CN116972910A - Monitoring method and system for electrical equipment of thermal power plant - Google Patents
Monitoring method and system for electrical equipment of thermal power plant Download PDFInfo
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Abstract
The application relates to the technical field of equipment monitoring, in particular to a monitoring method and a system for electrical equipment of a thermal power plant, wherein the method comprises the following steps: acquiring the operation data of the relevant measuring points of each device in real time, and processing the operation data according to a preset algorithm to obtain an operation data set of each device; based on the operation data set, calculating corresponding equipment state parameters, and carrying out equipment state evaluation on the electrical equipment; based on the equipment state evaluation result, life prediction is carried out on equipment with normal equipment state, and early warning reminding is carried out on equipment with abnormal equipment state. The application solves the problems that the running state of the equipment cannot be judged in time due to the diversification of the monitoring parameters of the equipment, and the fault cannot be found in time or the error judgment is made.
Description
Technical Field
The application relates to the technical field of equipment monitoring, in particular to a monitoring system and method for electrical equipment of a thermal power plant.
Background
Along with the improvement of the automation degree of the power plant, the traditional overhaul mode causes economic resource waste of the power plant, and meanwhile equipment maintenance deficiency or excessive maintenance is caused, so that the health management of the equipment is not facilitated. Economic losses due to equipment failure are also increasingly appreciated and focused by power generation enterprises. In this case, the operation and maintenance mode of performing state monitoring before the equipment fails, that is, pre-judging the possible failure in advance, must draw attention from the power plant.
In the prior art, the equipment operation state cannot be judged in time due to the diversification of equipment monitoring parameters, so that faults cannot be found in time or error judgment is made, and therefore, the application provides a monitoring method and a monitoring system for electric equipment of a thermal power plant.
Disclosure of Invention
Aiming at the problems in the prior art, the application aims to provide a monitoring method for electrical equipment of a thermal power plant, which solves the problems that the running state of the equipment cannot be judged in time, faults cannot be found in time or wrong judgment is made due to the diversification of equipment monitoring parameters.
In order to achieve the above purpose, the application provides a method and a system for monitoring electrical equipment of a thermal power plant, wherein the method comprises the following steps:
acquiring the operation data of the relevant measuring points of each device in real time, and processing the operation data according to a preset algorithm to obtain an operation data set of each device;
based on the operation data set, calculating corresponding equipment state parameters, and carrying out equipment state evaluation on the electrical equipment;
based on the equipment state evaluation result, life prediction is carried out on equipment with normal equipment state, and early warning reminding is carried out on equipment with abnormal equipment state.
In some embodiments of the application, calculating the corresponding device state parameters based on the operational data set includes:
comparing the operation data set with a preset standard data set to obtain a comparison result, and calculating corresponding equipment state parameters based on the comparison result, wherein the preset standard data set is operation data of each equipment associated measuring point when the equipment normally operates;
the corresponding equipment state parameter calculation formula is as follows:
wherein P is a corresponding equipment state parameter, si is a weight value of an ith equipment association measuring point in the operation data set, n is the number of corresponding equipment association measuring points in the operation data set, ti is the operation data of the ith equipment association measuring point in the operation data set, and T0i is the operation data of the ith equipment association measuring point in the preset standard data set.
In some embodiments of the application, device state evaluation of an electrical device according to a device state parameter comprises:
presetting a first preset equipment state parameter P01, a second preset equipment state parameter P02, a third preset equipment state parameter P03 and a fourth preset equipment state parameter P04, wherein P01 is more than P02 and less than P03 is more than P04;
when P01 is more than P and less than or equal to P02, determining that the equipment state of the current electrical equipment is a fault;
when P02 is more than P and less than or equal to P03, determining that the equipment state of the current electrical equipment is abnormal;
when P03 is less than or equal to P04, determining the current equipment state of the electrical equipment as attention;
when P04 is less than P, determining that the current equipment state of the electrical equipment is normal;
when the equipment state is attention, abnormality and fault, the equipment state is abnormal, and early warning reminding is carried out on equipment with the equipment state of attention, abnormality and fault.
