Pumped storage transition process data dynamic cutting method based on mass operation data

Abstract

A dynamic cutting method for pumped storage transition process data based on mass operation data relates to the field of intelligent operation and maintenance of a pumped storage system, and is triggered through a sensor instruction, automatically cuts sensor data according to a trigger condition, segments and stores the acquired data, realizes reasonable and rapid data interception, and improves the data processing efficiency. The completeness and accuracy of the data in the transition process are ensured, and the real-time data are stored, so that later analysis or data tracking is facilitated. Rich state information contained in the pumped storage transition process can be obtained, actual operation data in the transition processes of frequent start-stop, working condition conversion and the like can be obtained on the premise that normal production and power generation of the hydroelectric generating set are not affected, the safety of the set in the flexible adjustment process can be analyzed and predicted, and the safety of the set is improved. And an effective technical means is provided for guaranteeing safe and stable operation of the hydroelectric generating set.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent operation and maintenance of pumped - storage systems, and particularly relates to a method for dynamically cutting pumped - storage transient process data based on a large amount of operation data. Background Art

[0002] The new power system with new energy as the main body puts forward higher requirements for the flexible regulation ability and intelligent operation and maintenance level of pumped - storage units. As the key equipment of the power station, the pumped - storage units need to frequently experience transient processes such as starting, stopping, and operating condition conversion to give full play to the role of flexible regulation. Under this operation mode, the damage suffered by the hydraulic system of the power station is much higher than that in the case of long - term stable power generation operation of the units. Therefore, it is necessary to monitor the operation data of the pumped - storage units to ensure the safety of the transient process during the flexible regulation of the pumped - storage power station.

[0003] At the same time, due to the uncertain occurrence time and frequency of the pumped - storage transient process, monitoring the operation data of the pumped - storage units requires the storage and management of full - time operation data. The full - time operation data has the characteristics of many data types, large data volume, and a lot of garbage data. Most of the data in the full - time operation database belongs to steady - state data. How to extract transient process data from the massive data is the key.

[0004] To achieve the function of extracting transient process data from massive data, it is necessary to involve a variety of technologies and tools, especially those related to data acquisition, real - time processing, signal processing, and database management. In terms of real - time data acquisition and processing, the message queue technology can process sensor data in a real - time system, help handle large - scale data streams, and quickly respond after a triggering event. In terms of signal processing, time - series databases, such as ClickHouse, can efficiently store and query a large amount of time - series data. Using window functions or segmentation techniques, sensor data can be divided into different time periods or data segments for subsequent analysis or storage. In terms of the event - driven architecture, when a sensor emits a specific instruction or signal, the corresponding cutting operation is triggered through the event - driven architecture.

[0005] In summary, the technical problem to be solved by the present invention is: how to obtain the actual operation data during transient processes such as frequent starting, stopping, and operating condition conversion without affecting the normal power generation of the hydropower unit, and use it to analyze and predict the safety during the flexible regulation process of the unit. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a dynamic cutting method for pumped-storage transition process data based on a large amount of operation data, so as to obtain the actual operation data during the transition process such as frequent start-stop and condition conversion without affecting the normal production and power generation of the hydropower unit, in order to analyze and predict the safety during the flexible regulation process of the unit, and provide an effective technical means for ensuring the safe and stable operation of the hydropower unit.

[0007] To solve the above technical problems, the technical solution adopted by the present invention is:

[0008] A dynamic cutting method for pumped-storage transition process data based on a large amount of operation data, the steps are as follows:

[0009] Step 1: Quickly read and write all sensor data and communication data through MQTT and the time series database ClickHouse, and the data is transmitted to the computer or server in real time through the interface; the MQTT herein represents Message Queuing Telemetry Transport; ClickHouse represents an open-source columnar database management system;

[0010] Step 2: Compile a transient process condition trigger instruction module according to the digital input data, command data and main status data;

[0011] Step 3: Transmit the digital input data, command data and main status data in the communication data to the transient process condition trigger instruction module in real time;

[0012] The digital input data includes the circuit breaker position, pumping state, power generation state, and shutdown state; the command data includes the start command, shutdown command, and emergency shutdown command; the main status data includes the given guide vane opening and the given power;

[0013] Step 4: Determine whether the current transient process condition is triggered according to the transient process condition trigger instruction module. If the trigger condition is not met, the task is discarded. If the condition is met, the timed cutting task module is triggered;

[0014] Step 5: Start the timed cutting task module, execute the indexing task from ClickHouse with a time stamp, and intercept the data packets for a period of time before and after the command trigger from the database as the transient process data under this condition;

[0015] Step 6: Store the transient process data according to the specified path, and push it to the front end through the HyperText Transfer Protocol (HTTP) for data playback and display.

