CN114114006A - Wind driven generator diagnosis device and diagnosis method thereof - Google Patents
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Abstract
The invention discloses a diagnosis device and a diagnosis method of a wind driven generator, relating to the technical field of wind driven generators, and comprising a software system and a diagnosis method, wherein the software system comprises a central control module, a state monitoring module, a parameter setting module, a fault diagnosis module, a data management module and a user management module; the state monitoring module is used for collecting state data of the wind driven generator during working and sending the collected data to the central control module; the parameter setting module is used for setting a temperature threshold, a rotating speed and a vibration frequency threshold of the wind driven generator during working; the method can quickly find out the fault position and timely display the fault form through the neural network, so that personnel can quickly know the fault of the wind driven generator, the maintenance personnel can conveniently maintain the wind driven generator, the stable work of the wind driven generator is ensured, the service life of the wind driven generator is prolonged, and resources are saved.
Description
Technical Field
The invention relates to the technical field of wind driven generators, in particular to a wind driven generator diagnosis device and a diagnosis method thereof.
Background
The wind power generator is an electric power device which converts wind energy into mechanical work, and the mechanical work drives a rotor to rotate so as to finally output alternating current. The wind-driven generator generally comprises wind wheels, a generator (including a device), a direction regulator (empennage), a tower, a speed-limiting safety mechanism, an energy storage device and other components. The wind driven generator has simple working principle, the wind wheel rotates under the action of wind force, the kinetic energy of the wind is converted into mechanical energy of a wind wheel shaft, and the generator rotates under the drive of the wind wheel shaft to generate electricity. In a broad sense, wind energy is also solar energy, so that the wind power generator is a heat energy utilization generator which uses solar energy as a heat source and uses the atmosphere as a working medium.
At present, when a wind driven generator is diagnosed, the working state of the wind driven generator cannot be diagnosed in real time, and a fault part and a fault form cannot be found out quickly, so that the service life of the wind driven generator is shortened, and unnecessary economic loss is brought.
Disclosure of Invention
The invention aims to solve the problems that the working state of a wind driven generator cannot be diagnosed in real time, and the fault position and the fault form cannot be found out quickly in the prior art, so that the service life of the wind driven generator is shortened, and unnecessary economic loss is caused.
In order to achieve the purpose, the invention adopts the following technical scheme:
a diagnosis device for a wind driven generator comprises a software system, wherein the software system comprises a central control module, a state monitoring module, a parameter setting module, a fault diagnosis module, a data management module and a user management module;
the central control module is used for controlling the whole system through a multifunctional operation interface;
the state monitoring module is used for collecting state data of the wind driven generator during working and sending the collected data to the central control module;
the parameter setting module is used for setting a temperature threshold, a rotating speed and a vibration frequency threshold of the wind driven generator during working;
the fault diagnosis module is used for carrying out comparison analysis according to the data acquired by the state monitoring module and the data in the data management module, and carrying out diagnosis and alarm on the fault of the wind driven generator;
the data management module is used for storing data in real time, processing and analyzing the data and providing support for the data use of the whole system;
the user management module is used for managing the logged-in user and setting the authority of the user according to the user level stored in the system.
Preferably, the state monitoring module comprises a temperature detection unit, a rotating speed detection unit, a bearing detection unit, a real-time trend unit and a historical trend unit;
the temperature detection unit is used for detecting the temperature of the rolling bearing of the wind driven generator and sending the detected data to the diagnosis system;
the rotating speed detection unit is used for detecting the rotating speed of a rolling bearing of the wind driven generator and sending detected data to the diagnosis system;
the bearing detection unit is used for detecting the vibration frequency of a rolling bearing of the wind driven generator and sending the detected data to the diagnosis system;
the real-time trend unit displays the real-time dynamic trend of the monitored variable in a curve form, and the updating period of the real-time trend unit is set to be 1 second;
the historical trend unit can display the data stored in the data management module, and is convenient for observing and acquiring the historical operation trend of each monitored object of the unit.