In some embodiments of the present application, early warning reminding is performed on devices with attention, abnormality and faults, including:
acquiring first operation data of equipment association measuring points with equipment states of attention, abnormality and fault, and determining second operation data when the current electrical equipment is abnormally operated, and second fault probability and second fault type corresponding to the second operation data according to a historical database;
training a preset fault probability model according to the second operation data, and obtaining the fault probability model when the accuracy of the output result of the preset fault probability model is greater than the preset accuracy;
inputting first operation data based on a fault probability model to obtain a first fault probability and a first fault type of corresponding equipment;
a first preset probability and a second preset probability are preset, and the first preset probability is smaller than the second preset probability;
when the first fault probability is smaller than a first preset probability, performing feature monitoring on the current equipment, and not sending an early warning signal;
when the first fault probability is in the first preset probability and the second preset probability, a first early warning signal is sent;
when the first fault probability is larger than a second preset probability, a second early warning signal is sent;
the first early warning signal is smaller than the second early warning signal, and the early warning signal comprises a fault grade and a fault type.
In some embodiments of the present application, if the difference value of the fault probabilities of the plurality of equipment-associated measurement points is smaller than the preset fault probability difference value, the corresponding fault probabilities are corrected according to the corresponding weights and the working sequences of the plurality of equipment-associated measurement points, and the corresponding early warning signals are sent according to the corrected fault probabilities.
In some embodiments of the present application, life prediction is performed on a device with normal device status, including:
acquiring equipment operation data and equipment environment data of normal equipment states;
determining at least one similar environment of the corresponding equipment and similar environment data and historical loss data in a historical database according to the equipment environment data;
constructing a device life prediction model based on the historical loss data;
determining loss data of the current equipment according to the equipment environment data and the similar environment data, and inputting the loss data of the current equipment into a life prediction model to obtain a life prediction value of the current equipment;
and optimizing the current equipment according to the life predicted value and the preset life value.
In some embodiments of the application, determining characteristic environmental data according to equipment environmental data and environmental parameter influence degree, wherein the characteristic environmental data comprises environmental temperature, environmental humidity and corrosion degree, and setting a first environmental index function (x, y, z) according to a preset index under the characteristic environmental data;
determining historical characteristic environment data according to similar environment data and the influence degree of the environment parameters, wherein the historical characteristic environment data comprises historical environment temperature, historical environment humidity and historical corrosion, and setting a second environment index function (x 0, y0, z 0) according to preset indexes;
when x=x0, y=y0, and z=z0, determining a first environment and first loss data in a similar environment;
when x is not equal to x0 or y is not equal to y0 or z is not equal to z0, calculating a first environment adjustment coefficient r1 between x and x0 or a second environment adjustment coefficient r2 between y and y0 or a third environment adjustment coefficient r3 between z and z 0;
a first preset coefficient interval R01, a second preset coefficient interval R02, a third preset coefficient interval R03 and a fourth preset coefficient interval R04 are preset, and R01 is more than 0.6 and less than R02 and less than 1 and R03 and less than R04 and less than 1.5;
determining a second environment and second loss data in a similar environment when R01 < ri < R02;
when R02 is less than ri and less than or equal to R03, determining a third environment and third loss data in a similar environment;
when R03 is less than ri and less than or equal to R04, determining a fourth environment and fourth loss data in similar environments;
determining a fifth environment and fifth loss data in a similar environment when R04 < ri;
and inputting the loss data into a life prediction model to obtain life prediction values corresponding to different loss data, and transmitting a first early warning signal when the life prediction values are smaller than preset life values.
In some embodiments of the present application, processing the operational data according to a preset algorithm includes:
the preset algorithm comprises a double difference algorithm, a standardization algorithm and a dimension reduction algorithm;
the double method algorithm is used for analyzing the operation data of each equipment association measuring point, clearing abnormal data, setting a data reasonable interval of each equipment association measuring point, judging whether the operation data is in the data reasonable interval, if so, reserving the operation data, and if not, deleting the operation data, wherein the reserved operation data forms a clean data set;
the standardized algorithm is used for carrying out standardized processing on the operation data in the clean data set, and the standardized processing converts the operation data into dimensionless data which form a standard data set;
the dimension reduction algorithm is used for carrying out data dimension reduction on the operation data in the standard data set by adopting a factor analysis method, and the dimension reduced data form an operation data set.
In some embodiments of the application, a monitoring system for electrical equipment of a thermal power plant is further included:
the acquisition module is used for acquiring the operation data of each equipment associated measuring point in real time, and processing the operation data according to a preset algorithm to obtain an operation data set of each equipment;
the evaluation module is used for calculating corresponding equipment state parameters based on the operation data set and evaluating the equipment state of the electrical equipment;
and the early warning module is used for predicting the service life of equipment with normal equipment state based on the equipment state evaluation result and carrying out early warning reminding on equipment with abnormal equipment state.
