CN115788771A - Intelligent operation and maintenance management system of wind power generation system based on Internet of things technology - Google Patents
Intelligent operation and maintenance management system of wind power generation system based on Internet of things technology Download PDFInfo
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- CN115788771A CN115788771A CN202211497225.3A CN202211497225A CN115788771A CN 115788771 A CN115788771 A CN 115788771A CN 202211497225 A CN202211497225 A CN 202211497225A CN 115788771 A CN115788771 A CN 115788771A
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Abstract
The invention provides an intelligent operation and maintenance management system of a wind power generation system based on the technology of the Internet of things. The method comprises the following steps: a data receiving module: the system is used for acquiring state data of each fan corresponding to the wind power generation system and acquiring system monitoring data of the wind power generation system; a data processing module: the system comprises a data processing module, a data processing module and a data processing module, wherein the data processing module is used for processing data based on acquired state data and system monitoring data and combining external influence parameters; a data analysis module: the system is used for carrying out prediction diagnosis on the processed data by combining historical operation and maintenance information of the wind power generation system; the data early warning module: and the system is used for sending the data exceeding the set threshold value to the operation and maintenance terminal based on the prediction and diagnosis result, and performing alarm notification and system maintenance. By processing and analyzing the state data and the monitoring data of the wind power generation system and predicting based on the historical operation and maintenance data of the wind power generation system, the current state of the wind power generation system can be more accurately judged, and the working efficiency of the wind power generation system is ensured.
Description
Technical Field
The invention relates to the field of intelligent operation and maintenance management, in particular to an intelligent operation and maintenance management system of a wind power generation system based on the technology of the Internet of things.
Background
At present, the level of mechanized production and manufacturing is improved, the technology of internet of things also permeates the aspects of the industrial field, especially in a wind power generation system, the maintenance cost of the system is greatly increased, operation and maintenance management also needs to carry out a lot of daily maintenance operations, a great amount of repetitive labor is included in daily work of maintenance personnel, and an intelligent operation and maintenance system is produced at the discretion of operation.
However, the existing intelligent operation and maintenance management system has the disadvantages of poor operation and maintenance detection and judgment capability and long judgment time, so that the operation and maintenance time of the wind power generation system is increased, and the working efficiency of the system is reduced.
Therefore, the invention provides an intelligent operation and maintenance management system of a wind power generation system based on the technology of the Internet of things.
Disclosure of Invention
The invention provides an intelligent operation and maintenance management system of a wind power generation system based on the Internet of things technology, which is used for more accurately judging the state of the current wind power generation system by processing and analyzing state data and monitoring data of the wind power generation system and predicting based on historical operation and maintenance data of the wind power generation system, so that the operation and maintenance of the wind power generation system can be realized in a shorter time, and the working efficiency of the wind power generation system is ensured.
The invention provides an intelligent operation and maintenance management system of a wind power generation system based on the technology of the Internet of things, which comprises:
a data receiving module: the system is used for acquiring state data of each fan corresponding to the wind power generation system and acquiring system monitoring data of the wind power generation system;
a data processing module: the system comprises a data processing module, a data processing module and a data processing module, wherein the data processing module is used for processing data based on acquired state data and system monitoring data and combining external influence parameters;
a data analysis module: the system is used for carrying out prediction diagnosis on the processed data by combining historical operation and maintenance information of the wind power generation system;
the data early warning module: and the system is used for sending the data exceeding the set threshold value to the operation and maintenance terminal based on the prediction and diagnosis result, and performing alarm notification and system maintenance.
In one possible implementation manner, the data receiving module includes:
a status data acquisition unit: the system comprises a data base, a state database and a data processing module, wherein the data base is used for acquiring state data of each fan in a wind power generation system and respectively constructing the state database of each fan;
a monitoring data acquisition unit: the system monitoring data acquisition module is used for acquiring system monitoring data of the wind power generation system and constructing a monitoring database;
a data transmission unit: and the monitoring database is used for storing all the state databases and the monitoring database to the data processing module.
