System based on unattended power station platform

Abstract

The invention discloses a system based on an unattended power station platform. The system comprises a system architecture module, an information processing module and an operation module. The system architecture is divided into four areas, the safety area III is an overall architecture construction area, hardware is deployed in the safety area III based on a J2EE technical system, and the safety area III covers the functions of model management and the like. And the information processing module stores and reads the alarm data by adopting a Kafka message queue technology, and acquires the in-station real-time alarm data by using a 104 scheduling protocol. The running operation module is provided with a home page submodule for displaying key data and alarm and event information of a power station; the basic data management sub-module manages roles, users, an equipment library and a point table information library; the signal identification sub-module classifies the signals and constructs a working condition signal reference library; the event handling sub-module displays and handles emergency events and integrates experience; and the report management sub-module realizes unified generation and management of reports. The system aims at solving the problem in unattended transformation of the power station and improving the operation efficiency and safety.

Description

Technical Field

[0001] The present invention relates to the field of power system automation, in particular to a system based on an unattended power station platform. Background Art

[0002] With the development of the power industry, the intensive management of power stations has been continuously deepened, and the construction of unmanned power stations has become a trend. The Shuoduogang River cascade power station of a certain power generation company faces many problems in the process of transformation from manned to unmanned. The power station has a reduced number of personnel and uneven technical strength. The monitoring screen alarm signals are complicated and diverse. Valid and invalid alarm signals jump frequently. The monitoring work pressure of personnel is high, and there are risks such as omissions, misreading, and untimely processing, which affect the safe and stable operation of the power plant. At the same time, there is a lack of effective intelligent means in signal identification, event handling, and report management, which cannot meet the needs of efficient operation of unmanned power stations.

[0003] Since the 21st century, my country's rapid development of automation and intelligent technology has provided a technical foundation for the realization of unmanned power stations. Information digitization technology makes it possible to manage substation automation systems. At the same time, the integration of multiple technologies such as power inverters and intelligent fault diagnosis technology has improved operating efficiency and fault handling capabilities.

[0004] There are many deficiencies in the current existing technology. In terms of signal processing, there is a lack of effective classification and screening mechanisms for a large number of alarm signals. Existing technologies cannot accurately distinguish different types of signals, such as being unable to effectively identify which signals are valid signals directly related to equipment failures and which are invalid signals caused by equipment operation, on-site operations, etc., resulting in a large number of invalid signals interfering with the judgment of the equipment operation status by the on-duty personnel. There is a lack of real-time intelligent analysis capabilities for signals, and it is impossible to analyze the changing trends and historical data of the signals in a timely manner, making it difficult to warn of possible equipment failures in advance, and unable to provide effective decision support for on-duty personnel.

[0005] In terms of incident handling, the notification and handling of emergency events rely on manual labor, which is inefficient. When an emergency occurs, it is necessary to manually notify relevant personnel, which is prone to untimely notification. In the process of handling, judgments are often made based on experience or by checking relevant documents to assist in judgment, which can easily lead to the omission of important alarm information, affecting the timeliness and accuracy of incident handling; there is a lack of effective integration and utilization of incident handling experience. There is no systematic mechanism to collect and organize previous emergency incident handling experience, which cannot provide reference and guidance for the handling of subsequent similar incidents.

[0006] In terms of report management, the automation level of report generation and management is low. The existing technology cannot achieve effective docking with the monitoring system and water system, and cannot collect data in real time for report generation, resulting in the inability to guarantee the accuracy and timeliness of the report, and requires a lot of manual data entry and sorting work, which increases the workload and the possibility of errors.

[0007] By collecting data from the monitoring system of the centralized control center, obtaining the operating data of each power station equipment, and conducting modeling analysis, the association between the power station, equipment, and data points is established, and the effective alarm information is intelligently pushed in combination with the label rule library to prompt the power station on-duty personnel, thereby improving the alarm signal recognition capability; through the collected alarm data and combined with the accident expert rule library, the relevant accident event information is intelligently pushed, and the operation personnel are provided with disposal strategies and assisted in judging the accident, thereby improving the efficiency of accident disposal and thus improving the efficiency of emergency event disposal; a centralized storage report center is established to display all basic electricity statistics reports and reporting reports of the centralized control center, and to achieve the unified generation and management of a series of data filling reports, and to achieve the establishment of a centralized storage report center, to display all basic electricity statistics reports and reporting reports of the centralized control center, and to achieve the unified generation and management of a series of data filling reports. Summary of the invention

[0008] The technical problem to be solved by the present invention is a system based on an unattended power station platform. The data system is based on a built-in multi-module structure to realize that in the event of an accident, through the emergency response platform, it can assist the operating personnel to accurately and quickly judge the accident phenomenon in the first time, avoid missing important alarms, provide a multi-dimensional and visual emergency response process, and simultaneously push accident information to ensure that relevant personnel are organized to handle the accident in a short time, while ensuring the efficiency of emergency response and the safe operation of the centralized control equipment.

[0009] In order to solve the above technical problems, the technical solutions adopted by the present invention are as follows: A system based on an unattended power station platform, characterized by comprising the following modules: A-1 system architecture module, A-2 information processing module, A-3 operation module.

[0010] As a preferred solution of the present invention, the A-1 system architecture module is specifically as follows: the system architecture is divided into four major areas, namely, safety zone I real-time control area, safety zone II non-controlled production area, safety zone III production management area, and safety zone IV management information area; wherein, the overall architecture is constructed in safety zone III, including but not limited to model management, intelligent identification, accident handling, and statistical report function modules, and the 104 protocol files are obtained through safety zone I and the data of the monitoring system mirror database is directly obtained through safety zone III for including but not limited to intelligent identification of signals and accident handling; the hardware architecture is deployed in zone III based on the J2EE technology system, including but not limited to application servers, switches, database servers, parsing servers, and 104 protocol message server hardware devices.

[0011] As a preferred solution of the present invention, the A-2 information processing module is specifically: using Kafka message queue technology to store and read massive real-time monitoring alarm data, storing data by topic, including but not limited to producers, consumers, middleware and Zookeeper-type components, to achieve high availability and reliability; using 104 scheduling protocol as an online real-time file transfer protocol, for parsing and obtaining including but not limited to in-station telesignaling, telemetry and real-time alarm data and storing it; based on this technology, data encryption, identity authentication, two-way communication and transmission of multiple data types are performed.

[0012] As a preferred solution of the present invention, the A-3 operation module specifically includes: A-3-1 home page submodule, A-3-2 basic data management submodule, A-3-3 signal identification submodule, A-3-4 event handling submodule, A-3-5 report management submodule.

[0013] As a preferred solution of the present invention, the A-3-1 home page submodule specifically: displays key data indicators of all power stations, which is used to switch and view the data of each power station in real time, including but not limited to unit status, governor oil pump status data; as well as the number of alarm signals, pending events and processed event statistics. When new alarm information is generated, the home page alarm light flashes, and clicking the icon pops up the alarm monitoring screen window to display the alarm signal judged by the system.

