A coordinated dispatch optimization control system for a group of hydropower stations

Abstract

The present invention discloses a coordinated dispatch optimization control system for a group of hydropower stations, which relates to the technical field of power station control. A state division module obtains multi-source data of each hydropower station, and after comprehensively evaluating the current health state of each hydropower station based on the multi-source data, divides the health state of the hydropower station into an excellent state, a medium state, and a poor state. The optimization control module generates a first control strategy in combination with the hydropower stations in the excellent state and the medium state during the dry season, and generates a second control strategy in combination with the hydropower stations in the excellent state, the medium state, and the poor state during the flood season. The group of hydropower stations is coordinated and dispatched and optimized according to the generated first control strategy or the second control strategy. The control system generates different control strategies in the dry season and the flood season, respectively, and realizes flexible resource dispatch and optimization control by comprehensively considering the health state of the power station, thereby improving the power generation efficiency and grid stability of the hydropower station group.

Description

Technical Field

[0001] The present invention relates to the technical field of power station control, and in particular to a coordinated dispatching optimization control system for a group of hydropower stations. Background Art

[0002] A hydroelectric power station is a facility that uses the energy of water resources to generate electricity. It is usually located near a river, reservoir or dam. The basic principle is to use the gravitational potential energy or kinetic energy of the water flow to drive the turbine to rotate, and then drive the generator to generate electricity through the turbine. When water flows from high to low, its potential energy is converted into kinetic energy. The force of the water flow drives the turbine to rotate, and the mechanical energy of the turbine is converted into electrical energy through the generator. Depending on the water source and terrain, the hydroelectric power station control system is the core management and operation platform of the hydroelectric power station, responsible for coordinating and controlling all aspects of the hydroelectric power generation process;

[0003] The prior art has the following defects:

[0004] For cascade hydropower stations in a river basin, existing hydropower stations usually operate independently and lack coordinated control. They are unable to dynamically adjust power generation strategies according to different seasons and hydrological conditions, resulting in low resource utilization efficiency in different seasons or hydrological conditions, and reducing the grid stability of the hydropower station group.

[0005] Based on this, the present invention proposes a coordinated dispatching and optimization control system for a group of hydropower stations, which generates different control strategies in the dry season and the flood season respectively. By comprehensively considering the health status of the power station, flexible resource dispatching and optimization control are achieved, thereby improving the power generation efficiency and grid stability of the hydropower station group. Summary of the invention

[0006] The purpose of the present invention is to provide a coordinated dispatching optimization control system for a group of hydropower stations to solve the deficiencies in the background technology.

[0007] In order to achieve the above-mentioned object, the present invention provides the following technical solutions: a coordinated dispatch optimization control system for a group of hydropower stations, comprising an information acquisition module, a state division module, and an optimization control module;

[0008] Information acquisition module: obtains the hydropower station group information through the API interface of the management platform. The hydropower station group information includes the number of hydropower stations and the relative positions of the hydropower stations;

[0009] Status classification module: obtains multi-source data of each hydropower station, comprehensively evaluates the current health status of each hydropower station based on the multi-source data, and then classifies the health status of the hydropower station into excellent, medium and poor status;

[0010] Optimization control module: Controls the hydropower stations in poor condition to stop running and send warning signals to the management platform. In the dry season, the first control strategy is generated by combining the hydropower stations in excellent and medium conditions. In the flood season, the second control strategy is generated by combining the hydropower stations in excellent, medium and poor conditions. The hydropower station group is coordinated and dispatched for optimization control based on the generated first control strategy or second control strategy.

[0011] In a preferred embodiment, the state classification module obtains multi-source data of each hydropower station, the multi-source data including generator abnormality index, operation efficiency index and pressure pipeline fluctuation index;

[0012] The generator abnormality index, the operation efficiency index and the pressure pipeline fluctuation index are calculated comprehensively to obtain the state coefficient;

[0013] The state classification module compares the acquired state coefficient with a first abnormal threshold and a second abnormal threshold, wherein the first abnormal threshold is used to judge whether the health state of the hydropower station is good or bad, and the second abnormal threshold is used to judge the degree of decline of the health state of the hydropower station;

[0014] If the state coefficient is less than or equal to the first abnormal threshold, the health state of the hydropower station is classified as an excellent state. If the state coefficient is greater than the first abnormal threshold and the state coefficient is less than or equal to the second abnormal threshold, the health state of the hydropower station is classified as a medium state. If the state coefficient is greater than the second abnormal threshold, the health state of the hydropower station is classified as a poor state.

