Water turbine operation optimization control method and system

Abstract

The invention provides a water turbine operation optimization control method and system, and the method comprises the steps: obtaining operation parameters of a water turbine set, and carrying out the preprocessing of the operation parameters, and obtaining the preprocessing data; based on the preprocessed data, determining an abrasion risk index and a cavitation probability; based on the abrasion risk index and the cavitation probability, generating an optimization control strategy through an intelligent control algorithm; according to the optimization control strategy, operation of the water turbine set is controlled, quantification of abrasion and cavitation erosion risks is achieved, the optimization control strategy is generated based on the quantified risk index and cavitation erosion probability, then dynamic balance among the water turbine operation efficiency, the equipment service life and the operation stability is achieved, the water turbine operation economical efficiency is improved, and the energy consumption of the water turbine set is reduced. The service life of key parts is prolonged, and the risk of unplanned shutdown caused by equipment damage is reduced.

Description

Technical Field

[0001] This invention relates to the field of hydropower technology, and in particular to a method and system for optimizing the operation and control of a water turbine. Background Technology

[0002] Hydropower stations operating on rivers with high sediment content generally face severe erosion problems in their turbine-generator units. High-sediment-laden water flows cause continuous erosion and wear on flow components such as the runner and guide vanes, as well as rotating components such as the main shaft seal, leading to two major consequences: first, it directly causes component mass loss, disrupts rotor dynamic balance, and triggers abnormal unit vibration; second, it induces a series of cascading equipment defects, such as unit shaft tilting, accelerated failure of the main shaft seal, flooding of the water guide bearing, and exacerbated cavitation on the runner blades.

[0003] In existing technologies, countermeasures against erosion mainly focus on using anti-erosion materials, optimizing component structures, or relying on regular maintenance and post-incident treatment. At the operational control level, traditional strategies often rely on fixed operating procedures or make local adjustments only to a single parameter (such as vibration value), lacking integrated analysis and forward-looking judgment of multi-dimensional information such as inflow sediment conditions, real-time mechanical status of the unit, and grid load demand. This makes it difficult for the turbine to always operate within its optimal range under complex conditions, failing to effectively avoid erosion risks while sacrificing operating efficiency, resulting in high equipment maintenance costs and shortened service life.

[0004] Therefore, how to quantify the risks of abrasion and cavitation, and generate optimized control strategies based on the quantified risk index and cavitation probability, so as to achieve a dynamic balance between turbine operating efficiency, equipment life and operating stability, is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] This invention provides a method and system for optimizing the operation of a water turbine, which aims to achieve a dynamic balance between the operating efficiency, equipment lifespan, and operational stability of the water turbine, thereby improving the economic efficiency of water turbine operation, extending the service life of key components, and reducing the risk of unplanned downtime due to equipment damage.

[0006] On one hand, the present invention provides a method for optimizing the operation control of a water turbine, comprising: The operating parameters of the turbine unit are acquired and preprocessed to obtain preprocessed data. The preprocessed data includes inflow rate, sediment particle size distribution, sediment hardness, head, unit vibration, shaft inclination, main shaft sealing status, water guide bearing temperature, runner blade erosion status, load demand, frequency, and guide vane opening. Based on the preprocessed data, the abrasion risk index and cavitation probability are determined; Based on the abrasion risk index and the cavitation probability, an optimized control strategy is generated through an intelligent control algorithm. The operation of the turbine unit is controlled according to the optimized control strategy.

[0007] On the other hand, the present invention also provides a turbine operation optimization control system, which includes: The preprocessing module is used to acquire the operating parameters of the turbine unit and preprocess the operating parameters to obtain preprocessed data; wherein, the preprocessed data includes inflow rate, sediment particle size distribution, sediment hardness, head, unit vibration, shaft inclination, main shaft sealing status, water guide bearing temperature, runner blade erosion status, load demand, frequency, and guide vane opening. The determination module is used to determine the abrasion risk index and cavitation probability based on the preprocessed data; The optimization module is used to generate an optimized control strategy based on the abrasion risk index and the cavitation probability using an intelligent control algorithm. The control module is used to control the operation of the turbine unit according to the optimized control strategy.

