CN111486043A - Lower rack fault diagnosis method based on hydro-turbo generator set runout data - Google Patents
Lower rack fault diagnosis method based on hydro-turbo generator set runout data Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03B—MACHINES OR ENGINES FOR LIQUIDS
- F03B11/00—Parts or details not provided for in, or of interest apart from, the preceding groups, e.g. wear-protection couplings, between turbine and generator
- F03B11/008—Measuring or testing arrangements
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/20—Hydro energy
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Abstract
The invention discloses a lower rack fault diagnosis method based on runout data of a hydroelectric generating set, which comprises the following steps: calculating a peak value and a frequency doubling value of the horizontal vibration of the lower rack according to the vibration waveform; establishing a three-dimensional model of guide vane opening, water head, peak value and frequency doubling value of horizontal vibration of a lower frame; extracting a lower rack horizontal vibration characteristic value sample and a lower rack horizontal vibration frequency doubling characteristic value sample according to data of the three-dimensional model in a stable state of the vibration value of the unit; and introducing the lower rack horizontal vibration characteristic value sample and the lower rack horizontal vibration frequency multiplication characteristic value sample into a fault diagnosis algorithm model to obtain a fault prediction trend. The invention relates to a lower rack fault diagnosis method based on hydro-turbo generator set runout data, which starts with the research of characteristic parameters of a normal operation state of a set, establishes characteristic quantity health samples according to operation conditions, and realizes the health evaluation and performance degradation prediction of the operation state of the set.
Description
Technical Field
The invention relates to the technical field of hydroelectric generating set vibration faults, in particular to a lower rack fault diagnosis method based on hydroelectric generating set vibration data.
Background
The monitoring of the state of the hydroelectric generating set is the basis for carrying out state maintenance, the real-time running condition of the hydroelectric generating set is tracked, the running of the hydroelectric generating set is ensured to be in a healthy state, the monitoring of the stability, the pressure pulsation, the cavitation, the partial discharge and the like are mainly included, and the stability monitoring mainly based on vibration signal analysis is common. The characteristic frequency band of the monitored vibration signal is easily influenced by the background noise and the complicated electromagnetic interference when the unit operates, so that the real operating state of the unit is difficult to reflect. The noise reduction analysis is carried out on the monitoring signals through an advanced signal processing method, so that the time and frequency characteristics of the signals can be accurately acquired, and the abnormal operation of the unit can be found in time.
The fault diagnosis is used as an important component of intelligent construction of the power station, namely, on the basis that a monitoring system comprehensively collects unit operation data, a signal processing and neural network technology is comprehensively applied, the unit operation health state is analyzed, possible faults are diagnosed, and finally a decision suggestion is given to provide guidance and basis for state maintenance, so that major operation accidents are avoided. The fault diagnosis of the hydroelectric generating set comprises three steps of establishing a three-dimensional model, extracting a characteristic value and predicting a slow variable. The extraction of the characteristic value is a key for improving the fault accuracy, so that how to extract the characteristic information capable of fully representing the unit running state from the monitoring signal is a hot spot of great concern. The variable working condition, the starting and the stopping of the hydroelectric generating set are frequent, and the monitored vibration signal has the characteristics of obvious non-stationarity, non-linearity, non-gauss and the like under the coupling action of multiple excitation factors during running. Aiming at the signals, a new feature extraction method capable of deeply reflecting the change rule of a complex large power system of a hydroelectric generating set is explored, and the method has important theoretical and practical significance. In addition, due to the particularity of the hydroelectric generating set equipment, diagnosis is often faced with the conditions of uneven sample distribution and inclined quantity. Therefore, it is urgently needed to develop a new diagnosis method for such problems, and to improve the accuracy of fault prediction. The method has the advantages that the state trend of the hydroelectric generating set is accurately predicted, the early-stage operation abnormity of the hydroelectric generating set is found in time, the state maintenance plan is scientifically and reasonably formulated, and the comprehensive economic benefit of the power station is improved.
Disclosure of Invention
The invention aims to solve the problems and provide a lower rack fault diagnosis method based on the runout data of the hydroelectric generating set.