In one possible implementation, the data processing module includes:
a status data processing unit: the state data in each state database are subjected to first standardization processing to obtain a corresponding second state database, and first comparison is carried out on the second state database and first standard state data of a fan corresponding to the current wind power generation system;
obtaining a difference state database containing a plurality of comparison difference values based on the first comparison result;
the difference state database comprises state data of a corresponding fan after first standardization processing, first standard state data of the corresponding fan and a plurality of comparison difference values;
a parameter processing unit: the second standardization processing is carried out on the data of the first external influence factor corresponding to the fan, and different influence indexes of the first external influence factor on the corresponding fan are obtained by combining the weight coefficient corresponding to the data after the first standardization processing;
obtaining a corresponding first parameter library based on the influence indexes of the same fan and by combining with a difference state database;
a monitoring data processing unit: the monitoring database is used for carrying out second comparison on the standard monitoring data of the wind power generation system;
based on the second comparison result, obtaining the data condition of each monitoring data, and based on the second external influence factor, correcting each data condition to obtain a second parameter library;
a data acquisition unit: and the data processing unit is used for obtaining processed data based on the first parameter library and the second parameter library.
In one possible implementation manner, the data obtaining unit includes:
a position locking block: for locking a first position of each fan in the wind power system;
the mapping relation establishing block: the wind power generation system is used for establishing a factor mapping relation between each fan and the wind power generation system according to a first external influence factor of each fan and a second external influence factor of the wind power generation system;
a table optimization block: and the parameter mapping table is used for establishing a parameter mapping table of the first parameter base and the second parameter base based on the first position, optimizing the parameter mapping table based on the factor mapping relation to obtain an optimized mapping table, and regarding the parameters in the optimized mapping table as the processed parameters.
In one possible implementation, the data analysis module includes:
an analysis processing unit: the data processing device is used for performing data extraction, data cleaning and data mining on the processed data to generate a first data analysis result;
a second analysis unit: the system comprises a first data analysis result, a second data analysis result and historical operation and maintenance information of the wind power generation system, wherein the first data analysis result and the historical operation and maintenance information of the wind power generation system are analyzed to obtain the second data analysis result;
an analysis transmission unit: and the second data analysis result is uploaded to a data early warning module for early warning.
In one possible implementation manner, the data analysis module further includes:
the filling unit is used for filling the historical operation and maintenance information of the wind power generation system into an operation and maintenance analysis table according to different information types;
the average value obtaining unit is used for obtaining a first operation and maintenance average value in a normal range and a second operation and maintenance average value in an abnormal range in each type in the operation and maintenance analysis table;
the result extraction unit is used for carrying out third comparison on the processed data and the normal range of the corresponding type to obtain a third comparison result, and extracting a first normal result and a first abnormal result in the third comparison result;
the first comparison unit is used for performing first comparison on the basis of a first historical result matched with the first operation and maintenance average value, and obtaining a third operation and maintenance average value of the first normal result on the basis of the first operation and maintenance average value;
wherein Y1 represents a first operation and maintenance average value; n1 represents n1 result values extracted from the first normal result; h1 i1 Representing an i1 st result value among the n1 extracted results; h0 i1 Representing the i1 st result value in the n1 first history results; is at a position of 01 Representing a type weight corresponding to the first normal result;
the second comparison unit is used for performing second comparison on the basis of a second historical result matched with the second operation and maintenance average value and obtaining a fourth operation and maintenance average value of the first abnormal result on the basis of the second operation and maintenance average value;
and the array construction unit is used for carrying out operation and maintenance primary judgment on the processed data based on the third operation and maintenance average value and the fourth operation and maintenance average value, constructing to obtain an operation and maintenance array set, and enabling the operation and maintenance array to comprise the third operation and maintenance average value, the fourth operation and maintenance average value and a corresponding data type.
In one possible implementation manner, the data early warning module includes:
an analysis and comparison unit: the device is used for comparing the prediction diagnosis result with a preset monitoring threshold value one by one;
a judging unit: when a first numerical value larger than a preset monitoring threshold value exists in the prediction diagnosis result, judging that a fan corresponding to the first numerical value is in an abnormal state to work;
simultaneously, the current state of the wind power generation system is monitored:
if the current state of the wind power generation system is an early warning state, the state is kept unchanged, and a result which is related to the first numerical value and needs early warning is uploaded to an operation and maintenance terminal and displayed on a terminal page;
and if the state of the wind power generation system is a non-early-warning state, controlling the wind power generation system to be changed from the non-early-warning state to an early-warning state, uploading information which is related to the first numerical value and needs early warning to an operation and maintenance terminal, and displaying the information on a terminal page.