Preferably, the parameter setting module comprises a temperature setting unit, a rotating speed setting unit and a vibration frequency setting unit;
the temperature setting unit is used for setting the highest temperature threshold of the rolling bearing of the wind driven generator; the rotating speed setting unit is used for setting the rotating speed of a rolling bearing of the wind driven generator; the vibration frequency setting unit is used for setting a vibration frequency threshold value of a rolling bearing of the wind driven generator; when the temperature of the rolling bearing of the wind driven generator exceeds a highest threshold value or a maximum vibration frequency threshold value, the parameter setting module sends a signal to the central control module, and the central control module closes the whole system.
Preferably, the fault diagnosis module comprises a fault alarm unit, a time domain analysis unit and a frequency domain analysis unit;
the fault alarm unit is used for alarming when the wind driven generator is diagnosed to have a fault and sending data to the data management module;
the time domain analysis unit is used for processing and diagnosing through a wavelet packet and a neural network algorithm according to the collected vibration signals;
and the frequency domain analysis unit is used for carrying out fault identification according to the result variable value of the vibration of the time domain analysis unit and the temperature and rotating speed variable value, the fault diagnosis program is circularly executed by taking 2 seconds as a period, if the variable value is found to correspond to an alarm area or a fault value in the system, a piece of alarm information is generated, and the alarm reason and the fault occurrence part of the fault are analyzed according to the characteristic of the variable value.
Preferably, the data management module comprises a real-time database, a historical database, a data processing unit and a data analysis unit;
the real-time database is used for storing real-time data of the system;
the historical database is used for storing the historical data of the system;
the data processing unit is used for processing data in the system;
the data analysis unit is used for analyzing data in the system in real time, comparing and analyzing the data in the real-time database and the data in the historical database by reading the data in the same time period, and analyzing the states of the rolling bearings of the wind driven generators in the historical time period and the real-time period.
Preferably, the user management module comprises a right management unit and a right setting unit; the authority management unit is used for managing the authority of all users in the system; the permission setting unit is used for setting the use permission of all users in the system.
Preferably, the neural network algorithm steps are as follows:
according to the known number N of nodes of the input layerinNumber of nodes of output layer NoutNumber of samples NtrainThe target classification number NclaRespectively calculating the number N of the corresponding hidden layer nodes by combining formulashidTaking the minimum value and assigning it to nminTaking the maximum value and assigning it to nmaxFinally, determining the value range of the hidden layer as nmin≤Nhid≤nmax；
Let Nhid＝nminI.e. the number of hidden nodes is nminDetermining network structure, starting training network, obtaining networkMean square error Mse1The number of hidden layer nodes is selected according to the magnitude of the mean square error Mse and is used as a contrast parameter,
where m is the number of output nodes, p is the total number of training samples,
is the desired output value, ypjIs the actual output value;
taking another Nhid＝(nmin+nmax)/2，NhidIs an integer, the network is trained again to obtain the mean square error Mse2；
If Mse1≥Mse2Then let nmin＝Nhid，Mse1＝Mse2(ii) a Otherwise, let nmax＝Nhid；
If n ismin＜nmaxAnd returning to the second step, otherwise, exiting the loop and ending the algorithm.
A wind driven generator diagnosis method comprises the following steps:
a user logs in the diagnosis system through the user management module, and data of the rolling bearing of the wind driven generator is set through the parameter setting module;
the method comprises the steps that working state data of the wind driven generator are collected through a state monitoring module, and the collected data are sent to a fault diagnosis module and a data management module;
the fault diagnosis module diagnoses and analyzes the fault of the wind driven generator through a time domain analysis unit and a frequency domain analysis unit through a neural network algorithm;
when the fault is diagnosed and analyzed, the fault alarm unit starts to work and indicates the fault part;
and the data analysis unit reads the data in the historical database and the real-time database in the same time period after the data management module finishes processing the received data, performs comparative analysis and displays the historical state and the real-time state of the rolling bearing of the wind driven generator in the same time period.
Compared with the prior art, the invention has the beneficial effects that:
1. in addition, the improved combined neural network fault diagnosis model established after the number of hidden layer nodes of each unit neural network in the neural network is determined according to the method has certain universality, can carry out fault diagnosis on other mechanical parts similar to the bearing fault as long as learning samples of the neural network and network related learning parameters are changed, and has good applicability.
2. The method can quickly find out the fault position and timely display the fault form through the neural network, so that personnel can quickly know the fault of the wind driven generator, the maintenance personnel can conveniently maintain the wind driven generator, the stable work of the wind driven generator is ensured, the service life of the wind driven generator is prolonged, and resources are saved.
