CN113536969A - Defect diagnosis method and system for high-voltage reactor - Google Patents
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Abstract
The invention relates to the technical field of defect diagnosis of power transmission and transformation facilities, and discloses a defect diagnosis method for a high-voltage reactor, which comprises the steps of collecting vibration signals at the side of the high-voltage reactor, and carrying out analog-digital conversion on the collected signals; establishing a model, setting an empirical coefficient, substituting the empirical coefficient into signal data, calculating to obtain an initial diagnosis result, sending the diagnosis result, a vibration intensity map and a vibration signal segmentation discrete power map to a monitoring person, giving a field diagnosis result by the field monitoring person by synthesizing the information, feeding the result back to the model, performing machine learning by a neural network algorithm, correcting the corresponding empirical coefficient, substituting the signal data acquired in real time, and calculating to obtain a final diagnosis result. The invention also provides a system comprising a non-transitory readable recording medium storing the program of the method and a processing circuit, wherein the processing circuit can call the program to execute the method, and the method is suitable for diagnosing and analyzing the defects of the high-voltage reactor.
Description
Technical Field
The invention relates to the technical field of defect diagnosis of power transmission and transformation facilities, in particular to a defect diagnosis method for a high-voltage reactor.
Background
In order to enhance reactive compensation and reactive balance in a power system, inhibit overvoltage of the system, improve power quality and power supply reliability, the high-voltage shunt reactor is widely used. By the end of 2015, the operation amount of the 220 kV-1000 kV high-voltage shunt reactors of the national power grid company is 1703, and the annual growth rate is about 11.1%. With the increasing number of commissioning, monitoring and diagnosis of the reactor are more and more emphasized.
According to statistics, in the reactor defects and faults occurring in 2006-2015 by national grid companies, the fault caused by part loosening and damage caused by vibration accounts for 16.7% of all faults, and is the most main fault cause after assembly problems and design defects. The defects are difficult to find by the methods of electric quantity monitoring, oil chromatography monitoring and the like which are commonly used at present, and potential safety hazards exist.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides the high-voltage reactor defect diagnosis method which has the advantages of high accuracy and quick diagnosis and solves the problems.
The technical scheme provided by the invention comprises the following steps:
s1, collecting vibration signals at the side of a high-voltage reactor, and carrying out analog-digital conversion on the collected signals;
s2, establishing a model, setting an empirical coefficient, substituting the empirical coefficient into signal data, and calculating to obtain an initial diagnosis result;
s3, sending the diagnosis result, the vibration intensity map and the vibration signal segmentation discrete power map to a field monitoring person, and giving a field diagnosis result by the monitoring person by integrating the information;
and S4, feeding back the on-site diagnosis result to the model, performing machine learning through a neural network algorithm, correcting the corresponding experience coefficient, substituting the experience coefficient into the signal data acquired in real time, and calculating to obtain a final diagnosis result.
Preferably, the step S1 includes collecting vibration signals of the high-voltage reactor side including vibration signals of each side of the high-voltage reactor. Thus, the sampling is more comprehensive, and the diagnosis deviation is smaller.
Since most faults are caused by the loose iron core defect in the winding, the empirical coefficients in the step S2 preferably include characteristic values of the loose iron core and the reactor winding.
Further, the characteristic value includes a principal component coefficient Mhc and a parameter that reflects a vibration spectrum and a waveform.
Furthermore, the parameters comprise amplitude of 100Hz and integral multiple thereof, wavelet packet decomposition coefficient of the vibration signal and segmented discrete power spectrum.
Another aspect of the present invention is to provide a system for diagnosing defects of a high voltage reactor, comprising a non-transitory readable recording medium storing a program and a processing circuit, wherein the processing circuit can call the program to execute the steps S1-S4 of the method.
Preferably, processing circuit includes vibration acceleration sensor, signal shielded wire and detection device host computer, vibration acceleration sensor has a plurality ofly, sets up in treating every one side of monitoring high voltage reactor, with the detection device host computer passes through signal shielded wire electric connection, the detection device host computer still includes display screen, computer integrated circuit board and data acquisition card.
Still another aspect of the present invention is to provide a non-transitory readable recording medium storing one or more programs including instructions, the programs including the steps included in the method for diagnosing a defect of a high-voltage reactor.
Compared with the prior art, the method and the system for diagnosing the defects of the high-voltage reactor have the following beneficial effects:
according to the method for analyzing the vibration signal of the high-voltage reactor, experience judgment of field monitoring personnel of the loosening characteristic values of the reactor winding and the iron core is brought into the category of machine learning, so that the high-voltage reactor can be pre-disconnected as soon as possible when the vibration acceleration sensor displaces, measures are taken, and normal operation of the high-voltage reactor and accessory facilities thereof is guaranteed.
Drawings
FIG. 1 is a schematic diagram of a system according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a host of the diagnostic testing apparatus according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-2, the implementation steps of the present embodiment are as follows:
the high-voltage reactor mechanical defect diagnosis system comprises a plurality of vibration acceleration sensors, a signal shielding line and a detection device host, wherein the vibration acceleration sensors are arranged on each surface of the high-voltage reactor, the detection device host further comprises a display screen, a computer board card and a data acquisition card, and the output end of each vibration acceleration sensor is electrically connected with the input end of the detection device host through the signal shielding line.