In a possible implementation manner, the determining unit includes:
an optimum value determination block: the method comprises the steps of finding out the optimal value of a monitoring threshold in a preset monitoring range of the wind power generation system by adopting a threshold triggering model, wherein the optimal value is the corresponding preset monitoring threshold;
the range of the threshold triggering model changes along with the belonged time of the wind power generation system service, the monitoring threshold of the wind power generation system in the peak service period is compared with the monitoring threshold of the peak service period, and the monitoring threshold of the wind power generation system in the idle service period is compared with the monitoring threshold of the idle service period, so that the optimal value of the monitoring threshold is obtained.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a structural diagram of an intelligent operation and maintenance management system of a wind power generation system based on the internet of things technology in an embodiment of the invention;
fig. 2 is a structural diagram of a data processing module in the intelligent operation and maintenance management system of the wind power generation system based on the internet of things technology in the embodiment of the invention;
fig. 3 is a structural diagram of a data analysis module in an intelligent operation and maintenance management system of a wind power generation system based on the internet of things technology in the embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it should be understood that they are presented herein only to illustrate and explain the present invention and not to limit the present invention.
Example 1:
the embodiment of the invention provides an intelligent operation and maintenance management system of a wind power generation system based on the technology of the internet of things, which comprises the following components as shown in figure 1:
a data receiving module: the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring state data of each fan corresponding to the wind power generation system and acquiring system monitoring data of the wind power generation system;
a data processing module: the system is used for processing data based on the acquired state data and system monitoring data and in combination with external influence parameters;
a data analysis module: the system is used for carrying out prediction diagnosis on the processed data by combining historical operation and maintenance information of the wind power generation system;
the data early warning module: and the system is used for sending the data exceeding the set threshold value to the operation and maintenance terminal based on the prediction diagnosis result, and performing alarm notification and system maintenance.
In this embodiment, the status data is data of real-time working conditions of each fan corresponding to the wind power generation system, and includes power, voltage, rotation speed, conversion capability to wind speed, and the like.
In this embodiment, the system monitoring data is monitoring data for monitoring a system operation condition of the wind power generation system, such as power, voltage, current, efficiency of wind-to-electricity, and the like.
In this embodiment, the external influence parameter refers to a parameter that causes a loss influence on the system and the fan itself by a factor external to the system, for example, the fan is influenced by the wind power and the environmental factor, and the system is influenced by vibration, collision, electromagnetic interference, and the like.
In the embodiment, the data processing is to perform standardized processing based on the acquired state data and system monitoring data, and perform processing again by combining with external influence parameters to obtain a data processing result, so as to avoid that the fan is considered to have abnormality during subsequent analysis due to external influence.
In this embodiment, the historical operation and maintenance information is operation and maintenance management information corresponding to the historical operation and maintenance management of the current wind power generation system, and includes various types of maintenance information, such as an operation and maintenance instruction for the rotation speed of a fan blade in a wind turbine, a receiving and adjusting instruction for wind power, a wind power efficiency conversion instruction, and the like.
In this embodiment, the prediction diagnosis is to predict based on an analysis result of the wind power generation system, perform diagnosis, and determine whether the wind power generation system is in a normal operation state, for example, compare the prediction diagnosis result with a preset monitoring threshold one by one, if the prediction diagnosis result is greater than the preset monitoring threshold, determine that the fan is in an abnormal state, and monitor a current state of the wind power generation system, if the current state is an early warning state, the wind power generation system is in a normal operation state, and if the current state is an early warning state, the wind power generation system is in an abnormal operation state.
In this embodiment, the set threshold is determined based on the standard state data and the standard monitoring data of the wind power generation system, and the set threshold is changed when the wind power generation system is changed due to different set thresholds of the wind power generation system, for example, in a service peak period of the wind power generation system, a preset threshold corresponding to the monitoring threshold in the peak period is obtained, and in a service idle period of the wind power generation system, a preset threshold corresponding to the monitoring threshold in the idle period is obtained.
The beneficial effects of the above technical scheme are: by processing and analyzing the state data and the monitoring data of the wind power generation system and predicting based on historical operation and maintenance data of the wind power generation system, the state of the current wind power generation system can be more accurately judged, so that the operation and maintenance of the wind power generation system can be realized in a shorter time, and the working efficiency of the wind power generation system is ensured.