[0014] As a preferred solution of the present invention, the A-3-2 basic data management submodule includes B-1 role management module, B-2 user management module, B-3 equipment library module, B-4 point table information library module, specifically: B-1 Role Management Module: Displays role information in a list format, and maintains and manages roles in the system by adding, modifying, deleting, and configuring role permissions. When performing the above operations, query by role name and configure system menus and operation button permissions by role; B-2 User management module: Displays all users under the power station in a tree structure. Maintains and manages all users of the system through adding, viewing, modifying and deleting user information. Query by user name, name and contact information during specific operations; B-3 Equipment library module: query different types of equipment in a tree structure, display equipment information in a list form, maintain and manage all equipment in each power station through the addition, import, modification and deletion of equipment information, and query by equipment name during specific operations; B-4 Point table information library module: With the power plant as the processing dimension, the alarm information is displayed in a list format. All alarm information of the system is maintained and managed through the operations of adding, viewing, modifying, and enabling / disabling the alarm information. The specific operations are queried by signal ID, power plant name, equipment name, and alarm status conditions.

[0015] As a preferred solution of the present invention, the A-3-3 signal identification submodule includes C-1 label rule library module, C-2 operating condition management module, C-3 alarm signal identification module, C-4 statistical analysis module, specifically: C-1 Tag rule library module: customizes rules for each tag rule model, classifies different signals of different devices, and classifies and processes the signals through expert diagnosis models for real-time query of various signals; C-2 Operation Condition Management Module: Based on the "power station-equipment-point meter information" association relationship library, the signal reference library model and alarm sequence of each operation condition are constructed. The corresponding monitoring alarm information is associated with the association relationship library for different operation conditions, and the alarm signal data under the conditions is intelligently matched and pushed to the alarm monitoring screen; C-3 Alarm signal identification module: displays the alarm signal information classified by the system according to the label rule library, manually marks and modifies the label type, defines rules for a large number of alarm screen flashing characters, sets a variety of alarm signal analysis rules and label management rules, and combines the operating condition benchmark library to perform multi-target analysis and filtering of invalid alarm signals; C-4 Statistical Analysis Module: Perform multi-dimensional graphical statistics on all historical alarm data of each power station, including total alarm signal analysis, invalid alarm signal classification statistics, and total valid alarm signal analysis. By viewing the specific alarm signal list and comparing it with historical data, the module performs statistics.

[0016] Furthermore, the C-2 operating condition management module is specifically: C-2-1 Establish a "power station-equipment-point table information" association database: C-2-1-1 Data collection and organization: Collect raw data from various data sources of the power station, including but not limited to equipment operating parameter records, monitoring system data, and maintenance logs; clean and pre-process the collected data to remove noise data, erroneous data, and incomplete data records, and use methods including but not limited to data interpolation, outlier detection, and processing to improve data quality; C-2-1-2 Extraction and matching of equipment and point table information: Identify relevant information of different equipment through analysis of raw data. Equipment information includes but is not limited to equipment name, model, and location. At the same time, extract point table information related to the equipment from the data; establish the mapping relationship between equipment and point table information through a combination of data analysis algorithms and manual intervention; C-2-1-3 Construction of the association relationship library: Use the database management system to create an association relationship library, design the data table structure to store equipment information, point table information and the association relationship between them, store the cleaned and matched equipment and point table information and their association relationship in the corresponding data table, and maintain data consistency and integrity during the storage process; C-2-2 Construct the signal reference library model and alarm sequence for each operating condition: C-2-2-1 Construction of the working condition signal benchmark library model and alarm sequence: For different operating conditions, including but not limited to: maintenance, startup, and shutdown, extract the historical data of the corresponding equipment under the condition from the association library. These historical data contain the operating parameter values ​​of the equipment at different time points. Analyze the data under each condition to build the working condition signal benchmark library model and alarm sequence. At the same time, use the seasonal analysis method to determine the periodic change law of the equipment operating parameters in different time periods. According to the analysis results, determine the alarm sequence under each condition. The alarm sequence is defined as the expected value range of the equipment operating parameters at different time points and the alarm level that should be triggered when the range is exceeded; C-2-2-2 Signal association and matching: When the system obtains monitoring alarm information in real time, it first matches it with the association library based on the equipment information to determine the operating condition of the equipment. For the matched operating condition, the real-time monitoring data is compared with the operating condition signal benchmark library model and alarm timing under the condition. An algorithm is developed to measure the similarity between the real-time monitoring data and the benchmark model based on the similarity between the data. If the similarity is lower than a certain threshold, the real-time monitoring data is considered to be inconsistent with the expected operating condition. The system believes that there is a possibility of an abnormal situation, thereby triggering the corresponding alarm. In the process of signal association and matching, the time delay factor is also considered. Since there is a certain delay in data collection and transmission, a certain time deviation is allowed when comparing to improve the accuracy of the system.

[0017] The algorithm in the further C-2-2-2 signal association and matching is specifically as follows: in a three-dimensional space coordinate system, the data in the benchmark model and the real-time monitoring data are regarded as independent points on two lines in space, and these points represent data values ​​at different time points; determine the distance between each corresponding point, and after calculating these distances, obtain a data table of the distances between the data values ​​at different time points on the two lines, and then find a path from the upper left corner to the lower right corner in this table, and the sum of the distances along this path is the smallest, first determine the distance of the point starting from the upper left corner, and then for the first row and the first column of the table, determine the total distance of each point by accumulating the distance of the previous point and the distance of the current point, and then, for other points in the table, according to the total distance of the three points above, to the left and to the upper left, select the smallest of the three distances, and add the distance of the point itself, and the total distance of the point is obtained. Continue this step to obtain a filled table, and finally start from the point in the lower right corner, according to the shortest direction selected by each point is the required path, that is, the similarity index of the current real-time data.

[0018] As a preferred solution of the present invention, the A-3-4 event handling submodule includes D-1 emergency event statistics module and D-2 experience rule base module, specifically: D-1 Emergency event statistics module: displays all the emergency events to be processed and processed in the current power plant. After receiving the alarm information, the system generates emergency events according to the fault library rules, and displays the alarm information occurrence status and real-time operation data including event diagnosis. At the same time, it realizes the visualization of the event handling process, pushes the relevant information to the centralized control and power plant synchronously, and evaluates and archives the handling process through the centralized control; The D-2 experience rule base module includes an event base unit and a rule maintenance unit, specifically: Event library unit: establish an event library to maintain and manage emergency events of all power plants, display emergency event information in a list format, and perform operations such as adding, viewing, modifying, deleting, and enabling / disabling emergency event information. When performing specific operations, query by power plant name, event name, equipment name, and status conditions; Rule base maintenance unit: establish an expert library, set the relationship between emergency events, alarm information and operating data, display rule information in a list form, add, view, modify, delete, enable / disable rule information, set alarm information attributes, set operating data thresholds, and query by power station name, event name, alarm information, operating data, and status conditions.

[0019] As a preferred solution of the present invention, an accident handling model is established to conduct risk pre-control and handling analysis of hydropower equipment, and the handling process is visualized to assist in emergency handling of accidents and improve the efficiency of accident handling. Specifically, through the established fault analysis model, real-time early warning of hydropower equipment fault events is provided; through fault events, the system automatically analyzes and provides corresponding handling processes to provide a basis for operating personnel to deal with accidents; after a fault event, the system analyzes the operating data before and after the event, identifies the data exceeding the threshold, and provides an effective basis for operating personnel to judge the fault.

[0020] As a preferred solution of the present invention, the A-3-5 report management submodule is specifically: displaying all basic electricity statistics reports and reporting reports of the centralized control center, the data report template is consistent with the original reporting system, realizing the unified generation and management of a series of data reporting reports including but not limited to the MIS system, the integrated systems of various units, and the local and central dispatching systems, connecting with the monitoring system and the water situation system and collecting data in real time to improve the accuracy and timeliness of the reports.

