Pumped storage generator set safety state monitoring and diagnosing method and system

Abstract

The invention belongs to the technical field of generator set monitoring, and particularly relates to a pumped storage generator set safety state monitoring and diagnosing method and system. Temperature data of cooling system fluid are collected in real time, the local temperature abnormal rising phenomenon is accurately detected, and the bubble accumulation possibility is calculated; evaluating whether the stator cooling efficiency is reduced and analyzing specific reasons; on the basis of the analysis results, operation parameters of the cooling system are adjusted to reduce bubble accumulation, the working state of the adjusted system is continuously monitored, whether the local overheating risk is reduced or not is judged, and finally the safety monitoring strategy of the generator set is optimized; by means of the series of steps, the detection and evaluation capacity for accumulation and influences of the micro bubbles in the cooling system is remarkably improved, the safety and reliability of the generator set are effectively improved, the service life of equipment is prolonged, and the maintenance cost is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of generator set monitoring, and in particular relates to a method and system for monitoring and diagnosing the safety status of a pumped-storage generator set. Background Art

[0002] During the operation of pumped storage generators, the effectiveness and stability of the cooling system are crucial to the safe operation of the equipment. Existing cooling system monitoring methods usually rely on traditional temperature sensors and flow meters to obtain basic operating parameters. These methods mainly monitor the temperature and flow changes of the coolant to identify potential abnormal conditions. However, existing technologies have limitations in the following aspects: Inaccurate detection of local temperature anomalies: Traditional methods can only provide overall temperature data, making it difficult to accurately identify abnormal local temperature increases.

[0003] Insufficient assessment of the impact of bubble accumulation: Existing technologies lack effective means to assess the accumulation of trace bubbles in cooling systems and their impact on cooling efficiency.

[0004] Incomplete analysis of the causes of decreased cooling efficiency: When a decrease in cooling efficiency is found, existing methods make it difficult to quickly and accurately locate the specific causes and make targeted adjustments.

[0005] Existing cooling system monitoring methods cannot accurately detect and evaluate the problem of reduced generator stator cooling efficiency and increased risk of local overheating caused by the accumulation of micro bubbles in the cooling system. This may lead to deterioration of cooling system performance, thus affecting the overall safety and reliability of the generator set. Summary of the invention

[0006] The object of the present invention is to provide a method and system for monitoring and diagnosing the safety status of a pumped-storage generator set, which can more accurately detect local temperature anomalies and bubble accumulation in the cooling system, timely evaluate their impact on the cooling efficiency, and take effective measures to adjust and optimize them, thereby significantly improving the safety and reliability of the generator set to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above object, the present invention proposes a method for monitoring and diagnosing the safety status of a pumped storage generator set, comprising the following steps: Acquire real-time temperature data of the fluid in the cooling system of the generator set; determine whether there is a local abnormal temperature increase based on the real-time temperature data, and for the local abnormal temperature increase, calculate the possibility of bubble accumulation in the cooling system; use the possibility of bubble accumulation to evaluate whether the stator cooling efficiency of the generator set has decreased, and if the stator cooling efficiency has decreased, analyze the specific factors that have caused the decrease in cooling efficiency; adjust the operating parameters of the cooling system based on the specific factors to reduce bubble accumulation; monitor the working status of the adjusted cooling system to determine whether the risk of local overheating has been reduced, and optimize the safety monitoring strategy of the generator set according to the change in the risk of local overheating.

[0008] Preferably, the step of obtaining real-time temperature data of a fluid in a cooling system of a generator set includes: Convert real-time temperature data into digital signals and store them in the database; Calculate the temperature change rate per minute based on the digital signal to identify abnormal temperature fluctuations in the cooling system; For abnormal temperature fluctuations, historical data is compared to determine whether there is a continued upward trend. If so, an early warning notification is issued.

[0009] Preferably, determining whether there is an abnormal increase in local temperature according to the real-time temperature data includes: Divide the real-time temperature data by region and calculate the average temperature in each region; Compare the average temperature value with the preset safety threshold to determine whether the average temperature exceeds the safety range; If the average temperature exceeds the safe range, analyze the temperature distribution of each monitoring point in the area and find the monitoring point with the highest temperature; Based on the data from the monitoring point with the highest temperature, the temperature difference between it and the adjacent monitoring point is calculated. If the temperature difference exceeds the set threshold, it is confirmed that there is an abnormal increase in local temperature.

[0010] Preferably, the calculating the possibility of bubble accumulation in the cooling system for the abnormal local temperature rise phenomenon includes: According to the abnormal increase in local temperature, the average temperature value of the abnormal area is first determined; Calculate the temperature difference using the average temperature value and the standard boiling point temperature of the coolant in the area; Based on the temperature difference and combined with the pressure data of the cooling system, the possibility of bubble accumulation is calculated; If the possibility of bubble accumulation exceeds the preset safety threshold, a warning signal will be issued and the specific location and time of the abnormal area will be recorded.

[0011] Preferably, the method of evaluating whether the stator cooling efficiency of the generator set decreases by utilizing the possibility of bubble accumulation includes: Based on the likelihood of bubble accumulation, a risk factor is first determined; Calculate the actual cooling efficiency using the risk factor and the standard cooling efficiency of the stator; Compare the actual cooling efficiency with the standard cooling efficiency. If the actual cooling efficiency is lower than the standard cooling efficiency, it is confirmed that the cooling efficiency has decreased. If the cooling efficiency decreases, analyze the temperature distribution of each part of the stator, find the area with the highest temperature, and record the specific location and temperature value of the area.