[0016] Preferably, the transient process condition trigger instruction in Step 3 is as follows:

[0017] There are mainly two types of preset data cutting trigger instruction types, including:

[0018] (1) Triggered based on a threshold, that is, when a certain sensor reading, if the start command or stop command reaches a preset threshold, data cutting is triggered;

[0019] (2) Externally triggered. When an external control signal is received, the control signal includes a manual instruction or a signal from another device, and the data cutting process is started; According to the transient process condition trigger instruction module, it is determined whether the current transient process condition is triggered. If the trigger condition is not met, the task is discarded. If the condition is met, the timed cutting task module is triggered.

[0020] Preferably, the method for intercepting data packets for a period of time before and after the interception instruction trigger in step four is as follows:

[0021] Use a circular buffer of fixed size to store sensor data in chronological order, continuously saving data for a past period of time; When the timed cutting task module is started, perform an indexing task from ClickHouse with timestamps; Intercept data packets for a period of time before and after the interception instruction from the database as the transient process data under this condition; Data for a certain period before the trigger condition is met is extracted from memory; Data for a certain period after the trigger condition is met will be continuously collected and saved until a certain end condition is reached. The end condition includes a time interval or the sensor data returns to the normal level.

[0022] Preferably, in step five, the transient process data is stored according to the specified path, and the process of pushing to the front end through the HTTP protocol for data playback and display is as follows:

[0023] 1) Set up a Spring cloud backend service framework that supports the HTTP protocol on the server to respond to requests from the front end;

[0024] 2) Design a transient process condition historical playback interface on the front end; When the front-end page finishes loading, use JavaScript (a dynamic programming language) to initiate an HTTP request to obtain the content in the transient data packet in the folder from the server;

[0025] 3) After the server receives the HTTP request from the front end, read the content in the transient data packet and return it to the front end as the HTTP response body;

[0026] 4) After the front end receives the response from the server, extract the content in the transient data packet and insert it into the transient process condition historical playback interface for display.

[0027] Preferably, the sub-steps of step one are as follows:

[0028] Initialize various sensors (such as pressure, flow, speed sensors, etc.) and communication devices in the pumped-storage power station, and configure parameters to ensure that they can collect data normally and prepare for data transmission according to the requirements of the MQTT protocol;

[0029] Deploy an MQTT client at the sensor and communication device ends, and configure the connection parameters of the client, including the address, port, subscription topic, and publishing topic of the MQTT server; the subscription topic is used to receive control instructions issued by the server, and the publishing topic is used to send the collected data to the server;

[0030] The MQTT client actively connects to the MQTT server according to the configured parameters to establish a stable network connection; during the connection process, perform authentication and handshake to ensure the security and legality of data transmission;

[0031] Sensors and communication devices encapsulate the real-time collected data into messages conforming to the MQTT protocol format at regular time intervals and publish them to the corresponding topics through the established MQTT connection;

[0032] On the computer or server side, perform initialization operations on the ClickHouse database, including creating the database and designing the data table structure; according to the characteristics of sensor data and communication data, design appropriate data table fields, such as timestamps, sensor IDs, data values, etc., and set appropriate data types;

[0033] Deploy an MQTT client as a subscriber on the computer or server side to subscribe to the topics where sensors and communication devices publish data; when receiving an MQTT message, parse the message content and insert the data into the corresponding data table according to the format requirements of the ClickHouse database to complete the rapid storage of data.