[0008] The turbine operation optimization control method and system provided by this invention obtains sediment characteristics, mechanical state and real-time operating conditions by multi-dimensional preprocessing of the turbine unit's operating parameters, so as to calculate the erosion risk index and assess the cavitation probability. This realizes the quantification of erosion and cavitation risks, and generates an optimized control strategy based on the quantified risk index and cavitation probability. This achieves a dynamic balance between turbine operating efficiency, equipment life and operating stability, thereby improving the turbine's operating economy, extending the service life of key components, and reducing the risk of unplanned downtime due to equipment damage. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0010] Figure 1 This is a flowchart illustrating the turbine operation optimization control method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of the turbine operation optimization control system provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0011] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0012] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0013] Figure 1 This is a flowchart illustrating the turbine operation optimization control method provided in an embodiment of the present invention.

[0014] like Figure 1 As shown, the turbine operation optimization control method provided in this embodiment of the invention mainly includes the following steps: 101. Obtain the operating parameters of the turbine unit and preprocess the operating parameters to obtain preprocessed data; 102. Based on the preprocessed data, determine the erosion risk index and cavitation probability; 103. Based on the erosion risk index and the cavitation probability, an optimized control strategy is generated through an intelligent control algorithm; 104. Control the operation of the turbine unit according to the optimized control strategy.

[0015] In a specific implementation process, obtaining the operating parameters of a water turbine unit can refer to the process of monitoring the operating status of the water turbine in real time through sensors and data acquisition systems. For example, using a flow meter to measure the incoming water flow, a vibration sensor to collect the unit's vibration data, and a temperature sensor to monitor the temperature of the water guide bearing, etc., in order to obtain comprehensive operating information.

[0016] Preprocessed data refers to the standardized data set obtained after cleaning, normalizing and feature extraction of the original operating parameters. For example, converting water flow rate to standard units, performing statistical analysis on sediment particle size distribution, and performing frequency domain transformation on vibration signals are used for subsequent analysis.

[0017] The erosion risk index is a quantitative value used to assess the likelihood of damage to turbine components due to sediment abrasion and deterioration of mechanical condition. It can be calculated, for example, by combining the sediment-carrying capacity of water flow with the mechanical condition of the unit. The higher the value, the greater the risk.

[0018] Cavitation probability can refer to the likelihood estimate of cavitation damage caused by cavitation phenomena during the operation of a water turbine. For example, it can be calculated based on the cavitation safety margin under the current operating conditions. The higher the probability value, the higher the risk of cavitation.

[0019] Intelligent control algorithms, such as fuzzy logic, neural networks, or genetic algorithms, are used to generate control commands based on multi-objective constraints, balancing efficiency, lifespan, and stability. Optimized control strategies can refer to a set of commands that adjust the turbine's operating parameters, such as adjusting guide vane opening, changing output limits, or modifying the regulating rate, to achieve optimal operation. Controlling the turbine unit's operation can be achieved by implementing optimization strategies through actuators or control systems, such as adjusting guide vane opening via the hydraulic system or adjusting the unit load via the governor, to ensure the turbine's safe and efficient operation.

[0020] In detail, raw operating parameters such as inflow rate, head, unit vibration, shaft tilt, main shaft seal leakage, water guide bearing temperature, impeller blade images, grid load demand, system frequency, and guide vane opening can be continuously collected through ultrasonic flow meters deployed at the inlet of the pressure steel pipe, vibration acceleration sensors, temperature sensors, shaft monitoring instruments, underwater cameras, and SCADA systems installed at various parts of the unit. This data is preprocessed: abnormal jump values ​​caused by sensor communication interruptions are removed; the vibration time-domain signal is converted to a frequency-domain signal using a fast Fourier transform to extract characteristic frequency energy; noise reduction and edge enhancement are performed on the impeller blade images to identify erosion areas; and all parameters are normalized to a uniform numerical range, forming a preprocessed data set that can be used for analysis.

[0021] Subsequently, the core risk assessment phase begins. This phase comprehensively utilizes the pre-processed data to assess two main aspects: First, the risk of sediment abrasion is evaluated. By analyzing the inflow velocity, particle size distribution and hardness from sediment analysis reports, combined with the unit's own vibration intensity, shaft alignment, bearing temperature rise, and blade erosion images, a comprehensive abrasion risk index is calculated. Second, the risk of cavitation erosion is assessed. Based on the current head, flow rate, and guide vane opening, the actual pressure state at the runner inlet is calculated and compared with the critical cavitation pressure characteristics of this type of turbine under the same head and load, resulting in a quantified cavitation probability.