Example 2:
based on embodiment 1, the data receiving module includes:
a status data acquisition unit: the system comprises a data base, a state database and a data processing module, wherein the data base is used for acquiring state data of each fan in a wind power generation system and respectively constructing the state database of each fan;
a monitoring data acquisition unit: the system monitoring data acquisition module is used for acquiring system monitoring data of the wind power generation system and constructing a monitoring database;
a data transmission unit: and the monitoring database is used for storing all the state databases and the monitoring database to the data processing module.
In this embodiment, the status data is data of real-time working conditions of each wind turbine corresponding to the wind power generation system, and includes power, voltage, rotation speed, conversion capability to wind speed, and the like.
In this embodiment, the status database is a corresponding fan status database that is constructed based on the status data of each fan.
In this embodiment, the system monitoring data is monitoring data for monitoring the system operation condition of the wind power generation system, and includes power, voltage, current, efficiency of wind-to-electricity, and the like.
In this embodiment, the monitoring database is a database constructed based on monitoring data obtained by monitoring the system operation condition of the wind power generation system.
The technical scheme has the beneficial effects that: the state data of the fan is acquired, and meanwhile the monitoring data of the wind power generation system are acquired, so that the state data are judged in an auxiliary mode, and the state of the current wind power generation system can be judged more accurately.
Example 3:
based on embodiment 2, the data processing module, as shown in fig. 2, includes:
a status data processing unit: the state data in each state database are subjected to first standardization processing to obtain a corresponding second state database, and first comparison is carried out on the second state database and first standard state data of a fan corresponding to the current wind power generation system;
obtaining a difference state database containing a plurality of comparison difference values based on the first comparison result;
the difference state database comprises state data of the corresponding fan after first standardization processing, first standard state data of the corresponding fan and a plurality of comparison difference values;
a parameter processing unit: the second normalization processing is carried out on the data of the first external influence factor corresponding to the fan, and different influence indexes of the first external influence factor on the corresponding fan are obtained by combining the weight coefficient corresponding to the data after the first normalization processing;
obtaining a corresponding first parameter library based on the influence indexes of the same fan and by combining with a difference state database;
a monitoring data processing unit: the monitoring database is used for carrying out second comparison on the standard monitoring data of the wind power generation system;
based on the second comparison result, obtaining the data condition of each monitoring data, and based on the second external influence factor, correcting each data condition to obtain a second parameter library;
a data acquisition unit: and the data processing unit is used for obtaining processed data based on the first parameter library and the second parameter library.
In this embodiment, the second state database is a database constructed based on data normalized based on state data in the first state database.
In this embodiment, the first standard status data is the standard status data of the current fan in the standard operation mode, for example, the status data of the fan in the air state with the atmospheric pressure of 101.325Kpa, the atmospheric temperature of 0 degrees celsius, and the relative humidity of 50% may be used as the first standard status data of the current fan.
In this embodiment, the first comparison is to compare the state data after the first normalization processing with the standard state data of the current fan in the standard operating mode.
In this embodiment, the difference state database is a database obtained by comparing the state data after the first standardization process and the standard state data of the current fan in the standard working mode one by one on the basis of the second state database.
In this embodiment, the difference state database includes state data of the corresponding fan after the first standardization process, first standard state data of the corresponding fan, and a plurality of comparison difference values.
In this embodiment, the first external influencing factor refers to an external factor that influences the normal operation of the fan, for example, the magnitude of the wind force value, the condition of the local air index, and the like.
In this embodiment, the second normalization process is a process of normalizing the data of the acquired first external influencing factor.
In this embodiment, the weight coefficient refers to an influence weight of the external influence factor after the second normalization process on each set of data in the difference state database, for example, the influence weight coefficient of the wind power on the external fan of the wind turbine is 0.8, and the influence weight coefficient on the internal engine of the wind turbine is 0.4.
In this embodiment, the first parameter database is obtained based on the influence index of the same wind turbine in combination with the difference status database.
In this embodiment, the monitoring database is a database constructed based on monitoring data obtained by monitoring the system operation condition of the wind power generation system.
In this embodiment, the standard monitoring data is monitoring data monitored when a system of the wind power generation system is operating under a standard condition.
In this embodiment, the second comparison refers to comparing the monitoring database corresponding to the wind power generation system with the standard monitoring data of the wind power generation system.
In this embodiment, the second external influencing factor refers to an external factor that influences the normal operation of the wind power generation system, such as vibration, impact, electromagnetic interference, and the like.
In this embodiment, the second parameter library is obtained by modifying the external influence factor of the wind turbine system.