[0015] In a preferred embodiment, the state classification module comprehensively calculates the generator abnormality index, the operation efficiency index and the pressure pipeline fluctuation index to obtain the state coefficient, which is expressed as:

[0016] , where is the state coefficient, is the operating efficiency index, is the generator abnormality index, is the pressure pipeline fluctuation index, are the proportional coefficients of the operating efficiency index, the generator abnormality index and the pressure pipeline fluctuation index, respectively, and Both are greater than 0.

[0017] In a preferred embodiment, during the dry season, the optimization control module controls the hydropower station in poor condition to stop operating, then controls the drainage gate of the hydropower station in poor condition to be fully open, and controls the hydropower station in excellent condition to have the maximum water storage capacity;

[0018] The optimization control module adjusts the maximum water storage capacity of the medium-state hydropower station during the dry season. The adjustment algorithm is:

[0019] , where is the maximum water storage capacity after adjustment, is the maximum water storage capacity before regulation, is the state coefficient.

[0020] In a preferred embodiment, the optimization control module performs secondary scheduling optimization according to the positional relationship between the medium-state hydropower station and the excellent-state hydropower station;

[0021] If there is at least one hydropower station in an optimal state whose current water storage capacity is less than 90% of the maximum water flow rate, the optimization control module obtains the positional relationship between the hydropower station in a medium state and the hydropower station in an optimal state. If the hydropower station in a medium state is in the upstream area of ​​the hydropower station in an optimal state, the current maximum water storage capacity of the hydropower station in a medium state is reduced by 10%. If the hydropower station in a medium state is in the downstream area of ​​the hydropower station in an optimal state, no secondary scheduling optimization is performed.

[0022] In a preferred embodiment, during the flood season, the optimization control module controls the hydropower station in poor state to stop operating, controls the hydropower station in excellent state to the maximum water storage capacity, and adjusts the maximum water storage capacity of the hydropower station in medium state, and the adjustment algorithm is:

[0023] , where is the maximum water storage capacity after adjustment, is the maximum water storage capacity before regulation, is the state coefficient;

[0024] The optimization control module obtains the positional relationship between the hydropower station in poor state, the hydropower station in medium state, and the hydropower station in excellent state, and obtains the real-time water level line of the downstream area;

[0025] If the medium-state hydropower station and the excellent-state hydropower station both meet their respective maximum water storage requirements, and the real-time water level in the downstream area is less than or equal to the warning water level, the optimization control module does not perform secondary scheduling optimization;

[0026] If the medium-state hydropower station and the excellent-state hydropower station both meet their respective maximum water storage requirements, but the real-time water level in the downstream area is greater than the warning water level, the optimization control module first obtains the position of the poor-state hydropower station and controls the poor-state hydropower station to increase the water storage capacity until the real-time water level in the downstream area is less than or equal to the warning water level.

[0027] In a preferred embodiment, the calculation logic of the pressure pipeline fluctuation index is: obtaining the real-time pressure value of each position of the pressure pipeline through monitoring points set at different positions inside the pressure pipeline;

[0028] The pressure deviation value is calculated based on the real-time pressure value obtained at the monitoring point. The pressure deviation value is equal to the absolute value of the difference between the real-time pressure value and the standard pressure value. The average pressure deviation value and the pressure deviation standard deviation are calculated based on the pressure deviation values ​​of multiple monitoring points. The expression is:

[0029] , where is the average pressure deviation value, is the standard deviation of pressure deviation, For the The pressure deviation value of each monitoring point, is the number of monitoring points;

[0030] If the average pressure deviation value is less than or equal to the deviation threshold, and the pressure deviation standard deviation is less than or equal to the standard deviation threshold, the pressure pipeline fluctuation index ;

[0031] If the average pressure deviation value is less than or equal to the deviation threshold, and the pressure deviation standard deviation is greater than the standard deviation threshold, the pressure pipeline fluctuation index ;

[0032] If the average pressure deviation value is greater than the deviation threshold, and the pressure deviation standard deviation is greater than the standard deviation threshold, the pressure pipeline fluctuation index ;

[0033] If the average pressure deviation value is greater than the deviation threshold, and the pressure deviation standard deviation is less than or equal to the standard deviation threshold, the pressure pipeline fluctuation index .

