CN111046582A - Nuclear power station diesel generating set coil temperature early warning method and system - Google Patents
Nuclear power station diesel generating set coil temperature early warning method and system Download PDFInfo
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Abstract
The invention provides a nuclear power station diesel generator set coil temperature early warning method and a nuclear power station diesel generator set coil temperature early warning system, wherein the method comprises the following steps: acquiring an actual temperature value of a generator set coil and parameter data related to the temperature; inputting the parameter data related to the temperature into a temperature prediction model, and calculating to obtain a real-time prediction value of the temperature of the coil of the generator set; comparing the actual temperature value with a real-time predicted value, and judging whether early warning is needed or not according to the difference value of the actual temperature value and the real-time predicted value; and when the difference value exceeds a set threshold range, giving out early warning information. According to the invention, the synchronous acquisition system of the coil temperature of the diesel generating set is built, the acquired coil temperature is counted in real time, the abnormity analysis is carried out according to the real-time predicted value, and early warning is carried out in time when abnormity occurs, so that the normal operation of the diesel generating set is ensured.
Description
Technical Field
The invention relates to the technical field of generator set temperature measurement, in particular to a nuclear power station diesel generator set coil temperature early warning method and system.
Background
The nuclear power emergency diesel engine set is used as the last barrier of a nuclear power supply system, and the safety and the reliability of the nuclear power emergency diesel engine set are guaranteed to be of great significance. For the emergency diesel generator set of the nuclear power plant, multiple signal types of the diesel generator set belong to quasi-periodic non-stationary signals, so that the diesel generator set has the characteristics of complex fault mechanism, difficulty in uniform acquisition and processing of various signals, difficulty in extraction of fault characteristics and the like.
The existing emergency diesel engine of the nuclear power station is old, and a local generator set coil temperature display instrument can only display a current value, cannot check historical data and does not have data storage and transmission functions. The current detection mode is mainly through the mode of regularly patrolling and examining to manual record data, and artificially set for temperature early warning threshold value etc. this kind of mode can not be complete the running condition of reaction diesel engine, can not adapt to the complex condition of diesel engine operating condition, can't catch transient process and time sequence change process, therefore can't assess equipment health value and in time make the early warning, cause diesel generating set to break down very easily and the condition that can't in time maintain.
Disclosure of Invention
In view of the above, the main object of the present invention is to provide a method and a system for early warning a coil temperature of a diesel generator set in a nuclear power station, wherein a synchronous acquisition system of the coil temperature of the diesel generator set is built, the acquired coil temperature is counted in real time, an anomaly analysis is performed according to a real-time predicted value, and early warning is performed in time when an anomaly occurs, so as to ensure normal operation of the diesel generator set.
The invention adopts the technical scheme that a nuclear power station diesel generating set coil temperature early warning method comprises the following steps:
A. acquiring an actual temperature value of a generator set coil and parameter data related to the temperature;
B. inputting the parameter data related to the temperature into a temperature prediction model, and calculating to obtain a real-time prediction value of the temperature of the coil of the generator set;
C. comparing the actual temperature value with a real-time predicted value, and judging whether early warning is needed or not according to the difference value of the actual temperature value and the real-time predicted value; and
D. and when the difference value exceeds a set threshold range, giving out early warning information.
According to the method, the collected coil temperature is counted in real time, the real-time predicted value of the coil temperature is calculated according to the collected parameter data related to the temperature, then the difference value of the predicted value and the actual value is utilized to carry out abnormity analysis, and early warning is carried out in time when abnormity occurs, so that the normal operation of the diesel generating set is ensured
Wherein, the temperature prediction model in the step B is established through the following steps:
acquiring historical data of the coil temperature of the generator set and parameters related to the temperature from a database;
classifying the running states of the historical data by adopting a clustering algorithm, and aligning according to the time stamps;
performing pairwise correlation analysis on the classified temperature-related parameter historical data and coil temperature historical data to obtain a correlation coefficient of each parameter and the coil temperature;
and establishing a temperature prediction model by using the historical data and the correlation coefficient of each parameter and the coil temperature by taking the running state as a modeling direction.