[0022] Furthermore, based on these two key risk assessment results, an optimized control strategy can be generated using intelligent control algorithms. The optimization objective is to simultaneously pursue high power generation efficiency, low equipment losses, and stable operation. This process involves dynamic trade-offs: for example, when both the erosion risk index and cavitation probability are high, power generation efficiency may be appropriately reduced by generating an optimized control strategy that limits output or adjusts the guide vane opening sequence to mitigate the risk. Finally, this strategy is translated into specific execution commands, which are then distributed to actuators such as the governor and excitation system via the power plant's local control unit (LCU), achieving real-time, optimized control of the hydro-generator unit's operation.

[0023] The turbine operation optimization control method and system of this embodiment obtains sediment characteristics, mechanical state and real-time operating conditions by multi-dimensional preprocessing of the turbine unit's operating parameters, so as to calculate the erosion risk index and assess the cavitation probability. This realizes the quantification of erosion and cavitation risks, and generates an optimized control strategy based on the quantified risk index and cavitation probability. This achieves a dynamic balance between turbine operating efficiency, equipment life and operating stability, thereby improving the turbine's operating economy, extending the service life of key components and reducing the risk of unplanned downtime due to equipment damage.

[0024] In some embodiments, the process of determining the erosion risk index after obtaining preprocessed data may include: calculating a basic erosion factor reflecting the water flow's ability to carry sediment abrasion based on the inflow rate, sediment particle size distribution characteristics, and sediment hardness; calculating a state adjustment coefficient reflecting the impact of the unit's current mechanical state on erosion based on unit vibration, shaft inclination, main shaft sealing status, water guide bearing temperature, and impeller blade erosion status; and multiplying the basic erosion factor, the state adjustment coefficient, and a preset time decay factor to obtain the final erosion risk index.

[0025] The basic abrasion factor can be a quantified value reflecting the wear potential of sediment in the water flow on turbine components. It is calculated, for example, based on inflow rate, sediment particle size distribution, and hardness; a higher value indicates a stronger sediment-carrying abrasion capacity. The condition adjustment coefficient can be an adjustment factor used to correct the basic abrasion factor to reflect the impact of the unit's current mechanical condition. It is calculated, for example, using parameters such as vibration and shaft tilt; a larger coefficient indicates a deterioration in mechanical condition and increased abrasion risk. The time decay factor can be a time-dependent function used to simulate the decrease in abrasion resistance caused by material fatigue. For example, it decreases with increasing unit operating hours to characterize cumulative damage during long-term operation.

[0026] In detail, firstly, a basic abrasion factor is calculated based on the pre-treated influent flow rate, sediment particle size distribution data, and sediment hardness. Secondly, the abrasion aggravation effect of the unit's own "health" status is analyzed in greater depth, i.e., a condition adjustment coefficient is calculated. This coefficient is determined by analyzing vibration signals to assess rotor balance, shaft tilt data to assess alignment, historical trends in seal condition to assess seal failure, bearing temperature to assess lubrication and cooling, and blade erosion images to assess the current severity of wear. A unit in poor condition, even under the same water flow and sediment conditions, has a higher actual risk of damage; this coefficient quantifies this impact. Thirdly, a time decay factor is introduced. This is a function that slowly decreases as the unit's cumulative operating hours increase, used to simulate the decline in the inherent abrasion resistance of metal materials due to long-term service fatigue. Even if the unit's operating conditions and status remain unchanged, an older unit that has operated for tens of thousands of hours is more vulnerable than a newer unit. Finally, by multiplying the aforementioned basic abrasion factor, state adjustment coefficient, and time decay factor, we obtain the final abrasion risk index, which comprehensively reflects the impact of external conditions, immediate state, and long-term lifespan.

[0027] In some embodiments, the present invention further provides a process for calculating a fundamental abrasion factor reflecting the abrasion capacity of water flow carrying sediment based on inflow rate, sediment particle size distribution characteristics, and sediment hardness. This process may include: Based on the particle size distribution characteristics of the sediment, the sediment particles are divided into at least two particle size groups. For each particle size group, a group erosion contribution value is calculated based on the representative particle size of each group, the sediment hardness, and the inflow rate. The group erosion contribution value is positively correlated with a specified power of the representative particle size and the sediment hardness. The erosion contribution value of each group is weighted and summed according to the mass proportion of each particle size group to obtain an initial erosion factor. The initial erosion factor is corrected based on the inflow rate to obtain the basic erosion factor.