[0034] A method for optimizing the coordinated dispatching of a hydropower station group, the control method comprising the following steps:

[0035] The control system obtains the hydropower station group information through the API interface of the management platform. The hydropower station group information includes the number of hydropower stations and the relative positions of the hydropower stations;

[0036] The control system obtains multi-source data of each hydropower station, and after comprehensively evaluating the current health status of each hydropower station based on the multi-source data, divides the health status of the hydropower station into excellent, medium and poor states, controls the hydropower station in poor state to stop operating and sends warning signals to the management platform;

[0037] During the dry season, the first control strategy is generated by combining the hydropower stations in excellent and medium conditions. During the flood season, the second control strategy is generated by combining the hydropower stations in excellent, medium and poor conditions. The hydropower station group is coordinated and optimally controlled based on the generated first control strategy or the second control strategy.

[0038] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0039] The present invention obtains multi-source data of each hydropower station through a state classification module, and after comprehensively evaluating the current health state of each hydropower station based on the multi-source data, divides the health state of the hydropower station into an excellent state, a medium state, and a poor state. The optimization control module generates a first control strategy in combination with the hydropower stations in the excellent state and the medium state during the dry season, and generates a second control strategy in combination with the hydropower stations in the excellent state, the medium state, and the poor state during the flood season. The hydropower station group is coordinated and dispatched and optimized according to the generated first control strategy or the second control strategy. The control system generates different control strategies in the dry season and the flood season, respectively, and realizes flexible resource scheduling and optimization control by comprehensively considering the health state of the power station, thereby improving the power generation efficiency and grid stability of the hydropower station group. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0041] Figure 1 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] Example 1: Please refer to Figure 1 As shown, a coordinated dispatch optimization control system for a group of hydropower stations described in this embodiment includes an information acquisition module, a state division module, and an optimization control module;

[0044] Information acquisition module: obtains the information of the hydropower station group through the API interface of the management platform. The information of the hydropower station group includes the number of hydropower stations and the relative positions of the hydropower stations. The information of the hydropower station group is sent to the state division module and the optimization control module;

[0045] State classification module: obtains multi-source data of each hydropower station, comprehensively evaluates the current health status of each hydropower station based on the multi-source data, and then classifies the health status of the hydropower station into excellent, medium and poor states. The health status classification results are sent to the optimization control module;

[0046] Optimization control module: Controls the hydropower stations in poor condition to stop running and send warning signals to the management platform. In the dry season, the first control strategy is generated by combining the hydropower stations in excellent and medium conditions. In the flood season, the second control strategy is generated by combining the hydropower stations in excellent, medium and poor conditions. The hydropower station group is coordinated and dispatched for optimization control based on the generated first control strategy or second control strategy.

[0047] This application obtains multi-source data of each hydropower station through a state classification module, and after comprehensively evaluating the current health status of each hydropower station based on the multi-source data, divides the health status of the hydropower station into an excellent state, a medium state, and a poor state. The optimization control module generates a first control strategy in combination with the hydropower stations in the excellent state and the medium state during the dry season, and generates a second control strategy in combination with the hydropower stations in the excellent state, the medium state, and the poor state during the flood season. The hydropower station group is coordinated and optimized according to the generated first control strategy or the second control strategy. The control system generates different control strategies in the dry season and the flood season, respectively, and realizes flexible resource scheduling and optimization control by comprehensively considering the health status of the power station, thereby improving the power generation efficiency and grid stability of the hydropower station group.

[0048] The workflow of the control system is:

[0049] The control system obtains the information of the hydropower station group through the API interface of the management platform. The information of the hydropower station group includes the number of hydropower stations and the relative positions of the hydropower stations. The control system obtains multi-source data of each hydropower station, and after comprehensively evaluating the current health status of each hydropower station based on the multi-source data, divides the health status of the hydropower station into excellent, medium and poor states, controls the hydropower station in poor state to stop running and send warning signals to the management platform, and generates a first control strategy in combination with the hydropower stations in excellent and medium states during the dry season, and generates a second control strategy in combination with the hydropower stations in excellent, medium and poor states during the flood season, and performs coordinated scheduling and optimization control of the hydropower station group based on the generated first control strategy or the second control strategy.

[0050] Embodiment 2: The information acquisition module acquires the hydropower station group information through the API interface of the management platform, and the hydropower station group information includes the number of hydropower stations and the relative positions of the hydropower stations;

[0051] Through the API interface provided by the management platform, use authentication keys or OAuth to authenticate and ensure that the information acquisition module can connect to the management platform safely and reliably. Establishing a secure connection with the management platform lays the foundation for subsequent data acquisition.