The data acquisition card comprises a data acquisition module, a data storage module and a signal conversion module, wherein the input end of the data acquisition module is electrically connected with the output ends of a plurality of vibration acceleration sensors, the output end of the data acquisition module is electrically connected with the input end of the signal conversion module, the plurality of vibration acceleration sensors are marked according to the positions of the vibration acceleration sensors on the high-voltage reactor, the storage input end of the data storage module is electrically connected with the output end of the signal conversion module, in order to obtain more information as much as possible, the data storage module has an intelligent storage function, the number of days for data storage can be set, the data storage module is automatically cleared after the expiration date is exceeded, the data storage module is stored for 30 days by default, and the data storage module can store the data periodically and discontinuously, including original data and characteristic parameters, the characteristic parameters are stored in order to visually observe the change of each parameter of the reactor in long-term operation. The default is that 1S data is collected every 1h and is stored permanently, and meanwhile, the state change triggers storage. When the reactor has a fault, the storage function is automatically started, and the data of 10S is continuously stored and permanently stored.
The computer board card comprises an algorithm file storage module, a data analysis module, a chart generation module and a display module, wherein the algorithm file storage module stores KNN, a neural network and a decision tree algorithm model, the KNN, the neural network and the decision tree algorithm model are called in a DLL (delay locked loop) format through programming, the input end of the data analysis module is electrically connected with the output end of the conversion module, the output end of the data analysis module is electrically connected with the input end of the chart generation module, the output ends of the data analysis module and the chart generation module are electrically connected with the input end of the display module, the output end of the display module is electrically connected with the input end of the display screen, the signal analysis module can carry out real-time analysis and feature extraction on signals, besides a discrete power spectrum, software also provides common signal processing methods such as Fourier transform, wavelet transform and the like, and a peak-to-peak value, And displaying parameters such as deviation, standard deviation, kurtosis and the like in real time. The system operates according to the following steps:
1) acquiring a vibration signal of each side of the high-voltage reactor through a vibration acceleration sensor;
2) the vibration signal is transmitted to the data acquisition module through a signal shielding wire;
3) the vibration signal is respectively transmitted to the data storage module and the signal conversion module through the data acquisition module, the data storage module stores signal data, and the signal conversion module converts the vibration signal into an electric signal which can be recognized by a host of the detection device;
4) the data analysis module receives the vibration signal, generates various parameters, calls an algorithm to carry the parameters in, obtains an analysis result, establishes a model, inputs the various parameters into the chart generation module at the same time, and obtains a corresponding vibration intensity map and a corresponding dynamic signal segmentation discrete power map;
5) the diagnosis result generated by the algorithm and the output map are transmitted to a display screen through a display module, and an operator obtains a judgment result according to the display.
In the diagnosis in the step 4), the loosening characteristic values of the reactor winding and the iron core need to be extracted, the extraction comprises extraction of a principal component coefficient Mhc and parameters capable of reflecting frequency spectrum and waveform, the extraction of the frequency spectrum starts from three angles of 100Hz and integral multiple amplitude thereof, a vibration signal wavelet packet decomposition coefficient and a segmented discrete power spectrum, the segmented discrete power spectrum is a segmented discrete representation of a signal power spectrum, so that the signal power spectrum is more suitable for the description of the vibration characteristic of the reactor, and the calculation expression is as follows:
wherein F (N) is the result of Fourier transform of the vibration signal, fs is the sampling rate, N is the number of sampling points, and p (1) is the sum of the squares of all frequency component amplitudes within 50 Hz-150 Hz.
And 4) generating a vibration signal time domain graph, a reactor vibration signal frequency spectrum graph and a reactor surface vibration intensity distribution graph by the data analysis module according to the acquired vibration signals by using the graph generation module, wherein the graphs are displayed through a display screen.
The algorithm KNN, the neural network and the decision tree used in the step 4) need to be trained in Matlab software for machine learning, a trained model is generated into a DLL (delay locked loop) file for calling by utilizing the compiling function of Matlab, the DLL file is called in LabVIEW, when the acquired data is deficient, a threshold method is adopted for defect judgment, the power of a signal is used as a judgment basis, and the expression of the threshold method is as follows:
in the above formula, x (k)2For the discrete signal of collection, N is the number of sampling points, compares its normal state's power, just can judge that reactor has the component to take place to become flexible when exceeding certain threshold value.
In order to further improve the diagnosis accuracy, the defect diagnosis in the step 4) is judged based on the amplitude, the mean square error, the principal component coefficient and the characteristic vector formed by part of discrete power spectrums of the selected vibration signals, and the position parameters of the vibration acceleration sensor are also used for machine learning;
the expression of the composed judgment vector is as follows:
F＝[ν，σ，Mhc，P2，P3，P4，P5，P6]
the characteristic vector of actual input is obtained after the normalization of the formula, the extracted characteristic vector is only closely related to the mechanical state of the reactor and has no obvious relation with the operating voltage, and the characteristic value can effectively reduce the influence of power grid fluctuation on a diagnosis result and enable the diagnosis result to be more accurate.