Drawings
FIG. 1 is a schematic diagram of a software system of a wind turbine diagnostic apparatus according to the present invention;
FIG. 2 is a flow chart of a method for determining the number of hidden layer neuron nodes of the wind turbine diagnostic apparatus according to the present invention;
fig. 3 is a schematic flow chart of a diagnosis method of a wind turbine generator according to 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.
Referring to fig. 1 and 2, the wind driven generator diagnosis device comprises a software system, wherein the software system comprises a central control module, a state monitoring module, a parameter setting module, a fault diagnosis module, a data management module and a user management module;
the central control module is used for controlling the whole system through a multifunctional operation interface;
the state monitoring module is used for collecting state data of the wind driven generator during working and sending the collected data to the central control module;
the parameter setting module is used for setting a temperature threshold, a rotating speed and a vibration frequency threshold of the wind driven generator during working;
the fault diagnosis module is used for carrying out comparison and analysis according to the data acquired by the state monitoring module and the data in the data management module, and carrying out diagnosis and alarm on the fault of the wind driven generator;
the data management module is used for storing data in real time, processing and analyzing the data and providing support for the data use of the whole system;
the user management module is used for managing the logged-in user and setting the authority of the user according to the user level stored in the system.
The state monitoring module comprises a temperature detection unit, a rotating speed detection unit, a bearing detection unit, a real-time trend unit and a historical trend unit;
the temperature detection unit is used for detecting the temperature of the rolling bearing of the wind driven generator and sending the detected data to the diagnosis system;
the rotating speed detection unit is used for detecting the rotating speed of a rolling bearing of the wind driven generator and sending detected data to the diagnosis system;
the bearing detection unit is used for detecting the vibration frequency of a rolling bearing of the wind driven generator and sending the detected data to the diagnosis system;
the real-time trend unit displays the real-time dynamic trend of the monitored variable in a curve form, and the updating period is set to be 1 second;
the historical trend unit can display the data stored in the data management module, and is convenient for observing and acquiring the historical operation trend of each monitored object of the unit.
The parameter setting module comprises a temperature setting unit, a rotating speed setting unit and a vibration frequency setting unit;
the temperature setting unit is used for setting the highest temperature threshold of the rolling bearing of the wind driven generator; the rotating speed setting unit is used for setting the rotating speed of a rolling bearing of the wind driven generator; the vibration frequency setting unit is used for setting a vibration frequency threshold value of a rolling bearing of the wind driven generator; when the temperature of the rolling bearing of the wind driven generator exceeds the highest threshold or the maximum vibration frequency threshold, the parameter setting module sends a signal to the central control module, and the central control module closes the whole system.
The fault diagnosis module comprises a fault alarm unit, a time domain analysis unit and a frequency domain analysis unit;
the fault alarm unit is used for alarming when the wind driven generator is diagnosed to have faults and sending data to the data management module;
the time domain analysis unit is used for processing and diagnosing through wavelet packets and a neural network algorithm according to the acquired vibration signals;
the frequency domain analysis unit is used for carrying out fault identification according to the result variable value of the vibration of the time domain analysis unit and the temperature rotating speed variable value, the fault diagnosis program is executed in a cycle of 2 seconds, if the variable value is found to correspond to the alarm area or the fault value in the system, an alarm message is generated, and the alarm reason and the fault occurrence position of the fault are analyzed according to the characteristic of the variable value.
The data management module comprises a real-time database, a historical database, a data processing unit and a data analysis unit;
the real-time database is used for storing the real-time data of the system;
the historical database is used for storing the historical data of the system;
the data processing unit is used for processing data in the system;
the data analysis unit is used for analyzing the data in the system in real time, comparing and analyzing the data in the real-time database in the same time period and the data in the historical database, and analyzing the states of the rolling bearings of the wind driven generator in the historical time period and the real-time period.
The user management module comprises a right management unit and a right setting unit; the authority management unit is used for managing the authority of all users in the system; the permission setting unit is used for setting the use permission of all users in the system.