Therefore, historical data of the coil temperature and parameters related to the temperature are obtained from the database, the historical data are classified according to the running state of the generator set, pairwise correlation analysis is carried out on each parameter and the coil temperature to obtain a correlation coefficient, and a prediction model of the parameters related to the temperature on the coil temperature can be calculated by adopting a neural network algorithm according to the historical data and the correlation coefficient.
In a further improvement, before the classification of the operation state, the method further comprises the following steps:
and cleaning historical data of the generator set coil temperature and the temperature-related parameters, synchronously removing or replacing invalid data according to the time stamp, and eliminating inconsistency among the data.
By the above, when data at a certain moment of a certain parameter in collected historical data is abnormal or obviously wrong, the data at the moment of the parameter belongs to invalid data and needs to be removed, and meanwhile, in order to ensure synchronous alignment of timestamps, data of other parameters at the moment also need to be removed, so that the situation that data samples are insufficient due to excessive data removal is prevented, and invalid data can be replaced by the data of the same timestamp in a similar operation state by combining with the operation state of a generator set.
Wherein the threshold range of the difference is determined by:
performing inverse calculation on parameter historical data related to the temperature by using the temperature prediction model to obtain prediction data corresponding to the coil temperature historical data;
and calculating the difference value of the historical coil temperature data and the corresponding prediction data, and performing Gaussian distribution processing to obtain the mean value and the standard deviation of the difference value, thereby determining the threshold range of the difference value.
By adopting the temperature prediction model to perform inverse calculation on the historical data, performing difference calculation on the prediction data obtained by inverse calculation and the historical data of the coil temperature, performing Gaussian distribution processing to obtain the mean value of the difference and the range of the upper standard deviation and the lower standard deviation, thereby determining the threshold range of the difference,
in a further improvement, the step a further includes:
and C, comparing the acquired actual temperature value of the generator set coil and the parameter data related to the temperature with the corresponding set threshold respectively, if the actual temperature value and the parameter data are beyond the range of the set threshold, performing early warning on corresponding parameters and ending the flow of the early warning method, otherwise, turning to the step B.
By the above, by setting a threshold with a large range for the coil temperature of the generator set and the related parameters respectively, when the acquired data exceeds the threshold range, early warning of the corresponding parameters can be directly performed, so that the problem of hardware corresponding to the parameters can be solved in time, and if the acquired data of each parameter does not exceed the threshold range, detection of subsequent steps can be performed by using the temperature prediction model.
Further improved, the method also comprises the following steps:
and introducing the collected actual temperature value of the generator set coil and the parameter data related to the temperature into a temperature prediction model as data samples, and performing deep optimization on the temperature prediction model.
Therefore, the data acquired each time are introduced into the temperature prediction model for optimization, so that the temperature prediction model is more accurate and more accurate early warning information is output.
Wherein the parameter related to genset coil temperature comprises:
power, voltage, current, and ambient temperature of the genset.
Therefore, in actual operation, the coil temperature of the generator set is often influenced by parameters such as the power, voltage, current and ambient temperature of the generator set.
Based on the method, the invention also provides a coil temperature early warning system of the diesel generating set, which comprises the following steps:
the data acquisition module is used for acquiring the actual temperature value of the generator set coil and parameter data related to the temperature;
the early warning module is used for inputting the parameter data related to the temperature into the temperature prediction model, calculating to obtain a real-time predicted value of the temperature of the coil of the generator set, comparing the actual temperature value with the real-time predicted value, and giving early warning information when the difference value of the actual temperature value and the real-time predicted value exceeds a set threshold range;
the storage module is used for storing the data acquired by the data module, the data obtained by calculation of the early warning module and early warning information;
and the display module is used for displaying the acquired data, the real-time predicted value and the early warning information.
