state value with a safety threshold, judging that the difference between the two states is small when the total state value is less than or equal to the safety threshold, generating a two-identity signal, and judging that the difference between the two states is large when the total state value is greater than the safety threshold, and generating a two-difference signal;
and assigning the same signal and the different signal as follows: x1 and X2, assigning the diplomatic and the heterodiplomatic signals to R1 and R2, labeling X1 and X2 as Xc, c =1, 2, labeling R1 and R2 as Rv, v =1, 2;
converting the power operation evaluation into a value
And Xc and Rv are substituted into the calculation:
，
expressed as a calculated value of the transformation;
comparing the transformation calculation value with a safety range threshold value, judging the safety of the transformer when the transformation calculation value is smaller than the minimum value of the safety range threshold value, generating a transformation safety signal, judging the potential safety hazard of the transformer when the transformation calculation value belongs to the safety range threshold value, generating a transformation early warning signal, judging the damage of the transformer when the transformation calculation value is larger than the maximum value of the safety range threshold value, and generating a damage warning signal;
and displaying the transformation safety signal, the transformation early warning signal and the damage warning signal on the transformation electric power management and control platform, and sending out a prompt.
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