A hydropower operation fault early warning system and method based on self-learning

Abstract

The present invention provides a self-learning-based hydropower operation fault early warning system and method, comprising: in a first operating mode, starting a first controller to obtain a water flow-time curve and a power generation-time curve of each hydropower unit; calculating the power generation efficiency of each hydropower unit and the overall power generation efficiency of the system; when the overall power generation efficiency is lower than a first preset power generation efficiency, determining whether there are hydropower units with obviously abnormal power generation efficiency; when there are hydropower units with obviously abnormal power generation efficiency, shutting them down or reducing their power generation load; when there are no hydropower units with obviously abnormal power generation efficiency, switching the first operating mode to the second operating mode, starting the second controller, and the first and second controllers generating emergency adjustment instructions through coordinated control to improve the overall power generation efficiency; the present invention can effectively improve the safety and stability of hydropower generation.

Description

Technical Field

[0001] The present invention relates to the field of hydropower operation fault early warning, and in particular to a hydropower operation fault early warning system and method based on self-learning. Background Art

[0002] Hydropower is a form of power generation that converts the energy of water flow into electricity. It primarily utilizes the potential energy contained in natural bodies of water, such as rivers and lakes, as they flow from higher to lower elevations. This potential energy is converted into kinetic energy for turbines, which in turn drives the turbine motors to generate electricity. As a key component of renewable energy, hydropower offers significant environmental and economic advantages. With the increasing global demand for renewable energy and ongoing technological advancements, the intelligence and digitalization of hydropower generation are continuously improving, and features such as remote monitoring, intelligent scheduling, and fault warnings are emerging. However, this development also presents challenges, such as how to improve the safety and stability of hydropower generation and how to reduce its operating costs.

[0003] The existing hydropower operation fault warning system has the following main problems: First, hydropower units are customized products. The water head and single-unit capacity of each power station are different. Even for units of the same type, there are differences in their installation, which leads to different vibration characteristic values ​​of the units. Even for the same unit, there are obvious differences in its vibration values ​​at different operating points, which makes it difficult to accurately warn of faults by obtaining the vibration status of the hydropower unit. Secondly, the hydropower generation system has many functional modules and a complex structure. When monitoring it, it is often necessary to use sensors to obtain the operating parameters of each functional module. However, due to the limited installation location of the sensor, the operating parameters of some functional modules cannot be directly obtained and can only be simulated through special field simulation models and sensor data near the area. For example The stress state of the turbine motor impeller, the magnetic flux distribution of the turbine motor, etc. The simulation process requires a lot of time and computing power, which makes it difficult to quickly generate and execute control instructions under abnormal conditions. At the same time, the large time delay link in the hydropower system will also affect the rapid operation and maintenance of abnormal conditions. In addition, the operation failures of hydropower system equipment include the following situations: (1) Trend deterioration, which progresses slowly and generally does not substantially weaken the equipment efficiency. It is necessary to pay attention to its deterioration state and trigger abnormal warnings accordingly; (2) Sporadic failures, it is necessary to establish a rapid evaluation system based on pattern recognition to identify whether the sporadic abnormal data collected by online monitoring represent that a failure is about to occur; How to achieve accurate and timely detection and positioning of the above two types of operation failures is of great significance to improving the safety and stability of hydropower generation. Summary of the Invention

[0004] The purpose of the present invention is to provide a hydropower operation fault early warning system and method based on self-learning to solve the technical problems mentioned in the above background technology.

[0005] In order to achieve the above object, the specific technical solution adopted by the present invention is:

[0006] A hydropower operation fault early warning method based on self-learning comprises the following steps:

[0007] S1: In a first operating mode, start a first controller to obtain a water flow-time curve and a power generation-time curve of each hydropower unit;

[0008] S2: Calculate the power generation efficiency of each hydropower unit and the overall power generation efficiency of the system;

[0009] S3: When the overall power generation efficiency is lower than the first preset power generation efficiency, determining whether there is a hydropower unit with obviously abnormal power generation efficiency;

[0010] S4: When there are hydropower units with obviously abnormal power generation efficiency, shut them down or reduce their power generation load;

[0011] S5: When there is no hydropower unit with obvious abnormal power generation efficiency, the first operation mode is switched to the second operation mode, the second controller is started, and the first and second controllers generate emergency adjustment instructions through coordinated control to improve the overall power generation efficiency;

[0012] Optionally, also include:

[0013] S6: When the overall power generation efficiency is improved to the second preset power generation efficiency, the second operation mode is switched to the third operation mode, and the abnormal situation is evaluated, located, and operated and maintained;

[0014] S7: When the overall power generation efficiency is increased to the third preset power generation efficiency, switching the third operation mode to the first operation mode;

[0015] Optionally, the step S2: calculating the power generation efficiency of each hydropower unit and the overall power generation efficiency of the system includes:

[0016] S21: Counting the water flow-time curve and power generation-time curve of each hydropower unit and the system as a whole;

[0017] S22: Integrate the time period (t1-t2) based on the two time curves to obtain the water flow Q and power generation P within the time period;

[0018] S23: Determine the power generation efficiency of each hydropower unit and the overall power generation efficiency based on the following formula:

