System and method for monitoring mechanical overspeed of water-turbine generator set

Abstract

The mechanical overspeed monitoring system comprises a fastening ring, the fastening ring is connected with a main shaft, an overspeed pendulum and a balancing weight are installed on the fastening ring, the overspeed pendulum and a hydraulic switching valve are installed oppositely, and the states of the overspeed pendulum and the hydraulic switching valve are monitored by a high-speed camera; the hydraulic switching valve and the high-speed camera are electrically connected with the mechanical overspeed monitoring host, and the mechanical overspeed monitoring host is electrically connected with the vibration collecting system and the rotating speed collecting system. The system has the beneficial effects of improving the monitoring accuracy, ensuring the stable operation of the system, improving the equipment safety, facilitating fault diagnosis and maintenance and the like, can effectively ensure the safe, stable and efficient operation of the water-turbine generator set, and provides powerful technical support for the long-term stable operation of the set.

Description

Technical Field

[0001] The invention belongs to the technical field of state monitoring of a hydro-generator set, and in particular relates to a system and method for monitoring mechanical overspeed of a hydro-generator set. Background Art

[0002] Hydroelectric generators are generally equipped with mechanical overspeed protection devices as the last line of protection for the unit. The action value of the mechanical overspeed protection device has strict requirements, so that it can act effectively without malfunctioning and causing the unit to stop unexpectedly. At present, the action accuracy of the unit's mechanical overspeed device only depends on the factory settings of the manufacturer's products, and the power station overspeed test and the status monitoring of the mechanical overspeed action after the unit is running are blank.

[0003] In order to monitor and warn of mechanical overspeed in a timely manner, it is necessary to design a highly reliable and sensitive mechanical overspeed monitoring system. The technical difficulties that the system needs to overcome are mainly as follows: the mechanical overspeed monitoring system needs to collect the speed signal of the hydro-generator set in real time, and judge whether mechanical overspeed occurs based on the speed change trend. However, in a high-speed rotation environment, the acquisition of speed signals is easily affected by electromagnetic interference and mechanical vibration, resulting in signal distortion or interruption, affecting the accuracy and reliability of monitoring. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a hydraulic generator set mechanical overspeed monitoring system and method, which are used for data collection and analysis when the machinery is overspeeding, thereby improving the product manufacturing and installation reliability and the unit operation safety.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: A mechanical overspeed monitoring system for a hydro-generator set comprises a fastening ring, the fastening ring is connected to a main shaft, an overspeed pendulum and a counterweight are installed on the fastening ring, the overspeed pendulum is installed opposite to a hydraulic switching valve, and the states of the overspeed pendulum and the hydraulic switching valve are monitored by a high-speed camera; The hydraulic switching valve and the high-speed camera are electrically connected to the mechanical overspeed monitoring host, and the mechanical overspeed monitoring host is electrically connected to the vibration collection system and the speed collection system.

[0006] Preferably, the fastening ring is connected to the main shaft by clamping.

[0007] Preferably, an overspeed pendulum and a counterweight are symmetrically mounted on the fastening ring.

[0008] Preferably, the high-speed camera is fixed with a bracket so that the high-speed camera can include the hydraulic switching valve and the overspeed pendulum as a whole in the camera range.

[0009] Preferably, the vibration collection system and the speed collection system are connected to the mechanical overspeed monitoring host through a data transmission line to ensure the transmission of relevant data signals.

[0010] Preferably, the mechanical overspeed monitoring host includes a first signal receiving unit, a second signal receiving unit, a power module, a display screen and a rack. The signal receiving unit is inserted into the left side of the lower part of the installation rack, the second signal receiving unit is inserted into the middle side of the lower part of the installation rack, the power module is inserted into the right side of the lower part of the installation rack, and the display screen is embedded and installed on the top of the rack.

[0011] A method for operating a mechanical overspeed monitoring system for a hydro-generator set comprises the following steps: A high-precision speed sensor is used to collect the speed signal of the turbine generator set in real time. The influence of electromagnetic interference and mechanical vibration on the signal is eliminated through a filtering algorithm to obtain stable speed data. The filtered speed data is input into the preset speed change trend analysis model. If the speed change rate exceeds the preset threshold, it is judged as a mechanical overspeed state. When it is judged as a mechanical overspeed state, the hydraulic valve switching control module is triggered to calculate the switching time and force of the hydraulic valve according to the rotational inertia of the turbine generator set to avoid the water hammer effect. The hydraulic valve switching control module outputs a control signal to the hydraulic actuator to quickly switch the hydraulic valve state and achieve emergency shutdown. During the hydraulic valve switching process, the pressure change of the hydraulic system is monitored in real time. If the pressure fluctuation exceeds the preset range, the hydraulic valve switching parameters are adjusted to ensure system stability. The system has a built-in fault self-diagnosis module to regularly detect the working status of the speed sensor, hydraulic valve control module and signal processing module. If a fault is detected, the fault locking function is triggered to prevent false alarms. The fault self-diagnosis module stores the fault information in the system log and uploads it to the remote monitoring center through the communication module for subsequent analysis and maintenance.

[0012] Preferably, the high-precision speed sensor is used to collect the speed signal of the hydro-generator set in real time, and the influence of electromagnetic interference and mechanical vibration on the signal is eliminated by a filtering algorithm to obtain stable speed data, including: Use high-precision speed sensors to collect speed signals of hydro-generator sets in real time and obtain original speed data; For the collected original speed data, the wavelet transform algorithm is used for denoising to eliminate the influence of electromagnetic interference on the speed signal; According to the mechanical structure characteristics of the hydro-generator set, a mathematical model of mechanical vibration is established, and the speed signal is filtered through an adaptive filtering algorithm to eliminate the influence of mechanical vibration on the speed signal; Perform feature extraction on the denoised and filtered speed signal to obtain time domain and frequency domain feature parameters of the speed signal; The extracted characteristic parameters of the speed signal are input into the support vector machine model, and a stable and reliable speed estimation value is obtained through model training and optimization. The estimated speed value is compared with the preset speed threshold. If the speed exceeds the normal range, it is determined that the hydro-generator set is abnormal and an alarm signal is triggered; Based on the estimated speed and alarm signal, an operating status report of the hydro-generator set is generated to provide data support for equipment maintenance and fault diagnosis.

[0013] Preferably, the filtered speed data is input into a preset speed change trend analysis model, and if the speed change rate exceeds a preset threshold, it is judged as a mechanical overspeed state, including: Obtain real-time speed data of mechanical equipment, filter the speed data, remove noise interference in the speed data, and obtain filtered speed data; According to the filtered speed data, the speed change rate is calculated to obtain the speed change rate data within a certain time range; The speed change rate data is input into a preset speed change trend analysis model, and the speed change rate data is trend analyzed by the model to determine whether the speed change rate exceeds a preset threshold; If the speed change rate exceeds the preset threshold, the mechanical equipment is judged to be in an overspeed state, an overspeed alarm is triggered, and relevant personnel are notified to handle it; If the speed change rate does not exceed the preset threshold, the mechanical equipment is judged to be in normal working condition, and the real-time speed data continues to be obtained to perform the next round of speed change trend analysis; Based on the historical speed change rate data and overspeed state judgment results, the support vector machine algorithm is used to train and optimize the speed change trend analysis model to improve the accuracy of the model's overspeed state judgment; Through big data analysis technology, the association rules between the speed change rate and other operating parameters of mechanical equipment are mined, and the speed change trend analysis model is optimized based on the association rules to improve the adaptability and robustness of the model.