The technical scheme has the beneficial effects that: the state of the current wind power generation system can be more accurately judged by performing data processing on the acquired state data and system monitoring data in combination with external influence parameters, so that the working efficiency of the wind power generation system is ensured.
Example 4:
based on embodiment 3, the data acquisition unit includes:
a position locking block: for locking a first position of each wind turbine in the wind power system;
the mapping relation establishing block: the wind power generation system is used for establishing a factor mapping relation between each fan and the wind power generation system according to the first external influence factor of each fan and the second external influence factor of the wind power generation system;
a table optimization block: and the parameter mapping table is used for establishing a parameter mapping table of the first parameter base and the second parameter base based on the first position, optimizing the parameter mapping table based on the factor mapping relation to obtain an optimized mapping table, and regarding the parameters in the optimized mapping table as the processed parameters.
In this embodiment, the first position refers to the position coordinates of the wind power system, for example, the left longitude and latitude may be used for positioning.
In this embodiment, the first external influencing factor refers to an external factor that influences the normal operation of the fan, for example, the magnitude of the wind force value, the condition of the local air index, and the like.
In this embodiment, the second external influencing factor refers to an external factor that influences the normal operation of the wind power generation system, such as vibration, impact, electromagnetic interference, and the like.
In this embodiment, the factor mapping relationship refers to a mapping relationship between the first external influence factor and the second external influence factor and the wind power generation system and the wind turbine.
In this embodiment, the parameter mapping table is a mapping table obtained based on the first location and the first parameter library and the second parameter library.
In this embodiment, the optimized mapping table is a mapping table obtained by performing optimization based on a factor mapping relationship on the basis of a parameter mapping table.
The technical scheme has the beneficial effects that: the parameter mapping relation between the first parameter base and the second parameter base is optimized through the factor mapping relation, and the optimized mapping relation table is obtained, so that the current state of the wind power generation system can be judged more accurately, and the working efficiency of the wind power generation system is ensured.
Example 5:
based on embodiment 1, the data analysis module includes:
an analysis processing unit: the data processing device is used for performing data extraction, data cleaning and data mining on the processed data to generate a first data analysis result;
a second analysis unit: the system comprises a first data analysis result, a second data analysis result and historical operation and maintenance information of the wind power generation system, wherein the first data analysis result and the historical operation and maintenance information of the wind power generation system are analyzed to obtain the second data analysis result;
an analysis transmission unit: and the second data analysis result is uploaded to a data early warning module for early warning.
In this embodiment, the data extraction is to extract the processed data in the database, and convert the data into a format that can be recognized by the tool, for example, the data in the database can be extracted in a full-scale extraction manner without being changed.
In this embodiment, data cleansing is a process of reviewing and checking data, for example, for an error value in a database, a simple rule base (common sense rule, business specific rule, etc.) may be used to check a data value, or a constraint between different attributes, external data may be used to detect and cleanse data.
In this embodiment, data mining is a process that reveals potentially valuable information from the vast amount of data in an existing database.
In this embodiment, the first data analysis result and the second data analysis result are obtained by processing and analyzing data in the database in multiple ways.
In this embodiment, the historical operation and maintenance information is corresponding operation and maintenance management information when the historical operation and maintenance management of the current wind power generation system is performed.
The beneficial effects of the above technical scheme are: by analyzing the processed data and analyzing the existing analysis result again based on the historical operation and maintenance information, the obtained analysis result can be more accurate, and the state of the current wind power generation system can be more accurately judged.
Example 6:
based on embodiment 5, the data analysis module, as shown in fig. 3, further includes:
the filling unit is used for filling the historical operation and maintenance information of the wind power generation system into an operation and maintenance analysis table according to different information types;
the average value obtaining unit is used for obtaining a first operation and maintenance average value in a normal range and a second operation and maintenance average value in an abnormal range in each type in the operation and maintenance analysis table;
the result extraction unit is used for carrying out third comparison on the processed data and the normal range of the corresponding type to obtain a third comparison result, and extracting a first normal result and a first abnormal result in the third comparison result;
the first comparison unit is used for performing first comparison on the basis of a first historical result matched with the first operation and maintenance average value, and obtaining a third operation and maintenance average value of the first normal result on the basis of the first operation and maintenance average value;
wherein Y1 represents a first operation and maintenance average value; n1 represents n1 result values extracted from the first normal result; h1 i1 Representing an i1 st result value among the n1 extracted results; h0 i1 To representAn i1 st result value of the n1 first history results; is at a position of 01 Representing a type weight corresponding to the first normal result;
the second comparison unit is used for performing second comparison on the basis of a second historical result matched with the second operation and maintenance average value and obtaining a fourth operation and maintenance average value of the first abnormal result on the basis of the second operation and maintenance average value;
and the array construction unit is used for carrying out operation and maintenance primary judgment on the processed data based on the third operation and maintenance average value and the fourth operation and maintenance average value, constructing to obtain an operation and maintenance array set, and enabling the operation and maintenance array to comprise the third operation and maintenance average value, the fourth operation and maintenance average value and a corresponding data type.