The application provides a monitoring method and a system for electrical equipment of a thermal power plant, which have the following beneficial effects compared with the prior art:
the application discloses a monitoring method and a system for electrical equipment of a thermal power plant, wherein the method comprises the following steps: acquiring the operation data of the relevant measuring points of each device in real time, and processing the operation data according to a preset algorithm to obtain an operation data set of each device; based on the operation data set, calculating corresponding equipment state parameters, and carrying out equipment state evaluation on the electrical equipment; based on the equipment state evaluation result, predicting the service life of equipment with normal equipment state, and carrying out early warning reminding on equipment with abnormal equipment state;
according to the application, the operation data of each equipment associated measuring point is processed, the equipment state parameter is calculated through the operation data set of data cleaning, data standard and data dimension reduction, whether the current equipment state is normal or abnormal is determined, if the current equipment state is normal, the life prediction value of the corresponding equipment is predicted, if the current equipment state is abnormal, the fault probability and the fault type are obtained, and the corresponding early warning signal is sent according to the fault probability, so that the problem that the equipment operation state cannot be judged in time due to the equipment monitoring parameter diversification, and faults cannot be found in time or error judgment is made is solved.
Drawings
Fig. 1 is a schematic flow chart of a method for monitoring electrical equipment of a thermal power plant according to an embodiment of the application;
fig. 2 shows a schematic diagram of a monitoring system for electrical equipment of a thermal power plant according to an embodiment of the present application.
Detailed Description
The following describes in further detail the embodiments of the present application with reference to the drawings and examples. The following examples are illustrative of the application and are not intended to limit the scope of the application.
In the description of the present application, it should be understood that the terms "center," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate describing the present application and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present application.
The terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present application, unless otherwise indicated, the meaning of "a plurality" is two or more.
In the description of the present application, it should be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present application will be understood in specific cases by those of ordinary skill in the art.
The following is a description of preferred embodiments of the application, taken in conjunction with the accompanying drawings.
As shown in fig. 1, an embodiment of the application discloses a method for monitoring electrical equipment of a thermal power plant, which comprises the following steps:
step S101: acquiring the operation data of the relevant measuring points of each device in real time, and processing the operation data according to a preset algorithm to obtain an operation data set of each device;
step S102: based on the operation data set, calculating corresponding equipment state parameters, and carrying out equipment state evaluation on the electrical equipment;
step S103: based on the equipment state evaluation result, life prediction is carried out on equipment with normal equipment state, and early warning reminding is carried out on equipment with abnormal equipment state.
In this embodiment, each device has a plurality of associated measurement points, the operation data of the associated measurement points relate to a plurality of components of the device, such as voltage, current, vibration data, and the like, and the operation data is subjected to data cleaning, data standardization, and data dimension reduction according to a preset algorithm to obtain an operation data set, so that the technical problem that the operation state of the device cannot be timely judged due to the diversification of the monitoring parameters of the device is solved.
In some embodiments of the application, calculating the corresponding device state parameters based on the operational data set includes:
comparing the operation data set with a preset standard data set to obtain a comparison result, and calculating corresponding equipment state parameters based on the comparison result, wherein the preset standard data set is operation data of each equipment associated measuring point when the equipment normally operates;
the corresponding equipment state parameter calculation formula is as follows:
wherein P is a corresponding equipment state parameter, si is a weight value of an ith equipment association measuring point in the operation data set, n is the number of corresponding equipment association measuring points in the operation data set, ti is the operation data of the ith equipment association measuring point in the operation data set, and T0i is the operation data of the ith equipment association measuring point in the preset standard data set.
In this embodiment, the operation data set is mapped to the operation data in the preset standard data set one by one, the preset standard data set is identical to the processing program of the operation data set, the historical normal operation data is subjected to data cleaning, data standardization and data dimension reduction to obtain the preset standard data set, and the state parameters of the current device are calculated by performing processing calculation on a plurality of device association measuring points, so that the operation state of the current device is judged in a quantized mode, the current device is adjusted, more efficient operation of the current device is ensured, and the working performance of the device is improved.