In order to achieve the purpose, the present disclosure provides a lower rack fault diagnosis method based on oscillation data of a hydroelectric generating set, including the following steps:
s1, calculating the peak value and the frequency doubling value of the horizontal vibration of the lower frame according to the vibration waveform;
s2, establishing a three-dimensional model of guide vane opening, water head, peak value and frequency doubling value of lower frame horizontal vibration;
s3, extracting a lower frame horizontal vibration characteristic value sample and a lower frame horizontal vibration frequency doubling characteristic value sample according to data of the three-dimensional model in a stable state of the vibration value of the unit;
and S4, introducing the lower rack horizontal vibration characteristic value sample and the lower rack horizontal vibration frequency multiplication characteristic value sample into a fault diagnosis algorithm model to obtain a fault prediction trend.
The invention has the beneficial effects that:
1. the invention relates to a lower rack fault diagnosis method based on hydro-turbo generator set runout data, which starts with the research of characteristic parameters of a normal operation state of a unit, respectively establishes characteristic quantity health samples according to operation conditions, and realizes the health evaluation and performance degradation prediction of the operation state of the unit;
2. the characteristic values of the curves of the monitored quantities under different working conditions are analyzed in detail, association rules of the characteristic quantities and the working conditions are extracted by fusing the working conditions of the water turbine generator set and the monitored quantity signals, and the characteristic values of the health sample are extracted by using the monitored quantities, water heads, guide vane opening degrees, loads and other working condition parameters, so that the representativeness of the characteristic quantities is effectively improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure without limiting the disclosure. In the drawings:
FIG. 1 is a flow chart of a fault diagnosis method according to the present invention;
FIG. 2 is a horizontal vibration and frequency doubling trend chart of the lower frame according to the present invention;
FIG. 3 is a graph of the horizontal vibration, frequency multiplication, guide vane opening and water purification head trend of the lower frame of the present invention;
FIG. 4 is a model of the horizontal vibration characteristics of the lower frame according to the present invention;
FIG. 5 is a model of the frequency doubling characteristic of the horizontal vibration of the lower frame according to the present invention;
FIG. 6 is a plot of lower rack horizontal vibration versus lower rack horizontal vibration eigenvalue trend according to the present invention;
fig. 7 is a trend graph of the frequency multiplication characteristic value of the horizontal vibration of the lower rack and the frequency multiplication characteristic value of the horizontal vibration of the lower rack according to the present invention.
Detailed Description
The following detailed description of specific embodiments of the present disclosure is provided in connection with the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present disclosure, are given by way of illustration and explanation only, not limitation.
The frequency spectrum characteristic of the vibration signal is a main direction for researching the lower frame support loosening fault, the lower frame support loosening fault can cause major accident faults such as lower frame loosening, fracture and the like, and the main causes of the faults are bolt loosening of each part of the lower frame, foundation bolt loosening of a lower oil guide groove and the like. In order to more accurately predict and determine the lower frame support loose fault, characteristic values of a lower frame vibration frequency spectrum under different working conditions need to be analyzed.
The invention relates to a lower rack fault diagnosis method based on hydro-turbo generator set runout data, which comprises the following steps of:
and S1, calculating the peak-to-peak value and the frequency doubling value of the horizontal vibration of the lower rack according to the vibration waveform.
Specifically, using a vibration waveform algorithm template: the peak-to-peak value of the horizontal vibration of the lower frame and the frequency doubling of the horizontal vibration of the lower frame by 1 are calculated from the waveform in the direction of the lower frame X, Y by fourier transform.
And S2, establishing a three-dimensional model of guide vane opening, water head, peak value of horizontal vibration of the lower frame and frequency doubling value.
Specifically, the lower frame horizontal vibration peak value and the lower frame horizontal vibration frequency multiplication value calculated in S1 are used as a three-dimensional Z-axis, a water head and a guide vane opening degree are used as X, Y axes, and the three-dimensional Z-axis and the three-dimensional guide vane opening degree are subjected to convergence calculation in a three-dimensional model algorithm.
And S3, extracting a lower frame horizontal vibration characteristic value sample and a lower frame horizontal vibration frequency doubling characteristic value sample according to the data of the three-dimensional model in the stable state of the vibration value of the unit.
Specifically, the swing influence quantity converged in the three-dimensional model is circled, characteristic signals near a rated water head and with the opening degree of more than 50% are selected for extraction and output as characteristic values, and the characteristic values extracted from the three-dimensional model are subjected to early warning configuration according to actual operation conditions.