[0019] E=(P / Q)*k;

[0020] Wherein, E represents power generation efficiency, and k is a preset coefficient determined based on the relationship between water flow and power generation;

[0021] Optionally, the step S3: when the overall power generation efficiency is lower than a first preset power generation efficiency, determining whether there is a hydropower unit with obviously abnormal power generation efficiency includes:

[0022] S31: comparing the overall power generation efficiency of the system with a first preset power generation efficiency, and when the overall power generation efficiency is lower than the first preset power generation efficiency, calculating the average power generation efficiency Eav of each hydropower unit;

[0023] S32: Calculate the deviation degree W of the power generation efficiency e of each hydropower unit lower than the average power generation efficiency Eav based on the following formula;

[0024] W=(Eav-e) / Eav;

[0025] S33: determining a hydropower generating unit with obviously abnormal power generation efficiency based on the deviation degree, and when W exceeds a preset value, confirming the corresponding hydropower generating unit as a hydropower generating unit with obviously abnormal power generation efficiency;

[0026] Optionally, the first operating mode, the second operating mode, and the third operating mode correspond to a normal operating mode, an emergency operating mode, and a safe operating mode, respectively;

[0027] According to another aspect of the present invention, there is also provided a hydropower operation fault early warning system based on self-learning, comprising: a central control module, a data acquisition module, a storage module, and an alarm module;

[0028] The central control module can analyze and process the parameter information collected by the data acquisition module to achieve fault warning for hydropower operation and generate corresponding adjustment instructions;

[0029] The data acquisition module is used to collect different types of parameter information;

[0030] The storage module can store the parameter information collected by the data acquisition module;

[0031] The alarm module can sound an alarm when an abnormality occurs in the hydropower system;

[0032] Optionally, the central control module is capable of: in a first operating mode, starting a first controller to obtain a water flow-time curve and a power generation-time curve of each hydropower unit; calculating the power generation efficiency of each hydropower unit and the overall power generation efficiency of the system; when the overall power generation efficiency is lower than a first preset power generation efficiency, determining whether there are hydropower units with obviously abnormal power generation efficiency; when there are hydropower units with obviously abnormal power generation efficiency, shutting them down or reducing their power generation load; when there are no hydropower units with obviously abnormal power generation efficiency, switching the first operating mode to the second operating mode, starting the second controller, and the first and second controllers generating emergency adjustment instructions through collaborative control to improve the overall power generation efficiency;

[0033] Optionally, the central control module is further capable of: when the overall power generation efficiency is improved to a second preset power generation efficiency, switching the second operation mode to a third operation mode, and evaluating, locating, and operating and maintaining abnormal conditions; when the overall power generation efficiency is improved to the third preset power generation efficiency, switching the third operation mode to the first operation mode;

[0034] Optionally, when there is no hydropower unit with obviously abnormal power generation efficiency, switching the first operation mode to the second operation mode, starting the second controller, and the first and second controllers generating emergency adjustment instructions through coordinated control to improve the overall power generation efficiency include:

[0035] Start the second controller and increase the sensor data acquisition rate from H1 to H2;

[0036] The collected parameter information will no longer be automatically deleted, but will be stored and sent to the backend server;

[0037] Optionally, it also includes: shutting down the field simulation model with a large amount of calculation, and using a preset control law with a small amount of calculation to replace the field simulation model to determine the operating parameters of the functional modules that are difficult to directly collect.

[0038] The present invention provides a self-learning-based hydropower operation fault warning system and method, including: in a first operating mode, starting a first controller to obtain the water flow-time curve and power generation-time curve of each hydropower unit; calculating the power generation efficiency of each hydropower unit and the overall power generation efficiency of the system; when the overall power generation efficiency is lower than the first preset power generation efficiency, determining whether there are hydropower units with obvious abnormal power generation efficiency; when there are hydropower units with obvious abnormal power generation efficiency, shutting them down or reducing their power generation load; when there are no hydropower units with obvious abnormal power generation efficiency, switching the first operating mode to the second operating mode, starting the second controller, and the first and second controllers generating emergency adjustment instructions through collaborative control to improve the overall power generation efficiency. The present invention has at least the following technical effects:

[0039] (1) By calculating the overall power generation efficiency of each hydropower unit and the system, the operating status of the hydropower system can be judged, and the differences between the hydropower units can be avoided to accurately monitor the operating status of the hydropower system;

[0040] (2) Detect whether there are hydropower units with significantly abnormal power generation efficiency. If so, use parallel hydropower units to share their power generation load as much as possible, thereby improving the overall power generation efficiency of the hydropower system while ensuring the stability of the hydropower system power supply;

[0041] (3) Switching between different hydropower system operation modes under different circumstances can improve the generation speed of emergency regulation instructions and ensure the safety of hydropower system operation;

[0042] (4) Simultaneously monitor the deterioration trend and occasional failures of hydropower system operating equipment to facilitate rapid and accurate acquisition of the operating status of the hydropower system;

[0043] (5) Realize rapid detection and location of abnormal nodes in the hydropower system and improve operation and maintenance speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a flow chart of the hydropower operation fault early warning method based on self-learning of the present invention;