[0014] Preferably, when the mechanical overspeed state is determined, the hydraulic valve switching control module is triggered, and the switching time and force of the hydraulic valve are calculated according to the rotational inertia of the hydro-generator set to avoid the water hammer effect, including: The speed sensor is used to monitor the speed of the hydro-generator set in real time, and the collected speed data is transmitted to the mechanical overspeed monitoring host; After receiving the speed data, the mechanical overspeed monitoring host compares it with the preset mechanical overspeed threshold to determine whether it is currently in a mechanical overspeed state; If the result of the judgment is a mechanical overspeed state, the mechanical overspeed monitoring host immediately sends a trigger signal to the hydraulic valve switching control module; After receiving the trigger signal, the hydraulic valve switching control module obtains the rotational inertia parameters of the hydro-generator set from the equipment parameter database; Based on the acquired moment of inertia parameters, an optimization algorithm is used to calculate the optimal switching time and switching force of the hydraulic valve, so as to achieve fast and effective mechanical deceleration while avoiding the water hammer effect; The calculated hydraulic valve switching time and force parameters are transmitted to the hydraulic valve execution unit to control the hydraulic valve to switch according to the optimal solution; After the hydraulic valve is switched, the speed changes of the turbine generator set are continuously monitored until the speed returns to the normal range to ensure that the mechanical overspeed problem is effectively resolved.

[0015] Preferably, the hydraulic valve switching control module outputs a control signal to the hydraulic actuator to quickly switch the hydraulic valve state to achieve emergency shutdown, including: The hydraulic valve switching control module receives the emergency stop command and determines whether an emergency stop operation needs to be performed. If an emergency stop operation needs to be performed, the next step is entered; Otherwise, continue to monitor the emergency stop command; The hydraulic valve switching control module determines the hydraulic valve state to be switched according to the preset hydraulic valve switching control strategy, and generates a corresponding hydraulic valve control signal; The hydraulic valve switching control module outputs the generated hydraulic valve control signal to the hydraulic actuator, and drives the hydraulic actuator to quickly switch the hydraulic valve state through the control signal; After receiving the control signal output by the hydraulic valve switching control module, the hydraulic actuator quickly switches the hydraulic valve state according to the control signal to achieve rapid switching of the hydraulic valve; The hydraulic valve switching control module monitors the state feedback signal of the hydraulic actuator in real time to determine whether the hydraulic valve has been switched to the target state. If it has been switched to the target state, it proceeds to the next step; Otherwise, continue to output the control signal to drive the hydraulic actuator to switch the hydraulic valve state; The hydraulic valve switching control module stops outputting control signals after determining that the hydraulic valve has switched to the target state based on the state feedback signal of the hydraulic actuator, and feeds back the hydraulic valve state switching result to the upper control system; After receiving the hydraulic valve state switching result fed back by the hydraulic valve switching control module, the upper control system determines whether the emergency stop operation has been completed. If it has been completed, the emergency stop process ends; Otherwise, continue with other emergency stop operations.

[0016] Preferably, during the hydraulic valve switching process, the pressure change of the hydraulic system is monitored in real time, and if the pressure fluctuation exceeds a preset range, the hydraulic valve switching parameters are adjusted to ensure system stability, including: Obtain real-time pressure data during the hydraulic valve switching process, compare the pressure data with the preset range, and determine whether the pressure fluctuation exceeds the preset range; If the pressure fluctuation exceeds the preset range, the adjustment range of the hydraulic valve switching parameter is determined according to the degree to which the pressure fluctuation exceeds the range; A correlation model between pressure fluctuations and hydraulic valve switching parameters is established through machine learning algorithms, and the optimal hydraulic valve switching parameters are predicted based on the pressure fluctuations. Apply the adjusted hydraulic valve switching parameters to the hydraulic valve switching process, and continuously monitor the pressure changes to determine whether the adjusted parameters are effective; If the adjusted parameters fail to effectively reduce pressure fluctuations, the machine learning model is further optimized and the optimal hydraulic valve switching parameters are re-predicted; Continuously iterate and optimize the hydraulic valve switching parameters until the pressure fluctuation is controlled within the preset range to ensure the stable operation of the hydraulic system; The optimized hydraulic valve switching parameters are saved as preset parameters and directly applied in the subsequent hydraulic valve switching process to improve the system response speed and stability.

[0017] Preferably, the system has a built-in fault self-diagnosis module, which regularly detects the working status of the speed sensor, the hydraulic valve control module and the signal processing module. If a fault is detected, a fault locking function is triggered to prevent false alarms, including: According to the preset time interval, the system's built-in fault self-diagnosis module is regularly triggered to perform status detection on the speed sensor, hydraulic valve control module and signal processing module; When performing status detection, the fault self-diagnosis module obtains the real-time speed data of the speed sensor and compares it with the preset normal speed range to determine whether the speed sensor has a fault; At the same time, the fault self-diagnosis module determines whether the opening and closing state of the hydraulic valve is consistent with the control instruction by analyzing the feedback signal of the hydraulic valve control module, and determines whether there is a fault in the hydraulic valve control module; The fault self-diagnosis module also monitors the input and output data of the signal processing module in real time, and determines whether the signal processing module is working normally through the continuity and integrity analysis of the data; If the fault self-diagnosis module detects a fault in any module, it will immediately trigger the fault locking function to stop the operation of the corresponding module by cutting off the power supply or blocking the signal transmission; When the fault locking function is triggered, the fault self-diagnosis module generates a fault report, records the time and location of the fault, the fault type and other information, and sends the report to the system administrator for subsequent maintenance processing; The fault lockout function remains in effect until the system administrator confirms that the fault has been eliminated and manually releases the lockout state. The system can then resume normal operation, effectively preventing false alarms caused by continued operation of the faulty module.

[0018] Preferably, the fault self-diagnosis module stores the fault information in the system log and uploads it to the remote monitoring center through the communication module for subsequent analysis and maintenance, including: According to the fault information obtained by the fault self-diagnosis module, the fault information is written into the system log to obtain a system log file containing the fault information; Establish a communication connection with the remote monitoring center through the communication module. If the communication connection is successfully established, upload the system log file to the remote monitoring center; After the remote monitoring center receives the uploaded system log file, it uses natural language processing technology to analyze the system log file and extract key fault information; According to the extracted fault information, semantic analysis is performed through knowledge graph technology to determine the fault type and cause, and obtain the fault diagnosis result; Match the fault diagnosis results with the pre-established fault solution knowledge base to obtain the corresponding fault solution; Automatically generate a maintenance work order based on the fault solution obtained, assign the maintenance work order to the relevant maintenance personnel, and notify the maintenance personnel to perform fault maintenance; After completing the fault maintenance, the maintenance personnel will feed back the maintenance results to the remote monitoring center, and the remote monitoring center will update the fault solution knowledge base and optimize the fault diagnosis and resolution process.

[0019] The present invention can achieve the following beneficial effects: 1. The fastening ring and the main shaft are connected by clamping. This connection method can ensure the stable installation of the fastening ring on the main shaft, avoid the loosening of the fastening ring due to vibration and other factors during the operation of the hydro-turbine generator set, ensure the stability of the installation foundation of components such as the overspeed pendulum and counterweight, and provide reliable hardware support for subsequent overspeed monitoring work.

