CN115640698A - Fault early warning system for hydropower station operation equipment - Google Patents
Fault early warning system for hydropower station operation equipment Download PDFInfo
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Abstract
The invention discloses a hydropower station operation equipment fault early warning system, and particularly relates to the technical field of hydropower station early warning, which comprises an intelligent monitoring module, an equipment fault analysis module, a cloud service module, a fault evaluation module, a daily maintenance module and an emergency processing plan module, wherein the intelligent monitoring module is connected with each sensor of a hydropower station to acquire and monitor the operation state data of each operation equipment in real time, the equipment fault analysis module carries out modeling analysis on the operation data of the operation equipment through a fault similarity model and transmits the analysis result to the fault evaluation module, the cloud service module acquires expert knowledge and big data characteristics in a cloud database to provide algorithm support for the equipment fault analysis module process, and simultaneously stores and self-updates data and algorithms, and carries out fault evaluation and classification on the fault data through the fault evaluation module, thereby providing data support for solving various faults and improving the efficiency and accuracy for solving the faults.
Description
Technical Field
The invention relates to the technical field of hydropower station early warning, in particular to a hydropower station operation equipment fault early warning system.
Background
The hydropower station operation equipment fault early warning system specifically comprises a fault information acquisition module, a fault alarm database, an online analysis module and a man-machine interaction module. The fault information acquisition module acquires working state data of the operating equipment in real time and transmits the data to the fault alarm database; the fault database stores the fault information acquired by the information acquisition; the fault information acquisition module transmits fault data to the online analysis module, an expert unit of the online analysis module monitors, analyzes and gives an early warning to the fault information, an analysis result is sent to a display interface, the man-machine interaction module graphically displays the analysis data, and data support is provided for operation and maintenance personnel to perform daily maintenance.
The existing hydropower station operation equipment fault early warning system analyzes data in real time through an online analysis module and eliminates and repairs the existing faults, but the online analysis module in the existing early warning system has incomplete data analysis, so that errors occur in data analysis, and the situation of false alarm of fault data early warning occurs.
Disclosure of Invention
In order to overcome the above defects in the prior art, embodiments of the present invention provide a hydropower station operation equipment fault early warning system, which obtains and preprocesses operation data through an intelligent detection module, so as to improve accuracy of the operation data, judges a possibility of equipment fault occurrence through an equipment fault analysis module by using a fault similarity model, and performs fault evaluation classification on fault data through a fault evaluation module, so as to provide data support for solving various faults, improve efficiency and accuracy of fault solution, and solve the problems proposed in the background art.
In order to achieve the purpose, the invention provides the following technical scheme: the utility model provides a power station operational equipment trouble early warning system which characterized in that: the intelligent monitoring system comprises an intelligent monitoring module, an equipment fault analysis module, a cloud service module, a fault evaluation module, a daily maintenance module and an emergency treatment plan module. The intelligent monitoring module is connected with each sensor of the hydropower station, and is used for acquiring and monitoring the operation state data of each operation device in real time, and the device fault analysis module is used for carrying out modeling analysis on the operation data of the operation device through a fault similarity model and transmitting the analysis result to the fault evaluation module; the cloud service module acquires expert knowledge and big data characteristics in a cloud database, provides algorithm support for the equipment fault analysis module process, and simultaneously stores and self-updates data and algorithms; the fault evaluation module judges the analysis result, if the daily fault data fluctuation is caused, the daily fault instruction is sent to the daily maintenance module, and if the daily fault data fluctuation is caused, the emergency fault instruction is sent to the emergency treatment plan module; the daily maintenance module sets a fault code according to the evaluation type and sends the daily maintenance code and the fault position to mobile equipment of operation and maintenance personnel; the emergency treatment plan module creates a plan simulation laboratory, quickly searches the optimal emergency treatment plan and controls and processes each device according to the emergency treatment plan.
In a preferred embodiment, the intelligent monitoring module acquires the operation data of each operation device of the hydropower station in real time, automatically divides and arranges the operation data of each operation device into data samples according to the device state, acquires the data characteristics of the operation rate and efficiency of each operation device of the hydropower station by using a mathematical statistics method, and then calculates the absolute value of the residual error of the acquired data value, namely | S | x I > Gg, S x For a certain measurement value of x, gg is set residual pickAnd (2) removing the preset value, marking the calculation result as G1, accurately removing the data from the obtained data by using a Leide-Kerr method to obtain a data result wrongly, assuming that the operation data only contains random errors, calculating the operation data to obtain a standard deviation, then determining a partition according to a certain probability, removing the deviation in the partition to obtain an accurate data result, marking the result as G2, and comparing the removal results of G1 and G2 to improve the accuracy of data removal. The running speed and the running efficiency of the equipment are reference variables, the reference variables can be voltage, current and the like, the reference variables are not unique, and the running data are acquired and preprocessed through the intelligent addition and measurement module, so that the accuracy of the running data is improved.