Therefore, the synchronous acquisition system for the coil temperature of the diesel generating set is built, the acquired coil temperature is counted in real time, abnormal analysis is carried out according to a real-time predicted value, and early warning is carried out in time when an abnormality occurs, so that the normal operation of the diesel generating set is ensured.
And the early warning module is further used for comparing the acquired actual temperature value of the generator set coil and the parameter data related to the temperature with the corresponding set threshold respectively, and if the actual temperature value and the parameter data are beyond the range of the set threshold, giving out early warning information of the corresponding parameters.
By the above, the threshold with a large range is set for the coil temperature of the generator set and the related parameters respectively, and when the acquired data exceed the threshold range, the early warning of the corresponding parameters can be directly performed, so that the fault of hardware corresponding to the parameters can be solved in time.
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FIG. 1 is a schematic flow chart of a coil temperature early warning method for a diesel generator set of a nuclear power station according to the invention;
FIG. 2 is a schematic diagram of the coil temperature early warning system of the nuclear power station diesel generator set.
Detailed Description
The following describes in detail specific embodiments of a coil temperature early warning method and a coil temperature early warning system for a nuclear power plant diesel generator set provided by the present invention with reference to fig. 1 to 2.
Fig. 1 is a schematic flow chart of a nuclear power plant diesel generator set coil temperature early warning method according to an embodiment of the present invention, and as shown in fig. 1, the nuclear power plant diesel generator set coil temperature early warning method according to the embodiment of the present invention includes the following steps:
s100: acquiring an actual temperature value of a generator set coil and parameter data related to the temperature;
the method comprises the steps of periodically collecting actual temperature values of a generator set coil and parameter data related to the temperature, wherein the collection period can be set according to actual conditions;
in practical applications, the temperature-related parameter data may include data that the power, voltage, current, and ambient temperature of the genset are directly or indirectly related to the genset coil temperature;
in this step, the collected actual temperature value of the generator set coil and the parameter data related to the temperature may be compared with the corresponding set threshold value, the comparison process is a preliminary detection step for performing early warning on the obvious abnormal information, wherein the set threshold value range of the coil temperature and the related parameter is the maximum range that can be achieved in the normal working state, when any collected parameter value exceeds the set threshold value range, the early warning of the corresponding parameter is performed and the flow of the early warning method is ended, otherwise, the step S200 is switched to.
S200: inputting the parameter data related to the temperature into a temperature prediction model, and calculating to obtain a real-time prediction value of the temperature of the coil of the generator set;
in this step, the temperature prediction model is established by the following substeps:
s201: acquiring historical data of the coil temperature of the generator set and parameters related to the temperature from a database;
s202: cleaning historical data of the temperature of the generator set coil and parameters related to the temperature, synchronously eliminating or replacing invalid data according to a timestamp, and eliminating inconsistency among the data;
it is worth to be noted that, some invalid data exist in the historical data of multiple parameters collected from the database, so before using the data, the data which are obviously invalid need to be cleaned, and the cleaning can be in a mode of removing or replacing;
when the invalid data is removed, in order to ensure the alignment of the timestamps of all the parameters, other parameter data corresponding to the timestamps also need to be removed synchronously. When only one or two parameters of the data corresponding to the timestamp are invalid, an alternative mode can be adopted, namely the data characteristics are extracted from the similar operation states in other periods in combination with the operation state of the generator set to carry out data transplantation, and the transplanted data is controlled within a theoretical range to ensure the accuracy of the data.
S203: classifying the running states of the historical data by adopting a clustering algorithm, and aligning according to the time stamps;
for example, a diesel engine is generally divided into a hot standby state, an operating state and a cooling state according to an operating condition, the operating state is further divided into a loaded state and an unloaded state, and the states are reflected to collected parameter data, namely a rotating speed, active power, reactive power and the like. In this embodiment, a clustering algorithm may be used to divide the historical data of multiple parameters into three categories: and working, no-load running and non-running states are operated, and a prediction model of the coil temperature in each state needs to be established for each state.