[0034] Preferably, the sub-steps of step two are:

[0035] Deeply analyze the operation logic and transient process characteristics of the pumped-storage power station, and clarify the key roles of digital input data (breaker position, pumping state, generating state, shutdown state), command data (start command, shutdown command, emergency shutdown command), and main state variables (given guide vane opening, given power) in transient process determination;

[0036] Sort out the logical relationships between different types of data, such as the association between breaker position and start command, generating state, etc., and the coordination relationship between the change of given guide vane opening and power change, etc., to provide a logical basis for setting trigger conditions;

[0037] According to the data logical relationship and the actual operation requirements of the power station, set the trigger conditions for the transient process conditions; for example, set that when the breaker position changes from open to closed, and at the same time a start command is received and the current state is not the shutdown state, trigger the start transient process condition; or when the given power changes significantly within a short period of time and meets certain guide vane opening change conditions, trigger the load adjustment transient process condition;

[0038] Select a suitable programming language (such as Python, Java, etc.), and write the code for the transient process condition trigger instruction module according to the set trigger conditions; implement the functions of real-time monitoring of input data, logical judgment, and output of trigger signals in the code;

[0039] Test the written trigger instruction module, verify it using simulated data and actual operation data, and check the accuracy of the trigger conditions and the stability of the module; according to the test results, optimize and adjust the module to ensure that it can accurately and reliably determine the transient process conditions.

[0040] Preferably, the sub-steps of step six are:

[0041] Design the storage path structure of the transient process data according to the data management and retrieval requirements; for example, adopt a hierarchical structure by year / month / date / condition type for storage to ensure clear data classification and facilitate subsequent quick search and call;

[0042] On the server side, write a data storage program. When receiving the transient process data intercepted from the database, store the data in a suitable file format (such as CSV, JSON, etc.) to the specified storage medium according to the planned storage path; at the same time, measures such as redundant storage and regular backup can be adopted to ensure the security and integrity of the data; build a backend service framework: build a backend service framework that supports the HTTP protocol on the server side, such as Spring cloud; configure the relevant parameters of the framework, including the port number, routing rules, etc., so that it can receive HTTP requests sent by the front end and read the corresponding transient data packets from the storage path according to the request content;

[0043] Develop a historical playback interface for the transient process conditions on the front end, and use technologies such as HTML, CSS, and JavaScript to design a friendly user interaction interface, including elements such as a data display area, operation buttons, and a timeline, to facilitate users to view and analyze data;

[0044] After the front-end page is loaded, use JavaScript to write code to initiate an HTTP request to the backend server; the request contains relevant information about the required transient data packets, such as the time range, condition type, etc., so that the backend can accurately return the corresponding data;

[0045] After the front end receives the HTTP response returned by the back-end server, it parses the content of the transient data packet in the response body; according to the data characteristics and the front-end interface design, it uses a suitable visualization library (such as Echarts, D3.js, etc.) to display the data in the form of line charts, bar charts, tables, etc. on the historical playback interface, intuitively presenting the changes in the operating parameters of the pumped-storage power station during the transient process.

[0046] A dynamic data cutting system for the transient process of pumped-storage energy based on massive operation data adopts the described dynamic data cutting method for the transient process of pumped-storage energy based on massive operation data, and includes:

[0047] MQTT and ClickHouse integration module: used to quickly read and write all sensor data and communication data through MQTT and the time series database ClickHouse, and the data is transmitted to the computer or server in real time through the interface;

[0048] The real-time data transmission to the transient process condition trigger instruction module: used to transmit the switch data, instruction data and main status data in the communication data to the transient process condition trigger instruction module in real time;

[0049] Transient process condition determination module: used to determine whether the transient process condition is triggered currently according to the transient process condition trigger instruction module. If the trigger condition is not met, the task is discarded. If the condition is met, the timed cutting task module is triggered;

[0050] Start the timed cutting task module: used to start the timed cutting task module, execute the indexing task from Click House with a timestamp, and intercept the data packets for a period of time before and after the instruction trigger from the database as the transient process data under this condition;

[0051] Data storage and front-end display module: used to store the transient process data according to the specified path, and push it to the front end through the HTTP protocol for data playback and display.

[0052] A computer device includes:

[0053] One or more processors;

[0054] The described processor is used to store one or more programs;

[0055] When the one or more programs are executed by the one or more processors, the described dynamic data cutting method for the transient process of pumped-storage energy based on massive operation data is implemented.

[0056] A computer-readable storage medium stores a computer program, and when the computer program is executed, the described dynamic data cutting method for the transient process of pumped-storage energy based on massive operation data is implemented.

[0057] The present invention can achieve the following beneficial effects:

[0058] (1) The present invention discloses a dynamic cutting method for pumped-storage transition process data based on massive operation data, which can effectively extract the pumped-storage transition process data from the massive operation data and monitor and analyze the operation status of the hydraulic power generation system of the pumped-storage power station in real time. This method provides a new technical means for the operation monitoring of the pumped-storage power station.