In some embodiments of the application, device state evaluation of an electrical device according to a device state parameter comprises:
presetting a first preset equipment state parameter P01, a second preset equipment state parameter P02, a third preset equipment state parameter P03 and a fourth preset equipment state parameter P04, wherein P01 is more than P02 and less than P03 is more than P04;
when P01 is more than P and less than or equal to P02, determining that the equipment state of the current electrical equipment is a fault;
when P02 is more than P and less than or equal to P03, determining that the equipment state of the current electrical equipment is abnormal;
when P03 is less than or equal to P04, determining the current equipment state of the electrical equipment as attention;
when P04 is less than P, determining that the current equipment state of the electrical equipment is normal;
when the equipment state is attention, abnormality and fault, the equipment state is abnormal, and early warning reminding is carried out on equipment with the equipment state of attention, abnormality and fault.
In some embodiments of the present application, early warning reminding is performed on devices with attention, abnormality and faults, including:
acquiring first operation data of equipment association measuring points with equipment states of attention, abnormality and fault, and determining second operation data when the current electrical equipment is abnormally operated, and second fault probability and second fault type corresponding to the second operation data according to a historical database;
training a preset fault probability model according to the second operation data, and obtaining the fault probability model when the accuracy of the output result of the preset fault probability model is greater than the preset accuracy;
inputting first operation data based on a fault probability model to obtain a first fault probability and a first fault type of corresponding equipment;
a first preset probability and a second preset probability are preset, and the first preset probability is smaller than the second preset probability;
when the first fault probability is smaller than a first preset probability, performing feature monitoring on the current equipment, and not sending an early warning signal;
when the first fault probability is in the first preset probability and the second preset probability, a first early warning signal is sent;
when the first fault probability is larger than a second preset probability, a second early warning signal is sent;
the first early warning signal is smaller than the second early warning signal, and the early warning signal comprises a fault grade and a fault type.
In some embodiments of the present application, if the difference value of the fault probabilities of the plurality of equipment-associated measurement points is smaller than the preset fault probability difference value, the corresponding fault probabilities are corrected according to the corresponding weights and the working sequences of the plurality of equipment-associated measurement points, and the corresponding early warning signals are sent according to the corrected fault probabilities.
In some embodiments of the present application, life prediction is performed on a device with normal device status, including:
acquiring equipment operation data and equipment environment data of normal equipment states;
determining at least one similar environment of the corresponding equipment and similar environment data and historical loss data in a historical database according to the equipment environment data;
constructing a device life prediction model based on the historical loss data;
determining loss data of the current equipment according to the equipment environment data and the similar environment data, and inputting the loss data of the current equipment into a life prediction model to obtain a life prediction value of the current equipment;
and optimizing the current equipment according to the life predicted value and the preset life value.
In this embodiment, the history database includes history loss information corresponding to each device with the same device model, where the history loss information includes loss conditions of the device itself in the same environment or in different environments, where the same environment refers to loss conditions of the device itself in the same environment temperature, environment humidity, and corrosion, and where the different environments refer to loss conditions of the device itself in different combinations of environment temperature, environment humidity, and corrosion.
In some embodiments of the application, determining characteristic environmental data according to equipment environmental data and environmental parameter influence degree, wherein the characteristic environmental data comprises environmental temperature, environmental humidity and corrosion degree, and setting a first environmental index function (x, y, z) according to a preset index under the characteristic environmental data;
determining historical characteristic environment data according to similar environment data and the influence degree of the environment parameters, wherein the historical characteristic environment data comprises historical environment temperature, historical environment humidity and historical corrosion, and setting a second environment index function (x 0, y0, z 0) according to preset indexes;
when x=x0, y=y0, and z=z0, determining a first environment and first loss data in a similar environment;
when x is not equal to x0 or y is not equal to y0 or z is not equal to z0, calculating a first environment adjustment coefficient r1 between x and x0 or a second environment adjustment coefficient r2 between y and y0 or a third environment adjustment coefficient r3 between z and z 0;
a first preset coefficient interval R01, a second preset coefficient interval R02, a third preset coefficient interval R03 and a fourth preset coefficient interval R04 are preset, and R01 is more than 0.6 and less than R02 and less than 1 and R03 and less than R04 and less than 1.5;
determining a second environment and second loss data in a similar environment when R01 < ri < R02;
when R02 is less than ri and less than or equal to R03, determining a third environment and third loss data in a similar environment;
when R03 is less than ri and less than or equal to R04, determining a fourth environment and fourth loss data in similar environments;
determining a fifth environment and fifth loss data in a similar environment when R04 < ri;
and inputting the loss data into a life prediction model to obtain life prediction values corresponding to different loss data, and transmitting a first early warning signal when the life prediction values are smaller than preset life values.