And S4, introducing the sample of the characteristic value of the horizontal vibration of the lower rack and the sample of the frequency doubling characteristic value of the horizontal vibration of the lower rack into a fault diagnosis algorithm for calculation to obtain a fault prediction trend.
Specifically, the fault diagnosis algorithm is to perform logical operation on the extracted lower rack horizontal vibration characteristic value, the extracted slow-varying quantity, the extracted lower rack horizontal vibration frequency multiplication characteristic value and the extracted slow-varying quantity, wherein after any two quantities reach alarm values, the algorithm gives an alarm for the abnormal fault of the lower rack vibration.
The fault of the water turbine generator set is closely related to the operation condition of the set, and the correlation curve between the vibration amplitude of the set and the working condition parameters such as the rotating speed, the opening degree, the exciting current and the like is an effective expression form for reflecting the health state of the set. Therefore, the working condition process of the water turbine generator set is monitored, reliable signal resources can be provided for stability analysis of the water turbine generator set, and an effective judgment basis can be provided for analysis of the fault reason of the water turbine generator set. On the contrary, if the influence of the operation condition on the unit monitoring amount is neglected, the unit health sample is extracted only by adopting the vibration signal analysis technology of the water turbine generator set in an isolated mode, and the analysis result may be inaccurate.
Therefore, curve characteristic values of the monitored quantity under different working conditions are analyzed in detail, association rules of the characteristic quantity and the working conditions are extracted by fusing the working conditions of the water turbine generator set and the monitored quantity signals, and the characteristic quantity representativeness is effectively improved by adopting the monitored quantity and working condition parameters such as water heads, guide vane opening degrees and loads as the extraction of the characteristic values of the health samples.
And calculating the horizontal vibration frequency doubling value of the lower rack and the horizontal vibration frequency doubling value of the lower rack by using an intermediate algorithm. The frequency doubling value of the horizontal vibration of the lower frame and the horizontal vibration of the lower frame of the No. 1 machine of a certain power plant is used below. Fig. 2 shows the frequency multiplication values of the horizontal vibration of the lower frame and the horizontal vibration of the lower frame during a certain period of the unit number 1, and it can be seen from the figure that the time series of the frequency multiplication values of the horizontal vibration of the lower frame and the horizontal vibration of the lower frame are very complex due to the complex structure and frequent switching of working conditions of the unit, and it is difficult to accurately analyze the actual operation state of the unit from the figure.
As shown in fig. 3, the unit head fluctuates in the interval [42, 52] during the time period; the guide vane opening is concentrated on [0, 48] when the lower frame horizontal vibration and the frequency multiplication value fluctuation are large, and the guide vane opening is concentrated on [50, 68] when the lower frame horizontal vibration and the frequency multiplication value are stable. The unit has important influence on unit operation parameters under the power generation working condition, guide vane opening and working water head, and horizontal vibration and frequency doubling value change of the lower rack are complicated due to continuous conversion of the unit working water head and the operation working condition, so that the real state of the unit cannot be directly obtained from vibration data.
In order to obtain the real running state of the hydroelectric generating set in real time, a guide vane opening-water head-lower frame horizontal vibration and frequency doubling state three-dimensional model needs to be established for the hydroelectric generating set to obtain a sample in a healthy state, and fig. 4 and 5 show the lower frame horizontal vibration and lower frame horizontal vibration frequency doubling three-dimensional model.
After the three-dimensional model is established, the vibration frequency domain characteristics and the time domain characteristics of the unit in the healthy state are established by adopting the fault-free data of the unit in the stable vibration value state, and representative and healthy characteristic quantity samples are extracted. As can be seen from fig. 6 and 7, the trend of the health samples eliminates data in other states such as transient state, noise interference and the like, only the data of the health samples are retained, and the health condition of the unit under the actual operation condition can be effectively analyzed.
After the health sample is established, an artificial intelligence data algorithm model, such as neural network, linear regression machine learning, etc., is introduced, and the specific calculation process is shown in fig. 1.