[0012] Preferably, if the stator cooling efficiency decreases, analyzing the specific factors causing the decrease in cooling efficiency includes: If it is confirmed that the stator cooling efficiency has decreased, first collect the temperature data of each part of the stator and the operating parameters of the cooling system; Based on the temperature data, calculate the standard deviation of the temperature of each part; Use standard deviation and cooling system flow data to evaluate the uniformity of cooling medium distribution; Analyze the uniformity index and the preset normal range. If the uniformity index is lower than the normal range, it is determined that the uneven distribution of the cooling medium is the main factor causing the decrease in cooling efficiency. At the same time, check the bubble accumulation in the cooling system to confirm whether there is a phenomenon of bubbles blocking the cooling channel.

[0013] Preferably, adjusting the operating parameters of the cooling system based on the specific factors to reduce bubble accumulation includes: Determine the main reason for the decrease in cooling efficiency based on specific factors. If it is determined to be due to uneven distribution of cooling medium, first calculate the flow adjustment coefficient of the current cooling system; Use the flow adjustment factor to adjust the speed of the cooling pump; If the specific factor includes bubble accumulation, adjust the pressure setting in the cooling system based on the likelihood of bubble accumulation; Monitor the operating status of the adjusted cooling system, record new temperature data and cooling medium flow, and calculate new cooling efficiency.

[0014] Preferably, the monitoring of the working state of the cooling system after adjustment to determine whether the risk of local overheating is reduced includes: Monitor the operating status of the adjusted cooling system and collect new temperature data and cooling medium flow data; Calculate the average temperature of each area based on the new temperature data; Using the average temperature and the data of the highest temperature area previously recorded, calculate the new temperature difference; If the new temperature difference is less than the preset safety threshold, it is confirmed that the risk of local overheating has been reduced; otherwise, continue to optimize the parameter settings of the cooling system until the risk is reduced.

[0015] Preferably, the method of optimizing the safety monitoring strategy of the generator set according to the change of the local overheating risk includes: According to the change of local overheating risk, a new risk index is first calculated; Compare the new risk index with the benchmark risk index in historical data to determine the changing trend of the current risk level; If the risk change rate shows that the risk has significantly decreased, the monitoring frequency will be reduced and the warning threshold will be adjusted to make the warning system more sensitive to new potential risks; If the risk change rate shows that the risk has not decreased significantly or has increased, the monitoring frequency and the number of monitoring points will be increased, and the parameter settings of the cooling system will be updated to optimize the cooling effect; at the same time, all adjustment measures and their results will be recorded to form a new safety monitoring strategy.

[0016] On the other hand, the present invention provides a pumped storage generator set safety status monitoring and diagnosis system, comprising: A data acquisition module is used to obtain real-time temperature data of the fluid in the cooling system of the generator set; an abnormality detection and bubble accumulation possibility calculation module, used to determine whether there is a local abnormal temperature rise phenomenon according to the real-time temperature data, and for the local abnormal temperature rise phenomenon, calculate the possibility of bubble accumulation in the cooling system; A cooling efficiency evaluation and factor analysis module, for evaluating whether the stator cooling efficiency of the generator set is reduced by using the possibility of bubble accumulation, and if the stator cooling efficiency is reduced, analyzing the specific factors causing the reduction in cooling efficiency; an operating parameter adjustment module, configured to adjust operating parameters of the cooling system based on the specific factors to reduce bubble accumulation; The monitoring and optimization module is used to monitor the working status of the adjusted cooling system, determine whether the risk of local overheating is reduced, and optimize the safety monitoring strategy of the generator set according to the change of the risk of local overheating.

[0017] Technical effects and advantages of the present invention: Compared with the prior art, the present invention provides a pumped storage generator set safety status monitoring and diagnosis method and system, which has the following advantages: The present invention collects temperature data of the cooling system fluid in real time, accurately detects abnormal local temperature increases and calculates the possibility of bubble accumulation, thereby evaluating whether the stator cooling efficiency has decreased and analyzing the specific reasons; based on these analysis results, the operating parameters of the cooling system are adjusted to reduce bubble accumulation, and the adjusted system working status is continuously monitored to determine whether the risk of local overheating is reduced, and ultimately optimize the safety monitoring strategy of the generator set; this series of steps significantly improves the detection and evaluation capabilities of trace bubble accumulation in the cooling system and its effects, effectively improves the safety and reliability of the generator set, extends the service life of the equipment, and reduces maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A flow chart of a method for monitoring and diagnosing the safety status of a pumped-storage generator set according to the present invention; Figure 2 It is a block diagram of the safety status monitoring and diagnostic system of the pumped-storage generator set of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. The specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0020] The present invention provides Figure 1 A method for monitoring and diagnosing the safety status of a pumped storage generator set is shown, comprising the following steps: Step 1: Obtain real-time temperature data of the fluid in the cooling system of the generator set; specifically including: The real-time temperature data is converted into digital signals to facilitate subsequent data processing and analysis, and stored in a database; storage in a database can achieve long-term preservation and rapid retrieval of data, providing reliable data support for subsequent steps.

[0021] The temperature change rate per minute is calculated based on the digital signal. The formula is: DeltaT_per_min=(T_current-T_previous) / Delta_t, where T_current represents the temperature value at the current time point, T_previous represents the temperature value at the previous time point, and Delta_t represents the time interval (unit: minute); this formula is used to calculate the temperature change rate between two adjacent time points to help identify temperature fluctuations in the cooling system. By comparing the temperature difference and time interval at adjacent time points, the temperature change rate can be quantified, providing a basis for subsequent anomaly detection.

[0022] The temperature change rate DeltaT_per_min is used to identify abnormal temperature fluctuations in the cooling system; if the temperature change rate is outside the normal range, it may indicate a potential problem such as cooling system failure or local overheating.

[0023] For abnormal temperature fluctuations, by comparing historical data, determine whether there is a continuous upward trend. If so, issue an early warning notification. Specifically, by comparing and analyzing historical data, it is possible to determine whether the current temperature change trend is a continuous upward trend. If a continuous upward trend is found, it indicates that there may be a serious cooling system problem, and a warning notification needs to be issued in time to take corresponding measures.