[0045] Figure 2 Schematic diagram of switching between three operating modes of the present invention;

[0046] Figure 3 Schematic diagram of the three stages of the power generation process of the hydropower system of the present invention. DETAILED DESCRIPTION

[0047] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. The specific embodiments described herein are intended only to illustrate the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0048] A hydropower operation fault early warning method based on self-learning, such as Figure 1 As shown, it includes the following steps:

[0049] S1: In a first operating mode, start a first controller to obtain a water flow-time curve and a power generation-time curve of each hydropower unit;

[0050] S2: Calculate the power generation efficiency of each hydropower unit and the overall power generation efficiency of the system;

[0051] S3: When the overall power generation efficiency is lower than the first preset power generation efficiency, determine whether there is a water turbine unit with significantly abnormal power generation efficiency;

[0052] S4: When there is a water turbine unit with significantly abnormal power generation efficiency, shut it down or reduce its power generation load;

[0053] S5: When there is no water turbine unit with significantly abnormal power generation efficiency, switch the first operation mode to the second operation mode, start the second controller, and the first and second controllers generate an emergency regulation instruction through collaborative control to improve the overall power generation efficiency;

[0054] Optionally, it further includes:

[0055] S6: When the overall power generation efficiency is increased to the second preset power generation efficiency, switch the second operation mode to the third operation mode, and evaluate, locate, and perform operation and maintenance on the abnormal situation;

[0056] S7: When the overall power generation efficiency is increased to the third preset power generation efficiency, switch the third operation mode to the first operation mode.

[0057] The hydropower system includes multiple functional modules such as water storage, diversion, water diversion, water turbine units, power conversion and transmission, etc. Each functional module further includes sub-modules that are responsible for and complete different actions; sensors are used to obtain parameter information such as water level, flow rate, stress, voltage, current, magnetic flux, etc. of different functional modules or sub-modules, and send it to the storage module for storage. The storage module is based on a circular storage and deletion mechanism, and deletes it every preset time period, with the oldest first deleted, which saves storage space while facilitating later data traceability and viewing; the controller can analyze and process the collected parameter information to achieve fault warning for hydropower operation and generate corresponding regulation instructions to improve the operation status of the hydropower system; regarding the composition of the functional modules of the hydropower system, the types and installation methods of sensors, no specific limitations are provided here, and those skilled in the art can select and set according to actual needs;

[0058] The first operation mode, the second operation mode, and the third operation mode respectively correspond to the normal operation mode, the emergency operation mode, and the safe operation mode;

[0059] In the first and third operation modes, the data collection rates of the sensors are H1 and H3 respectively, and the collected parameter information is automatically deleted every preset time M1 and M2 respectively; in the second operation mode, the data collection rate of the sensor is H2, and the collected parameter information is no longer automatically deleted, but manually deleted by the staff to facilitate later analysis of the abnormal cause; H1 < H3 < H2; M1 < M2; As Figure 2 shown, the three operation modes can be automatically switched based on a preset control program; or can be switched manually;

[0060] The first controller is the main controller. Under normal circumstances, only the first controller is activated. The first controller can receive real-time water flow, voltage, and current information sent by sensors and generate water flow-time curves and power generation-time curves for each hydropower unit based on the information. The second controller is a backup controller, which is activated in special circumstances and can improve the system's data analysis and processing speed, thereby realizing the rapid generation of emergency adjustment instructions.

[0061] In the first operating mode, the first controller is turned on; in the second and third operating modes, the first and second controllers are turned on at the same time;

[0062] It can be understood that the first operating mode is an operating mode applicable to normal conditions, and its instruction generation speed, accuracy, computing power and energy consumption are moderate; the second operating mode is an operating mode applicable to emergency conditions, and its instruction generation speed is the fastest, accuracy is moderate, computing power and energy consumption are high, and it is convenient for quickly generating adjustment instructions in emergency conditions; the third operating mode is an operating mode applicable after the emergency condition is alleviated, and its instruction generation speed is moderate, accuracy is high, computing power and energy consumption are high, and it is convenient for fine adjustment of the system and fault location;

[0063] A hydropower system generally consists of multiple hydroelectric generating units connected in parallel. Under normal circumstances, different water flow rates per unit time in each branch correspond to different power generation. The water flow rate and power generation per unit time can be used to analyze the overall power generation efficiency of each hydroelectric generating unit and the system, thereby determining the degradation level of each hydroelectric generating unit and the system as a whole.

[0064] S2: Calculating the power generation efficiency of each hydropower unit and the overall power generation efficiency of the system, including:

[0065] S21: Counting the water flow-time curve and power generation-time curve of each hydropower unit and the system as a whole;

[0066] S22: Integrate the time period (t1-t2) based on the two time curves to obtain the water flow Q and power generation P within the time period;

[0067] S23: Determine the power generation efficiency of each hydropower unit and the overall power generation efficiency based on the following formula:

[0068] E=(P / Q)*k;

[0069] Wherein, E represents power generation efficiency, and k is a preset coefficient determined based on the relationship between water flow and power generation;

[0070] The overall power generation efficiency of a hydropower system can be affected by many factors, such as insufficient overall water storage, abnormalities in the power conversion and transmission modules, or low water flow due to stalling or leakage in a single hydropower unit connected in parallel.