The existing fault diagnosis concept is based on fault symptoms, and the method of classifying and comparing parameters obtained by state monitoring with the fault symptoms in a fault knowledge base is utilized to identify equipment faults, judge the fault occurrence positions, the fault properties and degrees, the generation reasons and the like of the equipment faults, and further predict the development trend of the equipment faults. Due to the particularity of the hydroelectric generating set, the 'fault' performance of the hydroelectric generating set can be diversified, a fault sample cannot be completed at present, and a practical achievement is difficult to obtain in a short time based on the fault characteristics of the hydroelectric generating set and a diagnosis method of the fault sample. Starting from the research on characteristic parameters of the normal operation state of the unit, a theoretical method for establishing a characteristic quantity health sample is provided on the basis of probability theory and mathematical statistics theory, and health evaluation and performance degradation prediction are carried out on the operation state of the unit by combining with an example.
The influence of the unit operation condition on the monitoring parameter characteristic quantity is analyzed and discussed by taking the actual monitoring quantity of the power station as an example. If the operating conditions of the unit are not considered when the health sample is established, the sample is dispersed, the standard deviation is large, the established health sample limit is too wide, and the health evaluation of the operating state of the unit is not facilitated. The theoretical method for respectively establishing the characteristic quantity health samples according to the running conditions under the stable working condition of the control unit is provided, so that relatively ideal health samples can be established.
According to the time series characteristics of the characteristic indexes of the monitoring quantity of the hydroelectric generating set, a hydroelectric generating set characteristic quantity trend prediction model based on time series change decomposition is established, and algorithms of trend prediction and performance degradation prediction based on the time series decomposition model are provided. The analysis model and the algorithm are verified by adopting the actual monitoring data of the power station, and the result shows that the prediction trend is well consistent with the actual monitoring trend, the quantitative trend prediction and the performance degradation prediction of the monitoring characteristic quantity of the hydroelectric generating set can be met, and the method has good practicability and application prospect for early warning of the potential abnormality of the hydroelectric generating set.
The preferred embodiments of the present disclosure are described in detail with reference to the accompanying drawings, however, the present disclosure is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solution of the present disclosure within the technical idea of the present disclosure, and these simple modifications all belong to the protection scope of the present disclosure.
It should be noted that, in the foregoing embodiments, various features described in the above embodiments may be combined in any suitable manner, and in order to avoid unnecessary repetition, various combinations that are possible in the present disclosure are not described again.
In addition, any combination of various embodiments of the present disclosure may be made, and the same should be considered as the disclosure of the present disclosure, as long as it does not depart from the spirit of the present disclosure.
Claims (3)
1. The lower rack fault diagnosis method based on the runout data of the hydroelectric generating set is characterized by comprising the following steps of:
s1, calculating the peak value and the frequency doubling value of the horizontal vibration of the lower frame according to the vibration waveform;
s2, establishing a three-dimensional model of guide vane opening, water head, peak value and frequency doubling value of lower frame horizontal vibration;
s3, extracting a lower frame horizontal vibration characteristic value sample and a lower frame horizontal vibration frequency doubling characteristic value sample according to data of the three-dimensional model in a stable state of the vibration value of the unit;
and S4, introducing the lower rack horizontal vibration characteristic value sample and the lower rack horizontal vibration frequency multiplication characteristic value sample into a fault diagnosis algorithm model to obtain a fault prediction trend.
2. The lower rack fault diagnosis method based on the runout data of the hydroelectric generating set according to claim 1, wherein S3 is used for extracting characteristic value samples according to the rated water head fluctuation range and the guide vane opening degree of more than 50%.
3. The method for diagnosing the fault of the lower rack based on the runout data of the hydroelectric generating set according to claim 1, wherein the characteristic values extracted from the three-dimensional model are subjected to early warning configuration.
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Address after: Huaneng building, No.47, section 4, Renmin South Road, Wuhou District, Chengdu, Sichuan 610000 Applicant after: Huaneng Sichuan Energy Development Co.,Ltd. Applicant after: CHINA HUANENG GROUP Co.,Ltd. Applicant after: BEIJING ORIENTGE TECHNOLOGY RESEARCH INSTITUTE Co.,Ltd. Address before: Huaneng building, No.47, section 4, Renmin South Road, Wuhou District, Chengdu, Sichuan 610000 Applicant before: Huaneng Sichuan Hydropower Co.,Ltd. Applicant before: CHINA HUANENG GROUP Co.,Ltd. Applicant before: BEIJING ORIENTGE TECHNOLOGY RESEARCH INSTITUTE Co.,Ltd. |
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