[0021] The beneficial effect of adopting the above technical solution is that the system can effectively guide remote on-duty personnel to quickly and accurately handle emergency events in centralized control power plants, significantly improving the company's safety management and control capabilities. The new energy industry is developing rapidly at home and abroad. The related functions of this system also have promotion value in the field of new energy industry, which can improve the production safety management level of the new energy industry. Through the application of the system, the emergency response capabilities of the company's hydropower production and operation personnel can be effectively improved, ensuring the safe and stable operation of the company's centralized control. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 : System architecture diagram; Figure 2 : Hardware equipment architecture diagram; Figure 3 : Signal processing flow chart; Figure 4 :Association relationship construction flow chart; Figure 5 :Flowchart for establishing expert database; Figure 6 : Real-time warning analysis chart; Figure 7 : Event processing flow chart; Figure 8 : Event processing analysis diagram; Fig. 9 : Home page module display picture; Fig.10 : User management display diagram; Fig.11 : Role management display diagram; Fig.12:Device library display diagram; Fig.13 : Alarm information database display diagram; Fig.14 : Run the database display diagram; Fig.15 : Tag rule library display diagram; Fig.16 : Operation condition management display diagram; Fig.17 : Alarm signal identification display diagram; Fig.18 :Statistical analysis display chart; Fig.19 : Event library display diagram; Fig. 20 : Rule base maintenance diagram; Fig.21 : Report management display diagram DETAILED DESCRIPTION

[0023] In the description of the following embodiments, specific details such as specific system structures, technologies, etc. are proposed for illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, and methods are omitted to prevent unnecessary details from hindering the description of the present application.

[0024] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, components.

[0025] Example 1: An unattended power station in a large factory Reference Figure 1 and attached Figure 2 , system architecture and hardware deployment The system architecture is strictly divided into four major areas. The real-time control area of ​​safety zone I is used to directly realize real-time monitoring functions, such as real-time monitoring of power parameters of key production equipment. The data real-time reaches the second level. The typical systems included in the data transmission through the dedicated channel include the power dispatching automation system inside the factory. Safety zone II non-control production area realizes the necessary production links but has no control function: the operation status of some auxiliary production equipment is monitored and the data real-time is at the minute level. It is connected through the dispatching data network. For example, the water cooling system monitoring of the factory belongs to this area. Safety zone III production management area mainly performs the management functions of the power station but does not involve control, such as the management of equipment maintenance plans, personnel scheduling, etc., and obtains data by connecting to the desktop terminal of the centralized control center or the control center staff. Safety zone IV management information area is responsible for power information management and office automation functions, such as generating production reports, sending notifications, etc. It uses the power data communication network and can be integrated with the internal office system of the enterprise.

[0026] The hardware architecture is deployed in Security Zone III based on the J2EE technology system. The application server uses a high-performance server with an Intel Xeon Gold 6248R processor, 512GB memory, and 10TB storage capacity to ensure efficient operation of the system. The switch is the Huawei S12700 series, providing high-speed and stable data exchange capabilities. The database server uses the Oracle19c database to ensure secure data storage and fast query.

[0027] Reference Figure 3 , Information Processing Kafka message queue technology is used to store and read massive real-time monitoring alarm data. The Kafka cluster consists of 5 nodes, each of which is configured with an Intel Core i7 - 9700K processor, 32GB memory, and 1TB hard disk. Data is stored by topic, divided into "equipment failure alarm" and "environmental parameter abnormality" topics. About 100,000 alarm data are generated every day, and the system can quickly process and store this data to ensure data integrity and availability.

[0028] The 104 dispatching protocol is used as an online real-time file transfer protocol to parse and obtain real-time alarm data of telesignaling and telemetry in the station, and to collect and transmit the switch status (telesignaling), voltage and current values ​​(telemetry) and other data of the high-voltage switch cabinet in the factory in real time. The data transmission rate reaches 100Mbps to meet the real-time requirements, and the AES encryption algorithm is used to encrypt the transmitted data to ensure the confidentiality and integrity of the data.

[0029] Reference Figure 4-6 , run the operation module Home submodule: Displays key data indicators of all power stations, such as the real-time power and operating temperature of each major production equipment with a total power generation of about 50,000 kWh per hour. You can switch to view the data of each power station in real time, including the status of the unit (unit 1 is operating normally, unit 2 is in maintenance), the status of the governor oil pump (the oil pump pressure is 3.5MPa), etc. The number of alarm signals shows that there are currently 20 unprocessed alarms, 5 events to be processed, and 30 events have been processed in the past 24 hours. When new alarm information is generated, the alarm light on the homepage flashes. Click the icon to pop up the alarm monitoring screen window to display all the alarm signals judged by the system: "Transformer oil temperature No. 1 is too high (red font mark)" "Abnormal current fluctuation of the motor of production line No. 3 (red font mark)", normal signal "Ambient temperature is normal (white font mark)".

[0030] Reference Fig.10 , basic data management submodule Role management module: 10 different role information are displayed in a list, including "system administrator", "production supervisor", "equipment maintainer", etc. Role information can be added, modified, and deleted: a new "safety inspector" role is added and the corresponding permissions are configured: only equipment safety-related data can be viewed. It is convenient and fast to query by role name conditions, and it also supports configuring system menus and operation button permissions by role: the "production supervisor" role can view the operating data of all production equipment and operate buttons related to the production process.

[0031] Reference Fig.11 ,User management module: Displays 200 user information under all power stations in the factory in a tree structure, covering different departments and positions. Perform operations such as adding, viewing, modifying, and deleting user information: Add a new electrical engineer user, fill in his user name "elec_eng001", full name "Zhang San", contact information "138xxxxxxxx", and affiliated unit "Electrical Maintenance Department".

[0032] Reference Fig.12 , Equipment library module: Management of more than 1,000 equipment information in the factory, including power station name ("Power Station No. 1"), bay name ("High Voltage Distribution Room"), equipment type (such as "Transformer", "Switch Cabinet", etc.), equipment name field (such as "Main Transformer No. 1", "High Voltage Switch Cabinet No. 3", etc.); query by equipment name condition to facilitate quick location of equipment information. Add, import, modify, and delete equipment information: If a new standby generator is purchased, the equipment information can be entered through the add operation, including detailed parameters such as equipment model, rated power, and production date.

[0033] Point table information library module: Based on the power station as the dimension, it maintains and manages all the alarm information of the system, displays about 5,000 alarm information in the form of a list, supports adding, viewing, modifying, and enabling / disabling operations of alarm information, and the alarm information includes signal ID ("ALM_001"), power station name ("Power Station No. 1"), equipment name ("Main Transformer No. 1"), signal description ("Oil Temperature Over-High Alarm"), and alarm status field ("Unprocessed"). It supports querying by signal ID, power station name, equipment name, and alarm status conditions, which is convenient for classifying and filtering alarm information and viewing detailed content.

[0034] Signal Identification Submodule Reference Fig.15 , Tag rule library module: Customize rules for each tag rule model. More than 20 tag rules have been configured, covering equipment failure types, abnormal operating status, etc.: "Oil temperature is too high" and "Oil level is too low" tag rules are set for transformers, and different signals of different equipment can be classified. Through the expert diagnosis model, the signals are classified and processed, and various signals can be queried in real time, such as querying all signals with the "Oil temperature is too high" tag, quickly locating the hidden dangers of related equipment failures, and providing users with auxiliary decision support functions.