[0052] According to the API documentation, construct a request containing query parameters and send an API request to the management platform to obtain information about the hydropower station group. The request contains necessary parameters, such as river basin identifier, time range, etc., to ensure that the returned data is accurate. Clarify the query scope to ensure that the acquired data is related to the cascade hydropower station group in the target river basin.

[0053] Receive the API response from the management platform, parse the JSON or XML format data in the response, and extract the information of the hydropower station group, including the number and relative location of the hydropower stations. Perform preliminary processing on the received raw data, such as format conversion, abnormal data filtering, etc., to prepare for subsequent use. Obtain and organize the basic information of the hydropower station group to provide data support for subsequent steps.

[0054] The acquired information such as the number and relative location of hydropower stations is stored in the local database or memory, and the data is managed and versioned for subsequent query and use. This ensures the persistent storage and traceability of data and provides a stable data source for the system.

[0055] The status classification module obtains multi-source data of each hydropower station, and after comprehensively evaluating the current health status of each hydropower station based on the multi-source data, classifies the health status of the hydropower station into excellent status, medium status, and poor status;

[0056] The state classification module obtains multi-source data of each hydropower station, including generator abnormality index, operation efficiency index and pressure pipeline fluctuation index;

[0057] The generator abnormality index, operation efficiency index and pressure pipeline fluctuation index are calculated comprehensively to obtain the state coefficient, and the expression is:

[0058] , where is the state coefficient, is the operating efficiency index, is the generator abnormality index, is the pressure pipeline fluctuation index, are the proportional coefficients of the operating efficiency index, the generator abnormality index and the pressure pipeline fluctuation index, respectively, and Both are greater than 0.

[0059] The calculation expression of generator abnormality index is: , where is the number of parameter deviation values ​​(including temperature deviation, vibration deviation, current deviation, voltage deviation), Indicates parameter deviation values, and Indicates The minimum and maximum allowed values ​​for the parameters, Indicates The weight of each parameter, the parameter deviation value is obtained by taking the absolute value of the difference between the actual parameter and the standard parameter;

[0060] The larger the generator abnormality index is, the worse the condition of the hydropower station is. This is because the generator abnormality index reflects the degree of deviation of the generator operating status. This index is obtained by comprehensively calculating the deviation values ​​of various key parameters of the generator, such as temperature deviation, vibration deviation, current deviation and voltage deviation.

[0061] Temperature deviation: When the generator is running, the temperature should be kept within a stable range. If the temperature deviation is large, it means that the generator may have problems such as overheating or poor heat dissipation, which may cause equipment damage or reduced operating efficiency.

[0062] Vibration deviation: A normally operating generator should have a stable vibration state. Large vibration deviations indicate that there may be mechanical failures inside the generator, such as bearing damage, rotor imbalance, etc. These problems will affect the stability and life of the generator.

[0063] Current deviation: The current deviation reflects the working condition of the generator under different load conditions. Large current deviation may indicate uneven load on the generator, or problems such as short circuit, overload, etc., which will threaten the normal operation of the generator.

[0064] Voltage deviation: The voltage should be kept near the design value, and a large deviation will affect the quality of power output. A large voltage deviation may indicate a problem with the generator regulator or electrical system, which may lead to unstable power supply and affect the operation of the entire power grid.

[0065] The calculation expression of the operating efficiency index is:

[0066] , where is the actual power generation of the hydropower station, is the actual water flow of the hydropower station, is the actual load rate of the hydropower station, is the standard power generation of the hydropower station. is the standard water flow of the hydropower station, is the standard load rate of the hydroelectric power station;

[0067] The larger the operating efficiency index, the better the status of the hydropower station. It reflects the efficiency of the power station in actual operation, based on the following key parameters:

[0068] Actual Generation: This is the amount of electricity that a power station actually generates over a period of time. If the actual generation is high, it means that the power station is more efficient in utilizing water energy.

[0069] Load Factor: reflects the ratio between the output power of a power station and its maximum design power. A higher load factor means that the power station is operating close to its design capacity to a greater extent and has a high utilization rate.

[0070] Water Flow: refers to the flow of water flowing through the turbine. Appropriate water flow enables the turbine to efficiently convert water energy into electrical energy. If the actual water flow is close to the optimal water flow, it means that the water energy conversion efficiency is high.