[0020] 2. Symmetrical installation of the overspeed pendulum and the counterweight on the fastening ring can make the overall force of the fastening ring more uniform, avoid additional centrifugal force or other additional forces caused by asymmetric installation, ensure the balance and stability of the overspeed monitoring system during operation, and improve the accuracy and reliability of monitoring.

[0021] 3. The high-speed camera is fixed with a bracket, which can include the hydraulic switching valve and the overspeed pendulum as a whole in the camera range, facilitating unified real-time monitoring of the status of these two key components, helping to grasp the operating status of the system as a whole, and also facilitating troubleshooting and analysis of possible problems in the later stage.

[0022] 4. The vibration collection system and the speed collection system are connected to the mechanical overspeed monitoring host through data transmission lines to ensure that the relevant data signals can be accurately and stably transmitted to the mechanical overspeed monitoring host, reduce interference and loss during signal transmission, and provide data guarantee for subsequent data analysis and processing.

[0023] 5. The mechanical overspeed monitoring host includes a first signal receiving unit, a second signal receiving unit, a power module, a display screen and a rack. The internal units are reasonably arranged. The signal receiving unit and the power module are respectively inserted at different positions at the bottom of the rack, and the display screen is embedded and installed on the top of the rack. This layout is not only convenient for the maintenance and management of each component, but also conducive to the orderly reception, processing and display of signals. At the same time, it avoids mutual interference between components and improves the overall performance and work efficiency of the host.

[0024] 6. The high-precision speed sensor is used to collect the speed signal of the hydro-generator set in real time. The combination of wavelet transform algorithm and adaptive filtering algorithm established according to the characteristics of mechanical structure can effectively eliminate the influence of electromagnetic interference and mechanical vibration on the speed signal and obtain stable and reliable speed data. This helps to monitor the speed of the unit more accurately, avoid misjudgment caused by interference signals, and improve the accuracy of monitoring.

[0025] 7. By extracting the features of the speed signal and inputting the extracted feature parameters into the support vector machine model for training and optimization, a stable speed estimation value can be obtained, further improving the accuracy and reliability of speed monitoring. At the same time, based on the comparison between the speed estimation value and the preset threshold, the abnormal situation of the hydro-generator set can be discovered in time and the alarm signal can be triggered, providing effective data support for equipment maintenance and fault diagnosis, which helps to prevent equipment failures in advance.

[0026] 8. The speed change rate is calculated for the filtered speed data, and the preset speed change trend analysis model is input for trend analysis. By continuously optimizing the model, using the support vector machine algorithm and big data analysis technology, the accuracy of overspeed state judgment is improved, and the adaptability and robustness of the model are enhanced. The system can more accurately judge the mechanical overspeed state, avoid the risks caused by inaccurate judgment, and ensure the safe and stable operation of the unit.

[0027] 9. When the machine is judged to be in overspeed state, the optimal switching time and force of the hydraulic valve are calculated according to the rotational inertia of the turbine generator set, which can effectively avoid the water hammer effect. While ensuring the rapid mechanical deceleration, it prevents the hydraulic system impact caused by the switching of the hydraulic valve from damaging the unit, thereby extending the service life of the unit and the hydraulic system.

[0028] 10. During the hydraulic valve switching process, the pressure changes of the hydraulic system are monitored in real time. The correlation model between pressure fluctuations and hydraulic valve switching parameters is established through machine learning algorithms. The hydraulic valve switching parameters can be adjusted according to the pressure fluctuations, and the optimization is continuously iterated until the pressure fluctuations are controlled within the preset range. This closed-loop adjustment mechanism ensures the stability of the hydraulic system and improves the adaptability and reliability of the system under various working conditions.

[0029] 11. The fault self-diagnosis module stores fault information in the system log and uploads it to the remote monitoring center. It uses natural language processing technology and knowledge graph technology to analyze faults, automatically generates maintenance work orders and assigns them to relevant maintenance personnel, facilitating subsequent fault analysis and maintenance work. At the same time, by updating the fault solution knowledge base through maintenance result feedback, it helps to continuously optimize the fault diagnosis and solution process and improve the maintainability and maintenance efficiency of the entire system. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The present invention will be further described below in conjunction with the accompanying drawings and embodiments: Figure 1 It is a system structure diagram of the present invention; Figure 2 This is a structural diagram of the mechanical overspeed monitoring host of the present invention; Figure 3 This is a flow chart of the operation of the system of the present invention. DETAILED DESCRIPTION

[0031] The preferred solution is Figures 1 to 3 As shown, a mechanical overspeed monitoring system for a hydro-turbine generator set is composed of a fastening ring 1, a main shaft 2, an overspeed pendulum 3, a counterweight 4, a hydraulic switching valve 5, a high-speed camera 6, a bracket 7, a vibration collection system 8, a speed collection system 9, and a mechanical overspeed monitoring host 10.

[0032] The fastening ring 1 is connected to the main shaft 2 by clamping, and sufficient pre-tightening force is applied to ensure firmness.

[0033] The overspeed pendulum 3 and the counterweight 4 are symmetrically mounted on the fastening ring 1, and their function is to ensure dynamic balance during the rotation process and prevent eccentric force from occurring.

[0034] The hydraulic switching valve 5 is installed opposite to the overspeed pendulum 3 with a gap of about 5 mm, so as to ensure that the hydraulic switching valve 5 can realize oil circuit switching when the machine is overspeeding.

[0035] The high-speed camera 6 is fixed by a bracket 7 so that the high-speed camera 6 can include the hydraulic switching valve 5 and the overspeed pendulum 3 as a whole in the camera range.

[0036] The vibration collection system 8 and the speed collection system 9 are connected to the mechanical overspeed monitoring host 10 through a data transmission line to ensure the transmission of relevant data signals.

[0037] The first signal receiving unit 101 in the mechanical overspeed monitoring host 10 is inserted in the lower left side of the mounting frame 105, the second signal receiving unit 102 is inserted in the lower middle side of the mounting frame 105, the power module 103 is inserted in the lower right side of the mounting frame 105, and the display screen 104 is embedded and installed on the top of the frame 105. After the mechanical overspeed monitoring host 10 is powered on, the built-in software can analyze the critical value of the device action speed, mechanical hysteresis, unit oscillation stability and other data during the mechanical overspeed process.

[0038] A method for operating a mechanical overspeed monitoring system for a hydro-generator set, comprising the following steps: S1. Use high-precision speed sensors to collect the speed signal of the hydro-generator set in real time, eliminate the influence of electromagnetic interference and mechanical vibration on the signal through filtering algorithms, and obtain stable speed data.

[0039] A high-precision speed sensor is used to collect the speed signal of the hydro-generator set in real time and obtain the original speed data. The wavelet transform algorithm is used to perform denoising on the collected original speed data to eliminate the influence of electromagnetic interference on the speed signal. According to the mechanical structure characteristics of the hydro-generator set, a mechanical vibration mathematical model is established, and the speed signal is filtered by an adaptive filtering algorithm to eliminate the influence of mechanical vibration on the speed signal. The de-noised and filtered speed signal is feature extracted to obtain the time domain and frequency domain characteristic parameters of the speed signal. The extracted speed signal characteristic parameters are input into the support vector machine model, and a stable and reliable speed estimation value is obtained through model training and optimization. The estimated speed value is compared with the preset speed threshold. If the speed exceeds the normal range, it is judged that the hydro-generator set is abnormal and an alarm signal is triggered. According to the speed estimation value and the alarm signal, an operating status report of the hydro-generator set is generated to provide data support for equipment maintenance and fault diagnosis.