In this embodiment, when determining an environment, a consistent historical environment and a similar environment are matched according to the current environmental temperature, environmental humidity and corrosion of the device, so as to obtain historical loss data of the historical environment or the similar environment, thereby obtaining a life prediction value, wherein the historical loss value is the loss condition of each associated measuring point of the device, the weight value and the loss condition of each associated measuring point determine the historical loss data, and the life prediction model is used for judging the loss condition of each measuring point correspondingly contained in the current device under the similar environment based on the historical loss information, so as to obtain the life prediction value of the current device.
In some embodiments of the present application, processing the operational data according to a preset algorithm includes:
the preset algorithm comprises a double difference algorithm, a standardization algorithm and a dimension reduction algorithm;
the double method algorithm is used for analyzing the operation data of each equipment association measuring point, clearing abnormal data, setting a data reasonable interval of each equipment association measuring point, judging whether the operation data is in the data reasonable interval, if so, reserving the operation data, and if not, deleting the operation data, wherein the reserved operation data forms a clean data set;
the standardized algorithm is used for carrying out standardized processing on the operation data in the clean data set, and the standardized processing converts the operation data into dimensionless data which form a standard data set;
the dimension reduction algorithm is used for carrying out data dimension reduction on the operation data in the standard data set by adopting a factor analysis method, and the dimension reduced data form an operation data set.
In this embodiment, the reasonable data interval is set according to the data mean value and the data variance, the data of the equipment associated measuring points have different dimensions and magnitudes, and the level difference between the measuring point indexes is relatively large. The method comprises the steps of processing cleaned data by adopting a Z-Score standardization method, mapping a sample characteristic value to a specific range, eliminating the influence of a data unit, converting the sample characteristic value into dimensionless data, facilitating comparison and weighting of multiple indexes, enabling the indexes of measurement points associated with equipment to be excessive, belonging to high-dimensional data, adopting a factor analysis method to reduce the dimension of the data, extracting useful comprehensive information according to a factor analysis step, and discarding useless information. And calculating a comprehensive score factor data set of the equipment by using data dimension reduction processing, and taking the comprehensive score factor data set as a compliance data set for training the equipment state model.
In some embodiments of the application, a monitoring system for electrical equipment of a thermal power plant is further included:
the acquisition module is used for acquiring the operation data of each equipment associated measuring point in real time, and processing the operation data according to a preset algorithm to obtain an operation data set of each equipment;
the evaluation module is used for calculating corresponding equipment state parameters based on the operation data set and evaluating the equipment state of the electrical equipment;
and the early warning module is used for predicting the service life of equipment with normal equipment state based on the equipment state evaluation result and carrying out early warning reminding on equipment with abnormal equipment state.
In summary, the application discloses a method and a system for monitoring electrical equipment of a thermal power plant, wherein the method comprises the following steps: acquiring the operation data of the relevant measuring points of each device in real time, and processing the operation data according to a preset algorithm to obtain an operation data set of each device; based on the operation data set, calculating corresponding equipment state parameters, and carrying out equipment state evaluation on the electrical equipment; based on the equipment state evaluation result, predicting the service life of equipment with normal equipment state, and carrying out early warning reminding on equipment with abnormal equipment state;
according to the application, the operation data of each equipment associated measuring point is processed, the equipment state parameter is calculated through the operation data set of data cleaning, data standard and data dimension reduction, whether the current equipment state is normal or abnormal is determined, if the current equipment state is normal, the life prediction value of the corresponding equipment is predicted, if the current equipment state is abnormal, the fault probability and the fault type are obtained, and the corresponding early warning signal is sent according to the fault probability, so that the problem that the equipment operation state cannot be judged in time due to the equipment monitoring parameter diversification, and faults cannot be found in time or error judgment is made is solved.
In the description of the above embodiments, particular features, structures, materials, or characteristics may be combined in any suitable manner in any one or more embodiments or examples.