[0071] A high operating efficiency index means efficient operation: The calculation formula of the operating efficiency index is usually measured by comparing the ratio of actual parameters to optimal or designed parameters. For example, when the power generation, load factor, and water flow are close to their designed or optimal values, the value of the operating efficiency index will be large. This means that the power station operates efficiently under the current conditions, the equipment utilization rate is high, and the water energy is effectively converted into electrical energy.

[0072] Low losses and high output: A higher operating efficiency index indicates that the power plant is able to produce more electricity with lower losses for the same water input. This usually means that the equipment is in good condition and operating parameters are correct.

[0073] Economic and environmental benefits: High efficiency means that power stations can generate more electricity while consuming fewer resources, which not only reduces operating costs but also reduces the impact on the environment.

[0074] The calculation logic of the pressure pipeline fluctuation index is: obtain the real-time pressure value of each position of the pressure pipeline through the monitoring points set at different positions inside the pressure pipeline, and comprehensively analyze the overall pressure condition of the pressure pipeline based on the real-time pressure values ​​obtained from multiple monitoring points;

[0075] The pressure deviation value is calculated based on the real-time pressure value obtained at the monitoring point. The pressure deviation value is equal to the absolute value of the difference between the real-time pressure value and the standard pressure value. The average pressure deviation value and the pressure deviation standard deviation are calculated based on the pressure deviation values ​​of multiple monitoring points. The expression is:

[0076] , where is the average pressure deviation value, is the standard deviation of pressure deviation, For the The pressure deviation value of each monitoring point, is the number of monitoring points;

[0077] If the average pressure deviation value is less than or equal to the deviation threshold, and the pressure deviation standard deviation is less than or equal to the standard deviation threshold, the overall pressure condition of the pressure pipeline is good, and the pressure pipeline fluctuation index is ;

[0078] If the average pressure deviation value is less than or equal to the deviation threshold, and the pressure deviation standard deviation is greater than the standard deviation threshold, the overall pressure condition of the pressure pipeline is good (but the average pressure deviation value of some monitoring points is greater than the deviation threshold), and the pressure pipeline fluctuation index is ;

[0079] If the average pressure deviation value is greater than the deviation threshold, and the pressure deviation standard deviation is greater than the standard deviation threshold, the overall pressure condition of the pressure pipeline is medium (but the average pressure deviation value of some monitoring points is less than or equal to the deviation threshold), and the pressure pipeline fluctuation index is ;

[0080] If the average pressure deviation value is greater than the deviation threshold, and the pressure deviation standard deviation is less than or equal to the standard deviation threshold, the overall pressure condition of the pressure pipeline is analyzed to be poor, and the pressure pipeline fluctuation index is .

[0081] The larger the state coefficient is, the worse the overall health state of the hydropower station is. The state classification module compares the acquired state coefficient with the first abnormal threshold and the second abnormal threshold. The first abnormal threshold is used to judge the health state of the hydropower station, and the second abnormal threshold is used to judge the degree of decline in the health state of the hydropower station.

[0082] If the state coefficient is less than or equal to the first abnormal threshold, the health state of the hydropower station is classified as an excellent state. If the state coefficient is greater than the first abnormal threshold and the state coefficient is less than or equal to the second abnormal threshold, the health state of the hydropower station is classified as a medium state. If the state coefficient is greater than the second abnormal threshold, the health state of the hydropower station is classified as a poor state.

[0083] The optimization control module stops the operation of the hydropower station in poor condition and sends a warning signal to the management platform;

[0084] During the dry season (dry season, the water flow of rivers decreases), a first control strategy is generated by combining the hydropower stations in excellent and medium states. During the flood season, a second control strategy is generated by combining the hydropower stations in excellent, medium and poor states. The hydropower station group is coordinated and dispatched for optimal control based on the generated first control strategy or second control strategy.

[0085] During the dry season, the optimization control module controls the hydropower station in poor condition to stop operating, and then controls the drainage gate of the hydropower station in poor condition to be fully open (that is, the hydropower station in poor condition can maintain the minimum water storage capacity), and controls the hydropower station in good condition to the maximum water storage capacity;

[0086] During the dry season, the optimization control module adjusts the maximum water storage capacity of the medium-state hydropower station, thereby reducing the load of the hydropower station with reduced performance. The adjustment algorithm is:

[0087] , where is the maximum water storage capacity after adjustment, is the maximum water storage capacity before regulation, is the state coefficient.