In a preferred embodiment, the device fault analysis module establishes a multi-layer mapping for the operating state, the operating data abnormality and the device fault of the device through a fault similarity model, first obtains the operating data of the intelligent monitoring module, and sets a fault similarity model, wherein a calculation formula of the fault similarity model is as follows:
In a preferred embodiment, the fault evaluation module performs fault evaluation through a data model of the equipment fault analysis module to obtain a fault type, calculates in real time by using a big data analysis technology and obtains a fault code from a cloud database according to the fault type in advance and gives a fault reason when an abnormal condition occurs through the health state of the running equipment. If the data obtained by the intelligent sensor has small deviation, the fault evaluation module obtains required data from the cloud database unit for analysis, and obtains abnormal faults in advance, wherein the required data comprises weather forecast and equipment maintenance indexes. The fault types are divided into a daily fault type and an emergency fault type, the daily fault type is abnormal basic maintenance of the operating equipment, and the emergency fault type is that the operating equipment floods a workshop, catches fire or stops operating the equipment. The fault evaluation module is used for carrying out fault evaluation classification on fault data, so that data support is provided for solving various faults, and the efficiency and the accuracy of solving the faults are improved.
In a preferred embodiment, the daily maintenance module receives fault data and daily maintenance instructions transmitted by the fault evaluation module, sorts fault processing logic steps according to the fault data, sorts and extracts codes to a corresponding method of each step, then enhances parameters and execution instruction logic required by code interface receiving operation data of each step, extracts operation data logic related codes to daily maintenance codes, defines and separates and isolates the daily fault data from daily service logic by an isolation method, and then changes the file name of the daily fault data into the file name of the whole daily data, puts the file name of the whole daily data into the codes and keeps the same isolated file name, thereby assisting the daily fault maintenance work of operation and maintenance personnel.
In a preferred embodiment, the emergency treatment plan module provides professional knowledge support according to an expert unit in the cloud service module, fault similarity matching is carried out on the faults of the emergency equipment to obtain emergency fault types, the emergency plans are matched according to the emergency fault types, experiments are carried out by using a virtual simulation laboratory, a data base is improved for the reliability and accuracy of the emergency plans, and the emergency plans are rapidly controlled after being audited by the expert unit.
In a preferred embodiment, a control method of a hydropower station operation equipment fault early warning system specifically includes the following steps:
step S10: firstly, an intelligent monitoring module is connected with each sensor of a hydropower station, and acquires and monitors the running state data of each running device in real time;
step S20: then, the equipment fault analysis module carries out modeling analysis on the operation data of the operation equipment through a fault similarity model, and transmits an analysis result to the fault evaluation module, and the cloud service module acquires expert knowledge and big data characteristics in a cloud database, provides algorithm support for the equipment fault analysis module process, and simultaneously stores, self-updates data and an algorithm;
step S30: then, the fault evaluation module judges an evaluation result through a fault similarity model, if the evaluation result is daily fault data fluctuation, a daily fault instruction is sent to a daily maintenance module, and if the evaluation result is emergent fault fluctuation, an emergent fault instruction is sent to an emergency treatment plan module;
step S40: and finally, the daily maintenance module sets a fault code according to the evaluation type and sends the daily maintenance code and the fault position to the mobile equipment of the operation and maintenance personnel, and the emergency treatment plan module creates a plan simulation laboratory, quickly searches the optimal emergency treatment plan and controls and processes each equipment according to the emergency treatment plan.
The invention has the technical effects and advantages that:
the intelligent detection module is used for acquiring and preprocessing the operation data, the accuracy of the operation data is improved, the equipment fault analysis module is used for judging the possibility of equipment fault occurrence by using the fault similarity model, and the fault evaluation module is used for carrying out fault evaluation and classification on the fault data, so that data support is provided for solving various faults, and the efficiency and the accuracy of solving the faults are improved.
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FIG. 1 is a block diagram of the system architecture of the present invention.
FIG. 2 is a flow chart of the method 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 obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Embodiments of the application are applicable to computer systems/servers that are operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well known computing systems, environments, and/or configurations that may be suitable for use with computer systems/servers include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, hand-held or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, networked personal computers, minicomputer systems, mainframe computer systems, distributed cloud computing environments that include any of the above, and the like.