[0035] Reference Fig.16 , Operation condition management module: Based on the "power station-equipment-point table information" association relationship library, build the signal reference library model and alarm sequence of each operation condition. There are 7 main operation conditions: maintenance (such as a comprehensive maintenance of major equipment every year, setting relevant alarm thresholds and timings during the maintenance period), startup (recording the normal range and timing of various parameters when the equipment is started), shutdown (different parameter judgments and timings for normal shutdown and fault shutdown of equipment), load adjustment (adjusting the parameter change range and timing when the equipment load is adjusted according to production needs), switching operation (signal reference and timing when the switch equipment in the substation is switched), plant power switching operation (relevant signal reference and timing when switching the power in the plant), communication channel switching (equipment signal status reference and timing when the communication system is switched). For each operating condition, the corresponding monitoring alarm information is associated with the "power station-equipment-point meter information" association library. When the relevant condition occurs, the system intelligently matches the alarm signal data under the condition by manually setting the condition information or automatically collecting it, and pushes the alarm information of the operating condition to the alarm monitoring screen: During the equipment startup process, if a parameter exceeds the normal range under the startup condition, the system will push the alarm information in time.

[0036] Reference Fig.17,Alarm signal identification module: displays the alarm signal information classified by the system according to the label rule library, and supports manual signal marking and modification of label types. Rules are defined for a large number of alarm screen flashing characters other than various operating conditions, mainly including "frequent (such as a sensor frequently appears abnormal signals) and "instantaneous (such as instant short circuit fault signals) signals. At the same time, according to the special circumstances that may occur during the monitoring process, in addition to frequent and instantaneous signals, it also sets up various alarm signal judgment rules and label management rules such as accompanying (such as a certain equipment failure triggering abnormal signals of related equipment), suppression (such as temporarily suppressing the display of some unimportant alarm signals), blocking (such as blocking false alarm signals), overdue non-reset (such as a protection signal that has not returned to normal for a long time after being activated), telemetry unchanged (such as a certain equipment The voltage remains unchanged for a long time), telemetry mutation (such as a sudden and significant change in the current of a certain equipment), overdue non-action (such as a certain standby equipment does not act on time when it needs to be started), and at the same time, combined with the operating condition benchmark library, multi-objective judgment and filtering of invalid alarm signals are carried out in multiple directions.

[0037] Reference Fig.18 , Statistical analysis module: multi-dimensional graphical statistics of all historical alarm data of each power station. All system users can view the statistical analysis of alarm signals, including total alarm signal analysis (e.g., 1,000 alarm signals were generated in the past month), invalid alarm signal classification statistics (e.g., invalid alarm signals generated due to environmental interference account for 30%), and total valid alarm signal analysis (700 valid alarm signals). Supports viewing of specific alarm signal lists and statistical comparison and analysis with historical data: compares the frequency changes of alarm signals of the same equipment in different time periods to provide a basis for equipment maintenance and fault prevention.

[0038] Reference Figure 7-8 , event handling submodule Emergency event statistics module: displays all the emergency events that are pending and processed in the current power plant. After receiving the alarm information, the system generates an emergency event according to the fault library rules, and displays the alarm information status and real-time operation data including event diagnosis: a key equipment fails, and the system shows that the fault occurred at 10:30 on August 15, 2024, and the alarm information is "the stator temperature of generator No. 1 is too high". The real-time operation data includes the current stator temperature of 120℃ (the normal range should be 80-100℃), and the event handling process is visualized, and the relevant information is pushed to the centralized control and power plant simultaneously. After the event is handled, the centralized control can classify the emergency event handling process to determine whether it is qualified and close the event for archiving. For unqualified handling processes, the centralized control needs to give handling suggestions so as to optimize the handling process when similar events occur in the future.

[0039] Reference Figure 5 , Experience rule base module Event library unit: Establish an event library to maintain and manage emergency events of all power plants. Display more than 100 emergency event information in the form of a list, and add, view, modify, delete, enable / disable emergency event information. Specific operations are performed by querying according to the power plant name, event name, equipment name, and status conditions: For the "No. 1 generator stator temperature is too high" event, record the detailed process of the event, the disposal measures taken, and the final result.

[0040] Reference Fig. 20 ,Rule base maintenance unit: establish an expert library, set the relationship between emergency events, alarm information and operating data, display rule information in a list form, add, view, modify, delete, enable / disable rule information, set alarm information attributes, set operating data threshold operations, and query according to power plant name, event name, alarm information, operating data, and status conditions during specific operations: For the "No. 1 generator stator temperature is too high" event, set a high-level alarm to be triggered when the temperature exceeds 110°C, and associate the corresponding disposal process and operating data monitoring requirements.

[0041] Reference Fig.21 , Report management submodule: Display all basic electricity statistics reports and reporting reports of the centralized control center. The data report template is consistent with the original reporting system, realizing the unified generation and management of a series of data reporting reports including MIS system, integrated system of various departments, local dispatching and central dispatching system, etc. The centralized control center currently involves about 80 statistical reports, and many reports have time limits. This system is connected with the monitoring system and water condition system to collect the daily electricity data (such as the total power generation of the day is 1.2 million kWh) and water condition data (such as the average water temperature of the day is 25℃) in real time, thereby improving the accuracy and timeliness of the report.

[0042] Example 2: Small hydropower station in remote mountainous area (temperature alarm) Data collection and relationship database construction By collecting raw data from various data sources of the power station, including equipment operation parameter records (such as generator speed, turbine flow, etc., collected every 10 minutes), monitoring system data (such as power station ambient temperature and humidity collected every hour), maintenance logs (detailed records of each equipment maintenance), etc. The collected data is cleaned and pre-processed to remove noise data (such as abnormal fluctuation data caused by sensor failure), erroneous data (such as incorrect parameter values ​​entered) and incomplete data records (such as data missing in some time periods), and data interpolation (such as linear interpolation of missing flow data), outlier detection and processing methods (such as treating values ​​that exceed 3 times the standard deviation of the normal range as outliers and correcting them) are used to improve data quality.

[0043] Identify the relevant information of different equipment from the original data. The equipment information includes the equipment name (such as "No. 1 turbine" and "No. 2 generator"), model (such as "HL240-LJ-250" turbine model), location (such as "underground first floor machine room"), and extract the point table information related to the equipment from the data (such as the point table information corresponding to the pressure, flow and other parameters related to the turbine). The mapping relationship between the equipment and the point table information is established by combining data analysis algorithms and manual intervention. Finally, the database management system is used to create an association library, and the data table structure is designed to store the equipment information, point table information and the association relationship between them. The cleaned and matched equipment and point table information and their association relationship are stored in the corresponding data table. The data is kept consistent and complete during the storage process.

[0044] Operation condition signal benchmark library model and alarm sequence construction For different operating conditions, including maintenance (a comprehensive maintenance is carried out once a year during the dry season), startup (parameter setting at startup at 8 am every day), and shutdown (parameter setting at shutdown at 8 pm every day), the historical data of the corresponding equipment under the operating condition is extracted from the association database. These historical data contain the operating parameter values ​​of the equipment at different time points. For example, for the turbine under the startup condition, the speed, flow, pressure and other parameter values ​​of the past 10 startups are extracted. Display all pending and processed emergency events of the power plant. After receiving the alarm information, the system generates emergency events according to the fault library rules, and displays the alarm information occurrence status and real-time operation data including event diagnosis. For example, a key equipment fails, and the system shows that the fault occurred at 10:30 on August 15, 2024, and the alarm information is "the stator temperature of generator No. 1 is too high". The real-time operation data includes the current stator temperature of 120℃ (the normal range should be 80-100℃), and the event handling process is visualized, and the relevant information is pushed to the centralized control and power plant simultaneously. After the incident is handled, the centralized control system can classify the emergency incident handling process to determine whether it is qualified and close and archive the incident. For unqualified handling processes, the centralized control system must provide handling suggestions in order to optimize the handling process when similar incidents occur in the future.