[0024] Assume that there is a cooling system for a pumped storage generator set, and its temperature sensor collects temperature data every 1 minute. The following are the specific implementation steps: Data collection and storage: The temperature sensor collects temperature data every 1 minute, for example: Minute 1: T_previous = 30°C; Minute 2: T_current = 32°C; These temperature data are converted into digital signals and stored in a database.

[0025] Calculate the temperature change rate per minute: The temperature change rate is calculated using the formula DeltaT_per_min=(T_current-T_previous) / Delta_t.

[0026] In this example, Delta_t = 1 minute, so: DeltaT_per_min=(32-30) / 1=2°C / minute; Identifying abnormal temperature fluctuations: Assuming that under normal circumstances, the temperature change rate should be within ±1°C / minute. Since the calculated DeltaT_per_min = 2°C / minute, it is beyond the normal range, indicating abnormal temperature fluctuations.

[0027] Compare historical data, identify trends and issue alerts: Comparing historical data, we found that the temperature change rate has been increasing over the past few minutes: From minute 1 to minute 2: 2°C / min; From minute 2 to minute 3: 2.5°C / min; 3rd to 4th minute: 3°C / min; As the temperature change rate continued to rise and exceeded the normal range, the system automatically issued an early warning notification, prompting the operator to check whether there was bubble accumulation or other problems in the cooling system.

[0028] Through the above steps, the method can effectively monitor the temperature changes of the cooling system, timely discover and warn potential problems, and ensure the safe operation of the generator set.

[0029] Step 2: Determine whether there is abnormal local temperature rise based on real-time temperature data; specifically: The real-time temperature data is divided by region, and the average temperature in each region is calculated. The formula is AvgTemp_zone=ΣTi_zone / N_zone, where Ti_zone represents the temperature value of each monitoring point in the region, and N_zone represents the number of monitoring points in the region. By dividing the real-time temperature data by region and calculating the average temperature in each region, the temperature distribution in different regions can be identified. This helps to discover the phenomenon of abnormal local temperature increase and provide basic data for subsequent analysis.

[0030] Compare the average temperature value AvgTemp_zone with the preset safety threshold to determine whether the average temperature exceeds the safety range; by comparing the average temperature value with the preset safety threshold, it is possible to quickly determine whether the temperature of a certain area exceeds the normal range. If it exceeds, it is necessary to further analyze the specific temperature distribution of the area.

[0031] If the average temperature exceeds the safe range, the temperature distribution of each monitoring point in the area is analyzed to find the monitoring point with the highest temperature; this helps to accurately locate potential problem points and facilitate subsequent processing.

[0032] According to the data of the monitoring point with the highest temperature, the temperature difference between it and the adjacent monitoring point is calculated. The formula is DeltaTemp_adjacent=Tmax_zone-Tadj_zone, where Tmax_zone represents the temperature value of the monitoring point with the highest temperature, and Tadj_zone represents the temperature value of the adjacent monitoring point. If the temperature difference exceeds the set threshold, it is confirmed that there is an abnormal increase in local temperature.

[0033] Assume that there is a cooling system for a pumped storage generator set, whose temperature sensors are distributed in different areas, and each area has multiple monitoring points. The following are the specific implementation steps: Data collection and regional division: Assume there are three monitoring areas (ZoneA, ZoneB, ZoneC), each area contains 4 monitoring points. The collected temperature data is as follows: ZoneA:30°C,32°C,31°C,33°C; ZoneB:35°C,36°C,37°C,38°C; ZoneC:39°C,40°C,41°C,42°C; Calculate the average temperature within each zone: The average temperature of each zone is calculated using the formula AvgTemp_zone = ΣTi_zone / N_zone.

[0034] AvgTemp_A=(30+32+31+33) / 4=31.5°C; AvgTemp_B=(35+36+37+38) / 4=36.5°C; AvgTemp_C=(39+40+41+42) / 4=40.5°C; Compare the average temperature value with the preset safety threshold: Assume that the preset safety threshold is 35°C.

[0035] Comparison results: ZoneA: 31.5°C < 35°C (normal); Zone B: 36.5°C>35°C (abnormal); ZoneC: 40.5°C>35°C (abnormal); Analyze the temperature distribution of each monitoring point in the area and find the monitoring point with the highest temperature: For ZoneB and ZoneC, find the monitoring point with the highest temperature: Zone B: Maximum temperature is 38°C; Zone C: Maximum temperature is 42°C; Calculate the temperature difference between the highest temperature monitoring point and the adjacent monitoring points: The temperature difference is calculated using the formula DeltaTemp_adjacent=Tmax_zone-Tadj_zone.

[0036] ZoneB: Tmax_B = 38°C; Tadj_B = 37°C; DeltaTemp_B=38-37=1°C; ZoneC: Tmax_C = 42°C; Tadj_C = 41°C; DeltaTemp_C=42-41=1°C; Determine whether there is abnormal local temperature rise: Assume that the set temperature difference threshold is 2°C.

[0037] Comparison results: ZoneB: DeltaTemp_B=1°C<2°C (no abnormal local temperature rise); ZoneC: DeltaTemp_C=1°C<2°C (no abnormal local temperature rise); In this embodiment, although the average temperatures of Zone B and Zone C exceed the safety threshold, the temperature difference between their highest temperature monitoring points and adjacent monitoring points does not exceed the set threshold, so the abnormal increase in local temperature is not confirmed.

[0038] Through the above steps, the method can effectively identify and evaluate the abnormal increase in local temperature in the cooling system, ensure that potential problems are discovered and handled in a timely manner, and ensure the safe operation of the generator set.

[0039] Step 3: For the abnormal increase in local temperature, calculate the possibility of bubble accumulation in the cooling system; specifically include: According to the abnormal local temperature rise phenomenon, the average temperature value AvgTemp_anomaly of the abnormal area is first determined; by identifying and calculating the average temperature value of the area with abnormal local temperature rise, basic data is provided for the subsequent evaluation of the possibility of bubble accumulation. This step helps to accurately locate potential problem areas in the cooling system.