[0071] S3: when the overall power generation efficiency is lower than the first preset power generation efficiency, determining whether there is a hydropower unit with obviously abnormal power generation efficiency includes:

[0072] S31: comparing the overall power generation efficiency of the system with a first preset power generation efficiency, and when the overall power generation efficiency is lower than the first preset power generation efficiency, calculating the average power generation efficiency Eav of each hydropower unit;

[0073] S32: Calculate the deviation degree W of the power generation efficiency e of each hydropower unit lower than the average power generation efficiency Eav based on the following formula;

[0074] W=(Eav-e) / Eav;

[0075] S33: determining a hydropower generating unit with obviously abnormal power generation efficiency based on the deviation degree, and when W exceeds a preset value, confirming the corresponding hydropower generating unit as a hydropower generating unit with obviously abnormal power generation efficiency;

[0076] like Figure 3 As shown in Figure 2, the power generation process of a hydropower system can be divided into the following three stages:

[0077] Phase 1: When the water flow rate increases from zero to the first preset value, the hydropower unit starts to rotate and start generating electricity;

[0078] The second stage: when the water flow rate is between the first preset value and the second preset value, the power generation of the hydropower unit increases as the water flow rate increases;

[0079] The third stage: when the water flow exceeds the second preset value, the power generation of the hydropower unit is at its maximum value and will not increase with the increase of water flow;

[0080] S4: When there is a hydropower unit with obviously abnormal power generation efficiency, shut it down or reduce its power generation load, including:

[0081] S41: Determine the location and power generation load of the hydropower generating units with obvious abnormal power generation efficiency;

[0082] S42: Detecting whether there is a hydropower generating unit in the second stage of power generation among the other parallel hydropower generating units. If no hydropower generating unit is present, the hydropower generating unit with obvious abnormality is allowed to continue power generation.

[0083] S43: If the abnormality exists, determining whether other hydropower generating units in the second stage of power generation can share the power generation load of the hydropower generating unit with the obvious abnormality;

[0084] S44: When the other hydropower generating units in the second stage of power generation are able to fully share the power generation load of the hydropower generating unit with obvious abnormality, the hydropower generating unit with obvious abnormality in power generation efficiency is shut down, and the corresponding water flow is distributed to the other hydropower generating units in the second stage of power generation by adjusting the corresponding valve state;

[0085] S45: When other hydropower generating units in the second stage of power generation can only partially share the power generation load of the hydropower generating unit with obvious abnormality, the corresponding water flow is partially distributed to other hydropower generating units in the second stage of power generation by adjusting the corresponding valve state, thereby reducing the power generation load of the hydropower generating unit with obvious abnormality in power generation efficiency;

[0086] The above solution can ensure the stability of the overall power supply of the hydropower system and improve the overall power generation efficiency;

[0087] When the overall power generation efficiency of the hydropower system is lower than the first preset power generation efficiency and there are no hydropower units with significantly abnormal power generation efficiency, it is considered that a key link of the hydropower system as a whole has failed, such as a power collection module or a power conversion module. In this case, in order to avoid equipment damage and causing significant property losses, the system switches to the second operating mode, namely the emergency operating mode, to achieve rapid generation and execution of control instructions;

[0088] S5: When there is no hydropower unit with obviously abnormal power generation efficiency, switching the first operation mode to the second operation mode, starting the second controller, and the first and second controllers generating emergency adjustment instructions through coordinated control to improve the overall power generation efficiency, including:

[0089] S51: Start the second controller and increase the data acquisition rate of the sensor from H1 to H2;

[0090] S52: The collected parameter information is no longer automatically deleted, and the collected parameter information is stored and sent to the backend server;

[0091] As mentioned earlier, due to the limited installation locations of sensors in hydropower systems, the operating parameters of certain functional modules must be simulated using specialized field simulation models and sensor data from nearby areas. This consumes a significant amount of time and computing power. The large time lag in hydropower systems also hinders rapid operation and maintenance in abnormal situations, making it difficult to quickly generate and execute control commands in these situations.

[0092] Optionally, also include:

[0093] S53: closing the field simulation model with a large computational load, and using a preset control law with a small computational load to replace the field simulation model to determine the operating parameters of the functional modules that are difficult to directly collect;

[0094] The field simulation model may be a force distribution simulation model of the turbine motor impeller and a magnetic flux distribution simulation model of the turbine motor, which can be combined with data from impeller force sensors and magnetometers installed near the area to calculate and simulate the force distribution of the turbine motor impeller and the magnetic flux distribution of the turbine motor;

[0095] The preset control law refers to pre-set regular information used to approximately determine the operating parameters of functional modules that are difficult to directly collect; for example, when the water flow velocity is u1, the impeller force of the turbine motor is determined to be f1, that is, the impeller force is indirectly approximately determined by the water flow velocity; when the speed of the turbine motor is v1, the magnetic flux distribution state of the turbine motor is determined to be Ψ1, that is, the magnetic flux distribution of the turbine motor is indirectly approximately determined by the speed of the turbine motor; this is only an example, and other regular information that can approximately determine the operating parameters of functional modules that are difficult to directly collect can also be used; it can be understood that the preset control law can sacrifice the acquisition accuracy of some operating parameters, speed up the acquisition speed of operating parameters, save computing power, and thereby improve the generation speed of control instructions in emergency situations;

[0096] Optionally, it also includes:

[0097] S54: Based on historical operation data, determine the time interval between the input and expected output data of each link. If the time interval is greater than a preset value, determine that it is a link with a large time delay;

[0098] S55: Collect historical input and output data of the large time-delay link, train an input and output prediction model for the large time-delay link, and in the second operating mode, predict the output of the large time-delay link based on the input and output prediction model. The controller will quickly generate corresponding emergency adjustment instructions based on the predicted output.