[0045] Experience rule base module Event library unit: Establish an event library to maintain and manage emergency events of all power plants, display more than 100 emergency event information in a list form, and perform operations such as adding, viewing, modifying, deleting, and enabling / disabling emergency event information. When performing specific operations, query according to power plant name, event name, equipment name, and status conditions. For example, for the event "No. 1 generator stator temperature is too high", record the detailed process of the event, the disposal measures taken, and the final result.

[0046] Rule base maintenance unit: Establish an expert library, set the relationship between emergency events, alarm information and operating data, display rule information in a list form, add, view, modify, delete, enable / disable rule information, set alarm information attributes, set operating data thresholds, and query by power station name, event name, alarm information, operating data, and status conditions. For example, for the "No. 1 generator stator temperature is too high" event, set a high-level alarm to be triggered when the temperature exceeds 110°C, and associate the corresponding disposal process and operating data monitoring requirements.

[0047] Report management submodule: displays all basic electricity statistics reports and reporting reports of the centralized control center. The data report template is consistent with the original reporting system, realizing the unified generation and management of a series of data reporting reports including MIS system, integrated systems of various departments, local dispatching and central dispatching systems, etc. The centralized control center currently involves about 80 statistical reports, and many reports have time limits. This system is connected with the monitoring system and water condition system to collect the daily electricity data (such as the total power generation of the day is 1.2 million kWh) and water condition data (such as the average water temperature of the day is 25°C) in real time, thereby improving the accuracy and timeliness of the report.

[0048] Example 3: Small hydropower station in remote mountainous area (pressure alarm) Data collection and relationship database construction By collecting raw data from various data sources of the power station, including equipment operation parameter records (such as generator speed, turbine flow, etc., collected every 10 minutes), monitoring system data (such as power station ambient temperature and humidity collected every hour), maintenance logs (detailed records of each equipment maintenance), etc. The collected data is cleaned and pre-processed to remove noise data (such as abnormal fluctuation data caused by sensor failure), erroneous data (such as incorrectly entered parameter values) and incomplete data records (such as missing data in some time periods), and data interpolation (such as linear interpolation of missing flow data), outlier detection and processing methods (such as treating values ​​that exceed 3 times the standard deviation of the normal range as outliers and correcting them) are used to improve data quality.

[0049] Relevant information of different equipment is identified from the original data. Equipment information includes equipment name (such as "No. 1 turbine" and "No. 2 generator"), model (such as "HL240-LJ-250" turbine model), location (such as "underground first floor machine room"), and point table information related to the equipment is extracted from the data (such as point table information corresponding to pressure, flow and other parameters related to the turbine). The mapping relationship between equipment and point table information is established by combining data analysis algorithms and manual intervention. Finally, a database management system is used to create an association library, and a data table structure is designed to store equipment information, point table information and their associations. The cleaned and matched equipment and point table information and their associations are stored in the corresponding data table, and the data is kept consistent and complete during the storage process.

[0050] Operation condition signal benchmark library model and alarm sequence construction For different operating conditions, including maintenance (a comprehensive maintenance is carried out once a year during the dry season), startup (parameter settings for startup at 8 a.m. every day), and shutdown (parameter settings for shutdown at 8 p.m. every day), historical data of the corresponding equipment under the operating condition is extracted from the association database. These historical data contain the operating parameter values ​​of the equipment at different time points: for the turbine under startup conditions, the speed, flow, pressure and other parameter values ​​of the past 10 startups are extracted. The data under each operating condition is analyzed to build a working condition signal benchmark library model and alarm sequence.

[0051] At the same time, seasonal analysis methods are used to determine the periodic changes in equipment operating parameters in different time periods: in summer, due to the high water temperature, the efficiency of the turbine may change, and the corresponding operating parameters will also have a certain range of fluctuations.

[0052] The alarm sequence under each working condition is determined based on the analysis results. The alarm sequence is defined as the expected value range of the equipment operating parameters at different time points and the alarm level that should be triggered when it exceeds this range: the turbine speed should be 800-1000 rpm during normal operation. When the speed is lower than 700 rpm or higher than 1100 rpm, a level 1 alarm is triggered.

[0053] Signal Correlation and Matching When the system obtains monitoring alarm information in real time, it first matches it with the association library based on the device information to determine the operating condition to which the device belongs. For the matched operating condition, the real-time monitoring data is compared with the operating condition signal benchmark library model and alarm timing under the condition. An algorithm is developed to measure the similarity between the real-time monitoring data and the benchmark model based on the similarity between the data. If the similarity is lower than a certain threshold, it is considered that the real-time monitoring data does not match the expected operating condition, and the system believes that there is a possibility of abnormality, thereby triggering the corresponding alarm. In the process of signal association and matching, the time delay factor is also considered. Since there is a certain delay in data acquisition and transmission, a certain time deviation is allowed when comparing to improve the accuracy of the system: the time deviation is set to 5 minutes. When the monitoring data shows that the turbine speed exceeds the normal range at 8:05, but returns to normal at 8:10, the system may not trigger an alarm, but record the data and observe further.

[0054] Run the operation module Home submodule: Displays key data indicators of small hydropower stations, such as real-time power generation of 500 kWh per hour, reservoir water level of 100 meters, turbine speed of 900 rpm, etc. Supports viewing the number of alarm signals of this power station (currently there are 3 unprocessed alarms), pending events (1 pending equipment maintenance event) and statistics of processed events (5 events have been processed in the past 24 hours). When new alarm information is generated, the alarm light on the homepage flashes. Click the icon to pop up the alarm monitoring screen window to display all alarm signals judged by the system, such as "turbine pressure is too low (red font mark)" and "reservoir water level drops too fast (red font mark)", and normal signals such as "normal ambient temperature (white font mark)".

[0055] Basic data management submodule Role management module: Displays 5 different role information in a list, including "power station administrator", "operation duty officer", "maintenance engineer", etc., and adds, modifies, and deletes role information: adds a "safety inspector" role and configures corresponding permissions, such as only being able to view equipment safety-related data. It is convenient and quick to query by role name, and supports configuring system menus and operation button permissions by role. For example, the "power station administrator" role can view the operation data of all equipment and operate buttons related to power station management.

[0056] User management module: Displays 20 user information under the power station in a tree structure, covering different positions. Supports adding, viewing, modifying, and deleting user information: Add a new operation duty user, fill in his user name "run_duty001", name "Li Si", contact information "139xxxxxxxx", and affiliated unit "Operation Department". The query function by user name, name, and contact information is practical and convenient for finding specific users.

[0057] Equipment library module: supports the management of more than 50 equipment information in the power station, including the power station name ("small hydropower station"), interval name (such as "turbine room" and "generator room"), equipment type (such as "turbine", "generator", "transformer", etc.), equipment name field (such as "turbine No. 1", "generator No. 2", "main transformer", etc.). Supports query by equipment name condition, which is convenient for quickly locating equipment information. Equipment information can be added, imported, modified, and deleted. For example, if a new standby generator is purchased, the equipment information can be entered through the addition operation, including detailed parameters such as equipment model, rated power, and production date.