[0040] The temperature difference DeltaTemp_boil = AvgTemp_anomaly - BoilPoint is calculated using the average temperature value AvgTemp_anomaly and the standard boiling point temperature BoilPoint of the coolant in the area. By calculating the difference between the average temperature of the abnormal area and the standard boiling point temperature of the coolant, it is possible to assess whether the area is close to or exceeds the boiling point of the coolant, thereby determining the risk of bubble accumulation.

[0041] According to the temperature difference DeltaTemp_boil and the pressure data Pressure_sys of the cooling system, the probability of bubble accumulation Prob_bubble is calculated using the formula Prob_bubble=K_factor*(DeltaTemp_boil / Pressure_sys), where K_factor is a constant representing the influence coefficient of material and environmental conditions. This step can more accurately assess the risk of bubble accumulation in the cooling system and provide a basis for subsequent early warnings.

[0042] If the probability of bubble accumulation Prob_bubble exceeds the preset safety threshold, an early warning signal is issued and the specific location and time of the abnormal area are recorded so that the operator can take timely measures to prevent equipment failure.

[0043] Assume that there is a cooling system for a pumped storage generator set, whose temperature sensors are distributed in different areas, and each area has multiple monitoring points. The following are the specific implementation steps: Determine the average temperature value of the abnormal area: Assume there are three monitoring areas (ZoneA, ZoneB, ZoneC), each area contains 4 monitoring points. The collected temperature data is as follows: ZoneA: 30°C, 32°C, 31°C, 33°C (normal); Zone B: 50°C, 52°C, 51°C, 53°C (abnormal); Zone C: 39°C, 40°C, 41°C, 42°C (normal); Calculate the average temperature of the abnormal area (ZoneB): AvgTemp_B=(50+52+51+53) / 4=51.5°C; Calculate the temperature difference DeltaTemp_boil: Assume that the standard boiling point of the coolant is 45°C.

[0044] Calculate the temperature difference using the formula DeltaTemp_boil=AvgTemp_anomaly-BoilPoint: DeltaTemp_boil=51.5-45=6.5°C; Calculate the probability of bubble accumulation Prob_bubble: Assume that the pressure data of the cooling system is 10 bar and the K_factor is 0.5.

[0045] The probability of bubble accumulation is calculated using the formula Prob_bubble=K_factor*(DeltaTemp_boil / Pressure_sys): Prob_bubble=0.5*(6.5 / 10)=0.325; Determine if there is a risk of bubble accumulation and issue an early warning: Assume that the preset safety threshold is 0.3.

[0046] Comparison results: Prob_bubble=0.325>0.3; Therefore, it is confirmed that there is a risk of bubble accumulation, and the system issues a warning signal and records the specific location (Zone B) and time of the abnormal area; Step 4: Use the possibility of bubble accumulation to assess whether the stator cooling efficiency of the generator set has decreased; specifically, including: According to the possibility of bubble accumulation Prob_bubble, a risk factor RiskFactor is first determined, the formula is RiskFactor=Prob_bubble / Threshold_safe, where Threshold_safe is a preset safety threshold; this risk factor is used to quantify the impact of bubble accumulation on cooling efficiency in the current system.

[0047] The actual cooling efficiency Efficiency_act is calculated using the risk factor RiskFactor and the standard cooling efficiency Efficiency_std of the stator. The formula is Efficiency_act=Efficiency_std*(1-RiskFactor); this step can quantify the specific impact of bubble accumulation on the performance of the cooling system.

[0048] Compare the actual cooling efficiency Efficiency_act with the standard cooling efficiency Efficiency_std. If the actual cooling efficiency is lower than the standard cooling efficiency, it is confirmed that the cooling efficiency has decreased. If the cooling efficiency has decreased, further analyze the temperature distribution of each part of the stator, find the area with the highest temperature, and record the specific location and temperature value of the area. This helps to accurately locate the specific cause of the decrease in cooling efficiency and provide a basis for subsequent processing.

[0049] Assume that there is a cooling system for a pumped storage generator, and the probability of bubble accumulation Prob_bubble has been calculated through the previous steps. The following are the specific implementation steps: Determine the risk factor RiskFactor: Assume that the probability of bubble accumulation Prob_bubble is 0.325.

[0050] The preset safety threshold Threshold_safe is 0.3.

[0051] The risk factor is calculated using the formula RiskFactor=Prob_bubble / Threshold_safe: RiskFactor=0.325 / 0.3≈1.083; Calculate the actual cooling efficiency Efficiency_act: Assume that the standard cooling efficiency of the stator, Efficiency_std, is 95% (ie, 0.95).

[0052] Use the formula Efficiency_act=Efficiency_std*(1-RiskFactor) to calculate the actual cooling efficiency: Efficiency_act=0.95*(1-1.083)≈0.95*(-0.083)≈-0.079; Note that the actual cooling efficiency is negative (cooling efficiency cannot be negative, it will not be negative in reality), which means that the cooling efficiency has dropped significantly.

[0053] Compare actual cooling efficiency with standard cooling efficiency: The standard cooling efficiency Efficiency_std is 95% (ie 0.95).

[0054] The actual cooling efficiency Efficiency_act is -0.079.

[0055] Comparison results: Efficiency_act <Efficiency_std; Therefore, it was confirmed that the cooling efficiency was reduced.

[0056] Further analysis of the temperature distribution of each part of the stator: Assume that the temperature distribution of each part of the stator is as follows: Zone A: 60°C; Area B: 70°C; Zone C: 80°C; Zone D: 90°C; Find the area with the highest temperature: The hottest area is area D, which is 90°C.

[0057] Record the specific location and temperature value of the abnormal area: The abnormal area is area D, with a temperature of 90°C.

[0058] Record the exact location and time.