The computer system/server may be described in the general context of a computer system being executed by a computer to perform instructions, such as program modules. Generally, program modules may include routines, programs, objects, components, logic, data structures, etc. that perform particular tasks or implement particular abstract data types. The computer system/server may be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media including memory storage devices.
Examples
The embodiment provides a hydropower station operation equipment fault early warning system as shown in fig. 1, which comprises an intelligent monitoring module, an equipment fault analysis module, a cloud service module, a fault evaluation module, a daily maintenance module and an emergency treatment plan module. The intelligent monitoring module is connected with each sensor of the hydropower station and is used for acquiring and monitoring the running state data of each running device in real time; the equipment fault analysis module carries out modeling analysis on the operation data of the operation equipment through a fault similarity model and transmits an analysis result to the fault evaluation module; the cloud service module acquires expert knowledge and big data characteristics in a cloud database, provides algorithm support for the equipment fault analysis module process, and simultaneously stores and self-updates data and algorithms; the fault evaluation module judges the analysis result, if the daily fault data fluctuation is caused, the daily fault instruction is sent to the daily maintenance module, and if the daily fault data fluctuation is caused, the emergency fault instruction is sent to the emergency treatment plan module; the daily maintenance module sets a fault code according to the evaluation type and sends the daily maintenance code and the fault position to mobile equipment of operation and maintenance personnel; the emergency treatment plan module creates a plan simulation laboratory, quickly searches the optimal emergency treatment plan and controls and processes each device according to the emergency treatment plan.
The hydropower station operation equipment can perform fault early warning according to the system, and is particularly one of the booster transformer and the hydro-generator sets, and no specific limitation is made on the system.
The embodiment provides a control method of a hydropower station operation equipment fault early warning system as shown in fig. 2, and the specific operation steps are as follows:
step S10: firstly, an intelligent monitoring module is connected with each sensor of the hydropower station, and acquires and monitors the running state data of each running device in real time;
specifically, in this embodiment, the intelligent monitoring module acquires the operation data of each operating device of the hydropower station in real time, automatically divides and arranges the operation data of each operating device into data samples according to the device states, acquires the data characteristics of the operation rate and efficiency of each operating device of the hydropower station by using a mathematical statistics method, and then calculates the absolute value of the residual error of the acquired data value, that is, the absolute value of | S x G, | > Gg, wherein S x For a certain measurement value at the x-th time, gg is a set residual elimination preset value, a calculation result is marked as G1, the obtained data is accurately processed by utilizing a Leide-based algorithm, the Leide-based algorithm assumes that the operation data only contains random errors, the operation data is calculated to obtain a standard deviation, and then the standard deviation is determined according to a certain probabilityAnd partitioning, removing the deviation in the partition to obtain an accurate data result, marking the result as G2, and comparing the rejection results of G1 and G2 to improve the accuracy of data rejection. The reference variable can be voltage, current and the like, and the reference variable is not unique, and the required variable is changed according to the setting of a user. The intelligent additional measurement module is used for acquiring and preprocessing the operation data, so that the accuracy of the operation data is improved.
Step S20: then, the equipment fault analysis module carries out modeling analysis on the operation data of the operation equipment through the fault similarity model, and transmits the analysis result to the fault evaluation module, and the cloud service module acquires expert knowledge and big data characteristics in the cloud database, provides algorithm support for the equipment fault analysis module process, and simultaneously stores and self-updates data and algorithms;
what should be specifically described in this embodiment is that the device fault analysis module establishes a multi-layer mapping for the operating state, the abnormal operating data, and the device fault of the device through the fault similarity model, first obtains the operating data of the intelligent monitoring module, and sets up the fault similarity model, where a calculation formula is as follows:
Step S30: then, the fault evaluation module judges the analysis result, if the daily fault data fluctuation is caused, the daily fault instruction is sent to the daily maintenance module, and if the daily fault data fluctuation is caused, the emergency fault instruction is sent to the emergency treatment plan module;
specifically, in this embodiment, the fault evaluation module performs fault evaluation through the data model of the device fault analysis module to obtain a fault type, calculates in real time by using a big data analysis technology, and obtains a fault code from the cloud database according to the fault type in advance and gives a fault reason when an abnormal condition occurs by operating the health state of the device. And if the data obtained by the intelligent sensor has small deviation, the fault evaluation module obtains required data from the cloud database unit for analysis, and obtains abnormal faults in advance, wherein the required data comprises weather forecast and equipment maintenance indexes. The purpose of weather forecast is to improve the possibility of influence on operating equipment according to climate change, for example, in high-temperature weather, fire easily occurs, and whether equipment is burnt due to high temperature in the operating process is judged through a fault evaluation module. The fault types are divided into daily fault types and emergency fault types, the daily fault types are abnormal basic maintenance of the operation equipment, the emergency fault types are plant flooding, fire catching or equipment stopping operation of the operation equipment, fault evaluation and classification are carried out on fault data through the fault evaluation module, data support is provided for solving various faults, and the fault solving efficiency and accuracy are improved.