[0099] The large time-delay link may be a link for determining the peak power generation voltage and current of the hydropower system, which is related to factors such as the water storage height and water flow rate. Under normal circumstances, after the water storage height and water flow rate and other factors change, it often takes a long time to determine the corresponding peak power generation voltage and current. The present invention can quickly obtain the corresponding peak voltage and peak current at a certain water storage height and water flow rate through a trained peak power generation voltage and current input and output prediction model, thereby further improving the speed of generating control instructions in emergency situations.

[0100] Optionally, the input and output prediction model of the large time-delay link can be iteratively trained based on the collected data information periodically;

[0101] S56: The first and second controllers coordinate control to generate emergency adjustment instructions to adjust the operating parameters of the corresponding functional modules to improve and enhance the overall power generation efficiency;

[0102] Specifically, the emergency adjustment instruction may be to increase, decrease, or close the opening state of the valve, or other control instructions that can alleviate the emergency abnormality, which are not specifically limited here;

[0103] When the emergency situation of the hydropower system is alleviated by the emergency regulation command and the overall power generation efficiency is temporarily restored, the hydropower system enters the safe operation mode, also known as the third operation mode, to further analyze, locate and deal with abnormal components and causes of abnormalities;

[0104] S6: When the overall power generation efficiency is increased to the second preset power generation efficiency, switching the second operation mode to the third operation mode, and evaluating, locating, and operating the abnormal situation, including:

[0105] S61: Using sensors and transfer functions of each link to determine the actual input, ideal output and actual output data of each link;

[0106] Each link in the hydropower system is connected in sequence; the transfer function refers to the input and output relationship of each link, which can be obtained by analyzing historical data;

[0107] S62: Rapidly determine the location of abnormal links based on the dichotomy method, including:

[0108] S621: Based on the actual input of the first node and the total transfer function from the first node to the intermediate node, the ideal output of the intermediate node is calculated, and the ideal output of the intermediate node is compared and analyzed with the actual output of the intermediate node obtained by the sensor to determine whether there is an abnormal node between the first node and the intermediate node;

[0109] The comparison and analysis of the ideal output of the intermediate node and the actual output of the intermediate node obtained by the sensor includes: analyzing the mean, variance, peak value, fluctuation, etc. of the two, which are not specifically limited here;

[0110] S622: When an abnormal node exists between the first node and the intermediate node, the node segment with the abnormal node is analyzed separately. Similar to the above process, the abnormal node status of the node segment with the abnormal node is continuously determined until all abnormal nodes are determined.

[0111] S623: After all abnormal nodes are identified, the abnormal conditions of the abnormal nodes are reviewed in combination with parameter information of the upstream and downstream nodes of the abnormal nodes;

[0112] S624: When a misjudged node is found, the sensor of the misjudged node is considered to be abnormal, and the operating status of the sensor is verified;

[0113] Optionally, the operating status of sensors can be periodically verified by combining upstream and downstream node data with unmanned inspection equipment equipped with fixed sensors to avoid misjudgments caused by sensor anomalies.

[0114] Optionally, the method further includes: S625: when a sensor at a certain node is abnormal, the controller may approximately replace parameter information collected by the abnormal sensor based on the transfer function of the node and data information of adjacent sensors, and continue to control the hydropower system;

[0115] As mentioned above, operational failures of hydropower system equipment include deterioration trends. To facilitate real-time monitoring of the degree of deterioration and to formulate scientific and accurate inspection plans for hydropower equipment, the following options are optional:

[0116] S63: in a third operating mode, determining an ideal output of the node or node segment based on an actual input of the node or node segment, a transfer function of the node or a total transfer function of the node segment, and comparing the ideal output of the node or node segment with an actual output to determine a degree of deterioration of the node or node segment;

[0117] Through the above solution, accurate monitoring of the degree of deterioration of nodes or node segments can be achieved. It can be understood that in the third operating mode, the data acquisition rate is high and the second controller is turned on. The activation of the field simulation model also makes data acquisition more accurate, and the analysis and monitoring of trend deterioration are also more accurate.

[0118] Optionally, the step S7: switching the third operation mode to the first operation mode when the overall power generation efficiency is increased to a third preset power generation efficiency, includes:

[0119] S71: After the assessment, location, and operation and maintenance of abnormal conditions are completed, the overall power generation efficiency of the hydropower system will be improved;

[0120] S72: When the overall power generation efficiency is increased to a third preset power generation efficiency, switching the third operation mode to the first operation mode;

[0121] The first preset power generation efficiency < the second preset power generation efficiency < the third preset power generation efficiency, and those skilled in the art can set its specific value according to the operation conditions of the hydropower system.