[0059] (2) The dynamic cutting method for pumped-storage transition process data based on massive operation data disclosed by the present invention can realize the automatic detection and cutting of the pumped-storage transition process data, without manual discrimination operation, and has extremely high feasibility and good engineering application value.

[0060] (3) The dynamic cutting method for pumped-storage transition process data based on massive operation data disclosed by the present invention can also be used for the development and application of system twin model training, unit fault warning and other contents, and has a wide application prospect. It is of great significance for the intelligent operation and maintenance of pumped-storage power stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] The present invention will be further described below with reference to the drawings and embodiments:

[0062] Figure 1 It is a flow sample diagram of the method of the present invention.

[0063] Figure 2 It is a schematic diagram of the model test bench and unit measuring point layout of the embodiment of the present invention.

[0064] Figure 3 It is a display diagram of the intercepted data in the load-increasing power generation condition of the embodiment of the present invention Figure 1 .

[0065] Figure 4 It is a display diagram of the intercepted data in the load-increasing power generation condition of the embodiment of the present invention Figure 2 .

[0066] Figure 5 It is a display diagram of the intercepted data in the load-increasing power generation condition of the embodiment of the present invention Figure 3 .

[0067] Figure 6 It is a display diagram of the intercepted data in the load-increasing power generation condition of the embodiment of the present invention Figure 4 .

[0068] Figure 7 It is a display diagram of the intercepted data in the load-increasing power generation condition of the embodiment of the present invention Figure 5 .

[0069] Figure 8 Intercepted data display diagram of the embodiment of the present invention under the condition of starting up and connecting to the grid (generating electricity) Figure 1 .

[0070] Figure 9 Intercepted data display diagram of the embodiment of the present invention under the condition of starting up and connecting to the grid (generating electricity) Figure 2 .

[0071] Figure 10 Intercepted data display diagram of the embodiment of the present invention under the condition of starting up and connecting to the grid (generating electricity) Figure 3 .

[0072] Figure 11 Intercepted data display diagram of the embodiment of the present invention under the condition of starting up and connecting to the grid (generating electricity) Figure 4 .

[0073] Figure 12 Intercepted data display diagram of the embodiment of the present invention under the condition of starting up and connecting to the grid (generating electricity) Figure 5 .

[0074] Figure 13 Intercepted data display diagram of the embodiment of the present invention under the condition of shutting down and generating electricity Figure 1 .

[0075] Figure 14 Intercepted data display diagram of the embodiment of the present invention under the condition of shutting down and generating electricity Figure 2 .

[0076] Figure 15 Intercepted data display diagram of the embodiment of the present invention under the condition of shutting down and generating electricity Figure 3 .

[0077] Figure 16 Intercepted data display diagram of the embodiment of the present invention under the condition of shutting down and generating electricity Figure 4 .

[0078] Figure 17 Intercepted data display diagram of the embodiment of the present invention under the condition of shutting down and generating electricity Figure 5 .

[0079] Figure 18 Intercepted data display diagram of the embodiment of the present invention under the condition of load rejection and generating electricity Figure 1 .

[0080] Figure 19 Intercepted data display diagram of the embodiment of the present invention under the condition of load rejection and generating electricity Figure 2 .

[0081] Figure 20 Intercepted data display diagram of the embodiment of the present invention under the condition of load rejection and generating electricity Figure 3 .

[0082] Figure 21 It is a diagram showing the intercepted data in the load rejection power generation condition of the embodiment of the present invention Figure 4 。

[0083] Figure 22 It is a diagram showing the intercepted data in the load rejection power generation condition of the embodiment of the present invention Figure 5 。

[0084] Figure 23 It is a diagram showing the intercepted data in the load reduction power generation condition of the embodiment of the present invention Figure 1 。

[0085] Figure 24 It is a diagram showing the intercepted data in the load reduction power generation condition of the embodiment of the present invention Figure 2 。

[0086] Figure 25 It is a diagram showing the intercepted data in the load reduction power generation condition of the embodiment of the present invention Figure 3 。

[0087] Figure 26 It is a diagram showing the intercepted data in the load reduction power generation condition of the embodiment of the present invention Figure 4 。

[0088] Figure 27 It is a diagram showing the intercepted data in the load reduction power generation condition of the embodiment of the present invention Figure 5 。

[0089] Figure 28 It is a diagram showing the intercepted data in the emergency shutdown power generation condition of the embodiment of the present invention Figure 1 。