Step S40: and finally, the daily maintenance module sets a fault code according to the evaluation type and sends the daily maintenance code and the fault position to the mobile equipment of the operation and maintenance personnel, and the emergency treatment plan module creates a plan simulation laboratory, quickly searches the optimal emergency treatment plan and controls and processes each equipment according to the emergency treatment plan.
The embodiment specifically describes that the daily maintenance module receives fault data and daily maintenance instructions transmitted by the fault evaluation module, sorts and extracts codes according to fault data processing logic steps, and a method corresponding to each step is provided, then a code interface of each step is enhanced to receive parameters and execution instruction logic required by operation data, and related codes of the operation data logic are extracted into daily maintenance codes, the extraction method defines and separates daily fault data from daily service logic in an isolation manner and isolates the daily fault data, and then the file name of the daily fault data is changed into a whole daily data file name and is put into the codes and kept isolated, so as to assist the daily fault maintenance work of an operation and maintenance worker.
What this embodiment needs to specifically explain is that emergency treatment plan module provides professional knowledge support according to the expert unit in the cloud service module, carry out the fault similarity matching with emergency equipment trouble, obtain emergency fault type, and match emergency plan according to emergency fault type, utilize the virtual simulation laboratory to carry out the experiment, improve the data basis for reliability and the accuracy of emergency plan, the emergency plan passes through to each equipment quick control after expert unit audits, accomplish the emergency measure of operation equipment, reduce the bigger economic loss that leads to in response to emergency fault.
And finally: the above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that are within the spirit and principle of the present invention are intended to be included in the scope of the present invention.
Claims (7)
1. The utility model provides a power station operational equipment trouble early warning system which characterized in that: the intelligent monitoring module is connected with each sensor of a hydropower station, and acquires and monitors running state data of each running device in real time, the running data of the running devices is modeled and analyzed by the device fault analysis module through a fault similarity model, an analysis result is transmitted to the fault evaluation module, the cloud service module acquires expert knowledge and big data characteristics in a cloud database, algorithm support is provided for the device fault analysis module process, data and algorithms are stored, updated automatically and updated at the same time, the fault evaluation module judges the analysis result, if the analysis result is daily fault data fluctuation, a daily fault instruction is transmitted to the daily maintenance module, if the analysis result is emergency fault fluctuation, an emergency fault instruction is transmitted to the emergency processing plan module, the daily maintenance module sets a fault code according to an evaluation type and transmits the daily maintenance code and the fault position to mobile devices of operation and maintenance personnel, the emergency processing plan module establishes a plan simulation laboratory, searches for an optimal emergency processing plan quickly, and controls and processes each device according to the emergency processing plan.
2. The hydropower station operation equipment fault early warning system according to claim 1, characterized in that: the intelligent monitoring module acquires the operation data of each operation device of the hydropower station in real time, automatically divides and arranges the operation data of each operation device into data samples according to the device state, acquires the data characteristics of the operation speed and efficiency of each operation device of the hydropower station by using a mathematical statistical method, and calculates the absolute value of the residual error of the acquired data value, namely | S |, of which x |>Gg, said S x And for a certain x-th measured value, gg is a set residual elimination preset value, a calculation result is marked as G1, a data result is obtained by accurately removing data by a Leideda method, the data result is obtained by removing data wrongly, the Leideda method is used for assuming that the operation data only contains random errors, the operation data is calculated to obtain a standard deviation, then a partition is determined according to a certain probability, the deviation in the partition is removed to obtain an accurate data result, the result is marked as G2, and the elimination results of G1 and G2 are compared to improve the accuracy of data elimination.