[0040] Specifically, a high-precision speed sensor with an accuracy of 1% is used to collect the speed signal of the hydro-turbine generator set in real time, sampling 1000 times per second to obtain the original speed data. For the collected original speed data, the wavelet transform algorithm db4 wavelet basis function is used to decompose the signal into 5 layers, and then the high-frequency component is denoised by the threshold method, which effectively eliminates the influence of electromagnetic interference on the speed signal. According to the mechanical structure characteristics of the hydro-turbine generator set, a mechanical vibration mathematical model considering factors such as bearings, rotors and excitation windings is established. The speed signal is filtered by the least squares adaptive filtering algorithm to filter out the influence of mechanical vibration on the speed signal. The speed signal after denoising and filtering is feature extracted, and the frequency domain feature parameters of the speed signal, including fundamental frequency, harmonic components, etc., are obtained by the fast Fourier transform algorithm. At the same time, the time domain feature parameters such as mean and variance are extracted. The 10 key speed signal feature parameters extracted are input into the support vector machine model, and the radial basis kernel function is used to train and optimize the model through cross-validation and grid search, and finally a stable and reliable speed estimation value is obtained, and the estimation accuracy reaches 2%. The estimated speed value is compared with the preset threshold of ±1% of the rated speed. If the speed exceeds the normal range, it is judged that the turbine generator set is abnormal and the sound and light alarm signal is triggered. Based on the speed estimation value and the alarm signal, a turbine generator set operation status report containing speed trend chart, alarm log and other contents is automatically generated, providing reliable data support for equipment maintenance and fault diagnosis.

[0041] S2. Input the filtered speed data into a preset speed change trend analysis model. If the speed change rate exceeds a preset threshold, it is determined to be a mechanical overspeed state.

[0042] The real-time speed data of the mechanical equipment is obtained, and the speed data is filtered to remove the noise interference in the speed data to obtain the filtered speed data. According to the filtered speed data, the speed change rate is calculated to obtain the speed change rate data within a certain time range. The speed change rate data is input into the preset speed change trend analysis model, and the speed change rate data is trend analyzed by the model to determine whether the speed change rate exceeds the preset threshold. If the speed change rate exceeds the preset threshold, it is determined that the mechanical equipment is in an overspeed state, and the overspeed alarm is triggered to notify relevant personnel to handle it. If the speed change rate does not exceed the preset threshold, it is determined that the mechanical equipment is in a normal working state, and the real-time speed data continues to be obtained to perform the next round of speed change trend analysis. According to the historical speed change rate data and the overspeed state judgment results, the support vector machine algorithm is used to train and optimize the speed change trend analysis model to improve the accuracy of the model's overspeed state judgment. Through big data analysis technology, the association rules between the speed change rate and other operating parameters of the mechanical equipment are mined, and the speed change trend analysis model is optimized in combination with the association rules to improve the adaptability and robustness of the model.

[0043] Specifically, after the real-time speed data of the mechanical equipment is collected by the sensor, the speed data is filtered using the Kalman filter algorithm, and the filter parameters Q=001, R=1 are set. After filtering, smooth speed data is obtained. According to the filtered speed data, the speed change rate every 1 second is calculated by the differential method to obtain the speed change rate data sequence within a period of time. The speed change rate data sequence is input into the preset LSTM neural network model, and the model can perform trend prediction on the current speed change rate data by training the historical speed change rate data. By comparing the predicted speed change rate with the preset threshold value 5, it is judged whether the mechanical equipment is in an overspeed state. If the predicted speed change rate exceeds 5, an overspeed alarm is triggered, and relevant personnel are notified by SMS and email for processing; if the predicted speed change rate does not exceed 5, it is judged that the mechanical equipment is in a normal working state, and the next round of speed change trend analysis is continued. At the same time, the historical speed change rate data and the overspeed state judgment results are used to optimize the parameters of the LSTM model by using the support vector machine algorithm, and the optimal model parameter combination is found by grid search to improve the accuracy of the model's overspeed state judgment. In addition, through big data analysis technology, using the Apriori association rule mining algorithm, with a minimum support of 05 and a minimum confidence of 8, the association rules between the speed change rate and other operating parameters of mechanical equipment, such as vibration frequency and temperature, were mined. It was found that when the speed change rate exceeds 5, the vibration frequency will often exceed 100Hz and the temperature will exceed 80 degrees Celsius. According to the mined association rules, the input features of the LSTM model are optimized, and the vibration frequency and temperature are also used as inputs to the model to improve the adaptability and robustness of the model. The optimized LSTM model can more accurately judge the overspeed state of mechanical equipment and effectively avoid equipment damage and safety accidents caused by overspeed.

[0044] S3. When it is determined to be a mechanical overspeed state, the hydraulic valve switching control module is triggered to calculate the switching time and force of the hydraulic valve according to the rotational inertia of the hydro-generator set to avoid the water hammer effect.

[0045] The speed sensor is used to monitor the speed of the turbine generator set in real time, and the collected speed data is transmitted to the mechanical overspeed monitoring host. After receiving the speed data, the mechanical overspeed monitoring host compares it with the preset mechanical overspeed threshold to determine whether it is currently in a mechanical overspeed state. If the judgment result is a mechanical overspeed state, the mechanical overspeed monitoring host immediately sends a trigger signal to the hydraulic valve switching control module. After receiving the trigger signal, the hydraulic valve switching control module obtains the moment of inertia parameters of the turbine generator set from the equipment parameter database. According to the obtained moment of inertia parameters, the optimization algorithm is used to calculate the optimal switching time and switching force of the hydraulic valve to achieve fast and effective mechanical deceleration while avoiding the water hammer effect. The calculated hydraulic valve switching time and force parameters are transmitted to the hydraulic valve execution unit to control the hydraulic valve to switch according to the best solution. After the hydraulic valve switching is completed, the speed change of the turbine generator set is continuously monitored until the speed returns to the normal range to ensure that the mechanical overspeed problem is effectively solved.