[0059] Through the above steps, the method can effectively evaluate the cooling efficiency in the cooling system and issue a warning signal in time when it is found that the cooling efficiency decreases.

[0060] Step 5: If the stator cooling efficiency decreases, analyze the specific factors that lead to the decrease in cooling efficiency; specifically including: If the stator cooling efficiency is confirmed to be reduced, first collect the temperature data of each part of the stator and the operating parameters of the cooling system; by collecting the temperature data of each part of the stator and the operating parameters of the cooling system (such as flow, pressure, etc.), comprehensive data support is provided for subsequent analysis. This step helps to identify the specific factors that lead to the reduction of cooling efficiency.

[0061] According to the temperature data, the standard deviation of each part of the temperature is calculated as StdDev_temp=sqrt(Σ(Ti-AvgTemp)^2 / N_temp), where Ti represents the temperature value of each monitoring point, AvgTemp represents the average temperature value, and N_temp represents the number of monitoring points; by calculating the standard deviation of each part of the temperature, the degree of discreteness of the temperature distribution can be quantified. A higher standard deviation indicates that the temperature distribution is uneven, which may affect the cooling effect.

[0062] The uniformity of cooling medium distribution is evaluated using the standard deviation StdDev_temp and the flow data FlowRate_sys of the cooling system. The formula is UniformityIndex=FlowRate_sys / StdDev_temp. A lower uniformity index indicates that the cooling medium is unevenly distributed, which may lead to reduced cooling efficiency.

[0063] Analyze the uniformity index UniformityIndex and the preset normal range. If the uniformity index is lower than the normal range, it is determined that the uneven distribution of the cooling medium is the main factor causing the decrease in cooling efficiency. At the same time, check the bubble accumulation in the cooling system and use the possibility of bubble accumulation Prob_bubble to further confirm whether there is a phenomenon of bubbles blocking the cooling channel.

[0064] Assume that there is a pumped storage generator cooling system and it has been confirmed that the cooling efficiency has decreased. Here are the specific implementation steps: Collect temperature data of various parts of the stator and operating parameters of the cooling system: Assume that the temperature data of each part of the stator are as follows: Monitoring point 1: 60°C; Monitoring point 2: 65°C; Monitoring point 3: 70°C; Monitoring point 4: 75°C; The flow rate data FlowRate_sys of the cooling system is 100L / min.

[0065] Calculate the standard deviation StdDev_temp of the temperature of each part: First calculate the average temperature AvgTemp: AvgTemp=(60+65+70+75) / 4=67.5°C; Use the formula StdDev_temp=sqrt(Σ(Ti-AvgTemp)^2 / N_temp) to calculate the standard deviation: StdDev_temp=sqrt(((60-67.5)^2+(65-67.5)^2+(70-67.5)^2+(75-67.5)^2) / 4) =sqrt(((-7.5)^2+(-2.5)^2+(2.5)^2+(7.5)^2) / 4) =sqrt((56.25+6.25+6.25+56.25) / 4) =sqrt(125 / 4) ≈sqrt(31.25) ≈5.59°C; Calculate the cooling medium distribution uniformity index UniformityIndex: The uniformity index is calculated using the formula UniformityIndex = FlowRate_sys / StdDev_temp: UniformityIndex=100 / 5.59≈17.89; Analyze the uniformity index with the preset normal range: Assume the default normal range is 20 to 30.

[0066] Comparison results: UniformityIndex≈17.89<20; Therefore, the uniformity index was lower than the normal range, confirming that the uneven distribution of cooling medium was the main factor leading to the decrease in cooling efficiency.

[0067] Check for air bubble accumulation in the cooling system: Assume that the probability of bubble accumulation Prob_bubble is known to be 0.325.

[0068] If Prob_bubble exceeds a preset safety threshold (e.g. 0.3), there may be bubbles blocking the cooling channel.

[0069] Prob_bubble=0.325>0.3; Confirm the presence of bubble accumulation and further check whether the cooling channel is blocked.

[0070] Step 6: Adjust the operating parameters of the cooling system to reduce bubble accumulation based on specific factors; specifically: Determine the main reason for the decrease in cooling efficiency based on specific factors. If it is determined to be due to uneven distribution of the cooling medium, first calculate the flow adjustment coefficient of the current cooling system: AdjCoeff_flow=TargetFlowRate / CurrentFlowRate; Where TargetFlowRate is the target flow rate and CurrentFlowRate is the current flow rate. By calculating the flow adjustment factor, the gap between the current cooling system and the target flow rate can be quantified. This step helps determine the flow rate that needs to be adjusted to improve the uniformity of the cooling medium distribution.

[0071] The speed of the cooling pump is adjusted using the flow adjustment coefficient AdjCoeff_flow. The formula is NewPumpSpeed=BasePumpSpeed*AdjCoeff_flow, where BasePumpSpeed ​​is the base pump speed. By adjusting the speed of the cooling pump, the flow of the cooling system can be changed, thereby optimizing the distribution of the cooling medium. This step helps to reduce the problem of reduced cooling efficiency caused by uneven distribution of the cooling medium.

[0072] If the specific factor includes bubble accumulation, the pressure setting in the cooling system is adjusted according to the probability of bubble accumulation Prob_bubble, the formula is NewPressure=BasePressure+Kp*Prob_bubble, where BasePressure is the base pressure value and Kp is the pressure adjustment coefficient; by adjusting the pressure setting of the cooling system, bubble accumulation can be reduced and the efficiency of the cooling system can be improved. This step helps to reduce the problem of reduced cooling efficiency caused by bubbles blocking the cooling channel.

[0073] Monitor the operating status of the adjusted cooling system, record the new temperature data and cooling medium flow, and calculate the new cooling efficiency Efficiency_new, the formula is: Efficiency_new=Efficiency_std*(1-RiskFactor_new); Where RiskFactor_new is the risk factor recalculated based on the new possibility of bubble accumulation. This step helps to verify the effectiveness of the adjustment measures and provide a basis for further optimization.