3. The system of claim 1, wherein the system comprises: the equipment fault analysis module establishes multilayer mapping on the running state, the running data abnormity and the equipment fault of the equipment through a fault similarity model, firstly obtains the running data of the intelligent monitoring module, sets the fault similarity model, and has the following calculation formula:
4. The system of claim 1, wherein the system comprises: the fault evaluation module carries out fault evaluation through a data model of the equipment fault analysis module to obtain a fault type, calculates in real time by using a big data analysis technology and operates the health state of the equipment, and obtains a fault code from the cloud database in advance according to the fault type and gives a fault reason when an abnormal condition occurs. If the data obtained by the intelligent sensor has small deviation, the fault evaluation module obtains required data from the cloud database unit for analysis, and obtains abnormal faults in advance, wherein the required data comprises weather forecast and equipment maintenance indexes, the fault types are divided into a daily fault type and an emergency fault type, the daily fault type is abnormal basic maintenance of operating equipment, and the emergency fault type is that the operating equipment floods a workshop, catches fire or stops running.
5. The system of claim 1, wherein the system comprises: the daily maintenance module receives fault data and daily maintenance instructions transmitted by the fault evaluation module, combs fault processing logic steps according to the fault data, codes are arranged and extracted to a corresponding method of each step, then parameters and execution instruction logics required by the code interface of each step for receiving operation data are enhanced, operation data logic related codes are extracted to daily maintenance codes, the extraction method defines the daily fault data in an isolation mode, the daily fault data are separated from the daily service logics and isolated, then the file name of the daily fault data is changed into the file name of the whole daily data, the whole daily data is placed in the codes and kept isolated, and the daily fault maintenance work of operation and maintenance personnel is assisted.
6. The system of claim 1, wherein the system comprises: the emergency treatment plan module provides professional knowledge support according to an expert unit in the cloud service module, fault similarity matching is conducted on emergency equipment faults, emergency fault types are obtained, emergency plans are matched according to the emergency fault types, experiments are conducted through a virtual simulation laboratory, data bases are improved for reliability and accuracy of the emergency plans, and the emergency plans are rapidly controlled after being audited through the expert unit.
7. The control method of the early warning system of faults of the hydropower station operation equipment according to any one of claims 1 to 6, characterized by comprising the following steps: the method specifically comprises the following steps:
step S10: firstly, an intelligent monitoring module is connected with each sensor of a hydropower station, and acquires and monitors the running state data of each running device in real time;
step S20: then, the equipment fault analysis module carries out modeling analysis on the operation data of the operation equipment through the fault similarity model, and transmits the analysis result to the fault evaluation module, and the cloud service module acquires expert knowledge and big data characteristics in the cloud database, provides algorithm support for the equipment fault analysis module process, and simultaneously stores and self-updates data and algorithms;
step S30: then, the fault evaluation module judges an evaluation result through a fault similarity model, if the evaluation result is daily fault data fluctuation, a daily fault instruction is sent to a daily maintenance module, and if the evaluation result is emergent fault fluctuation, an emergent fault instruction is sent to an emergency treatment plan module;
step S40: and finally, the daily maintenance module sets a fault code according to the evaluation type and sends the daily maintenance code and the fault position to the mobile equipment of the operation and maintenance personnel, and the emergency treatment plan module creates a plan simulation laboratory, quickly searches the optimal emergency treatment plan and controls and processes each equipment according to the emergency treatment plan.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116882968A (en) * | 2023-06-29 | 2023-10-13 | 三峡科技有限责任公司 | Design and implementation method for fault defect overall process treatment |
| CN117332121A (en) * | 2023-09-26 | 2024-01-02 | 华能澜沧江水电股份有限公司 | Hydropower plant non-electric quantity protection logic map generation system and method |
| CN117411184A (en) * | 2023-10-26 | 2024-01-16 | 唐山昌宏科技有限公司 | Intelligent command system for emergency treatment of medium-low voltage power supply |
| CN116882968B (en) * | 2023-06-29 | 2024-04-26 | 三峡科技有限责任公司 | Design and implementation method for fault defect overall process treatment |
-
2022
- 2022-11-09 CN CN202211398973.6A patent/CN115640698A/en not_active Withdrawn
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116882968A (en) * | 2023-06-29 | 2023-10-13 | 三峡科技有限责任公司 | Design and implementation method for fault defect overall process treatment |
| CN116882968B (en) * | 2023-06-29 | 2024-04-26 | 三峡科技有限责任公司 | Design and implementation method for fault defect overall process treatment |
| CN117332121A (en) * | 2023-09-26 | 2024-01-02 | 华能澜沧江水电股份有限公司 | Hydropower plant non-electric quantity protection logic map generation system and method |
| CN117411184A (en) * | 2023-10-26 | 2024-01-16 | 唐山昌宏科技有限公司 | Intelligent command system for emergency treatment of medium-low voltage power supply |
| CN117411184B (en) * | 2023-10-26 | 2024-05-03 | 唐山昌宏科技有限公司 | Intelligent command system for emergency treatment of medium-low voltage power supply |
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