[0046] Specifically, by installing a high-precision speed sensor on the hydro-generator set, the speed data is collected in real time. The sampling frequency of the sensor is set to 1000Hz, which can meet the needs of real-time monitoring. The speed data is transmitted to the mechanical overspeed monitoring host through industrial Ethernet, and the transmission delay is less than 10ms. The mechanical overspeed threshold is set to 1200rpm inside the mechanical overspeed monitoring host, and the speed data is judged by a real-time comparison algorithm. When the speed data of three consecutive sampling cycles exceeds the threshold, it is judged as mechanical overspeed. The mechanical overspeed monitoring host completes the judgment within 2ms and sends a trigger signal to the hydraulic valve switching control module through a high-speed optical fiber network. The hydraulic valve switching control module adopts an equipment parameter management system based on the Internet of Things. The moment of inertia parameters of the hydro-generator set are queried from the database through a unique equipment ID, and the query time is less than 5ms. According to the moment of inertia parameters, the fuzzy PID control algorithm is used to calculate the optimal switching time and switching force of the hydraulic valve within 10ms, and the switching time is controlled within 100ms, and the switching force is controlled to be more than 80% of the rated value. The calculation results are transmitted to the hydraulic valve actuator unit through a high-speed optical fiber network, and the transmission delay is less than 2ms. The hydraulic valve actuator adopts a high-precision servo control system, and completes the switching action of the hydraulic valve within 5ms according to the received switching parameters. After the switching is completed, the mechanical overspeed monitoring host continues to monitor the speed change at a frequency of 1000Hz, and uses the Kalman filter algorithm to filter the speed data to predict the speed recovery trend in real time. When the speed data of 10 consecutive sampling cycles falls below 1000rpm, it is determined that the mechanical overspeed problem has been effectively solved, and the entire process control is completed within 1s.

[0047] S4, the hydraulic valve switching control module outputs a control signal to the hydraulic actuator to quickly switch the hydraulic valve state to achieve emergency shutdown.

[0048] The hydraulic valve switching control module receives the emergency stop command and determines whether the emergency stop operation needs to be performed. If the emergency stop operation needs to be performed, it enters the next step; otherwise, it continues to monitor the emergency stop command. The hydraulic valve switching control module determines the hydraulic valve state that needs to be switched according to the preset hydraulic valve switching control strategy and generates the corresponding hydraulic valve control signal. The hydraulic valve switching control module outputs the generated hydraulic valve control signal to the hydraulic actuator, and drives the hydraulic actuator to quickly switch the hydraulic valve state through the control signal. After the hydraulic actuator receives the control signal output by the hydraulic valve switching control module, it quickly switches the hydraulic valve state according to the control signal to achieve rapid switching of the hydraulic valve. The hydraulic valve switching control module monitors the state feedback signal of the hydraulic actuator in real time to determine whether the hydraulic valve has switched to the target state. If it has switched to the target state, it enters the next step; otherwise, it continues to output the control signal to drive the hydraulic actuator to switch the hydraulic valve state. After the hydraulic valve switching control module determines that the hydraulic valve has switched to the target state according to the state feedback signal of the hydraulic actuator, it stops outputting the control signal and feeds back the hydraulic valve state switching result to the upper control system. After receiving the hydraulic valve state switching result fed back by the hydraulic valve switching control module, the upper control system determines whether the emergency stop operation has been completed. If it has been completed, the emergency stop process ends; otherwise, other emergency stop operations continue to be performed.

[0049] Specifically, after the hydraulic valve switching control module receives the emergency stop command, it determines whether the emergency stop operation needs to be performed through the preset threshold. If the priority of the emergency stop command is higher than the threshold, the control module determines the hydraulic valve state that needs to be switched through the fuzzy control algorithm according to the preset hydraulic valve switching control strategy, and generates a 4-20mA standard current signal to output to the hydraulic actuator. After the hydraulic actuator receives the control signal, it quickly adjusts the opening of the hydraulic valve through the PID control algorithm to achieve rapid switching of the hydraulic valve within 100ms. At the same time, the hydraulic valve switching control module monitors the position feedback signal of the hydraulic actuator in real time through high-speed AD sampling, and filters the feedback signal through the Kalman filter algorithm to determine whether the hydraulic valve has switched to the target position. If the hydraulic valve does not switch to the target position within 200ms, the control module continues to output the control signal, and adjusts the control parameters in real time through the adaptive control algorithm to ensure that the hydraulic valve switches to the target state quickly and smoothly. When the hydraulic valve switches to the target state, the control module stops outputting the control signal and feeds back the hydraulic valve state switching result to the upper control system through industrial Ethernet. The upper control system determines whether the emergency shutdown operation has been completed based on the feedback results through the decision tree algorithm. If not, it continues to perform other emergency shutdown operations until the entire system is safely shut down.

[0050] S5. During the hydraulic valve switching process, monitor the pressure changes of the hydraulic system in real time. If the pressure fluctuation exceeds the preset range, adjust the hydraulic valve switching parameters to ensure system stability.

[0051] Acquire real-time pressure data during the hydraulic valve switching process, compare the pressure data with the preset range, and determine whether the pressure fluctuation exceeds the preset range; if the pressure fluctuation exceeds the preset range, determine the adjustment range of the hydraulic valve switching parameters according to the degree to which the pressure fluctuation exceeds the range; establish a correlation model between pressure fluctuations and hydraulic valve switching parameters through a machine learning algorithm, and predict the optimal hydraulic valve switching parameters according to the pressure fluctuations; apply the adjusted hydraulic valve switching parameters to the hydraulic valve switching process, and continuously monitor the pressure changes to determine whether the adjusted parameters are effective; if the adjusted parameters fail to effectively reduce the pressure fluctuations, further optimize the machine learning model and re-predict the optimal hydraulic valve switching parameters; continuously iterate and optimize the hydraulic valve switching parameters until the pressure fluctuations are controlled within the preset range to ensure the stable operation of the hydraulic system; save the optimized hydraulic valve switching parameters as preset parameters and directly apply them in subsequent hydraulic valve switching processes to improve the system response speed and stability.

[0052] Specifically, during the hydraulic valve switching process, the pressure data is collected in real time by the pressure sensor, and the sampling frequency is 1000Hz. The collected pressure data is compared with the preset pressure range, and the preset range is 5-0MPa. When it is detected that the pressure fluctuation exceeds the preset range, the adjustment amplitude of the hydraulic valve switching parameter is determined by the fuzzy control algorithm according to the degree of exceeding the range. The greater the pressure fluctuation, the greater the adjustment amplitude, and the adjustment amplitude range is 1-5MPa. At the same time, the historical pressure fluctuation data and the corresponding hydraulic valve switching parameters are used to establish the association model between the pressure fluctuation and the hydraulic valve switching parameters through the support vector machine algorithm, and the optimal hydraulic valve switching parameters are predicted according to the current pressure fluctuation. The optimized hydraulic valve switching parameters are applied to the hydraulic valve switching process, and the pressure changes are continuously monitored with a cycle of 10ms. The pressure fluctuation is analyzed by statistical methods to determine whether the adjusted parameters are effective. If the adjusted parameters fail to control the pressure fluctuation within the preset range, the machine learning model is optimized by the incremental learning algorithm, and the genetic algorithm is introduced to perform parameter optimization and re-predict the optimal hydraulic valve switching parameters. Through continuous iterative optimization, the pressure fluctuation is finally controlled within the preset range of 5-0MPa to ensure the stable operation of the hydraulic system. The optimized hydraulic valve switching parameters are saved as preset parameters and directly called in the subsequent hydraulic valve switching process, which can shorten the system response time to less than 50ms and significantly improve the real-time performance and stability of the system.

[0053] S6. The system has a built-in fault self-diagnosis module, which regularly detects the working status of the speed sensor, hydraulic valve control module and signal processing module. If a fault is detected, the fault locking function is triggered to prevent false alarms.