[0122] According to another aspect of the present invention, there is also provided a hydropower operation fault early warning system based on self-learning, the system comprising: a central control module, a data acquisition module, a storage module, and an alarm module;

[0123] The central control module includes a first controller and a second controller. The central control module can analyze and process the parameter information collected by the data acquisition module to achieve fault warning for hydropower operation and generate corresponding adjustment instructions to improve the operating status of the hydropower system;

[0124] The data acquisition module includes a water level sensor, a water flow sensor, a stress sensor, a voltage sensor, a current sensor, and a magnetometer, which are respectively used to collect different types of parameter information;

[0125] The data acquisition modules are installed at corresponding positions of each functional module or functional submodule of the hydropower system. The type and installation method of the data acquisition modules are not specifically limited here, and those skilled in the art can select and set them according to actual needs.

[0126] The hydropower system includes multiple functional modules such as water storage, flow diversion, flow diversion, hydropower units, power conversion and transmission, etc. Each functional module includes submodules responsible for and completing different actions;

[0127] The storage module can store the parameter information collected by the data acquisition module. The storage module is based on a ring storage and deletion mechanism, which deletes data after a preset time period. The first-in-first-out mechanism facilitates data tracing and viewing in the future while saving storage space.

[0128] The alarm module can sound an alarm when an abnormality occurs in the water and electricity system;

[0129] The central control module is capable of: in a first operating mode, starting a first controller to obtain a water flow-time curve and a power generation-time curve of each hydropower unit; calculating the power generation efficiency of each hydropower unit and the overall power generation efficiency of the system; when the overall power generation efficiency is lower than a first preset power generation efficiency, determining whether there are hydropower units with obviously abnormal power generation efficiency; if there are hydropower units with obviously abnormal power generation efficiency, shutting them down or reducing their power generation load; if there are no hydropower units with obviously abnormal power generation efficiency, switching the first operating mode to the second operating mode and starting the second controller, so that the first and second controllers generate emergency adjustment instructions through coordinated control to improve the overall power generation efficiency;

[0130] Optionally, the central control module can further: when the overall power generation efficiency is improved to a second preset power generation efficiency, switch the second operation mode to a third operation mode to evaluate, locate and operate the abnormal situation; when the overall power generation efficiency is improved to the third preset power generation efficiency, switch the third operation mode to the first operation mode;

[0131] The first operating mode, the second operating mode, and the third operating mode correspond to the normal operating mode, the emergency operating mode, and the safety operating mode, respectively;

[0132] In the first and third operating modes, the sensor data acquisition rate is H1 and H3 respectively, and the collected parameter information is automatically deleted every preset time M1 and M2 respectively; in the second operating mode, the sensor data acquisition rate is H2, and the collected parameter information is no longer automatically deleted, but is manually deleted by the staff; the H1 <H3<H2;M1<M2;

[0133] The first controller is a main controller and the second controller is a standby controller; in the first operating mode, the first controller is turned on; in the second and third operating modes, the first and second controllers are turned on at the same time;

[0134] The calculation of the power generation efficiency of each hydropower unit and the overall power generation efficiency of the system includes:

[0135] Statistical analysis of the water flow-time curve and power generation-time curve of each hydropower unit and the system as a whole;

[0136] Based on the two time curves, the time period (t1-t2) is integrated to obtain the water flow Q and power generation P within the time period;

[0137] The power generation efficiency of each hydropower unit and the overall power generation efficiency are determined based on the following formula:

[0138] E=(P / Q)*k;

[0139] Wherein, E represents power generation efficiency, and k is a preset coefficient determined based on the relationship between water flow and power generation;

[0140] When the overall power generation efficiency is lower than the first preset power generation efficiency, determining whether there is a hydropower unit with obviously abnormal power generation efficiency includes:

[0141] Compare the overall power generation efficiency of the system with the first preset power generation efficiency; when the overall power generation efficiency is lower than the first preset power generation efficiency, calculate the average power generation efficiency Eav of each hydropower unit;

[0142] The deviation degree W of the power generation efficiency e of each hydropower unit lower than the average power generation efficiency Eav is calculated based on the following formula:

[0143] W=(Eav-e) / Eav;

[0144] Determine the hydropower generating unit with obviously abnormal power generation efficiency based on the deviation degree, and when W exceeds a preset value, confirm the corresponding hydropower generating unit as a hydropower generating unit with obviously abnormal power generation efficiency;

[0145] When a hydropower unit has obviously abnormal power generation efficiency, shutting it down or reducing its power generation load includes:

[0146] Determine the location and power generation load of hydropower units with significant power generation efficiency anomalies;

[0147] Detect whether there is a hydropower unit in the second stage of power generation among other parallel hydropower units. If no hydropower unit is present, the hydropower unit with obvious abnormality is allowed to continue power generation;

[0148] If there is an abnormality, determine whether other hydropower units in the second stage of power generation can share the power generation load of the hydropower unit with obvious abnormality;