The neural network algorithm comprises the following steps:
according to the known number N of nodes of the input layerinNumber of nodes of output layer NoutNumber of samples NtrainThe target classification number NclaThe channels and combined formula
Nhid＝2Nin+1, wherein, NinRepresenting the number of input layer neuron nodes, NhidRepresenting the number of output layer neuron nodes;
Nhid≤Ntrain/R×(Nin+Nout) Wherein N istrainRepresenting the number of training samples, NoutRepresenting the number of nodes of an output layer, wherein R represents an integer between 5 and 10;
Nhid＝log2Nin
respectively calculating the number N of corresponding hidden layer nodeshidTaking the minimum value and assigning it to nminTaking the maximum value and assigning it to nmaxFinally, determining the value range of the hidden layer as nmin≤Nhid≤nmax；
Let Nhid＝nminI.e. the number of hidden nodes is nminDetermining the network structure, starting to train the network, and obtaining the mean square error Mse of the network1The number of hidden layer nodes is selected according to the magnitude of the mean square error Mse and is used as a contrast parameter,
where m is the number of output nodes, p is the total number of training samples,
is the desired output value, ypjIs the actual output value;
taking another Nhid＝(nmin+nmax)/2，NhidIs an integer, the network is trained again to obtain the mean square error Mse2；
If Mse1≥Mse2Then let nmin＝Nhid，Mse1＝Mse2(ii) a Otherwise, let nmax＝Nhid；
If n ismin＜nmaxAnd returning to the second step, otherwise, exiting the loop and ending the algorithm.
Referring to fig. 3, a wind turbine diagnosis method includes the following steps:
a user logs in the diagnosis system through the user management module, and data of the rolling bearing of the wind driven generator is set through the parameter setting module;
the method comprises the steps that working state data of the wind driven generator are collected through a state monitoring module, and the collected data are sent to a fault diagnosis module and a data management module;
the fault diagnosis module diagnoses and analyzes the fault of the wind driven generator through a time domain analysis unit and a frequency domain analysis unit through a neural network algorithm;
when the fault is diagnosed and analyzed, the fault alarm unit starts to work and indicates the fault part;
and the data analysis unit reads the data in the historical database and the real-time database in the same time period after the data management module finishes processing the received data, performs comparative analysis and displays the historical state and the real-time state of the rolling bearing of the wind driven generator in the same time period.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (8)
1. A diagnosis device for a wind driven generator comprises a software system, and is characterized in that the software system comprises a central control module, a state monitoring module, a parameter setting module, a fault diagnosis module, a data management module and a user management module;
the central control module is used for controlling the whole system through a multifunctional operation interface;
the state monitoring module is used for collecting state data of the wind driven generator during working and sending the collected data to the central control module;
the parameter setting module is used for setting a temperature threshold, a rotating speed and a vibration frequency threshold of the wind driven generator during working;
the fault diagnosis module is used for carrying out comparison analysis according to the data acquired by the state monitoring module and the data in the data management module, and carrying out diagnosis and alarm on the fault of the wind driven generator;
the data management module is used for storing data in real time, processing and analyzing the data and providing support for the data use of the whole system;
the user management module is used for managing the logged-in user and setting the authority of the user according to the user level stored in the system.
2. The wind driven generator diagnosis device according to claim 1, wherein the condition monitoring module comprises a temperature detection unit, a rotating speed detection unit, a bearing detection unit, a real-time trend unit and a historical trend unit;
the temperature detection unit is used for detecting the temperature of the rolling bearing of the wind driven generator and sending the detected data to the diagnosis system;
the rotating speed detection unit is used for detecting the rotating speed of a rolling bearing of the wind driven generator and sending detected data to the diagnosis system;
the bearing detection unit is used for detecting the vibration frequency of a rolling bearing of the wind driven generator and sending the detected data to the diagnosis system;
the real-time trend unit displays the real-time dynamic trend of the monitored variable in a curve form, and the updating period of the real-time trend unit is set to be 1 second;
the historical trend unit can display the data stored in the data management module, and is convenient for observing and acquiring the historical operation trend of each monitored object of the unit.
3. The wind driven generator diagnosis device according to claim 1, wherein the parameter setting module comprises a temperature setting unit, a rotating speed setting unit and a vibration frequency setting unit;
the temperature setting unit is used for setting the highest temperature threshold of the rolling bearing of the wind driven generator; the rotating speed setting unit is used for setting the rotating speed of a rolling bearing of the wind driven generator; the vibration frequency setting unit is used for setting a vibration frequency threshold value of a rolling bearing of the wind driven generator; when the temperature of the rolling bearing of the wind driven generator exceeds a highest threshold value or a maximum vibration frequency threshold value, the parameter setting module sends a signal to the central control module, and the central control module closes the whole system.