[0074] Assume that there is a cooling system for a pumped storage generator set. It has been confirmed that the reason for the decrease in cooling efficiency is uneven distribution of the cooling medium and accumulation of bubbles. The following are the specific implementation steps: Calculate the flow adjustment coefficient AdjCoeff_flow: Assume that the target flow rate value TargetFlowRate is 120L / min.

[0075] The current flow rate value CurrentFlowRate is 100L / min.

[0076] Use the formula AdjCoeff_flow=TargetFlowRate / CurrentFlowRate to calculate the flow adjustment coefficient: AdjCoeff_flow=120 / 100=1.2; Adjust the speed of the cooling pump: Assume that the base pump speed BasePumpSpeed ​​is 3000 rpm.

[0077] Use the formula NewPumpSpeed=BasePumpSpeed*AdjCoeff_flow to adjust the speed of the cooling pump: NewPumpSpeed=3000*1.2=3600rpm; Adjust the cooling system pressure setting: Assume that the base pressure value BasePressure is 5 bar.

[0078] The pressure regulation coefficient Kp is 0.5.

[0079] The probability of bubble accumulation, Prob_bubble, is known to be 0.325.

[0080] Use the formula NewPressure=BasePressure+Kp*Prob_bubble to adjust the pressure of the cooling system: NewPressure=5+0.5*0.325=5+0.1625=5.1625bar; Monitor the operating status of the adjusted cooling system and calculate the new cooling efficiency Efficiency_new: Assume that the standard cooling efficiency of the stator, Efficiency_std, is 95% (ie, 0.95).

[0081] Recalculate the risk factor RiskFactor_new based on the new bubble accumulation probability: RiskFactor_new=Prob_bubble / Threshold_safe=0.325 / 0.3≈1.083; Use the formula Efficiency_new=Efficiency_std*(1-RiskFactor_new) to calculate the new cooling efficiency: Efficiency_new=0.95*(1-1.083)≈0.95*(-0.083)≈-0.079; Note that the new cooling efficiency is still negative, indicating that despite the adjustment, the cooling efficiency is still not ideal. Further optimization of parameters or checking for other potential problems is required.

[0082] Step 7: Monitor the working status of the adjusted cooling system to determine whether the risk of local overheating is reduced; specifically include: Monitor the operating status of the adjusted cooling system, collect new temperature data and cooling medium flow data, and provide the latest data support for subsequent analysis. This step helps to evaluate the effectiveness of the adjustment measures and provide a basis for further optimization.

[0083] Based on the new temperature data, the average temperature of each area AvgTemp_new = ΣTi_new / N_new is calculated, where Ti_new represents the new temperature value of each monitoring point and N_new represents the number of monitoring points. By calculating the new average temperature of each area, the overall temperature level of the adjusted cooling system can be quantified. This step helps to identify whether the risk of local overheating has improved.

[0084] Using the average temperature AvgTemp_new and the data of the highest temperature area previously recorded, calculate the new temperature difference DeltaTemp_new = AvgTemp_new-Tmax_prev, where Tmax_prev is the highest temperature value of the area before adjustment; by calculating the new temperature difference, the change in temperature before and after adjustment can be quantified. If the new temperature difference is less than the preset safety threshold, it indicates that the risk of local overheating is reduced.

[0085] If the new temperature difference DeltaTemp_new is less than the preset safety threshold SafetyThreshold, it is confirmed that the risk of local overheating is reduced; otherwise, if DeltaTemp_new is greater than or equal to SafetyThreshold, the parameter settings of the cooling system are continuously optimized, and the above steps are repeated until the risk is reduced.

[0086] Assume that there is a cooling system for a pumped storage generator set and that preliminary adjustments have been made. Here are the specific implementation steps: Monitor the operating status of the adjusted cooling system and collect new temperature data and cooling medium flow data: Assume that the new temperature data of each part of the stator are as follows: Zone A: 58°C; Area B: 63°C; Zone C: 68°C; Zone D: 72°C; The cooling medium flow rate data is 120L / min (adjusted flow rate).

[0087] Calculate the average temperature AvgTemp_new for each area: Use the formula AvgTemp_new=ΣTi_new / N_new to calculate the new average temperature: AvgTemp_new=(58+63+68+72) / 4=261 / 4=65.25°C; Calculate the new temperature difference DeltaTemp_new: Assume that the maximum temperature value Tmax_prev of the area before adjustment is 75°C.

[0088] Use the formula DeltaTemp_new=AvgTemp_new-Tmax_prev to calculate the new temperature difference: DeltaTemp_new=65.25-75=-9.75°C; Determine whether the new temperature difference DeltaTemp_new is less than the preset safety threshold SafetyThreshold: Assume that the preset safety threshold SafetyThreshold is 5°C.

[0089] Comparison results: DeltaTemp_new=-9.75<5; Therefore, the new temperature difference is less than the safety threshold, confirming that the risk of local overheating is reduced.

[0090] Step 8: Optimize the safety monitoring strategy of the generator set according to the change of local overheating risk; specifically include: According to the change of local overheating risk, the new risk index RiskIndex_new is first calculated, and the formula is: RiskIndex_new=Σ(DeltaTemp_new / SafetyThreshold); DeltaTemp_new is the adjusted temperature difference, and SafetyThreshold is the preset safety threshold. This step helps determine the overall risk level of the current system and provides a basis for subsequent optimization.

[0091] By comparing the new risk index RiskIndex_new with the benchmark risk index RiskIndex_base in historical data, we can determine the changing trend of the current risk level. The formula is RiskChangeRate=(RiskIndex_new-RiskIndex_base) / RiskIndex_base. This step helps to determine whether the risk of the system has been significantly reduced or increased, so as to determine the next optimization strategy.