[0028] Here, particle size grouping refers to classifying sediment particles into multiple categories based on their size range, such as fine sand (particle size less than 0.1 mm), medium sand (particle size 0.1-0.5 mm), and coarse sand (particle size greater than 0.5 mm). Representative particle size refers to the typical size value of each particle size group, such as using the median or average particle size of the group. Group abrasion contribution refers to the contribution of each particle size group to overall abrasion, calculated based on the power (e.g., square) of the representative particle size and the sediment hardness. Initial abrasion factor refers to the preliminary abrasion assessment value obtained through weighted summation.

[0029] In this embodiment, after receiving the preprocessed data of sediment particle size distribution, it is not treated as a whole, but is first finely grouped, for example, into three particle size groups: "fine sand (<0.1mm)", "medium sand (0.1-0.5mm)" and "coarse sand (>0.5mm)".

[0030] For each group, its group abrasion contribution is calculated. This contribution is not simply proportional to the weight of the sediment in that group, but rather proportional to a power (e.g., the square) of its representative particle size (usually the median particle size of the group), and also proportional to the hardness of the sediment. This is because large, hard particles exert a much greater destructive force on materials than small, soft particles.

[0031] Next, based on the mass percentage of each particle size group in the sediment analysis report, the contribution values ​​of each group are weighted and summed to obtain an initial abrasion factor. Finally, the inflow rate is introduced to correct this initial factor. Even with the same sediment conditions, a larger flow rate means a greater total amount of sediment impacting the impeller per unit time, resulting in stronger abrasion. Through a correction function based on the flow rate, the final output is a basic abrasion factor that accurately reflects the current water flow's sediment-carrying abrasion capacity.

[0032] For example, if the inflow rate is 100 cubic meters per second and the sediment particle size distribution shows that it is mainly medium sand with high hardness, the system calculates the initial abrasion factor through weighted calculation and corrects it with the inflow rate to obtain the basic abrasion factor.

[0033] In some embodiments, the present invention further provides a process for calculating a state adjustment coefficient based on unit vibration, shaft tilt, main shaft sealing status, water guide bearing temperature, and impeller blade erosion status. This process may include: acquiring the real-time vibration spectrum of the unit and extracting vibration energy values ​​in specific frequency bands related to impeller dynamic imbalance; calculating the shaft misalignment deviation based on shaft tilt; evaluating the rate of seal performance degradation based on historical and current data of the main shaft sealing status; comparing the water guide bearing temperature with a preset safe operating threshold to obtain a temperature influence factor; and quantitatively evaluating the impeller blade erosion status based on image recognition technology or acoustic monitoring technology to obtain an erosion status index. These parameters are then input into a pre-trained fuzzy inference system for analysis, and the state adjustment coefficient is output.

[0034] Among these, vibration energy value refers to the vibration signal energy in a specific frequency band (such as the rotational frequency harmonics), used to assess the degree of dynamic imbalance of the turbine runner. Shaft misalignment deviation refers to the deviation caused by shaft tilt, reflecting the alignment status of the unit. Sealing performance degradation rate refers to the rate at which the main shaft sealing condition deteriorates over time, derived from a comparison of historical and current data. Temperature influence factor refers to the quantitative value of the degree to which the water-guided bearing temperature deviates from the safe threshold. Erosion state index refers to the quantitative assessment value of the degree of erosion of the turbine blades, obtained through image or acoustic techniques. Fuzzy inference system refers to a decision-making system based on fuzzy logic, used to handle uncertainty and multi-input fusion.

[0035] In detail, the calculation process of the condition adjustment coefficient is a process of integrating multiple mechanical condition information into a single adjustment value, which may include vibration analysis, shaft alignment analysis, seal condition assessment, bearing temperature assessment, and blade erosion assessment.

[0036] Vibration analysis: This allows for the analysis of real-time vibration spectra, with a particular focus on the rotational frequency and its harmonics, which are closely related to the dynamic imbalance of the impeller, and the extraction of vibration energy values ​​in these specific frequency bands. Higher energy values ​​indicate more severe dynamic imbalance and exacerbate localized wear.

[0037] Axis alignment analysis: Based on the axis inclination data, a quantified axis misalignment deviation can be calculated. The larger the deviation, the less stable the unit operation and the higher the risk of erosion.

[0038] Sealing condition assessment: Historical leakage data and current values ​​of the spindle seal can be retrieved, and the rate of seal performance degradation can be assessed through trend analysis. The faster the degradation, the greater the risk of external foreign matter intrusion.

[0039] Bearing temperature assessment: The real-time temperature of the water-guided bearing can be compared with a preset safe operating threshold to derive a temperature influence factor. Abnormally high temperatures may indicate poor lubrication, which will exacerbate wear.