[0149] When the other hydropower generating units in the second stage of power generation are able to fully share the power generation load of the hydropower generating unit with obvious abnormality, the hydropower generating unit with obvious abnormality in power generation efficiency is shut down, and its corresponding water flow is distributed to the other hydropower generating units in the second stage of power generation by adjusting the corresponding valve state;

[0150] Optionally, the method further includes: when other hydropower generating units in the second stage of power generation can only partially share the power generation load of the hydropower generating unit with obvious abnormality, by adjusting the corresponding valve state, the corresponding water flow is partially distributed to other hydropower generating units in the second stage of power generation, thereby reducing the power generation load of the hydropower generating unit with obvious abnormality in power generation efficiency;

[0151] When there is no hydropower unit with obvious abnormal power generation efficiency, switching the first operation mode to the second operation mode, starting the second controller, and the first and second controllers generating emergency adjustment instructions through coordinated control to improve the overall power generation efficiency, including:

[0152] Start the second controller and increase the sensor data acquisition rate from H1 to H2;

[0153] The collected parameter information will no longer be automatically deleted, but will be stored and sent to the backend server;

[0154] Optionally, also include:

[0155] Close the field simulation model with large computational load, and use the preset control law with small computational load to replace the field simulation model to determine the operating parameters of the functional modules that are difficult to directly collect;

[0156] The field simulation model may be a force distribution simulation model of a hydraulic motor impeller or a magnetic flux distribution simulation model of the hydraulic motor;

[0157] The preset control law refers to the regular information preset for approximately determining the operating parameters of the functional modules that are difficult to directly collect;

[0158] Based on historical operation data, the time interval between the input and expected output data of each link is determined. When the interval is greater than a preset value, it is determined to be a link with a large time lag.

[0159] Collect historical input and output data of the large time-delay link and train an input and output prediction model for the large time-delay link. In the second operating mode, the output of the large time-delay link is predicted based on the input and output prediction model. The controller will quickly generate corresponding emergency adjustment instructions based on the predicted output.

[0160] The first and second controllers cooperate to generate emergency adjustment instructions and adjust the operating parameters of the corresponding functional modules to improve and enhance the overall power generation efficiency;

[0161] The emergency adjustment instruction may be to increase, decrease or close the opening state of the valve;

[0162] Optionally, the input and output prediction model of the large time-delay link can be iteratively trained based on the collected data information periodically;

[0163] When the overall power generation efficiency is increased to the second preset power generation efficiency, the second operation mode is switched to the third operation mode, and abnormal conditions are evaluated, located, and operated and maintained, including:

[0164] Use sensors and transfer functions of each link to determine the actual input, ideal output and actual output data of each link;

[0165] Quickly determine the location information of abnormal links based on dichotomy;

[0166] in a third operating mode, determining an ideal output of the node or node segment based on an actual input of the node or node segment, a transfer function of the node, or a total transfer function of the node segment, and comparing the ideal output of the node or node segment with an actual output to determine a degree of deterioration of the node or node segment;

[0167] The method of quickly determining the location information of the abnormal link based on the dichotomy method includes:

[0168] Based on the actual input of the first node and the total transfer function from the first node to the intermediate node, the ideal output of the intermediate node is calculated. The ideal output of the intermediate node is compared with the actual output of the intermediate node obtained by the sensor to determine whether there is an abnormal node between the first node and the intermediate node.

[0169] When there are abnormal nodes between the first node and the intermediate node, the node segment with the abnormality is analyzed separately. Similar to the above process, the abnormal node status of the node segment with the abnormality is continuously determined until all abnormal nodes are determined.

[0170] After all abnormal nodes are identified, the abnormal conditions of the abnormal nodes are reviewed in combination with the parameter information of the upstream and downstream nodes of the abnormal nodes;

[0171] When a misjudged node is found, the sensor of the misjudged node is considered to be abnormal and the operating status of the sensor is verified;

[0172] Optionally, the operating status of sensors can be periodically verified by combining upstream and downstream data of nodes and unmanned inspection equipment equipped with fixed sensors;

[0173] Optionally, when a sensor at a certain node is abnormal, the controller may approximately replace the parameter information collected by the abnormal sensor based on the transfer function of the node and the data information of the adjacent sensors, and continue to control the hydropower system;

[0174] When the overall power generation efficiency is increased to the third preset power generation efficiency, switching the third operation mode to the first operation mode includes:

[0175] After the assessment, location, and operation and maintenance of abnormal conditions are completed, the overall power generation efficiency of the hydropower system will be improved;

[0176] When the overall power generation efficiency is increased to a third preset power generation efficiency, switching the third operation mode to the first operation mode;

[0177] The first preset power generation efficiency<the second preset power generation efficiency<the third preset power generation efficiency.