[0054] According to the preset time interval, the built-in fault self-diagnosis module of the system is triggered regularly to perform status detection on the speed sensor, hydraulic valve control module and signal processing module. When performing status detection, the fault self-diagnosis module obtains the real-time speed data of the speed sensor and compares it with the preset normal speed range to determine whether the speed sensor is faulty. At the same time, the fault self-diagnosis module determines whether the opening and closing state of the hydraulic valve is consistent with the control instruction by analyzing the feedback signal of the hydraulic valve control module, and determines whether the hydraulic valve control module is faulty. The fault self-diagnosis module also monitors the input and output data of the signal processing module in real time, and determines whether the signal processing module is working normally by analyzing the continuity and integrity of the data. If the fault self-diagnosis module detects that any module is faulty, the fault lockout function is immediately triggered to stop the corresponding module by cutting off the power supply or blocking the signal transmission. While triggering the fault lockout function, the fault self-diagnosis module generates a fault report, records the time, location and fault type of the fault, and sends the report to the system administrator for subsequent maintenance processing. The fault lockout function remains in effect until the system administrator confirms that the fault has been eliminated and manually releases the lockout state. The system can then resume normal operation, effectively preventing false alarms caused by continued operation of the faulty module.

[0055] Specifically, the built-in fault self-diagnosis module of the system is automatically triggered every 30 minutes to perform a comprehensive status detection on the speed sensor, hydraulic valve control module and signal processing module. The fault self-diagnosis module obtains the speed data of the speed sensor in real time through the RS-485 bus, and compares it with the preset normal speed range of 800-1200rpm. If the actual speed exceeds this range, it is determined that the speed sensor is faulty. At the same time, the fault self-diagnosis module adopts the feedback signal analysis method based on the PID control algorithm to analyze the feedback signal of the hydraulic valve control module. By calculating the deviation value between the control instruction and the actual execution state, if the deviation value is greater than 5%, it is determined that the hydraulic valve control module is faulty. In addition, the fault self-diagnosis module also uses the CRC check algorithm to monitor the input and output data of the signal processing module in real time. By analyzing the continuity and integrity of the data frame, if it is found that three consecutive data frames have check errors, it is determined that the signal processing module is working abnormally. Once the fault self-diagnosis module detects that any module is faulty, it will immediately trigger the fault lock function, cut off the power supply of the faulty module through the relay, and send a fault report containing information such as fault time, location and type to the system administrator. The faulty module will remain in a shutdown state until the system administrator manually releases the locked state, thus avoiding false alarms caused by the continued operation of the faulty module.

[0056] S7. The fault self-diagnosis module stores the fault information in the system log and uploads it to the remote monitoring center through the communication module for subsequent analysis and maintenance.

[0057] According to the fault information obtained by the fault self-diagnosis module, the fault information is written into the system log to obtain a system log file containing the fault information. A communication connection is established with the remote monitoring center through the communication module. If the communication connection is successfully established, the system log file is uploaded to the remote monitoring center. After the remote monitoring center receives the uploaded system log file, the system log file is analyzed using natural language processing technology to extract key fault information. According to the extracted fault information, semantic analysis is performed using knowledge graph technology to determine the fault type and cause, and the fault diagnosis result is obtained. The fault diagnosis result is matched with the pre-established fault solution knowledge base to obtain the corresponding fault solution. According to the obtained fault solution, a fault maintenance work order is automatically generated, and the maintenance work order is assigned to the relevant maintenance personnel, and the maintenance personnel are notified to perform fault maintenance. After completing the fault maintenance, the maintenance personnel will feedback the maintenance results to the remote monitoring center, and the remote monitoring center will update the fault solution knowledge base to optimize the fault diagnosis and resolution process.

[0058] Specifically, the fault self-diagnosis module obtains relevant information when the system fails by real-time monitoring of the system operation status, such as the fault time is 14:30:25 on May 10, 2023, the fault code is ERR_0x001, and the fault description is "the system cannot start normally". These fault information are written into the system log file system_log_2023051txt. The communication module uses the TCP / IP protocol to establish a communication connection with the remote monitoring center, and ensures the reliability of the connection through a three-way handshake mechanism. After the connection is successfully established, the system log file is uploaded to the remote monitoring center through the FTP protocol. The remote monitoring center uses a natural language processing model based on deep learning to analyze the received system log file, and extracts key information such as the fault occurrence time, fault code, and fault description through technologies such as part of speech tagging and named entity recognition. Then, using the pre-built fault knowledge graph, the semantic analysis technology determines that the fault type is "system startup failure" and the fault cause is "configuration file damage". According to the fault type and cause, the corresponding solution retrieved from the fault solution knowledge base is "reconfigure system parameters and repair damaged configuration files". The system automatically generates a fault maintenance work order M_20230510001 and assigns it to the system administrator Zhang San, notifying Zhang San through SMS and email to perform fault maintenance. Zhang San reconfigures and repairs the system according to the instructions of the maintenance work order, and after completing the maintenance, he feeds back the maintenance result "the system has resumed normal operation" to the remote monitoring center. Based on the feedback of the maintenance results, the remote monitoring center optimizes the fault solutions in the knowledge base, increases the effectiveness of the solutions from 85% to 95%, and fine-tunes the fault diagnosis model to improve the accuracy of fault diagnosis. Through this series of intelligent fault diagnosis and maintenance processes, the reliability and maintenance efficiency of the system are greatly improved, and the need for manual intervention is reduced.

[0059] S8. The system adopts a modular design. Each functional module is waterproof, dustproof, and corrosion-resistant, can adapt to a wide range of temperature changes, supports plug-and-play functions, and is easy to install and maintain.

[0060] According to the modular design of the system, the waterproof, dustproof and corrosion-resistant property parameters of each functional module are input into the module property database. By querying the module property database, the waterproof, dustproof and corrosion-resistant level parameters of each functional module are obtained. According to the obtained waterproof, dustproof and corrosion-resistant level parameters, the fuzzy comprehensive evaluation method is used to calculate the comprehensive protection level of each functional module. The comprehensive protection level of each functional module is compared with the preset protection level threshold. If the comprehensive protection level is greater than or equal to the threshold, it is judged that the module meets the system protection requirements. According to the temperature adaptation property, the operating temperature range parameters of each functional module are obtained and input into the module temperature adaptation database. By querying the module temperature adaptation database, the maximum operating temperature and minimum operating temperature of all modules of the system are obtained. According to the obtained maximum operating temperature and minimum operating temperature, the overall operating temperature range of the system is determined, and it is judged whether the temperature range meets the system temperature adaptation requirements.

[0061] Specifically, according to the modular design of the system, the waterproof, dustproof and corrosion-resistant property parameters of each functional module are input into the module property database. For example, the waterproof level of module A is IPX7, the dustproof level is IP6X, and the corrosion-resistant level is H; the waterproof level of module B is IPX5, the dustproof level is IP5X, and the corrosion-resistant level is M. The module property database is queried through SQL statements to obtain the waterproof, dustproof and corrosion-resistant level parameters of each functional module. According to the obtained waterproof, dustproof and corrosion-resistant level parameters, the fuzzy comprehensive evaluation method is adopted, and the weight vector A=(3, 3, 4) is set. The comprehensive protection level of each functional module is calculated by weighted average, such as the comprehensive protection level of module A is 85, and the comprehensive protection level of module B is 76. The comprehensive protection level of each functional module is compared with the preset protection level threshold 8. Module A meets the system protection requirements, but module B does not. According to the temperature adaptation attribute, the operating temperature range parameters of each functional module are obtained and input into the module temperature adaptation database. For example, the operating temperature range of module A is -20℃~60℃, and the operating temperature range of module B is -10℃~50℃. By querying the module temperature adaptation database, the maximum operating temperature of all modules in the system is 60℃, and the minimum operating temperature is -20℃. According to the obtained maximum and minimum operating temperatures, the overall operating temperature range of the system is determined to be -20℃~60℃, and it is judged whether the temperature range meets the system temperature adaptation requirements, that is, whether it is within the preset range of -30℃~70℃. In this case, the requirements are met. In summary, through the modular design of the system, the protection and temperature adaptation attributes of each module are parameterized, and the comprehensive protection level and temperature adaptation range of the system can be quantitatively evaluated, providing a quantitative basis for system design.