[0040] Blade erosion assessment: Image recognition technology can be used to analyze photos of turbine blades taken by underwater cameras, or acoustic monitoring technology can be used to analyze cavitation noise, quantify erosion pits and cracks on the blade surface, and generate an erosion state index.

[0041] The above five dimensions of indicators are simultaneously input into a pre-trained fuzzy inference system. This fuzzy inference system incorporates rules formed by expert experience (e.g., "If the vibration energy is high and the axis misalignment deviation is large, the state adjustment coefficient should be adjusted towards 'high'"). Through fuzzy logic operations, it ultimately outputs a comprehensive state adjustment coefficient that reflects the degree of influence of the current mechanical state on abrasion.

[0042] In some embodiments, the present invention further provides a process for determining the probability of cavitation erosion, which may include: calculating the actual cavitation number under the current operating conditions based on the head, inflow rate, and guide vane opening; querying pre-stored turbine characteristic data based on the head and load demand to obtain the critical cavitation number under the current operating conditions; calculating the cavitation erosion safety margin between the actual cavitation number and the critical cavitation number; and determining the current probability of cavitation erosion based on the cavitation erosion safety margin and a preset probability mapping relationship.

[0043] The actual cavitation number reflects the tendency of cavitation to occur under current operating conditions and is calculated based on head, inflow rate, and guide vane opening. The critical cavitation number refers to the threshold cavitation number for cavitation occurrence under specific operating conditions stored in the turbine characteristic data. The cavitation safety margin refers to the safety boundary between the actual cavitation number and the critical cavitation number; a larger margin indicates a lower risk of cavitation. The probability mapping relationship refers to a pre-defined mapping table or function that converts the cavitation safety margin into a cavitation probability value.

[0044] In detail, cavitation erosion is closely related to the pressure distribution inside the turbine runner. In this embodiment, firstly, based on the current operating head, inflow rate, and guide vane opening, the actual cavitation number of the turbine under the current operating conditions is calculated using a fluid dynamics model. This value reflects the margin between the lowest pressure point inside the runner and the vaporization pressure; the lower the actual cavitation number, the stronger the tendency for cavitation to occur. Then, based on the current head and load requirements, a pre-stored database of turbine characteristics obtained through model tests is consulted to find the minimum cavitation number required to prevent cavitation under the current operating conditions, i.e., the critical cavitation number. Next, the cavitation erosion safety margin between the actual cavitation number and the critical cavitation number is calculated. The larger the margin, the safer the operation. Finally, a probability mapping relationship is preset, which is usually an empirical function or lookup table, mapping the calculated safety margin to a cavitation erosion probability value between 0 and 1. For example, when the safety margin is large, the probability is close to 0; when the safety margin is negative (i.e., the actual cavitation number is lower than the critical value), the probability will increase significantly, thereby quantitatively assessing the possibility of cavitation damage occurring.

[0045] In some embodiments, the present invention further provides a process for calculating the actual cavitation number under the current operating conditions based on the water head, inflow rate, and guide vane opening. This process may include: calculating the reference pressure at the turbine runner inlet based on the water head and inflow rate; determining the pressure loss value in the current flow channel based on the guide vane opening using a pre-stored model relating guide vane opening and flow channel pressure loss; obtaining the atmospheric pressure at the turbine installation location and the saturated steam pressure corresponding to the current water temperature; and calculating the actual cavitation number based on the reference pressure, the pressure loss value, the atmospheric pressure, and the saturated steam pressure.

[0046] The reference pressure refers to the theoretical pressure at the turbine runner inlet, calculated based on the head and inflow rate. The pressure loss value refers to the pressure reduction within the flow channel caused by the guide vane opening, determined through a pre-stored model. The atmospheric pressure value refers to the atmospheric pressure at the installation location, for example, obtained through a pressure sensor. The saturated vapor pressure value refers to the saturated vapor pressure of water at the current water temperature, obtained through temperature lookup. The actual cavitation number refers to the cavitation number calculated using the above parameters, used for cavitation risk assessment.