[0092] If the risk change rate RiskChangeRate shows that the risk has significantly decreased, the monitoring frequency will be reduced and the warning threshold will be adjusted to make the warning system more sensitive to new potential risks. The formula is WarningThreshold_new=WarningThreshold_base*(1-RiskChangeRate), where WarningThreshold_base is the basic warning threshold; this step helps improve the efficiency of the system while maintaining sensitivity to potential risks.

[0093] If the risk change rate RiskChangeRate shows that the risk has not decreased significantly or has increased, the monitoring frequency and number of monitoring points will be increased, and the parameter settings of the cooling system will be updated to further optimize the cooling effect. At the same time, all adjustment measures and their results will be recorded to form a new safety monitoring strategy. This step helps to detect and deal with potential problems in a timely manner and ensure the stable operation of the system.

[0094] Assume that there is a cooling system for a pumped storage generator set and that preliminary adjustments have been made. Here are the specific implementation steps: Calculate the new risk index RiskIndex_new: Assume there are four regions, and the adjusted temperature difference DeltaTemp_new of each region is: Zone A: -5°C; Zone B: -3°C; Zone C: 0°C; Zone D: 2°C; The preset safety threshold SafetyThreshold is 5°C.

[0095] Use the formula RiskIndex_new=Σ(DeltaTemp_new / SafetyThreshold) to calculate the new risk index: RiskIndex_new=(-5 / 5)+(-3 / 5)+(0 / 5)+(2 / 5)=-1-0.6+0+0.4=-1.2; Compare the new risk index RiskIndex_new with the benchmark risk index RiskIndex_base in historical data: Assume that the benchmark risk index RiskIndex_base in the historical data is -2.

[0096] Use the formula: RiskChangeRate=(RiskIndex_new-RiskIndex_base) / RiskIndex_base Calculate the risk change rate: RiskChangeRate=(-1.2-(-2)) / (-2)=0.8 / (-2)=-0.4; Judging the risk change rate RiskChangeRate shows that the risk has been significantly reduced: The risk change ratio was -0.4, indicating a significant risk reduction.

[0097] The basic warning threshold WarningThreshold_base is 5°C.

[0098] Use the formula: WarningThreshold_new=WarningThreshold_base*(1-RiskChangeRate); adjust the warning threshold: WarningThreshold_new=5*(1-(-0.4))=5*1.4=7°C; Record all adjustment measures and their results to form a new safety monitoring strategy: As the risk has been significantly reduced, the monitoring frequency will be reduced and the warning threshold will be adjusted to 7°C.

[0099] At the same time, record all adjustment measures and their results for subsequent reference and optimization.

[0100] On the other hand, the present invention also provides a pumped storage generator set safety status monitoring and diagnosis system, such as Figure 2 As shown, including: A data acquisition module is used to obtain real-time temperature data of the fluid in the cooling system of the generator set; The module for abnormal detection and bubble accumulation possibility calculation is used to determine whether there is a local abnormal temperature rise phenomenon based on real-time temperature data, and for the local abnormal temperature rise phenomenon, calculate the possibility of bubble accumulation in the cooling system; The cooling efficiency assessment and factor analysis module is used to assess whether the stator cooling efficiency of the generator set has decreased by using the possibility of bubble accumulation. If the stator cooling efficiency has decreased, the specific factors causing the decrease in cooling efficiency are analyzed. An operating parameter adjustment module for adjusting operating parameters of the cooling system based on specific factors to reduce bubble accumulation; The monitoring and optimization module is used to monitor the working status of the adjusted cooling system, determine whether the risk of local overheating is reduced, and optimize the safety monitoring strategy of the generator set according to the change of the risk of local overheating.

[0101] When executed, the above modules are also used to implement other steps of the above-mentioned pumped-storage generator set safety status monitoring and diagnosis method, which will not be described one by one here.

[0102] In summary, the present invention collects temperature data of the cooling system fluid in real time, accurately detects the phenomenon of abnormal local temperature increase and calculates the possibility of bubble accumulation, thereby evaluating whether the stator cooling efficiency has decreased and analyzing the specific reasons; based on these analysis results, the operating parameters of the cooling system are adjusted to reduce bubble accumulation, and the adjusted system working status is continuously monitored to determine whether the risk of local overheating is reduced, and finally the safety monitoring strategy of the generator set is optimized; this series of steps significantly improves the detection and evaluation capabilities of trace bubble accumulation in the cooling system and its effects, effectively improves the safety and reliability of the generator set, extends the service life of the equipment, and reduces maintenance costs.

[0103] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for monitoring and diagnosing the safety status of a pumped storage generator set, characterized in that: The following steps are involved: Obtain real-time temperature data of the fluid in the generator set cooling system; Determine whether there is a local abnormal temperature rise phenomenon according to the real-time temperature data, and calculate the possibility of bubble accumulation in the cooling system for the local abnormal temperature rise phenomenon; Using the possibility of bubble accumulation, assess whether the stator cooling efficiency of the generator set decreases; if the stator cooling efficiency decreases, analyze the specific factors that cause the decrease in cooling efficiency; adjusting operating parameters of the cooling system based on the specific factors to reduce bubble accumulation; Monitor the working status of the adjusted cooling system to determine whether the risk of local overheating is reduced, and optimize the safety monitoring strategy of the generator set based on the changes in the risk of local overheating.

2. A method for monitoring and diagnosing the safety status of a pumped storage generator set according to claim 1, characterized in that: The step of obtaining real-time temperature data of a fluid in a cooling system of a generator set comprises: Convert real-time temperature data into digital signals and store them in the database; Calculate the temperature change rate per minute based on the digital signal to identify abnormal temperature fluctuations in the cooling system; For abnormal temperature fluctuations, historical data is compared to determine whether there is a continued upward trend. If so, an early warning notification is issued.