[0090] Figure 29 It is a diagram showing the intercepted data in the emergency shutdown power generation condition of the embodiment of the present invention Figure 2 。

[0091] Figure 30 It is a diagram showing the intercepted data in the emergency shutdown power generation condition of the embodiment of the present invention Figure 3 。

[0092] Figure 31 It is a diagram showing the intercepted data in the emergency shutdown power generation condition of the embodiment of the present invention Figure 4 。 Detailed implementation manners

[0093] Based on the above technical problems, the present invention proposes a dynamic data cutting method for the transient process of pumped-storage power generation based on a large amount of operation data. First, all sensor data and communication data are quickly read and written through MQTT and the time-series database ClickHouse, and the data is transmitted to a computer or server in real time through an interface. Secondly, the relevant data in the communication data is transmitted to the transient process condition trigger instruction module in real time, and it is determined whether the transient process condition is triggered according to the transient process condition trigger instruction module. Finally, the timed cutting task module is started, and an indexing task is executed from ClickHouse with timestamps, and data packets for a period of time before and after the instruction trigger are intercepted from the database as the transient process data under this condition. It provides an effective technical means for obtaining the operation data of the transient process of pumped-storage power generation and ensuring the operation safety of pumped-storage power stations. The specific implementation is as follows:

[0094] Embodiment:

[0095] Applied to a conventional model test platform device of a pumped-storage power station, a distorted model with a radial scale ratio of 17.357 and an axial scale ratio of 42.366 is selected for the model device. The experimental platform mainly consists of nine subsystems, namely, a model pipeline system, a circulating water channel system, a model unit, an AC excitation and electrical protection system, a speed regulation system, a monitoring system, a load system, a frequency conversion system, and a measurement system. The upstream and downstream of the experimental platform are both open water tanks, and overflow weirs are arranged in the water tanks to keep the water level constant. The two-machine-one-tunnel layout is adopted, and the unit part consists of two symmetrical pump-turbines, and the two units share a surge chamber. The circulating water system provides a stable water source for the experimental platform. The upstream water tank can pump water from the circulating water system through a water supply pump, and after passing through the pipeline system, the unit, and the tailwater system, it flows into the downstream water tank. The water flowing out of the downstream water tank flows into the circulating water system to realize the recycling of water. The conventional model test platform for pumped-storage power stations can not only conduct steady-state characteristic experiments on pump-turbines, but also conduct transient process experiments such as load increase and decrease, load rejection, runaway, and pump power-off of the turbine.

[0096] The layout of relevant measuring points and sensors on the model test bench is shown in Table 1.

[0097] Table 1 Detailed information on the selection of measuring point sensors for the model test bench unit

[0098]

[0099] In terms of condition monitoring, the model test bench accesses the status monitoring quantities of units #1-2 into the 485 board of the data acquisition instrument through the modbus-485 serial communication form, and the 485 module is installed in the excitation cabinets of units #1-2. The specific communication protocols can be seen in Tables 2-4.

[0100] Adopt Modbus-485 serial communication to achieve real-time communication between the local cabinet (LCU) and the data server. The address of LCU No. 1 is 01, and the address of LCU No. 2 is 02. Read the corresponding measured values according to the attachment address. The slave address can be set, and the read function code is 03. Among them, the data structures of LCU No. 1 and LCU No. 2 are exactly the same. Only the module data structure of one unit is given here, and the data structure of the other unit is completely copied from that of the other unit.

[0101] Table 2 Unit Serial Communication Registers

[0102]

[0103]

[0104] Table 3 4001 Digital Quantity Structure

[0105] Position Name 0 Circuit Breaker Position 1 Circuit Breaker Closed 2 Startup and Grid Connection Order 3 Sudden Load Increase Order 4 Sudden Load Decrease Order 5 Power Generation Condition 6 Pumping Condition 7 Load Rejection 8 Pumping Startup Order 9 Guide Valve Not Closing Order 10 Guide Valve Closing Order 11 Guide Valve Not Closing and Closing Order 12 Guide Valve Not Closing and Not Closing Order 13 Pump Power Off Order 14 Emergency Shutdown Order 15 Accident Shutdown Order

[0106] Table 4 4002 Digital Quantity Structure

[0107]

[0108]

[0109] Affected by the Siemens TIA Portal communication format and following the principle of "low byte first, high byte second", after data communication, it presents the format of "low byte + high byte", which needs to be exchanged. The corresponding relationship of unit status variables in 40033 - 40034 is shown in the following table:

[0110] Table 5 Corresponding Relationship of Unit Status Variables

[0111] 1 2 3 4 5 6 7 8 Shutdown State Idle Running State No-Load State Power Generation State Uncertain State Pumping State Startup State Unit Standby State

[0112] The corresponding relationship of grid connection regulation modes in 40035 - 40036 is shown in the following table:

[0113] Table 6 Corresponding Relationship of Grid Connection Regulation Modes

[0114] 0 1 2 Opening Degree Regulation Power Regulation Frequency Regulation

[0115] According to the measuring point layout scheme of the model test bench involved above, collect and store multi-source signals of hydraulics - mechanics - electricity by arranging hardware devices such as data acquisition instruments and data servers.

[0116] In the measurement room of the model test bench, a high-performance DH5922D 64-channel data acquisition device is installed to carry out data acquisition work. According to the signal transmission characteristics of the sensors, the data acquisition instrument is equipped with 8 current boards, 4 voltage boards, 1 digital input / output board, 2 485 module boards, and 1 frequency measurement board.

[0117] The data acquisition instrument is connected to the industrial control computer via network cable, and the electrical signals are transmitted to the Donghua PHM upper computer software in the industrial control computer through the TCP / IP communication protocol for calibration and pushing. The Donghua PHM upper computer software is installed on the Advantech industrial control computer to synchronize, calibrate, and push the signals of the data acquisition instrument. First, different channel variables are defined on the PHM software. Then, the signals of different channels are calibrated to convert them from electrical signals into analog quantities with physical meanings. Finally, the analog quantity signals are transmitted to the data server through MQTT. The data acquisition instrument, industrial control computer, data server, and simulation server are built in a local area network through network cable / switch, and the data is uploaded to the data server through MQTT for data storage.

[0118] The triggering rules for the transient process conditions of the model test bench are as follows:

[0119] 1. Startup and grid connection (power generation condition): The startup and grid connection command changes from 0 to 1, and the current power generation condition is 1;

[0120] 2. Increase the opening degree (power generation condition): The difference between the guide vane setting value at the later time and the previous time is greater than or equal to 10%, and the breaker positions before and after are both 1;

[0121] 3. Decrease the opening degree (power generation condition): The difference between the guide vane setting value at the previous time and the later time is greater than or equal to 10%, and the breaker positions before and after are both 1;

[0122] 4. Normal shutdown (power generation condition): The current guide vane opening is at the no-load opening, the breaker position changes from 1 to 0, and the emergency shutdown commands are all 0;

[0123] 5. Normal load rejection (power generation condition): The current guide vane opening is not at the no-load opening, the current power generation condition is 1, the breaker position changes from 1 to 0, and the emergency shutdown commands are all 0;

[0124] 6. Accident load rejection (power generation condition): The current guide vane opening is not at the no-load opening, the breaker position changes from 1 to 0, and the emergency shutdown command changes from 0 to 1;

[0125] 7. Pump startup (pumping condition): The pump startup command changes from 0 to 1, and the pumping condition at the next moment changes from 0 to 1;

[0126] 8. Pump shutdown / pump power off (pumping condition): The shutdown command changes from 1 to 0, the breaker changes from 1 to 0, and the pumping condition changes from 1 to 0;

[0127] 9. Emergency shutdown (pumping mode): The emergency shutdown order changes from 0 to 1, and the pumping mode changes from 1 to 0;

[0128] Based on the conventional model experimental platform device of this pumped-storage power station, model experiments were carried out for accident shutdown (power generation) mode, load increase (power generation) mode, load decrease (power generation) mode, shutdown pumping mode and pumping startup mode. Based on this method, all sensor data and communication data were quickly read and written through MQTT and the time series database ClickHouse, and the relevant data in the communication data were transmitted in real time to the transient process condition trigger instruction module for determination. Finally, the data packets 50 s before and 550 s after the instruction trigger were intercepted from the database as the transient process data under this condition. The data includes the main pipe pressure of the upstream bifurcated pipe, the branch pipe pressure of the upstream bifurcated pipe, the inlet pressure of the spiral case, the inlet pressure of the draft tube, the outlet pressure of the draft tube and the elbow pressure of the draft tube. Figures 3 - 16 The results shown verify the effectiveness of the method involved in the present invention.