[0088] Furthermore, during the dry season, the hydropower stations in the best condition may not be able to maintain the maximum water storage capacity. At this time, we perform secondary scheduling optimization based on the positional relationship between the hydropower stations in the medium condition and the hydropower stations in the best condition, as follows:

[0089] If there is at least one hydropower station in optimal state whose current water storage capacity is less than 90% of the maximum water flow rate, the optimization control module obtains the positional relationship between the medium-state hydropower station and the optimal-state hydropower station. If the medium-state hydropower station is in the upstream area of ​​the optimal-state hydropower station, the current maximum water storage capacity of the medium-state hydropower station is reduced by 10% (that is, the water storage of the medium-state hydropower station is discharged to the optimal-state hydropower station). This can further reduce the operating load of the medium-state hydropower station. If the medium-state hydropower station is in the downstream area of ​​the optimal-state hydropower station (at this time, even if the medium-state hydropower station discharges water, it cannot enter the optimal-state hydropower station), no secondary scheduling optimization is performed.

[0090] During the flood season, the optimization control module stops the operation of the hydropower station in poor condition, controls the hydropower station in excellent condition to the maximum water storage capacity, and adjusts the maximum water storage capacity of the hydropower station in medium condition, thereby reducing the load of the hydropower station with reduced performance. The adjustment algorithm is:

[0091] , where is the maximum water storage capacity after adjustment, is the maximum water storage capacity before regulation, is the state coefficient;

[0092] Due to the high water volume in the flood season, it is necessary to coordinate with the hydropower stations in poor condition to adjust the water storage capacity of the hydropower stations in good condition and medium condition (to avoid overload operation of the hydropower stations in good condition and medium condition, and to avoid flood disasters in the downstream);

[0093] The optimization control module obtains the positional relationship between the hydropower station in poor state, the hydropower station in medium state, and the hydropower station in excellent state, and obtains the real-time water level line of the downstream area;

[0094] If the medium-state hydropower station and the excellent-state hydropower station both meet their respective maximum water storage requirements, and the real-time water level in the downstream area is less than or equal to the warning water level, the optimization control module does not perform secondary scheduling optimization;

[0095] If the medium-state hydropower station and the excellent-state hydropower station both meet their respective maximum water storage requirements, but the real-time water level in the downstream area is greater than the warning water level, the optimization control module first obtains the position of the poor-state hydropower station and controls the poor-state hydropower station to increase the water storage capacity until the real-time water level in the downstream area is less than or equal to the warning water level.

[0096] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0097] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0098] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A coordinated dispatch optimization control system for a group of hydropower stations, characterized in that: It includes information acquisition module, state division module and optimization control module; Information acquisition module: obtains the hydropower station group information through the API interface of the management platform. The hydropower station group information includes the number of hydropower stations and the relative positions of the hydropower stations; Status classification module: obtains multi-source data of each hydropower station, comprehensively evaluates the current health status of each hydropower station based on the multi-source data, and then classifies the health status of the hydropower station into excellent, medium and poor status; Optimization control module: Controls the hydropower station in poor state to stop running and sends a warning signal to the management platform. In the dry season, it generates a first control strategy by combining the hydropower stations in excellent and medium states. In the flood season, it generates a second control strategy by combining the hydropower stations in excellent, medium and poor states. The hydropower station group is coordinated and dispatched for optimization control based on the generated first control strategy or second control strategy. The state classification module obtains multi-source data of each hydropower station, the multi-source data including generator abnormality index, operation efficiency index and pressure pipeline fluctuation index; The generator abnormality index, the operation efficiency index and the pressure pipeline fluctuation index are calculated comprehensively to obtain the state coefficient; The state classification module compares the acquired state coefficient with a first abnormal threshold and a second abnormal threshold, wherein the first abnormal threshold is used to judge whether the health state of the hydropower station is good or bad, and the second abnormal threshold is used to judge the degree of decline of the health state of the hydropower station; If the state coefficient is less than or equal to the first abnormal threshold, the health state of the hydropower station is classified as an excellent state. If the state coefficient is greater than the first abnormal threshold and the state coefficient is less than or equal to the second abnormal threshold, the health state of the hydropower station is classified as a medium state. If the state coefficient is greater than the second abnormal threshold, the health state of the hydropower station is classified as a poor state.