In order to verify the verification effect of the three feature extraction methods of the harmonic component feature value, the discrete power feature value and the wavelet packet energy feature value, different classification learning algorithms are adopted to perform classification testing on the three feature extraction methods, 1872 groups of signals acquired at different time periods and different measuring points are subjected to feature extraction according to the three methods respectively, a parameter with a correlation coefficient larger than 0.4, a signal amplitude value, a mean square error and a principal component coefficient are selected to form a feature vector together, normalization processing is performed, PCA dimension reduction is performed, and the correlation among the parameters is removed. Setting the normal data label to 0; the loose 60% status label is 1; the fully loosened state label is 2, 50% of the data is used for training and the remaining 50% is used for testing, with the test results as shown in the following table:
it can be seen that the discrete power spectrum has better effect than the other two methods. The reason is that compared with the amplitude of the harmonic component, the discrete power spectrum contains frequency spectrum information near each harmonic component, so that frequency spectrum leakage and the influence caused by the change of the sampling rate and the number of sampling points are reduced, and the calculation result is more stable and accurate. Compared with the energy of the wavelet packet, the segmented discrete power spectrum is directly aimed at the information contained in 100Hz and the higher harmonics thereof, is more targeted and simpler in calculation, so that the method is more suitable for the expression of the vibration information of the reactor, and the advantages of the wavelet packet are difficult to embody; the number of decomposition layers of wavelet packets, the selection of basis functions and the feature extraction method are also difficult in practical application.
The method is implemented by implanting the method into hardware equipment of a general computer in a software programming mode, so as to form another embodiment of the invention, namely: a vibration signal analysis system of a high-voltage reactor.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computers, usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. A high-voltage reactor defect diagnosis method is characterized by comprising the following steps:
s1, collecting vibration signals at the side of a high-voltage reactor, and carrying out analog-digital conversion on the collected signals;
s2, establishing a model, setting an empirical coefficient, substituting the empirical coefficient into signal data, and calculating to obtain an initial diagnosis result;
s3, sending the diagnosis result, the vibration intensity map and the vibration signal segmentation discrete power map to a field monitoring unit, and giving a field diagnosis result by the monitoring unit through synthesizing the information;
and S4, feeding back the on-site diagnosis result to the model, performing machine learning through a neural network algorithm, correcting the corresponding experience coefficient, substituting the experience coefficient into the signal data acquired in real time, and calculating to obtain a final diagnosis result.
2. The method for diagnosing the defects of the high-voltage reactor according to claim 1, wherein: the step S1 includes acquiring vibration signals of the high-voltage reactor side including vibration signals of each side of the high-voltage reactor.
3. The method for diagnosing the defects of the high-voltage reactor according to claim 1, wherein: the empirical coefficients in the step S2 include reactor winding and iron core loosening characteristic values.
4. A method for diagnosing defects of a high-voltage reactor according to claim 3, characterized in that: the characteristic values comprise main component coefficients Mhc and parameters capable of reflecting vibration frequency spectrums and waveforms.
5. The method according to claim 4, wherein the parameters include amplitude of 100Hz and integer multiples thereof, wavelet packet decomposition coefficient of vibration signal, and piecewise discrete power spectrum.
6. A high voltage reactor fault diagnosis system comprising a non-transitory readable recording medium storing a program and a processing circuit, wherein the program is called by the processing circuit to execute steps S1-S4 in the high voltage reactor fault diagnosis method according to any one of claims 1 to 5.
7. The high-voltage reactor mechanical defect diagnosis system of claim 6, wherein the processing circuit comprises a plurality of vibration acceleration sensors, a signal shielding wire and a detection device host, the plurality of vibration acceleration sensors are arranged on each side of the high-voltage reactor to be monitored and electrically connected with the detection device host through the signal shielding wire, and the detection device host further comprises a display screen, a computer board card and a data acquisition card.
8. A non-transitory readable recording medium storing one or more programs containing instructions, wherein the programs include the steps included in a method for diagnosing a defect in a high-voltage reactor according to any one of claims 1 to 5.
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| CN102086784A (en) * | 2010-12-16 | 2011-06-08 | 浙江大学 | Distributed remote vibration monitoring and fault diagnosis system of large steam turbine-generator |
| CN103984951A (en) * | 2014-04-25 | 2014-08-13 | 西南科技大学 | Automatic defect recognition method and system for magnetic particle testing |
| CN109932053A (en) * | 2019-03-19 | 2019-06-25 | 国网江苏省电力有限公司检修分公司 | A kind of state monitoring apparatus and method for high-voltage shunt reactor |
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| CN112163018A (en) * | 2020-09-27 | 2021-01-01 | 国家电网有限公司 | Method, device and system for determining life cycle of photovoltaic module |
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Application publication date: 20211022 |