[0062] The above embodiments are only preferred technical solutions of the present invention and should not be regarded as limiting the present invention. The protection scope of the present invention shall be the technical solutions recorded in the claims, including equivalent replacement solutions of the technical features in the technical solutions recorded in the claims. That is, equivalent replacement improvements within this scope are also within the protection scope of the present invention.

Claims

1. A mechanical overspeed monitoring system for a hydro-generator set, characterized in that: The invention comprises a fastening ring (1), the fastening ring (1) is connected to a main shaft (2), an overspeed pendulum (3) and a counterweight (4) are installed on the fastening ring (1), the overspeed pendulum (3) and a hydraulic switching valve (5) are installed opposite to each other, and the states of the overspeed pendulum (3) and the hydraulic switching valve (5) are monitored by a high-speed camera (6); The hydraulic switching valve (5) and the high-speed camera (6) are electrically connected to a mechanical overspeed monitoring host (10), and the mechanical overspeed monitoring host (10) is electrically connected to a vibration collection system (8) and a rotation speed collection system (9).

2. A mechanical overspeed monitoring system for a hydro-generator set according to claim 1, characterized in that: The fastening ring (1) and the main shaft (2) are connected by clamping.

3. A mechanical overspeed monitoring system for a hydro-generator set according to claim 1, characterized in that: An overspeed pendulum (3) and a counterweight (4) are symmetrically mounted on the fastening ring (1).

4. A mechanical overspeed monitoring system for a hydro-generator set according to claim 1, characterized in that: The high-speed camera (6) is fixed by a bracket (7), so that the high-speed camera (6) can include the hydraulic switching valve (5) and the overspeed pendulum (3) as a whole in the camera range.

5. A mechanical overspeed monitoring system for a hydro-generator set according to claim 1, characterized in that: The vibration collection system (8) and the speed collection system (9) are connected to the mechanical overspeed monitoring host (10) via a data transmission line to ensure that relevant data signals are transmitted.

6. A mechanical overspeed monitoring system for a hydro-generator set according to claim 1, characterized in that: The mechanical overspeed monitoring host (10) comprises a first signal receiving unit (101), a second signal receiving unit (102), a power module (103), a display screen (104) and a rack (105); the signal receiving unit (101) is inserted into the left side of the lower part of the mounting rack (105); the second signal receiving unit (102) is inserted into the middle side of the lower part of the mounting rack (105); the power module (103) is inserted into the right side of the lower part of the mounting rack (105); and the display screen (104) is embedded and installed on the top of the rack (105).

7. The operating method of a mechanical overspeed monitoring system for a hydro-generator set according to claim 1, characterized in that The following steps are involved: A high-precision speed sensor is used to collect the speed signal of the hydro-generator set in real time. The influence of electromagnetic interference and mechanical vibration on the signal is eliminated through a filtering algorithm to obtain stable speed data. The filtered speed data is input into a preset speed change trend analysis model. If the speed change rate exceeds a preset threshold, it is judged as a mechanical overspeed state. When the machine is judged to be in an overspeed state, the hydraulic valve switching control module is triggered to calculate the switching time and force of the hydraulic valve according to the rotational inertia of the turbine generator set to avoid the water hammer effect; the hydraulic valve switching control module outputs a control signal to the hydraulic actuator to quickly switch the hydraulic valve state to achieve emergency shutdown; during the hydraulic valve switching process, the pressure change of the hydraulic system is monitored in real time. If the pressure fluctuation exceeds the preset range, the hydraulic valve switching parameters are adjusted to ensure system stability; The system has a built-in fault self-diagnosis module, which regularly detects the working status of the speed sensor, hydraulic valve control module and signal processing module. If a fault is detected, the fault lockout function is triggered to prevent false alarms. The fault self-diagnosis module stores the fault information in the system log and uploads it to the remote monitoring center through the communication module to facilitate subsequent analysis and maintenance.

8. The method for operating a mechanical overspeed monitoring system for a hydro-generator set according to claim 7, characterized in that The following steps are involved: The high-precision speed sensor is used to collect the speed signal of the hydro-generator set in real time, and the influence of electromagnetic interference and mechanical vibration on the signal is eliminated by the filtering algorithm to obtain stable speed data, including: Use high-precision speed sensors to collect speed signals of hydro-generator sets in real time and obtain original speed data; For the collected original speed data, the wavelet transform algorithm is used for denoising to eliminate the influence of electromagnetic interference on the speed signal; According to the mechanical structure characteristics of the hydro-generator set, a mathematical model of mechanical vibration is established, and the speed signal is filtered through an adaptive filtering algorithm to eliminate the influence of mechanical vibration on the speed signal; Perform feature extraction on the denoised and filtered speed signal to obtain time domain and frequency domain feature parameters of the speed signal; The extracted characteristic parameters of the speed signal are input into the support vector machine model, and a stable and reliable speed estimation value is obtained through model training and optimization. The estimated speed value is compared with the preset speed threshold. If the speed exceeds the normal range, it is determined that the hydro-generator set is abnormal and an alarm signal is triggered; Based on the estimated speed and alarm signal, an operating status report of the hydro-generator set is generated to provide data support for equipment maintenance and fault diagnosis.

9. The method for operating a mechanical overspeed monitoring system for a hydro-generator set according to claim 1, characterized in that The following steps are involved: The filtered speed data is input into a preset speed change trend analysis model, and if the speed change rate exceeds a preset threshold, it is judged as a mechanical overspeed state, including: Obtain real-time speed data of mechanical equipment, filter the speed data, remove noise interference in the speed data, and obtain filtered speed data; According to the filtered speed data, the speed change rate is calculated to obtain the speed change rate data within a certain time range; The speed change rate data is input into a preset speed change trend analysis model, and the speed change rate data is trend analyzed by the model to determine whether the speed change rate exceeds a preset threshold; If the speed change rate exceeds the preset threshold, the mechanical equipment is judged to be in an overspeed state, an overspeed alarm is triggered, and relevant personnel are notified to handle it; If the speed change rate does not exceed the preset threshold, the mechanical equipment is judged to be in normal working condition, and the real-time speed data continues to be obtained to perform the next round of speed change trend analysis; Based on the historical speed change rate data and overspeed state judgment results, the support vector machine algorithm is used to train and optimize the speed change trend analysis model to improve the accuracy of the model's overspeed state judgment; Through big data analysis technology, the association rules between the speed change rate and other operating parameters of mechanical equipment are mined, and the speed change trend analysis model is optimized based on the association rules to improve the adaptability and robustness of the model.