S204: performing pairwise correlation analysis on the classified temperature-related parameter historical data and coil temperature historical data to obtain a correlation coefficient of each parameter and the coil temperature;
in this step, since each parameter has different weight on the influence of the coil temperature, it is necessary to perform pairwise correlation analysis on the historical data of each parameter and the corresponding coil temperature data to obtain a correlation coefficient between each parameter and the coil temperature.
S205: and establishing a temperature prediction model by using the historical data and the correlation coefficient of each parameter and the coil temperature by taking the running state as a modeling direction.
According to the classified states, sequencing the timestamps of the historical data of the parameters in each state, ensuring that the timestamps of the parameters are the same, and establishing a prediction model of the parameters to the coil temperature by using a neural network algorithm according to the historical data with aligned timestamps and the calculated correlation coefficient of each parameter and the coil temperature;
and introducing the acquired parameter data related to the coil temperature into the temperature prediction model to obtain a real-time predicted value of the coil temperature.
S300: comparing the actual temperature value with a real-time predicted value, and judging whether early warning is needed or not according to the difference value of the actual temperature value and the real-time predicted value;
s400: when the difference value exceeds a set threshold range, giving out early warning information;
in this step, the threshold range of the difference between the actual temperature and the real-time predicted value may be determined by the coil temperature and the historical data of the parameter related to the coil temperature, specifically:
performing inverse calculation on parameter historical data related to the temperature by using the temperature prediction model to obtain prediction data corresponding to the coil temperature historical data;
and calculating the difference value of the historical coil temperature data and the corresponding prediction data, performing Gaussian distribution processing to obtain the mean value and the standard deviation of the difference value, and determining the threshold range of the difference value by using the mean value and the standard deviation, wherein the range of the standard deviation can be set according to the actual situation.
S500: the method comprises the steps of taking an actual temperature value of a coil of a generator set and parameter data related to temperature as data samples, introducing the data samples into a temperature prediction model, and carrying out deep optimization on the temperature prediction model;
in the step, the actual temperature value of the generator set coil and the parameter data related to the temperature acquired each time are taken as data samples and introduced into the temperature prediction model, so that the temperature prediction model is continuously corrected and deeply optimized, the operation condition of the diesel generator set is more met, and the abnormal conditions of the coil temperature and the related parameters of the diesel generator set are accurately detected.
Fig. 2 is a schematic diagram of a principle of a coil temperature early warning system of a nuclear power plant diesel generator set according to another embodiment of the present invention, and as shown in fig. 2, the coil temperature early warning system of the nuclear power plant diesel generator set according to the embodiment of the present invention includes:
the data acquisition module 100 is used for acquiring an actual temperature value of a generator set coil and parameter data related to the temperature;
a communication module 200, configured to implement data communication between modules of the system;
the early warning module 300 is configured to compare the acquired actual temperature value of the generator set coil and the parameter data related to the temperature with a corresponding set threshold, respectively, if the actual temperature value and the parameter data related to the temperature exceed the range of the set threshold, give early warning information of a corresponding parameter, if the actual temperature value and the parameter data related to the temperature do not exceed the range of the set threshold, input the parameter data related to the temperature into a temperature prediction model, calculate a real-time predicted value of the generator set coil temperature, compare the actual temperature value with the real-time predicted value, and give early warning information when a difference value between the actual temperature value and the real-time predicted value exceeds the range of the;
the storage module 400 is used for storing the data acquired by the data module, the data calculated by the early warning module and early warning information;
and the display module 500 is used for displaying the acquired data, the real-time predicted value and the early warning information.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (9)
1. A nuclear power station diesel generating set coil temperature early warning method is characterized by comprising the following steps:
A. acquiring an actual temperature value of a generator set coil and parameter data related to the temperature;
B. inputting the parameter data related to the temperature into a temperature prediction model, and calculating to obtain a real-time prediction value of the temperature of the coil of the generator set;
C. comparing the actual temperature value with a real-time predicted value, and judging whether early warning is needed or not according to the difference value of the actual temperature value and the real-time predicted value; and