[0058] Point table information library module: Based on the power station as the dimension, all alarm information of the system is maintained and managed. About 200 alarm information are displayed in the form of a list, and the alarm information can be added, viewed, modified, enabled / disabled. The alarm information includes signal ID (such as "ALM_001"), power station name "small hydropower station"), equipment name (such as "No. 1 turbine"), signal description (such as "low pressure alarm"), and alarm status field (such as "unprocessed" and "processed"). It supports query by signal ID, power station name, equipment name, and alarm status conditions, which is convenient for classifying and filtering alarm information and viewing detailed content.

[0059] Signal Identification Submodule Label rule library module: Customize the rule configuration for each label rule model. More than 10 label rules have been configured, covering equipment failure types, abnormal operating status, etc. For example, label rules such as "pressure is too low" and "speed abnormality" are set for turbines to classify different signals of different equipment. By classifying and processing the signals through the expert diagnosis model, various signals can be queried in real time, such as querying all signals with the "pressure is too low" label, quickly locating the hidden dangers of related equipment failures, and providing users with auxiliary decision support functions.

[0060] Operation condition management module: Based on the "power station-equipment-point meter information" association relationship library, the signal reference library model and alarm sequence of each operation condition are constructed. There are 7 main operation conditions: maintenance (such as a comprehensive maintenance during the dry season every year, setting the relevant alarm thresholds and sequence during the maintenance period), startup (parameter setting when starting at 8 am every day), shutdown (parameter setting when shutting down at 8 pm every day), load adjustment (parameter change range and sequence when adjusting the power generation power according to power demand), switching operation (signal reference and sequence when switching the switch equipment in the power station), plant power switching operation (relevant signal reference and sequence when switching the power in the plant), communication channel switching (equipment signal status reference and sequence when switching the communication system). For each operating condition, the corresponding monitoring alarm information is associated with the "power station-equipment-point table snapshot relationship library. When a related condition occurs, the system intelligently matches the alarm signal data under the condition by manually setting the condition information or automatically collecting it, and pushes the alarm information of the operating condition to the alarm monitoring screen. For example, during the equipment startup process, if a parameter exceeds the normal range under the startup condition, the system will push the alarm information in time.

[0061] Alarm signal identification module: displays the alarm signal information classified by the system according to the labels of the label rule library, and supports manual signal marking and modification of label types. Rules are defined for a large number of alarm screen flashing characters other than various operating conditions, mainly "frequent" (such as a sensor frequently showing abnormal signals) and "instantaneous" (such as instant short-circuit fault signals). At the same time, according to special circumstances that may occur during the monitoring process, in addition to frequent and instantaneous signals, it also sets up various alarm signal judgment rules and label management rules such as accompanying (such as a certain equipment failure triggering abnormal signals of related equipment), suppression (such as temporarily suppressing the display of some unimportant alarm signals), blocking (such as blocking false alarm signals), overdue non-reset (such as a protection signal that has not returned to normal for a long time after being activated), telemetry unchanged (such as the voltage of a certain device remains unchanged for a long time), telemetry mutation (such as a sudden and significant change in the current of a certain device), overdue non-action (such as a backup device that does not act on time when it needs to be started), and at the same time, combined with the operating condition benchmark library, multi-objective judgment and filtering of invalid alarm signals are carried out in multiple directions.

[0062] Statistical analysis module: Multi-dimensional graphical statistics of all historical alarm data of each power station. All system users can view the statistical analysis of alarm signals, including total alarm signal analysis (e.g., 200 alarm signals were generated in the past month), invalid alarm signal classification statistics (e.g., invalid alarm signals generated due to environmental interference account for 20%), and total valid alarm signal analysis (160 valid alarm signals). It supports viewing a specific alarm signal list and statistical comparison and analysis with historical data, such as comparing the frequency changes of alarm signals of the same device in different time periods, to provide a basis for equipment maintenance and fault prevention.

[0063] Event handling submodule Emergency event statistics module: displays all the emergency events that are pending and processed in the current power plant. After receiving the alarm information, the system generates an emergency event according to the fault library rules, and displays the alarm information occurrence status and real-time operation data including event diagnosis. For example, a key equipment fails, and the system shows that the fault occurred at 10:30 on August 13, 2024, and the alarm information is "No. 1 turbine pressure is too low". The real-time operation data includes the current pressure of 1.5MPa (the normal range should be 2-3MPa). At the same time, the event handling process is visualized, and the relevant information is pushed to the centralized control and power plant simultaneously. After the event is handled, the centralized control can classify the emergency event handling process to determine whether it is qualified and close the event for archiving. For unqualified handling processes, the centralized control needs to give handling suggestions so as to optimize the handling process when similar events occur in the future.

[0064] Experience rule base module Event library unit: Establish an event library to maintain and manage emergency events of all power plants. Display more than 30 emergency event information in the form of a list, add, view, modify, delete, enable / disable emergency event information, and query by power plant name, event name, equipment name, and status conditions. For example, for the "No. 1 turbine pressure is too low" event, record the detailed process of the event, the disposal measures taken, and the final result.

[0065] Rule library maintenance unit: Establish an expert library, set the relationship between emergency events, alarm information and portrait data, display rule information in a list form, add, view, modify, delete, enable / disable rule information, set alarm information attributes, set operating data thresholds, and query according to power station name, event name, alarm information, operating data, and status conditions. For example, for the "No. 1 turbine pressure is too low" event, set a high-level alarm when the pressure is lower than 1.8MPa, and associate the corresponding disposal process and operating data monitoring requirements.

[0066] Report management submodule: displays all basic electricity statistics reports and reporting reports of the centralized control center. The data report template is consistent with the original reporting system, realizing the unified generation and management of a series of data reporting reports including MIS system, internal integration system of power station, etc. The centralized control center currently involves about 30 statistical reports, and many reports have time limits. This system is connected with the monitoring system and water condition system to collect the daily electricity data (such as the total power generation of the day is 120,000 kWh) and water condition data (such as the reservoir water level of the day is 98 meters) in real time, thereby improving the accuracy and timeliness of the report.

[0067] Example 4: Data tool development The data tool was jointly developed by power grids, university research forces, and software companies in the early stages of principle and algorithm development, and will later enter the practical application optimization stage.

[0068] The new power plant sensor data monitoring algorithm, which is a core development project, has an innovative design. Its core lies in the fact that conventional algorithms lack specificity in the data model and data flow execution process of power plant fault monitoring. They can only perform ordinary characterization of single data-oriented anomalies and the correspondence with known fault data or power plant fault maintenance plans is relatively simple, thereby reducing the value of abnormal characterization data. Therefore, a data tool with inherent geometric expansion properties is introduced, which can not only process the electrical data of equipment in the power plant, but also cover spatial distribution attribute data and associate these data combinations, so as to expand the depth and breadth of fault monitoring data.

[0069] Specifically, the data tool is constructed as a geometric balance vector or a geometric balance tensor, and its construction process is as follows: Adopt the existing method or our improved new method to construct the feature space according to the power station sensor data and call data: in the feature space formed by the pre-constructed time series-based sensor data set, we must first design a pattern of geometric proximity data and for each point in the feature space, define a local neighborhood, which includes this point and some of its nearest neighbors; further, for this point and its neighborhood, a certain degree of change in each dimension in the neighborhood is obtained through a specific calculation method, and then these changes are used to construct an estimate of the degree of balance of a discrete geometric proximity space.