In this embodiment, the historical operation and maintenance information is corresponding operation and maintenance management information when the historical operation and maintenance of the current wind power generation system is managed.
In this embodiment, the operation and maintenance analysis table is formed based on the historical operation and maintenance information and the analysis situation corresponding to the operation and maintenance information, and is a preset table, which is mainly used for conveniently filling the information to be analyzed into the table to perform effective analysis.
In this embodiment, the first operation and maintenance average value is obtained by screening out and averaging each type of data in the normal range based on the corresponding analysis condition in the operation and maintenance analysis table, and the first operation and maintenance average value is obtained because there may be a slight maintenance possibility even if the type of data is in the normal range.
In this embodiment, the second operation and maintenance average value is obtained by screening out data of each type that is not in the normal range based on the corresponding analysis condition in the operation and maintenance analysis table and calculating an average value thereof.
In this embodiment, the third comparison is to compare the processed data with a normal range of a corresponding type.
In this embodiment, the first normal result and the first abnormal result are results of comparing the processed data with the normal range of the corresponding type, the data in the normal range constitutes the first normal result, and the data not in the normal range constitutes the first abnormal result, for example, if the normal range of the corresponding type is 0.5 to 0.8, the historical operation and maintenance information in this range constitutes the first normal result, and the historical operation and maintenance information not in this range constitutes the first abnormal result.
In this embodiment, the third operation and maintenance average value is obtained by processing the first operation and maintenance average value based on a comparison between the first normal result and the first history result matched with the first operation and maintenance average value.
In this embodiment, the fourth operation and maintenance average value is obtained by processing the second operation and maintenance average value based on a comparison between the first abnormal result and the second history result matched with the second operation and maintenance average value.
In this embodiment, the operation and maintenance array set includes a third operation and maintenance average value, a fourth operation and maintenance average value, and a corresponding data type.
The beneficial effects of the above technical scheme are: historical operation and maintenance information of the wind power generation system is processed and classified and analyzed, so that a corresponding operation and maintenance array set is obtained to carry out prediction diagnosis on data of the wind power generation system, the state judgment of the current wind power generation system can be more accurate, and the working efficiency of the wind power generation system is ensured.
Example 7:
on the basis of embodiment 5, the data early warning module includes:
an analysis and comparison unit: the system is used for comparing the prediction diagnosis result with a preset monitoring threshold value one by one;
a judging unit: the method comprises the steps that when a first numerical value larger than a preset monitoring threshold value exists in a prediction diagnosis result, the fan corresponding to the first numerical value is judged to be in abnormal working state;
simultaneously, the current state of the wind power generation system is monitored:
if the current state of the wind power generation system is an early warning state, the state is kept unchanged, and a result which is related to the first numerical value and needs early warning is uploaded to an operation and maintenance terminal and displayed on a terminal page;
and if the state of the wind power generation system is a non-early-warning state, controlling the wind power generation system to be changed from the non-early-warning state to an early-warning state, uploading information which is related to the first numerical value and needs early warning to an operation and maintenance terminal, and displaying the information on a terminal page.
In this embodiment, the prediction diagnosis is to predict and diagnose the wind turbine system based on the analysis result of the wind turbine system, and determine whether the wind turbine system is in a normal operation state.
In this embodiment, the preset monitoring threshold is an optimal value of the monitoring threshold found in a preset monitoring range of the wind power generation system by using a threshold trigger model, where the optimal value is a corresponding preset monitoring threshold.
In this embodiment, the first value of the preset monitoring threshold refers to a first state threshold of the preset monitoring threshold.
In this embodiment, the current state includes an alert state and a non-alert state.