[0178] The present invention provides a self-learning-based hydropower operation fault early warning system and method, comprising: in a first operating mode, starting a first controller to obtain a water flow-time curve and a power generation-time curve of each hydropower unit; calculating the power generation efficiency of each hydropower unit and the overall power generation efficiency of the system; when the overall power generation efficiency is lower than a first preset power generation efficiency, determining whether there are hydropower units with obviously abnormal power generation efficiency; when there are hydropower units with obviously abnormal power generation efficiency, shutting them down or reducing their power generation load; when there are no hydropower units with obviously abnormal power generation efficiency, switching the first operating mode to the second operating mode, starting the second controller, and the first and second controllers generating emergency adjustment instructions through collaborative control to improve the overall power generation efficiency.

[0179] The above content is only the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A hydropower operation fault early warning method based on self-learning, characterized in that: The following steps are included: S1: In a first operating mode, start a first controller to obtain a water flow-time curve and a power generation-time curve of each hydropower unit; S2: Calculate the power generation efficiency of each hydropower unit and the overall power generation efficiency of the system; S3: When the overall power generation efficiency is lower than the first preset power generation efficiency, determining whether there is a hydropower unit with obviously abnormal power generation efficiency; S4: When there are hydropower units with obviously abnormal power generation efficiency, shut them down or reduce their power generation load; S5: When there is no hydropower unit with obvious abnormal power generation efficiency, the first operation mode is switched to the second operation mode, the second controller is started, and the first and second controllers generate emergency adjustment instructions through coordinated control to improve the overall power generation efficiency; S6: When the overall power generation efficiency is improved to the second preset power generation efficiency, the second operation mode is switched to the third operation mode, and the abnormal situation is evaluated, located, and operated and maintained; S7: When the overall power generation efficiency is increased to the third preset power generation efficiency, switching the third operation mode to the first operation mode; S2: Calculating the power generation efficiency of each hydropower unit and the overall power generation efficiency of the system, including: S21: Counting the water flow-time curve and power generation-time curve of each hydropower unit and the system as a whole; S22: Integrate the time period [t1, t2] based on the two time curves to obtain the water flow Q and power generation P within the time period; S23: Determine the power generation efficiency of each hydropower unit and the overall power generation efficiency based on the following formula: E=(P / Q)*k; Wherein, E represents power generation efficiency, k is a preset coefficient, which is determined based on the relationship between water flow and power generation; S3: when the overall power generation efficiency is lower than a first preset power generation efficiency, determining whether there is a hydropower unit with significantly abnormal power generation efficiency includes: S31: comparing the overall power generation efficiency of the system with a first preset power generation efficiency, and when the overall power generation efficiency is lower than the first preset power generation efficiency, calculating the average power generation efficiency Eav of each hydropower unit; S32: Calculate the deviation degree W of the power generation efficiency e of each hydropower unit lower than the average power generation efficiency Eav based on the following formula; W=(Eav-e) / Eav; S33: Determine the hydropower generating unit with obviously abnormal power generation efficiency based on the deviation degree, and when W exceeds a preset value, confirm the corresponding hydropower generating unit as a hydropower generating unit with obviously abnormal power generation efficiency.

2. The method according to claim 1, characterized in that The first operation mode, the second operation mode, and the third operation mode correspond to a normal operation mode, an emergency operation mode, and a safety operation mode, respectively.

3. A self-learning hydropower operation fault early warning system based on the method according to any one of claims 1-2, characterized in that: The system includes: central control module, data acquisition module, storage module, alarm module; The central control module can analyze and process the parameter information collected by the data acquisition module to achieve fault warning for hydropower operation and generate corresponding adjustment instructions; The data acquisition module is used to collect different types of parameter information; The storage module is capable of storing parameter information collected by the data collection module; The alarm module can sound an alarm when an abnormality occurs in the hydropower system.

4. The system according to claim 3, characterized in that The central control module is capable of: in a first operating mode, starting the first controller to obtain the water flow-time curve and power generation-time curve of each hydropower unit; calculating the power generation efficiency of each hydropower unit and the overall power generation efficiency of the system; when the overall power generation efficiency is lower than the first preset power generation efficiency, determining whether there are hydropower units with obviously abnormal power generation efficiency; when there are hydropower units with obviously abnormal power generation efficiency, shutting them down or reducing their power generation load; when there are no hydropower units with obviously abnormal power generation efficiency, switching the first operating mode to the second operating mode and starting the second controller, and the first and second controllers generate emergency adjustment instructions through collaborative control to improve the overall power generation efficiency.

5. The system according to claim 4, characterized in that The central control module is also capable of: when the overall power generation efficiency is improved to the second preset power generation efficiency, switching the second operating mode to the third operating mode, and evaluating, locating and operating abnormal situations; when the overall power generation efficiency is improved to the third preset power generation efficiency, switching the third operating mode to the first operating mode.

6. The system according to claim 5, characterized in that When there is no hydropower unit with obvious abnormal power generation efficiency, switching the first operation mode to the second operation mode, starting the second controller, and the first and second controllers generating emergency adjustment instructions through coordinated control to improve the overall power generation efficiency, including: Start the second controller and increase the sensor data acquisition rate from H1 to H2; The collected parameter information is no longer automatically deleted, and is stored and sent to the background server.

7. The system according to claim 6, characterized in that Also includes: The field simulation model with large computational complexity is turned off, and the preset control law with small computational complexity is used to replace the field simulation model to determine the operating parameters of the functional modules that are difficult to directly collect.

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