[0070] Among them, the specific construction method of geometric adjacent space balance estimation is as follows: considering that it is impossible to directly calculate a certain continuous related data at discrete data points, a specific method should be used to construct the data association degree in the geometric adjacent space. In the feature space formed by the pre-constructed sensor data set based on time series, several different calculations should be performed in a specific order for the power station sampling data set in one direction. These calculation methods are similar to a comparison of the data before and after and difference processing: there is a small factor related to the data, and this factor is also the step size in each dimension. It is related to the sampling density of the sensor data or the data refinement of the call data and the spatial location data of the data device source. On this basis, the three-dimensional geometric adjacent space balance estimation can be constructed by extending the above one-dimensional calculation method to three dimensions. Specifically, in the feature space formed by the pre-constructed sensor data set based on time series, for the power station sampling data set in the three-dimensional direction, its three parts related to the data space distribution balance should be obtained according to a specific calculation method. Here, it has an effect in each dimension. It is also related to the sampling density of the sensor data or the data refinement of the call data and the spatial location information of the data device source. It also serves as a certain measurement standard for the data. In general, the accuracy of power plant monitoring depends on the choice of step size and the distribution of data points. Too large a step size may lead to insufficient accuracy, while too small a step size may lead to numerical instability. The processing of boundary points also requires special attention, because they may not have enough neighboring points for a specific calculation method. In this case, the initial consideration is to use other alternative calculation methods or consider building new and more advanced boundary processing techniques.

[0071] Finally, the geometric balance vector or geometric balance tensor is constructed based on Python or MATLAB: For each, a geometric balance vector or geometric balance tensor is constructed, whose components are the discrete geometric neighboring space balance estimates of each point in the feature space in the neighborhood corresponding to each point. The above-mentioned geometric balance vector or geometric balance tensor is characterized as a second-order vector or a tensor of third order or higher according to its components and the dimensional characteristics of the original sensor data set of the power station being processed and the discrete geometric neighboring space balance estimate generated thereby. Usually, the discrete geometric neighboring space balance estimate itself is a second-order vector, and the geometric balance tensor is a third-order tensor in this case. This process can be vividly imagined as traversing each grid point of the power station feature data and using a similar calculation method to calculate the approximate value of the geometric balance.

[0072] A power plant monitoring model with deep data representation characteristics is constructed based on the newly constructed data tool with intrinsic geometric expansion properties, that is, the geometric balance vector or geometric balance tensor: its data process includes: first, a time series sensor data set is constructed for the sensor readings of the power plant. Each element in this data set corresponds to the sensor reading at a time point, and on this basis, it is necessary to consider the need to convert these data into a feature space suitable for analysis. Therefore, for each time point, a certain feature extraction method is used to convert the sensor reading into a feature vector. This feature vector belongs to a certain space, which is the feature space. Since the feature space is not a physical space, the physically defined supporting geometric balance cannot be directly used, so it must be introduced through the construction of analogical concepts. Specifically include: neighborhood definition, for each point in the feature space, a local neighborhood is defined, which includes this point and some of its nearest neighbors; discrete geometric proximity space balance estimation: for each data point and its neighborhood, a specific method is used to estimate the geometric balance, specifically calculating the gradient of the data point in each dimension in its neighborhood and using these gradients to construct a discrete geometric balance estimate; geometric balance vector or geometric balance tensor construction: for each data point, a geometric balance vector or geometric balance tensor is constructed, whose components are the discrete geometric balance estimates of the data point in the corresponding neighborhood; Furthermore, since the geometric balance vector or geometric balance tensor may be high-dimensional, an optional dimensionality reduction tool should be added here to map them to a low-dimensional space for analysis. In this part, the existing dimensionality reduction algorithm can be directly used, such as using principal component analysis PCA, t-SNE, autoencoder or other dimensionality reduction techniques to map the geometric balance vector or geometric balance tensor to a low-dimensional space; anomaly detection algorithms are used in the low-dimensional geometric balance feature space to identify abnormal patterns. Specifically, density-based algorithms such as DBSCAN, clustering-based algorithms such as K-Means, or isolation forest-based algorithms can be used to identify abnormal patterns; on this basis, anomaly score data integration can be further added to assign an anomaly score to each point in the mapped low-dimensional space corresponding to each point to represent the quantitative value of its abnormality degree. In this way, the abnormal pattern can be associated with the fault type, and intelligent detection and fault diagnosis of power plants based on new algorithm tools can be realized.

[0073] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0074] In various embodiments, the hardware implementation of the technology can directly adopt existing intelligent devices, including but not limited to industrial computers, PCs, smart phones, handheld stand-alone computers, floor-standing stand-alone computers, etc. Its input device preferably adopts a screen keyboard, its data storage and calculation module adopts existing memory, calculator, controller, its internal communication module adopts existing communication ports and protocols, and its remote communication adopts existing GPRS network, World Wide Internet, etc.

[0075] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0076] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0077] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A system based on an unattended power station platform, characterized in that It includes the following modules: A-1 system architecture module, A-2 information processing module, and A-3 operation module.

2. The system based on the unattended power station platform according to claim 1 is characterized in that: The A-1 system architecture module is specifically as follows: the system architecture is divided into four major areas, namely, the real-time control area of ​​safety zone I, the non-controlled production area of ​​safety zone II, the production management area of ​​safety zone III, and the management information area of ​​safety zone IV; the overall architecture is constructed in safety zone III, including but not limited to model management, intelligent identification, accident handling, and statistical report function modules, and the data of the monitoring system mirror database is directly obtained through safety zone I for including but not limited to intelligent identification of signals and accident handling of safety zone III; the hardware architecture is deployed in zone III based on the J2EE technology system, including but not limited to application servers, switches, database servers, parsing servers, and hardware devices such as 104 protocol message servers; The A-2 information processing module is specifically as follows: using Kafka message queue technology to store and read massive real-time monitoring alarm data, storing data by topic, including but not limited to producers, consumers, middleware and Zookeeper components, to achieve high availability and reliability; using 104 scheduling protocol as an online real-time file transfer protocol to parse and obtain real-time alarm data including but not limited to in-station telesignaling and telemetry and store it; based on this technology, data encryption, identity authentication, two-way communication and transmission of multiple data types are performed; The A-3 operation module specifically includes: A-3-1 home page submodule, A-3-2 basic data management submodule, A-3-3 signal identification submodule, A-3-4 event handling submodule, and A-3-5 report management submodule.

3. The system based on the unattended power station platform according to claim 2 is characterized in that The A-3-1 homepage submodule specifically displays key data indicators of all power stations, which is used to switch and view the data of each power station in real time, including but not limited to unit status, governor oil pump status data; as well as the number of alarm signals, pending events and processed event statistics. When new alarm information is generated, the alarm light on the homepage flashes, and clicking the icon pops up the alarm monitoring screen window to display the alarm signal judged by the system.

4. The system based on the unattended power station platform according to claim 2 is characterized in that The A-3-2 basic data management submodule includes B-1 role management module, B-2 user management module, B-3 equipment library module, and B-4 point table information library module, specifically: B-1 Role Management Module: Displays role information in a list format, and maintains and manages roles in the system by adding, modifying, deleting, and configuring role permissions. When performing the above operations, query by role name and configure system menus and operation button permissions by role; B-2 User management module: Displays all users under the power station in a tree structure. Maintains and manages all users of the system through adding, viewing, modifying and deleting user information. Query by user name, name and contact information during specific operations; B-3 Equipment library module: query different types of equipment in a tree structure, display equipment information in a list form, maintain and manage all equipment in each power station through adding, importing, modifying and deleting equipment information, and query by equipment name during specific operations; B-4 Point table information library module: With the power plant as the processing dimension, the alarm information is displayed in a list format. All alarm information of the system is maintained and managed through the operations of adding, viewing, modifying, and enabling / disabling the alarm information. The specific operations are queried by signal ID, power plant name, equipment name, and alarm status conditions.