4. The wind driven generator diagnosis device according to claim 1, wherein the fault diagnosis module comprises a fault alarm unit, a time domain analysis unit and a frequency domain analysis unit;
the fault alarm unit is used for alarming when the wind driven generator is diagnosed to have a fault and sending data to the data management module;
the time domain analysis unit is used for processing and diagnosing through a wavelet packet and a neural network algorithm according to the collected vibration signals;
and the frequency domain analysis unit is used for carrying out fault identification according to the result variable value of the vibration of the time domain analysis unit and the temperature and rotating speed variable value, the fault diagnosis program is circularly executed by taking 2 seconds as a period, if the variable value is found to correspond to an alarm area or a fault value in the system, a piece of alarm information is generated, and the alarm reason and the fault occurrence part of the fault are analyzed according to the characteristic of the variable value.
5. The wind turbine diagnostic device according to claim 1, wherein the data management module comprises a real-time database, a historical database, a data processing unit and a data analysis unit;
the real-time database is used for storing real-time data of the system;
the historical database is used for storing the historical data of the system;
the data processing unit is used for processing data in the system;
the data analysis unit is used for analyzing data in the system in real time, comparing and analyzing the data in the real-time database and the data in the historical database by reading the data in the same time period, and analyzing the states of the rolling bearings of the wind driven generators in the historical time period and the real-time period.
6. The diagnosis device and the diagnosis method for the wind driven generator according to claim 1, wherein the user management module comprises an authority management unit and an authority setting unit; the authority management unit is used for managing the authority of all users in the system; the permission setting unit is used for setting the use permission of all users in the system.
7. The wind turbine diagnostic device of claim 4, wherein the neural network algorithm comprises the following steps:
according to the known number N of nodes of the input layerinNumber of nodes of output layer NoutNumber of samples NtrainThe target classification number NclaRespectively calculating the number N of the corresponding hidden layer nodes by combining formulashidTaking the minimum value and assigning it to nminTaking the maximum value and assigning it to nmaxFinally, determining the value range of the hidden layer as nmin≤Nhid≤nmax；
Let Nhid＝nminI.e. the number of hidden nodes is nminDetermining the network structure, starting to train the network, and obtaining the mean square error Mse of the network1The number of hidden layer nodes is selected according to the magnitude of the mean square error Mse and is used as a contrast parameter,
where m is the number of output nodes, p is the total number of training samples,
is the desired output value, ypjIs the actual output value;
taking another Nhid＝(nmin+nmax)/2，NhidIs an integer, the network is trained again to obtain the mean square error Mse2；
If Mse1≥Mse2Then let nmin＝Nhid，Mse1＝Mse2(ii) a Otherwise, let nmax＝Nhid；
If n ismin＜nmaxAnd returning to the second step, otherwise, exiting the loop and ending the algorithm.
8. A wind driven generator diagnosis method is characterized by comprising the following steps:
a user logs in the diagnosis system through the user management module, and data of the rolling bearing of the wind driven generator is set through the parameter setting module;
the method comprises the steps that working state data of the wind driven generator are collected through a state monitoring module, and the collected data are sent to a fault diagnosis module and a data management module;
the fault diagnosis module diagnoses and analyzes the fault of the wind driven generator through a time domain analysis unit and a frequency domain analysis unit through a neural network algorithm;
when the fault is diagnosed and analyzed, the fault alarm unit starts to work and indicates the fault part;
and the data analysis unit reads the data in the historical database and the real-time database in the same time period after the data management module finishes processing the received data, performs comparative analysis and displays the historical state and the real-time state of the rolling bearing of the wind driven generator in the same time period.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116008701A (en) * | 2022-12-28 | 2023-04-25 | 国网山东省电力公司淄博供电公司 | Electric mechanism operation diagnosis system and method for intelligent high-voltage switch cabinet |
| CN116381490A (en) * | 2023-06-05 | 2023-07-04 | 江苏铭星智能家居有限公司 | Push rod motor performance detection system and method based on data analysis |
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