The beneficial effects of the above technical scheme are: the forecasting diagnosis result is compared with the preset monitoring threshold value one by one, and the wind power generation system is pre-warned based on the comparison result, so that the pre-warning result is more accurate, the operation and maintenance of the wind power generation system can be realized in a shorter time, and the working efficiency of the wind power generation system is ensured.
Example 8:
on the basis of embodiment 7, the judgment unit includes:
an optimum value determination block: the method comprises the steps of finding out an optimal value of a monitoring threshold in a preset monitoring range of the wind power generation system by adopting a threshold triggering model, wherein the optimal value is a corresponding preset monitoring threshold;
the range of the threshold triggering model changes along with the time of the wind power generation system service, the monitoring threshold of the wind power generation system in the peak service period is compared with the monitoring threshold of the wind power generation system in the peak service period, and the monitoring threshold of the wind power generation system in the idle service period is compared with the monitoring threshold of the wind power generation system in the idle service period, so that the optimal value of the monitoring threshold is obtained.
In this embodiment, the threshold triggering model is obtained by training based on a historical preset monitoring threshold of the wind power generation system.
In this embodiment, the range of the threshold trigger model varies with the time that the wind power system services belong to.
In this embodiment, the optimal value of the monitoring threshold is the corresponding preset monitoring threshold.
The beneficial effects of the above technical scheme are: by processing the preset monitoring threshold value of the wind power generation system, the optimal value is obtained, and then the optimal value is compared with the prediction diagnosis result, so that the comparison result is more accurate, the operation and maintenance of the wind power generation system can be realized in a shorter time, and the working efficiency of the wind power generation system is ensured.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (8)
1. The utility model provides a wind power generation system's intelligent operation and maintenance management system based on internet of things, its characterized in that includes:
a data receiving module: the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring state data of each fan corresponding to the wind power generation system and acquiring system monitoring data of the wind power generation system;
a data processing module: the system is used for processing data based on the acquired state data and system monitoring data and in combination with external influence parameters;
a data analysis module: the system is used for carrying out prediction diagnosis on the processed data by combining historical operation and maintenance information of the wind power generation system;
the data early warning module: and the system is used for sending the data exceeding the set threshold value to the operation and maintenance terminal based on the prediction diagnosis result, and performing alarm notification and system maintenance.
2. The intelligent operation and maintenance management system of the wind power generation system based on the internet of things technology as claimed in claim 1, wherein the data receiving module comprises:
a status data acquisition unit: the system comprises a wind power generation system, a state database and a database server, wherein the wind power generation system is used for acquiring state data of each fan in the wind power generation system and respectively constructing the state database of each fan;
a monitoring data acquisition unit: the system monitoring data acquisition module is used for acquiring system monitoring data of the wind power generation system and constructing a monitoring database;
a data transmission unit: and the monitoring database is used for storing all the state databases and the monitoring database to the data processing module.
3. The intelligent operation and maintenance management system of the wind power generation system based on the internet of things technology as claimed in claim 2, wherein the data processing module comprises:
a status data processing unit: the state data in each state database are subjected to first standardization processing to obtain a corresponding second state database, and first comparison is carried out on the second state database and first standard state data of a fan corresponding to the current wind power generation system;
obtaining a difference state database containing a plurality of comparison difference values based on the first comparison result;
the difference state database comprises state data of a corresponding fan after first standardization processing, first standard state data of the corresponding fan and a plurality of comparison difference values;
a parameter processing unit: the second normalization processing is carried out on the data of the first external influence factor corresponding to the fan, and different influence indexes of the first external influence factor on the corresponding fan are obtained by combining the weight coefficient corresponding to the data after the first normalization processing;
obtaining a corresponding first parameter library based on the influence indexes of the same fan and combined with a difference state database;
a monitoring data processing unit: the monitoring database is used for carrying out second comparison on the monitoring database corresponding to the wind power generation system and the standard monitoring data of the wind power generation system;
based on the second comparison result, obtaining the data condition of each monitoring data, and based on a second external influence factor, correcting each data condition to obtain a second parameter base;
a data acquisition unit: and the data processing unit is used for obtaining processed data based on the first parameter library and the second parameter library.