[0047] In detail, the reference pressure at the turbine runner inlet can be estimated using Bernoulli's equation based on the head and inflow rate. The theoretical inlet pressure, which does not consider flow channel losses, is not considered here. Then, based on the guide vane opening, a pre-stored model relating guide vane opening and flow channel pressure loss (calibrated through CFD simulation or field tests) is consulted to determine the pressure loss value generated when water flows through the guide vanes and volute. The smaller the guide vane opening and the more tortuous the flow channel, the greater the pressure loss is generally. Next, the local atmospheric pressure value of the hydropower station is read from meteorological sensors, and the saturated vapor pressure of the water is retrieved based on water temperature sensor data. Furthermore, by combining all the above pressure parameters, the pressure loss value is subtracted from the reference pressure to obtain the actual pressure at the runner inlet. This, combined with the atmospheric pressure and saturated vapor pressure, is calculated according to the physical definition of the cavitation number, ultimately outputting the accurate actual cavitation number.

[0048] In some embodiments, this application further provides a process for calculating the cavitation erosion safety margin between the actual cavitation number and the critical cavitation number. This process may include: calculating the difference between the critical cavitation number and the actual cavitation number; determining the ratio of the difference to the critical cavitation number as the current instantaneous cavitation erosion safety margin; obtaining a cavitation erosion safety margin sequence of the turbine within a preset historical time period, and calculating the changing trend of the cavitation erosion safety margin based on the sequence; and weightedly fusing the current instantaneous cavitation erosion safety margin with the changing trend to obtain the cavitation erosion safety margin.

[0049] The current instantaneous cavitation safety margin can refer to the instantaneous safety boundary value calculated based on the current operating conditions. The cavitation safety margin sequence can refer to the set of safety margin data over a historical time period (such as the past hour). The trend can refer to the direction of change of the sequence over time, for example, obtained through linear regression analysis. Weighted fusion can refer to combining instantaneous values ​​with trend values, for example, using weighting coefficients to balance short-term and long-term effects.

[0050] In detail, after calculating the instantaneous difference between the critical cavitation number and the actual cavitation number, this difference can be divided by the critical cavitation number itself to obtain a normalized, dimensionless current instantaneous cavitation safety margin. This approach makes the margin comparable across different operating conditions. To further improve the stability of the assessment, a series of cavitation safety margin data for the turbine over a preset historical time period (e.g., the past 30 minutes) can be obtained to form a cavitation safety margin sequence. By performing linear regression or moving average analysis on this sequence, the trend of the margin change (e.g., whether it is continuously deteriorating or improving) can be calculated. Furthermore, the current instantaneous cavitation safety margin (reflecting immediate risk) can be weighted and fused with the calculated trend (reflecting the direction of risk development). For example, even if the current instantaneous margin is acceptable, if the trend shows that it is rapidly deteriorating, the fused comprehensive cavitation safety margin will be reduced accordingly, thus issuing an early warning.

[0051] In some embodiments, this application further provides a process for obtaining the critical cavitation number under the current operating condition by querying pre-stored turbine characteristic data based on head and load demand. This process may include: pre-stored a turbine characteristic database containing the correspondence between different heads, different load demands and the critical cavitation number of the turbine; performing a matching query in the turbine characteristic database using the current net head and real-time load demand as joint query conditions; if a precisely matching combination of head and load demand is found, the corresponding critical cavitation number is directly read; if no precisely matching combination is found, the critical cavitation number under the current operating condition is calculated using a multidimensional interpolation algorithm based on the nearest multiple combinations of head and load demand and their corresponding critical cavitation numbers.

[0052] The turbine characteristic database can be implemented using a relational database or a distributed data storage system, aiming to provide a data foundation covering multiple operating conditions for querying critical cavitation numbers. Joint query conditions can be retrieval mechanisms that simultaneously rely on two parameters: head and load demand. This can be achieved through composite indexes or hash mapping techniques for efficient matching. Exact matching can be confirmed instantly using fast key-value lookup or binary search algorithms, aiming to ensure high reliability of results under standard operating conditions using verified accurate data. Multidimensional interpolation algorithms refer to computational methods that perform numerical fusion in a multidimensional parameter space. These can employ generalized implementations such as linear interpolation, spline interpolation, or kriging interpolation. Their purpose is to generate critical cavitation values ​​that conform to physical laws by utilizing the spatial distribution relationships of neighboring operating points when no exact match is found in the database, avoiding biases caused by single-dimensional interpolation.

[0053] In detail, a pre-stored turbine characteristic database can be used. This database, established through extensive model experiments and real-machine tests, stores the critical cavitation numbers corresponding to different combinations of head and load demand. The current net head and real-time load demand, monitored in real time, can be used as a set of joint query conditions for matching and searching within this database. If a record exists in the database that perfectly matches the current head and load, its corresponding critical cavitation number is directly retrieved.