5. The system based on the unattended power station platform according to claim 2 is characterized in that The A-3-3 signal identification submodule includes C-1 label rule base module, C-2 operation condition management module, C-3 alarm signal identification module, C-4 statistical analysis module, specifically: C-1 Tag rule library module: customizes rules for each tag rule model, classifies different signals of different devices, and classifies and processes the signals through expert diagnosis models for real-time query of various signals; C-2 Operation Condition Management Module: Based on the "power station-equipment-point meter information" association library, the signal reference library model and alarm sequence of each operation condition are constructed. The corresponding monitoring alarm information is associated with the association library for different operation conditions, and the alarm signal data under the conditions is intelligently matched and pushed to the alarm monitoring screen; C-3 Alarm signal identification module: displays the alarm signal information classified by the system according to the label rule library, manually marks and modifies the label type, defines rules for a large number of alarm screen flashing characters, sets a variety of alarm signal analysis rules and label management rules, and combines the operating condition benchmark library to perform multi-target analysis and filtering of invalid alarm signals; C-4 Statistical Analysis Module: Perform multi-dimensional graphical statistics on all historical alarm data of each power station, including total alarm signal analysis, invalid alarm signal classification statistics, and total valid alarm signal analysis. By viewing the specific alarm signal list and comparing it with historical data, the module performs statistics.

6. The system based on the unattended power station platform according to claim 5 is characterized in that The C-2 operating condition management module is specifically: C-2-1 Establish the "power station-equipment-point table information" association database: C-2-1-1 Data collection and organization: Collect raw data from various data sources of the power station, including but not limited to equipment operating parameter records, monitoring system data, and maintenance logs; clean and pre-process the collected data to remove noise data, erroneous data, and incomplete data records, and use methods including but not limited to data interpolation, outlier detection, and processing to improve data quality; C-2-1-2 Extraction and matching of equipment and point table information: Identify relevant information of different equipment through analysis of raw data. Equipment information includes but is not limited to equipment name, model, and location. At the same time, extract point table information related to the equipment from the data; establish the mapping relationship between equipment and point table information through a combination of data analysis algorithms and manual intervention; C-2-1-3 Construction of the association relationship library: Use the database management system to create an association relationship library, design the data table structure to store equipment information, point table information and the association relationship between them, store the cleaned and matched equipment and point table information and their association relationship in the corresponding data table, and maintain data consistency and integrity during the storage process; C-2-2 Construct the signal reference library model and alarm sequence for each operating condition: C-2-2-1 Construction of the working condition signal benchmark library model and alarm sequence: For different operating conditions, including but not limited to: maintenance, startup, and shutdown, extract the historical data of the corresponding equipment under the condition from the association library. These historical data contain the operating parameter values ​​of the equipment at different time points. Analyze the data under each condition to build the working condition signal benchmark library model and alarm sequence. At the same time, use the seasonal analysis method to determine the periodic change law of the equipment operating parameters in different time periods. According to the analysis results, determine the alarm sequence under each condition. The alarm sequence is defined as the expected value range of the equipment operating parameters at different time points and the alarm level that should be triggered when the range is exceeded; C-2-2-2 Signal association and matching: When the system obtains monitoring alarm information in real time, it first matches it with the association library based on the equipment information to determine the operating condition of the equipment. For the matched operating condition, the real-time monitoring data is compared with the operating condition signal benchmark library model and alarm timing under the condition. An algorithm is developed to measure the similarity between the real-time monitoring data and the benchmark model based on the similarity between the data. If the similarity is lower than a certain threshold, the real-time monitoring data is considered to be inconsistent with the expected operating condition. The system believes that there is a possibility of an abnormal situation, thereby triggering the corresponding alarm. In the process of signal association and matching, the time delay factor is also considered. Since there is a certain delay in data collection and transmission, a certain time deviation is allowed when comparing to improve the accuracy of the system.

7. The system based on the unattended power station platform according to claim 6 is characterized in that The algorithm in the C-2-2-2 signal association and matching is specifically as follows: in a three-dimensional spatial coordinate system, the data in the benchmark model and the real-time monitoring data are regarded as independent points on two lines in space, and these points represent data values ​​at different time points; the distance between each corresponding point is determined, and after all these distances are calculated, a data table of the distances between the data values ​​at different time points on the two lines is obtained, and then a path from the upper left corner to the lower right corner is found in this table, and the sum of the distances along this path is the smallest, first determine the distance of the point starting from the upper left corner, and then for the first row and the first column of the table, the total distance of each point is determined by accumulating the distance of the previous point and the distance of the current point, and then for other points in the table, according to the total distance of the three points above, to the left and to the upper left, select the smallest of the three distances, and add the distance of the point itself, and the total distance of the point is obtained. Continue this step to obtain a filled table, and finally start from the point in the lower right corner, and the shortest direction selected by each point is the required path, that is, the similarity index of the current real-time data.

8. The system based on the unattended power station platform according to claim 2 is characterized in that The A-3-4 event handling submodule includes D-1 emergency event statistics module and D-2 experience rule base module, specifically: D-1 Emergency event statistics module: displays all the emergency events to be processed and processed in the current power plant. After receiving the alarm information, the system generates emergency events according to the fault library rules, and displays the alarm information occurrence status and real-time operation data including event diagnosis. At the same time, it realizes the visualization of the event handling process, pushes the relevant information to the centralized control and power plant synchronously, and evaluates and archives the handling process through the centralized control; The D-2 experience rule base module includes an event base unit and a rule maintenance unit, specifically: Event library unit: establish an event library to maintain and manage emergency events of all power plants, display emergency event information in a list format, and perform operations such as adding, viewing, modifying, deleting, and enabling / disabling emergency event information. When performing specific operations, query by power plant name, event name, equipment name, and status conditions; Rule base maintenance unit: establish an expert library, set the relationship between emergency events, alarm information and operating data, display rule information in a list form, add, view, modify, delete, enable / disable rule information, set alarm information attributes, set operating data thresholds, and query by power station name, event name, alarm information, operating data, and status conditions.

9. The system based on the unattended power station platform according to claim 8, characterized in that An accident handling model is established to conduct risk pre-control and handling analysis of hydropower equipment, and the handling process is visualized to assist in emergency handling of accidents and improve the efficiency of accident handling. Specifically: through the established fault analysis model, real-time early warning of hydropower equipment failure events is provided; through fault events, the system automatically analyzes and provides corresponding handling processes to provide a basis for operators to deal with accidents; after a fault event, the system analyzes the operating data before and after the event, identifies the data that exceeds the threshold, and provides an effective basis for operators to judge the fault.

10. The system based on the unattended power station platform according to claim 2, characterized in that The A-3-5 report management submodule is specifically: displaying all basic electricity statistics reports and reporting reports of the centralized control center. The data report template is consistent with the original reporting system, realizing the unified generation and management of a series of data reporting reports including but not limited to the MIS system, the integrated systems of various units, and the local and central control systems, connecting with the monitoring system and the water situation system and collecting data in real time to improve the accuracy and timeliness of the reports.

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