4. The intelligent operation and maintenance management system of the wind power generation system based on the internet of things technology as claimed in claim 3, wherein the data acquisition unit comprises:
a position locking block: for locking a first position of each wind turbine in the wind power system;
the mapping relation establishment block: the wind power generation system is used for establishing a factor mapping relation between each fan and the wind power generation system according to the first external influence factor of each fan and the second external influence factor of the wind power generation system;
a table optimization block: and the parameter mapping table is used for establishing a parameter mapping table of the first parameter base and the second parameter base based on the first position, optimizing the parameter mapping table based on the factor mapping relation to obtain an optimized mapping table, and regarding the parameters in the optimized mapping table as the processed parameters.
5. The intelligent operation and maintenance management system of the wind power generation system based on the internet of things technology as claimed in claim 1, wherein the data analysis module comprises:
an analysis processing unit: the data processing device is used for performing data extraction, data cleaning and data mining on the processed data to generate a first data analysis result;
a second analysis unit: the system comprises a first data analysis result, a second data analysis result and historical operation and maintenance information of the wind power generation system, wherein the first data analysis result and the historical operation and maintenance information of the wind power generation system are analyzed to obtain the second data analysis result;
an analysis transmission unit: and the second data analysis result is uploaded to a data early warning module for early warning.
6. The intelligent operation and maintenance management system of the wind power generation system based on the internet of things technology as claimed in claim 5, wherein the data analysis module further comprises:
the filling unit is used for filling the historical operation and maintenance information of the wind power generation system into an operation and maintenance analysis table according to different information types;
the average value obtaining unit is used for obtaining a first operation and maintenance average value in a normal range and a second operation and maintenance average value in an abnormal range in each type in the operation and maintenance analysis table;
the result extraction unit is used for carrying out third comparison on the processed data and the normal range of the corresponding type to obtain a third comparison result, and extracting a first normal result and a first abnormal result in the third comparison result;
the first comparison unit is used for performing first comparison on the basis of a first historical result matched with the first operation and maintenance average value, and obtaining a third operation and maintenance average value of the first normal result on the basis of the first operation and maintenance average value;
wherein Y1 represents a first operation and maintenance average value; n1 represents n1 result values extracted from the first normal result; h1 i1 Representing an i1 st result value among the n1 extracted results; h0 i1 Representing the i1 st result value in the n1 first history results; is a direct change 01 Representing a type weight corresponding to the first normal result;
the second comparison unit is used for performing second comparison on the basis of a second historical result matched with the second operation and maintenance average value and obtaining a fourth operation and maintenance average value of the first abnormal result on the basis of the second operation and maintenance average value;
and the array construction unit is used for carrying out operation and maintenance primary judgment on the processed data based on the third operation and maintenance average value and the fourth operation and maintenance average value, constructing to obtain an operation and maintenance array set, and enabling the operation and maintenance array to comprise the third operation and maintenance average value, the fourth operation and maintenance average value and a corresponding data type.
7. The intelligent operation and maintenance management system of the wind power generation system based on the internet of things technology as claimed in claim 5, wherein the data early warning module comprises:
an analysis and comparison unit: the device is used for comparing the prediction diagnosis result with a preset monitoring threshold value one by one;
a judging unit: when a first numerical value larger than a preset monitoring threshold value exists in the prediction diagnosis result, judging that a fan corresponding to the first numerical value is in an abnormal state to work;
simultaneously, the current state of the wind power generation system is monitored:
if the current state of the wind power generation system is an early warning state, keeping the state unchanged, uploading a result which is related to the first numerical value and needs early warning to an operation and maintenance terminal, and displaying the result on a terminal page;
and if the state of the wind power generation system is a non-early-warning state, controlling the wind power generation system to be changed from the non-early-warning state to an early-warning state, uploading information which is related to the first numerical value and needs early warning to an operation and maintenance terminal, and displaying the information on a terminal page.
8. The intelligent operation and maintenance management system of the wind power generation system based on the internet of things technology as claimed in claim 7, wherein the judging unit comprises:
an optimum value determination block: the method comprises the steps of finding out an optimal value of a monitoring threshold in a preset monitoring range of the wind power generation system by adopting a threshold triggering model, wherein the optimal value is a corresponding preset monitoring threshold;
the range of the threshold triggering model changes along with the belonged time of the wind power generation system service, the monitoring threshold of the wind power generation system in the peak service period is compared with the monitoring threshold of the peak service period, and the monitoring threshold of the wind power generation system in the idle service period is compared with the monitoring threshold of the idle service period, so that the optimal value of the monitoring threshold is obtained.
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