[0129] The above embodiments are only the preferred technical solutions of the present invention and should not be regarded as limitations on the present invention. The protection scope of the present invention should be the technical solutions recorded in the claims, including the equivalent replacement solutions of the technical features in the technical solutions recorded in the claims. That is, the equivalent replacement improvements within this scope are also within the protection scope of the present invention.

Claims

1. A method for dynamically cutting data of pumped storage transition process based on massive operation data, characterized in that The following steps are involved: Step 1: All sensor data and communication data are quickly read and written through MQTT and the time series database ClickHouse, and the data is transmitted to the computer or server in real time through the interface; MQTT refers to message queue telemetry transmission; ClickHouse refers to an open source column-based database management system; Step 2: Write a transient process condition trigger instruction module based on the switch quantity data, instruction quantity data and main state quantity; Step 3: Transmit the switch quantity data, instruction quantity and main state quantity in the communication data to the transient process condition trigger instruction module in real time; The switch quantity data includes circuit breaker position, pumping state, power generation state, and shutdown state; the command quantity includes start command, shutdown command, and emergency shutdown command; the main state quantity includes given guide vane opening and given power; Step 4: According to the transient process condition trigger instruction module, determine whether the transient process condition is currently triggered. If the trigger condition is not met, the task is discarded. If the condition is met, the timed cutting task module is triggered; Step 5: Start the scheduled cutting task module, execute the indexing task from ClickHouse with timestamp, and intercept the data packets before and after the instruction trigger from the database as the transient process data under this working condition; Step 6: Store transient process data in the specified path and push the front end through the Hypertext Transfer Protocol (HTTP) protocol for data playback and display.

2. According to claim 1, a method for dynamically cutting data of a pumped storage power plant transition process based on massive operating data is characterized in that: The transient process condition triggering instructions in step 3 are as follows: There are two main types of preset data cutting trigger instructions, including: (1) Based on threshold triggering, that is, when a sensor reading, if the start command or shutdown command reaches the preset threshold, data cutting is triggered; (2) External triggering: when an external control signal is received, the control signal includes a manual command or a signal from other equipment, the data cutting process is started; according to the transient process condition, the instruction module is triggered to determine whether the transient process condition is currently triggered. If the trigger condition is not met, the task is discarded; if the condition is met, the timed cutting task module is triggered.

3. According to claim 1, a method for dynamically cutting data of a pumped storage power plant transition process based on massive operation data is characterized in that: The method for intercepting the data packets before and after the command trigger in step 4 is as follows: A fixed-size circular buffer is used to store sensor data in chronological order, and data from the past period of time is continuously saved. When the timed cutting task module is started, the indexing task is executed from ClickHouse with a timestamp. Data packets for a period of time before and after the instruction trigger are intercepted from the database as transient process data under this working condition. Data for a certain period of time before the trigger condition is met is extracted from the memory. Data for a certain period of time after the trigger condition is met will be continuously collected and saved until a certain end condition is reached, which includes the time interval or the sensor data returning to a normal level.

4. According to claim 1, a method for dynamically cutting data of a pumped storage power plant transition process based on massive operating data is characterized in that: In step 5, the transient process data is stored in the specified path, and the data playback and display process is pushed to the front end through the HTTP protocol as follows: 1) Set up a backend service framework Chunyun that supports HTTP protocol on the server to respond to front-end requests; 2) Design the transient process condition history playback interface on the front end; when the front end page is loaded, use JavaScript to initiate an HTTP request to obtain the contents of the transient data packet in the folder from the server; 3) After receiving the HTTP request from the front end, the server reads the content in the transient data packet and returns it to the front end as the HTTP response body; 4) After receiving the response from the server, the front end extracts the content of the transient data packet and inserts it into the transient process condition history playback interface for display.