Although the application has been described hereinabove with reference to embodiments, various modifications thereof may be made and equivalents may be substituted for elements thereof without departing from the scope of the application. In particular, the features of the disclosed embodiments may be combined with each other in any manner as long as there is no structural conflict, and the entire description of these combinations is not made in the present specification merely for the sake of omitting the descriptions and saving resources. Therefore, it is intended that the application not be limited to the particular embodiment disclosed, but that the application will include all embodiments falling within the scope of the appended claims.
Those of ordinary skill in the art will appreciate that: the above is only a preferred embodiment of the present application, and the present application is not limited thereto, but it is to be understood that the present application is described in detail with reference to the foregoing embodiments, and modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (9)
1. A method for monitoring electrical equipment of a thermal power plant, comprising:
acquiring the operation data of the relevant measuring points of each device in real time, and processing the operation data according to a preset algorithm to obtain an operation data set of each device;
based on the operation data set, calculating corresponding equipment state parameters, and carrying out equipment state evaluation on the electrical equipment;
based on the equipment state evaluation result, life prediction is carried out on equipment with normal equipment state, and early warning reminding is carried out on equipment with abnormal equipment state.
2. The method of monitoring electrical equipment of a thermal power plant according to claim 1, wherein calculating the corresponding equipment status parameters based on the operational data set comprises:
comparing the operation data set with a preset standard data set to obtain a comparison result, and calculating corresponding equipment state parameters based on the comparison result, wherein the preset standard data set is operation data of each equipment associated measuring point when the equipment normally operates;
the corresponding equipment state parameter calculation formula is as follows:
wherein P is a corresponding equipment state parameter, si is a weight value of an ith equipment association measuring point in the operation data set, n is the number of corresponding equipment association measuring points in the operation data set, ti is the operation data of the ith equipment association measuring point in the operation data set, and T0i is the operation data of the ith equipment association measuring point in the preset standard data set.
3. The method for monitoring electrical equipment of a thermal power plant according to claim 2, wherein the step of performing equipment status evaluation on the electrical equipment according to the equipment status parameter comprises the steps of:
presetting a first preset equipment state parameter P01, a second preset equipment state parameter P02, a third preset equipment state parameter P03 and a fourth preset equipment state parameter P04, wherein P01 is more than P02 and less than P03 is more than P04;
when P01 is more than P and less than or equal to P02, determining that the equipment state of the current electrical equipment is a fault;
when P02 is more than P and less than or equal to P03, determining that the equipment state of the current electrical equipment is abnormal;
when P03 is less than or equal to P04, determining the current equipment state of the electrical equipment as attention;
when P04 is less than P, determining that the current equipment state of the electrical equipment is normal;
when the equipment state is attention, abnormality and fault, the equipment state is abnormal, and early warning reminding is carried out on equipment with the equipment state of attention, abnormality and fault.
4. A method for monitoring electrical equipment in a thermal power plant according to claim 3, wherein the method for warning and reminding the equipment with attention, abnormality and fault states comprises the following steps:
acquiring first operation data of equipment association measuring points with equipment states of attention, abnormality and fault, and determining second operation data when the current electrical equipment is abnormally operated, and second fault probability and second fault type corresponding to the second operation data according to a historical database;
training a preset fault probability model according to the second operation data, and obtaining the fault probability model when the accuracy of the output result of the preset fault probability model is greater than the preset accuracy;
inputting first operation data based on a fault probability model to obtain a first fault probability and a first fault type of corresponding equipment;
a first preset probability and a second preset probability are preset, and the first preset probability is smaller than the second preset probability;
when the first fault probability is smaller than a first preset probability, performing feature monitoring on the current equipment, and not sending an early warning signal;
when the first fault probability is in the first preset probability and the second preset probability, a first early warning signal is sent;
when the first fault probability is larger than a second preset probability, a second early warning signal is sent;
the first early warning signal is smaller than the second early warning signal, and the early warning signal comprises a fault grade and a fault type.
5. The method for monitoring electrical equipment in a thermal power plant according to claim 4, wherein if the difference value of the fault probabilities of the plurality of equipment-associated measuring points is smaller than the preset fault probability difference value, the corresponding fault probabilities are corrected according to the corresponding weights and the working sequences of the plurality of equipment-associated measuring points, and the corresponding early warning signals are sent according to the corrected fault probabilities.