10. The operating method of the mechanical overspeed monitoring system of a hydro-generator set according to claim 1, characterized in that The following steps are involved: When the mechanical overspeed state is determined, the hydraulic valve switching control module is triggered, and the switching time and force of the hydraulic valve are calculated according to the rotational inertia of the hydro-generator set to avoid the water hammer effect, including: The speed sensor is used to monitor the speed of the hydro-generator set in real time, and the collected speed data is transmitted to the mechanical overspeed monitoring host; After receiving the speed data, the mechanical overspeed monitoring host compares it with the preset mechanical overspeed threshold to determine whether it is currently in a mechanical overspeed state; If the result of the judgment is a mechanical overspeed state, the mechanical overspeed monitoring host immediately sends a trigger signal to the hydraulic valve switching control module; After receiving the trigger signal, the hydraulic valve switching control module obtains the rotational inertia parameters of the hydro-generator set from the equipment parameter database; Based on the acquired moment of inertia parameters, an optimization algorithm is used to calculate the optimal switching time and switching force of the hydraulic valve, so as to achieve fast and effective mechanical deceleration while avoiding the water hammer effect; The calculated hydraulic valve switching time and force parameters are transmitted to the hydraulic valve execution unit to control the hydraulic valve to switch according to the optimal solution; After the hydraulic valve is switched, the speed changes of the turbine generator set are continuously monitored until the speed returns to the normal range to ensure that the mechanical overspeed problem is effectively resolved.

11. The method for operating a mechanical overspeed monitoring system for a hydro-generator set according to claim 1, characterized in that The following steps are involved: The hydraulic valve switching control module outputs a control signal to the hydraulic actuator to quickly switch the hydraulic valve state to achieve emergency shutdown, including: The hydraulic valve switching control module receives the emergency stop command and determines whether an emergency stop operation needs to be performed. If an emergency stop operation needs to be performed, the next step is entered; Otherwise, continue to monitor the emergency stop command; The hydraulic valve switching control module determines the hydraulic valve state to be switched according to the preset hydraulic valve switching control strategy, and generates a corresponding hydraulic valve control signal; The hydraulic valve switching control module outputs the generated hydraulic valve control signal to the hydraulic actuator, and drives the hydraulic actuator to quickly switch the hydraulic valve state through the control signal; After receiving the control signal output by the hydraulic valve switching control module, the hydraulic actuator quickly switches the hydraulic valve state according to the control signal to achieve rapid switching of the hydraulic valve; The hydraulic valve switching control module monitors the state feedback signal of the hydraulic actuator in real time to determine whether the hydraulic valve has been switched to the target state. If it has been switched to the target state, it proceeds to the next step; Otherwise, continue to output the control signal to drive the hydraulic actuator to switch the hydraulic valve state; The hydraulic valve switching control module stops outputting control signals after determining that the hydraulic valve has switched to the target state based on the state feedback signal of the hydraulic actuator, and feeds back the hydraulic valve state switching result to the upper control system; After receiving the hydraulic valve state switching result fed back by the hydraulic valve switching control module, the upper control system determines whether the emergency stop operation has been completed. If it has been completed, the emergency stop process ends; Otherwise, continue with other emergency stop operations.

12. The method for operating a mechanical overspeed monitoring system for a hydro-generator set according to claim 1, characterized in that The following steps are involved: During the hydraulic valve switching process, the pressure change of the hydraulic system is monitored in real time. If the pressure fluctuation exceeds a preset range, the hydraulic valve switching parameters are adjusted to ensure system stability, including: Obtain real-time pressure data during the hydraulic valve switching process, compare the pressure data with the preset range, and determine whether the pressure fluctuation exceeds the preset range; If the pressure fluctuation exceeds the preset range, the adjustment range of the hydraulic valve switching parameter is determined according to the degree to which the pressure fluctuation exceeds the range; A correlation model between pressure fluctuations and hydraulic valve switching parameters is established through machine learning algorithms, and the optimal hydraulic valve switching parameters are predicted based on the pressure fluctuations. Apply the adjusted hydraulic valve switching parameters to the hydraulic valve switching process, and continuously monitor the pressure changes to determine whether the adjusted parameters are effective; If the adjusted parameters fail to effectively reduce pressure fluctuations, the machine learning model is further optimized and the optimal hydraulic valve switching parameters are re-predicted; Continuously iterate and optimize the hydraulic valve switching parameters until the pressure fluctuation is controlled within the preset range to ensure the stable operation of the hydraulic system; The optimized hydraulic valve switching parameters are saved as preset parameters and directly applied in the subsequent hydraulic valve switching process to improve the system response speed and stability.

13. The operating method of the mechanical overspeed monitoring system of a hydro-generator set according to claim 1, characterized in that The following steps are involved: The system has a built-in fault self-diagnosis module, which regularly detects the working status of the speed sensor, hydraulic valve control module and signal processing module. If a fault is detected, the fault locking function is triggered to prevent false alarms, including: According to the preset time interval, the system's built-in fault self-diagnosis module is regularly triggered to perform status detection on the speed sensor, hydraulic valve control module and signal processing module; When performing status detection, the fault self-diagnosis module obtains the real-time speed data of the speed sensor and compares it with the preset normal speed range to determine whether the speed sensor has a fault; At the same time, the fault self-diagnosis module determines whether the opening and closing state of the hydraulic valve is consistent with the control instruction by analyzing the feedback signal of the hydraulic valve control module, and determines whether there is a fault in the hydraulic valve control module; The fault self-diagnosis module also monitors the input and output data of the signal processing module in real time, and determines whether the signal processing module is working normally through the continuity and integrity analysis of the data; If the fault self-diagnosis module detects a fault in any module, it will immediately trigger the fault locking function to stop the operation of the corresponding module by cutting off the power supply or blocking the signal transmission; When the fault locking function is triggered, the fault self-diagnosis module generates a fault report, records the time and location of the fault, the fault type and other information, and sends the report to the system administrator for subsequent maintenance processing; The fault lockout function remains in effect until the system administrator confirms that the fault has been eliminated and manually releases the lockout state. The system can then resume normal operation, effectively preventing false alarms caused by continued operation of the faulty module.

14. The method for operating a mechanical overspeed monitoring system for a hydro-generator set according to claim 1, characterized in that The following steps are involved: The fault self-diagnosis module stores the fault information in the system log and uploads it to the remote monitoring center through the communication module for subsequent analysis and maintenance, including: According to the fault information obtained by the fault self-diagnosis module, the fault information is written into the system log to obtain a system log file containing the fault information; Establish a communication connection with the remote monitoring center through the communication module. If the communication connection is successfully established, upload the system log file to the remote monitoring center; After the remote monitoring center receives the uploaded system log file, it uses natural language processing technology to analyze the system log file and extract key fault information; According to the extracted fault information, semantic analysis is performed through knowledge graph technology to determine the fault type and cause, and obtain the fault diagnosis result; Match the fault diagnosis results with the pre-established fault solution knowledge base to obtain the corresponding fault solution; Automatically generate a maintenance work order based on the fault solution obtained, assign the maintenance work order to the relevant maintenance personnel, and notify the maintenance personnel to perform fault maintenance; After completing the fault maintenance, the maintenance personnel will feed back the maintenance results to the remote monitoring center, and the remote monitoring center will update the fault solution knowledge base and optimize the fault diagnosis and resolution process.

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