[0054] However, the operating conditions of a water turbine are continuously changing, making it difficult to perfectly match the discrete test points in the database at all times. When no exact matching combination is found, the system initiates a multi-dimensional interpolation algorithm. This algorithm identifies several (e.g., four) head-load combinations in the database that are closest to the current operating point, along with their critical cavitation numbers. Interpolation calculations are then performed across these multiple dimensions to intelligently deduce the critical cavitation number applicable to the current precise operating condition, thus ensuring the continuity and accuracy of the characteristic query.

[0055] In some embodiments, this application further provides a process for determining the current cavitation probability based on the cavitation safety margin and a preset probability mapping relationship. This process may include: pre-setting a mapping table containing multiple cavitation safety margin intervals and basic cavitation probability values ​​corresponding to each interval; determining the interval to which the comprehensive cavitation safety margin belongs and obtaining the corresponding basic cavitation probability; real-time monitoring of high noise events and abnormal vibration events during turbine operation, and dynamically correcting the basic cavitation probability according to the frequency and intensity of the events; introducing an environmental correction factor related to the current water temperature and water flow gas content, and finally adjusting the dynamically corrected cavitation probability to output the current cavitation probability.

[0056] Among them, the preset mapping table refers to a pre-constructed discrete correspondence storage structure, which can be implemented using a database table, memory array, or lookup table. Its purpose is to divide the continuous cavitation safety margin into clear intervals, facilitating the rapid acquisition of initial probability values. Determining the interval based on the comprehensive cavitation safety margin refers to matching the preset interval boundary based on the input safety margin value. This can be implemented using a binary search algorithm or a linear search algorithm, aiming to efficiently locate the corresponding basic cavitation probability. Real-time monitoring of high-noise events and abnormal vibration events refers to collecting operational data and identifying abnormal physical characteristics through a sensor network. This can be implemented using an acoustic sensor array combined with a spectrum analysis module or a vibration acceleration sensor combined with a threshold detection algorithm, aiming to capture direct dynamic indications of cavitation occurrence. The environmental correction factor refers to the adjustment parameter reflecting the influence of external conditions. It can be implemented using a saturated vapor pressure calculation function based on water temperature or a conversion coefficient of real-time measured values ​​of water flow gas content, aiming to make the probability assessment fit the actual changes in the hydraulic environment.

[0057] In detail, this embodiment pre-defines a mapping table that divides the cavitation safety margin into several consecutive intervals and assigns a basic cavitation probability to each interval. For example, a margin > 0.05 corresponds to a probability of 0.1, a margin between 0.02 and 0.05 corresponds to a probability of 0.3, and a margin < 0.02 corresponds to a probability of 0.6. The calculated comprehensive cavitation safety margin can be used to find its corresponding interval and obtain the corresponding basic cavitation probability. Subsequently, a dynamic correction mechanism is introduced. Specifically, it can monitor in real time whether high-noise events (abnormal popping sounds captured by acoustic sensors) and abnormal vibration events (sudden increases in vibration energy at specific frequencies) occur during operation. These events are direct indicators of ongoing cavitation. The system dynamically adjusts the basic cavitation probability based on the recent frequency and intensity of these events. Next, an environmental correction factor is introduced for correction, which is related to the current water temperature (affecting saturated vapor pressure) and the gas content of the water flow (affecting cavitation collapse intensity). The higher the water temperature and the greater the gas content, the more destructive cavitation erosion becomes. Therefore, this correction factor will further fine-tune the probability value. After these two corrections, the system outputs the final cavitation probability, which reflects the real-time operating characteristics.

[0058] In some embodiments, this application further provides a process for generating an optimized control strategy based on the erosion risk index and the cavitation probability using an intelligent control algorithm. This process may include: constructing a multivariate optimization function with the objectives of unit operating efficiency, equipment lifespan loss, and operational stability, wherein the equipment lifespan loss term is a weighted sum of the erosion risk index and the cavitation probability; adaptively identifying operating scenarios based on current load demand, grid frequency, and inflow water, and assigning dynamic weights corresponding to the scenarios to the erosion risk index and the cavitation probability; inputting the erosion risk index and the cavitation probability into a fuzzy inference system to dynamically adjust operating constraint boundaries such as turbine output limits and guide vane opening adjustment rates; and solving the multivariate optimization function using an optimization algorithm based on the adjusted constraint boundaries to generate the final optimized control strategy.