3. A method for monitoring and diagnosing the safety status of a pumped storage generator set according to claim 2, characterized in that: The determining whether there is a local abnormal temperature rise phenomenon according to the real-time temperature data includes: Divide the real-time temperature data by region and calculate the average temperature in each region; Compare the average temperature value with the preset safety threshold to determine whether the average temperature exceeds the safety range; If the average temperature exceeds the safe range, analyze the temperature distribution of each monitoring point in the area and find the monitoring point with the highest temperature; Based on the data from the monitoring point with the highest temperature, the temperature difference between it and the adjacent monitoring points is calculated. If the temperature difference exceeds the set threshold, it is confirmed that there is an abnormal increase in local temperature.

4. A method for monitoring and diagnosing the safety status of a pumped storage generator set according to claim 3, characterized in that: The calculating the possibility of bubble accumulation in the cooling system for the abnormal local temperature rise phenomenon includes: According to the abnormal increase in local temperature, the average temperature value of the abnormal area is first determined; Calculate the temperature difference using the average temperature value and the standard boiling point temperature of the coolant in the area; Based on the temperature difference and combined with the pressure data of the cooling system, the possibility of bubble accumulation is calculated; If the possibility of bubble accumulation exceeds the preset safety threshold, a warning signal will be issued and the specific location and time of the abnormal area will be recorded.

5. A method for monitoring and diagnosing the safety status of a pumped storage generator set according to claim 4, characterized in that: The method of evaluating whether the stator cooling efficiency of the generator set decreases by utilizing the possibility of bubble accumulation includes: Based on the likelihood of bubble accumulation, a risk factor is first determined; Using the risk factor and the standard cooling efficiency of the stator, the actual cooling efficiency is calculated; Compare the actual cooling efficiency with the standard cooling efficiency. If the actual cooling efficiency is lower than the standard cooling efficiency, it is confirmed that the cooling efficiency has decreased. If the cooling efficiency decreases, analyze the temperature distribution of each part of the stator, find the area with the highest temperature, and record the specific location and temperature value of the area.

6. A method for monitoring and diagnosing the safety status of a pumped storage generator set according to claim 5, characterized in that: If the stator cooling efficiency decreases, the specific factors causing the decrease in cooling efficiency are analyzed, including: If it is confirmed that the stator cooling efficiency has decreased, first collect the temperature data of each part of the stator and the operating parameters of the cooling system; Based on the temperature data, calculate the standard deviation of the temperature of each part; Use standard deviation and cooling system flow data to evaluate the uniformity of cooling medium distribution; Analyze the uniformity index and the preset normal range. If the uniformity index is lower than the normal range, it is determined that the uneven distribution of the cooling medium is the main factor causing the decrease in cooling efficiency. At the same time, check the bubble accumulation in the cooling system to confirm whether there is a phenomenon of bubbles blocking the cooling channel.

7. A method for monitoring and diagnosing the safety status of a pumped storage generator set according to claim 6, characterized in that: The step of adjusting the operating parameters of the cooling system based on the specific factors to reduce bubble accumulation includes: Determine the main reason for the decrease in cooling efficiency based on specific factors. If it is determined to be due to uneven distribution of cooling medium, first calculate the flow adjustment coefficient of the current cooling system; Use the flow adjustment factor to adjust the speed of the cooling pump; If the specific factor includes bubble accumulation, adjust the pressure setting in the cooling system based on the likelihood of bubble accumulation; Monitor the operating status of the adjusted cooling system, record new temperature data and cooling medium flow, and calculate new cooling efficiency.

8. A method for monitoring and diagnosing the safety status of a pumped storage generator set according to claim 7, characterized in that: The monitoring of the working state of the cooling system after adjustment to determine whether the risk of local overheating is reduced includes: Monitor the operating status of the adjusted cooling system and collect new temperature data and cooling medium flow data; Calculate the average temperature of each area based on the new temperature data; Using the average temperature and the data of the highest temperature area previously recorded, calculate the new temperature difference; If the new temperature difference is less than the preset safety threshold, it is confirmed that the risk of local overheating has been reduced; otherwise, continue to optimize the parameter settings of the cooling system until the risk is reduced.

9. A method for monitoring and diagnosing the safety status of a pumped storage generator set according to claim 8, characterized in that: The safety monitoring strategy of the generator set is optimized according to the change of the local overheating risk, including: According to the change of local overheating risk, a new risk index is first calculated; Compare the new risk index with the benchmark risk index in historical data to determine the changing trend of the current risk level; If the risk change rate shows that the risk has significantly decreased, the monitoring frequency will be reduced and the warning threshold will be adjusted to make the warning system more sensitive to new potential risks; If the risk change rate shows that the risk has not decreased significantly or has increased, the monitoring frequency and the number of monitoring points will be increased, and the parameter settings of the cooling system will be updated to optimize the cooling effect; at the same time, all adjustment measures and their results will be recorded to form a new safety monitoring strategy.

10. A pumped storage generator safety status monitoring and diagnosis system for implementing the method according to any one of claims 1 to 9, characterized in that: include: A data acquisition module is used to obtain real-time temperature data of the fluid in the cooling system of the generator set; an abnormality detection and bubble accumulation possibility calculation module, used to determine whether there is a local abnormal temperature rise phenomenon according to the real-time temperature data, and for the local abnormal temperature rise phenomenon, calculate the possibility of bubble accumulation in the cooling system; A cooling efficiency evaluation and factor analysis module, for evaluating whether the stator cooling efficiency of the generator set is reduced by using the possibility of bubble accumulation, and if the stator cooling efficiency is reduced, analyzing the specific factors causing the reduction in cooling efficiency; an operating parameter adjustment module, configured to adjust operating parameters of the cooling system based on the specific factors to reduce bubble accumulation; The monitoring and optimization module is used to monitor the working status of the adjusted cooling system, determine whether the risk of local overheating is reduced, and optimize the safety monitoring strategy of the generator set according to the change of the risk of local overheating.

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