2. A coordinated dispatching optimization control system for a group of hydropower stations according to claim 1, characterized in that: The state classification module calculates the generator abnormality index, the operation efficiency index and the pressure pipeline fluctuation index to obtain the state coefficient, which is expressed as: , where is the state coefficient, is the operating efficiency index, is the generator abnormality index, is the pressure pipeline fluctuation index, are the proportional coefficients of the operating efficiency index, the generator abnormality index and the pressure pipeline fluctuation index, respectively, and Both are greater than 0.

3. A coordinated dispatching optimization control system for a group of hydropower stations according to claim 2, characterized in that: During the dry season, the optimization control module controls the hydropower station in poor condition to stop operating, then controls the drainage gate of the hydropower station in poor condition to be fully open, and controls the hydropower station in excellent condition to store water at the maximum capacity; The optimization control module adjusts the maximum water storage capacity of the medium-state hydropower station during the dry season. The adjustment algorithm is: , where is the maximum water storage capacity after adjustment, is the maximum water storage capacity before regulation, is the state coefficient.

4. A coordinated dispatching optimization control system for a group of hydropower stations according to claim 3, characterized in that: The optimization control module performs secondary dispatch optimization according to the positional relationship between the medium-state hydropower station and the excellent-state hydropower station; If there is at least one hydropower station in an optimal state whose current water storage capacity is less than 90% of the maximum water flow rate, the optimization control module obtains the positional relationship between the hydropower station in a medium state and the hydropower station in an optimal state. If the hydropower station in a medium state is in the upstream area of ​​the hydropower station in an optimal state, the current maximum water storage capacity of the hydropower station in a medium state is reduced by 10%. If the hydropower station in a medium state is in the downstream area of ​​the hydropower station in an optimal state, no secondary scheduling optimization is performed.

5. A coordinated dispatching optimization control system for a group of hydropower stations according to claim 1, characterized in that: During the flood season, the optimization control module controls the hydropower station in poor state to stop running, controls the hydropower station in excellent state to the maximum water storage capacity, and adjusts the maximum water storage capacity of the hydropower station in medium state. The adjustment algorithm is: , where is the maximum water storage capacity after adjustment, is the maximum water storage capacity before regulation, is the state coefficient; The optimization control module obtains the positional relationship between the hydropower station in poor state, the hydropower station in medium state, and the hydropower station in excellent state, and obtains the real-time water level line of the downstream area; If the medium-state hydropower station and the excellent-state hydropower station both meet their respective maximum water storage requirements, and the real-time water level in the downstream area is less than or equal to the warning water level, the optimization control module does not perform secondary scheduling optimization; If the medium-state hydropower station and the excellent-state hydropower station both meet their respective maximum water storage requirements, but the real-time water level in the downstream area is greater than the warning water level, the optimization control module first obtains the position of the poor-state hydropower station and controls the poor-state hydropower station to increase the water storage capacity until the real-time water level in the downstream area is less than or equal to the warning water level.

6. A coordinated dispatching optimization control system for a group of hydropower stations according to claim 4, characterized in that: The calculation logic of the pressure pipeline fluctuation index is: obtaining the real-time pressure value of each position of the pressure pipeline through the monitoring points set at different positions inside the pressure pipeline; The pressure deviation value is calculated based on the real-time pressure value obtained at the monitoring point. The pressure deviation value is equal to the absolute value of the difference between the real-time pressure value and the standard pressure value. The average pressure deviation value and the pressure deviation standard deviation are calculated based on the pressure deviation values ​​of multiple monitoring points. The expression is: , where is the average pressure deviation value, is the standard deviation of pressure deviation, For the The pressure deviation value of each monitoring point, is the number of monitoring points; If the average pressure deviation value is less than or equal to the deviation threshold, and the pressure deviation standard deviation is less than or equal to the standard deviation threshold, the pressure pipeline fluctuation index ; If the average pressure deviation value is less than or equal to the deviation threshold, and the pressure deviation standard deviation is greater than the standard deviation threshold, the pressure pipeline fluctuation index ; If the average pressure deviation value is greater than the deviation threshold, and the pressure deviation standard deviation is greater than the standard deviation threshold, the pressure pipeline fluctuation index ; If the average pressure deviation value is greater than the deviation threshold, and the pressure deviation standard deviation is less than or equal to the standard deviation threshold, the pressure pipeline fluctuation index .

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