5. The method for dynamically cutting data of the pumped storage transition process based on massive operation data according to claim 1 is characterized in that: The sub-steps of step one are as follows: Initialize and configure parameters of various sensors and communication equipment in the pumped storage power station to ensure that they can collect data normally and prepare for data transmission according to the MQTT protocol requirements; Deploy MQTT clients on sensors and communication devices, and configure the client connection parameters, including the address, port, subscription topic, and publishing topic of the MQTT server; the subscription topic is used to receive control instructions issued by the server, and the publishing topic is used to send the collected data to the server; The MQTT client actively connects to the MQTT server based on the configuration parameters to establish a stable network connection; During the connection process, authentication and handshake are performed to ensure the security and legitimacy of data transmission; Sensors and communication devices encapsulate the real-time collected data into messages that conform to the MQTT protocol format at certain time intervals, and publish them to the corresponding topics through the established MQTT connection; On the computer or server side, initialize the Click House database, including creating the database and designing the data table structure; design appropriate data table fields and set appropriate data types according to the characteristics of sensor data and communication data; Deploy the MQTT client on the computer or server as a subscriber to subscribe to the topics of data published by sensors and communication devices; when receiving the MQTT message, parse the message content and insert the data into the corresponding data table according to the format requirements of the Click House database to complete the rapid storage of data.

6. The method for dynamically cutting data of the pumped storage power generation transition process based on massive operation data according to claim 1 is characterized in that: The sub-steps of step 2 are: In-depth analysis of the operation logic and transient process characteristics of pumped storage power stations, clarifying the key role of switch quantity data, instruction quantity data and main state quantities in transient process judgment; Sort out the logical relationship between different types of data to provide a logical basis for setting trigger conditions; According to the data logic relationship and the actual needs of power station operation, the triggering conditions of the transient process conditions are set: when the circuit breaker position changes from open to closed, and a start-up command is received and the current state is not in the shutdown state, the startup transient process condition is triggered; or when the given power changes significantly in a short period of time and meets certain guide vane opening change conditions, the load adjustment transient process condition is triggered; Select a suitable programming language and write the code for the transient process condition trigger instruction module according to the set trigger conditions; Test the written trigger instruction module, verify it using simulation data and actual operation data, and check the accuracy of the trigger conditions and the stability of the module; Based on the test results, the module is optimized and adjusted to ensure that it can accurately and reliably determine transient process conditions.

7. The method for dynamically cutting data of the pumped storage power generation transition process based on massive operation data according to claim 1 is characterized in that: The sub-steps of step six are: Design the storage path structure of transient process data according to data management and retrieval requirements; On the server side, a data storage program is written. After receiving the transient process data intercepted from the database, the data is stored in the designated storage medium in a suitable file format according to the planned storage path. Build a backend service framework that supports the HTTP protocol on the server side; configure the relevant parameters of the framework, including port numbers and routing rules, so that it can receive HTTP requests sent by the front end and read the corresponding transient data packets from the storage path according to the request content; Develop a transient process condition history playback interface on the front end and design a user-friendly interactive interface, including a data display area, operation buttons, and a timeline, to facilitate users to view and analyze data; After the front-end page is loaded, use JavaScript to write code to initiate an HTTP request to the back-end server; the request contains relevant information about the required transient data packets; After the front end receives the HTTP response returned by the back-end server, it parses the transient data packet content in the response body; based on the data characteristics and the front-end interface design, it uses a suitable visualization library to display the data in the form of line graphs, bar graphs, and tables on the historical playback interface, visually presenting the changes in operating parameters of the pumped-storage power station during the transient process.

8. A system for dynamically cutting data of pumped storage transition process based on massive operation data, characterized in that: A method for dynamically cutting data of a pumped storage power plant transition process based on massive operating data according to any one of claims 1 to 7 is adopted, comprising: MQTT and ClickHouse integration module: used to quickly read and write all sensor data and communication data through MQTT and the time series database ClickHouse. The data is transmitted to the computer or server in real time through the interface; Transmitting data to the transient process condition trigger instruction module in real time: used to transmit the switch quantity data, instruction quantity and main state quantity in the communication data to the transient process condition trigger instruction module in real time; Transient process condition determination module: used to determine whether the transient process condition is currently triggered according to the transient process condition trigger instruction module. If the trigger condition is not met, the task is discarded. If the condition is met, the timed cutting task module is triggered; Start the timed cutting task module: used to start the timed cutting task module, execute the indexing task from Click House with timestamp, and intercept the data packets before and after the interception instruction is triggered from the database as the transient process data under this working condition; Data storage and front-end display module: used to store transient process data according to the specified path, and push the front-end through the HTTP protocol for data playback and display.

9. A computer device, characterized in that: include: one or more processors; The processor is used to store one or more programs; When the one or more programs are executed by the one or more processors, a method for dynamically cutting pumped storage transition process data based on massive operating data as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed, a method for dynamically cutting data of a pumped storage transition process based on massive operating data as described in any one of claims 1-7 is implemented.

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