6. The method for monitoring electrical equipment in a thermal power plant according to claim 5, wherein the life prediction of equipment with normal equipment status comprises:
acquiring equipment operation data and equipment environment data of normal equipment states;
determining at least one similar environment of the corresponding equipment and similar environment data and historical loss data in a historical database according to the equipment environment data;
constructing a device life prediction model based on the historical loss data;
determining loss data of the current equipment according to the equipment environment data and the similar environment data, and inputting the loss data of the current equipment into a life prediction model to obtain a life prediction value of the current equipment;
and optimizing the current equipment according to the life predicted value and the preset life value.
7. A method for monitoring electrical equipment of a thermal power plant according to claim 6, wherein,
determining characteristic environment data according to the equipment environment data and the influence degree of the environment parameters, wherein the characteristic environment data comprises environment temperature, environment humidity and corrosiveness, and setting a first environment index function (x, y, z) according to preset indexes under the characteristic environment data;
determining historical characteristic environment data according to similar environment data and the influence degree of the environment parameters, wherein the historical characteristic environment data comprises historical environment temperature, historical environment humidity and historical corrosion, and setting a second environment index function (x 0, y0, z 0) according to preset indexes;
when x=x0, y=y0, and z=z0, determining a first environment and first loss data in a similar environment;
when x is not equal to x0 or y is not equal to y0 or z is not equal to z0, calculating a first environment adjustment coefficient r1 between x and x0 or a second environment adjustment coefficient r2 between y and y0 or a third environment adjustment coefficient r3 between z and z 0;
a first preset coefficient interval R01, a second preset coefficient interval R02, a third preset coefficient interval R03 and a fourth preset coefficient interval R04 are preset, and R01 is more than 0.6 and less than R02 and less than 1 and R03 and less than R04 and less than 1.5;
determining a second environment and second loss data in a similar environment when R01 < ri < R02;
when R02 is less than ri and less than or equal to R03, determining a third environment and third loss data in a similar environment;
when R03 is less than ri and less than or equal to R04, determining a fourth environment and fourth loss data in similar environments;
determining a fifth environment and fifth loss data in a similar environment when R04 < ri;
and inputting the loss data into a life prediction model to obtain life prediction values corresponding to different loss data, and transmitting a first early warning signal when the life prediction values are smaller than preset life values.
8. The method for monitoring electrical equipment of a thermal power plant according to claim 1, wherein the processing of the operation data according to a preset algorithm comprises:
the preset algorithm comprises a double difference algorithm, a standardization algorithm and a dimension reduction algorithm;
the double method algorithm is used for analyzing the operation data of each equipment association measuring point, clearing abnormal data, setting a data reasonable interval of each equipment association measuring point, judging whether the operation data is in the data reasonable interval, if so, reserving the operation data, and if not, deleting the operation data, wherein the reserved operation data forms a clean data set;
the standardized algorithm is used for carrying out standardized processing on the operation data in the clean data set, and the standardized processing converts the operation data into dimensionless data which form a standard data set;
the dimension reduction algorithm is used for carrying out data dimension reduction on the operation data in the standard data set by adopting a factor analysis method, and the dimension reduced data form an operation data set.
9. A monitoring system for electrical equipment of a thermal power plant, comprising:
the acquisition module is used for acquiring the operation data of each equipment associated measuring point in real time, and processing the operation data according to a preset algorithm to obtain an operation data set of each equipment;
the evaluation module is used for calculating corresponding equipment state parameters based on the operation data set and evaluating the equipment state of the electrical equipment;
and the early warning module is used for predicting the service life of equipment with normal equipment state based on the equipment state evaluation result and carrying out early warning reminding on equipment with abnormal equipment state.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117454121A (en) * | 2023-12-22 | 2024-01-26 | 华能济南黄台发电有限公司 | Data analysis processing method and system based on power plant safety precaution |
| CN117953661A (en) * | 2024-01-18 | 2024-04-30 | 浙江金大门业有限公司 | Method and system for monitoring and evaluating running state of electronic burglary-resisting door |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117454121A (en) * | 2023-12-22 | 2024-01-26 | 华能济南黄台发电有限公司 | Data analysis processing method and system based on power plant safety precaution |
| CN117454121B (en) * | 2023-12-22 | 2024-04-05 | 华能济南黄台发电有限公司 | Data analysis processing method and system based on power plant safety precaution |
| CN117953661A (en) * | 2024-01-18 | 2024-04-30 | 浙江金大门业有限公司 | Method and system for monitoring and evaluating running state of electronic burglary-resisting door |
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