D. and when the difference value exceeds a set threshold range, giving out early warning information.
2. The method of claim 1, wherein the temperature prediction model of step B is created by:
acquiring historical data of the coil temperature of the generator set and parameters related to the temperature from a database;
classifying the running states of the historical data by adopting a clustering algorithm, and aligning according to the time stamps;
performing pairwise correlation analysis on the classified temperature-related parameter historical data and coil temperature historical data to obtain a correlation coefficient of each parameter and the coil temperature;
and establishing a temperature prediction model by using the historical data and the correlation coefficient of each parameter and the coil temperature by taking the running state as a modeling direction.
3. The method of claim 2, wherein prior to said performing an operational status classification, further comprising the steps of:
and cleaning historical data of the generator set coil temperature and the temperature-related parameters, synchronously removing or replacing invalid data according to the time stamp, and eliminating inconsistency among the data.
4. The method of claim 2, wherein the threshold range of differences is determined by:
performing inverse calculation on parameter historical data related to the temperature by using the temperature prediction model to obtain prediction data corresponding to the coil temperature historical data;
and calculating the difference value of the historical coil temperature data and the corresponding prediction data, and performing Gaussian distribution processing to obtain the mean value and the standard deviation of the difference value, thereby determining the threshold range of the difference value.
5. The method of claim 1, wherein step a further comprises:
and C, comparing the acquired actual temperature value of the generator set coil and the parameter data related to the temperature with the corresponding set threshold respectively, if the actual temperature value and the parameter data are beyond the range of the set threshold, performing early warning on corresponding parameters and ending the flow of the early warning method, otherwise, turning to the step B.
6. The method of claim 1, further comprising the step of:
and introducing the collected actual temperature value of the generator set coil and the parameter data related to the temperature into a temperature prediction model as data samples, and performing deep optimization on the temperature prediction model.
7. The method of claim 1, wherein the parameter related to genset coil temperature comprises:
power, voltage, current, and ambient temperature of the genset.
8. A nuclear power station diesel generator set coil temperature early warning system based on the method of any one of claims 1 to 7, characterized by comprising:
the data acquisition module is used for acquiring the actual temperature value of the generator set coil and parameter data related to the temperature;
the early warning module is used for inputting the parameter data related to the temperature into the temperature prediction model, calculating to obtain a real-time predicted value of the temperature of the coil of the generator set, comparing the actual temperature value with the real-time predicted value, and giving early warning information when the difference value of the actual temperature value and the real-time predicted value exceeds a set threshold range;
the storage module is used for storing the data acquired by the data module, the data obtained by calculation of the early warning module and early warning information;
and the display module is used for displaying the acquired data, the real-time predicted value and the early warning information.
9. The system of claim 8, wherein the early warning module is further configured to compare the acquired actual temperature value of the generator set coil and the parameter data related to the temperature with corresponding set thresholds, and if the actual temperature value and the parameter data related to the temperature exceed the range of the set thresholds, give early warning information of corresponding parameters.
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| CN112710413B (en) * | 2020-12-07 | 2022-11-29 | 潍柴动力股份有限公司 | Temperature sensor checking method and device, storage medium and electronic equipment |
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| CN112504511B (en) * | 2020-12-15 | 2023-08-15 | 润电能源科学技术有限公司 | Generator stator temperature monitoring method, device and medium |
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| CN114944698B (en) * | 2022-04-29 | 2024-05-03 | 湛江伟力机电设备有限公司 | Intelligent diesel generator group control method and system |
| CN116736115A (en) * | 2023-08-14 | 2023-09-12 | 山东开创电气有限公司 | Temperature monitoring method and system for coal mine belt conveying motor |
| CN116736115B (en) * | 2023-08-14 | 2023-10-20 | 山东开创电气有限公司 | Temperature monitoring method and system for coal mine belt conveying motor |
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Application publication date: 20200421 |