[0059] Among them, the multivariate optimization function refers to a mathematical model built on the basis of multi-objective optimization theory. It can be implemented using the weighted summation method or the Pareto optimal solution set method. Its purpose is to incorporate unit operating efficiency, equipment life loss and operating stability into a unified quantitative framework, avoiding the limitations of traditional methods that only focus on a single objective. Adaptive identification of operating scenarios can be understood as the process of dynamically dividing operating states according to real-time operating parameters. It can be implemented using cluster analysis or decision tree algorithms. Its purpose is to capture the real-time changing characteristics of power grid dispatch and hydrological conditions and assign scenario-appropriate weights to risk indicators. The fuzzy inference system specifically refers to the calculation module that processes fuzzy logic relationships. It can be implemented using Mamdani-type or Sugeno-type fuzzy controllers. Its purpose is to transform the nonlinear abrasion risk index and cavitation probability into quantifiable operating constraint boundary adjustment quantities. The optimization algorithm refers to the calculation method for solving the multivariate optimization function. It can be implemented using sequential quadratic programming or genetic algorithms. Its purpose is to search for the optimal solution within a dynamically set safety range.

[0060] In detail, this embodiment constructs a multivariate optimization function with the objectives of unit operating efficiency, equipment life loss, and operational stability, where the equipment life loss term is a weighted sum of the erosion risk index and the cavitation probability. Furthermore, the multivariate optimization function does not statically weigh these three objectives but adaptively identifies the operating scenario. For example, when grid load demand is high, frequency is stable, and inflow is abundant, the system identifies it as a "high efficiency priority" scenario, assigning lower weights to erosion and cavitation probabilities; conversely, during low load or frequency regulation operation, it is identified as a "equipment life priority" scenario, correspondingly increasing the weight of damage risk. Then, the real-time calculated erosion risk index and cavitation probability are input into a fuzzy inference system. This system dynamically and flexibly adjusts the turbine's operating constraints based on the risk level, for example, tightening output limits or reducing the guide vane opening adjustment rate during high-risk conditions to prevent sudden changes in operating conditions from exacerbating cavitation. Next, within the "safe operating zone" defined in the above manner, optimization algorithms (such as genetic algorithms and particle swarm optimization algorithms) can be used to solve the multivariate optimization function to find the optimal combination of operating parameters such as guide vane opening and blade angle under the current scenario objective, thereby generating the final optimized control strategy.

[0061] In this way, after obtaining the optimized control strategy, the operating parameters of the turbine unit can be adjusted in real time according to the optimized control strategy. For example, under the conditions of high sediment content and high erosion risk, the system will appropriately reduce the unit output, reduce the guide vane opening adjustment rate, and extend the unit operating time to reduce the wear of sediment on the runner blades; under the conditions of high cavitation risk, the system will increase the turbine's operating head and adjust the guide vane opening to ensure that the cavitation number is within a safe range.

[0062] In some embodiments, this application further provides a technical solution for fault early warning, which may include: calculating multiple fault risk indicators based on the preprocessed data; wherein the fault risk indicators include a vibration anomaly index, a temperature anomaly index, an erosion rate index, a sealing performance index, and a shaft misalignment index; inputting the fault risk indicators into a pre-trained fault diagnosis model to obtain a comprehensive fault risk score; wherein the fault diagnosis model is constructed based on a deep learning network and a fuzzy inference system, used to fuse the multiple fault risk indicators and output the comprehensive fault risk score; comparing the comprehensive fault risk score with a preset risk threshold to generate fault early warning information; wherein the fault early warning information includes the fault location, risk level, and recommended maintenance actions.

[0063] Among them, the vibration anomaly index refers to the degree to which the vibration of the unit deviates from the safe operating benchmark. It can be achieved by normalized statistical analysis of the energy values ​​of the relevant frequency bands of the dynamic imbalance of the impeller in the real-time vibration spectrum. Its purpose is to accurately identify vibration anomalies caused by mass loss due to abrasion. The temperature anomaly index can be understood as a quantitative indicator reflecting the temperature deviation of the water guide bearing from the preset safety threshold. It can be achieved by calculating the standard deviation of the temperature measurement value and historical operating data. Its purpose is to dynamically monitor the risk of bearing overheating. The erosion rate index is a parameter for assessing the material wear rate of the impeller blades. It can be achieved based on the feature extraction algorithm of the sediment particle size distribution characteristics and blade state image. Its purpose is to characterize the accelerating trend of the abrasion process. The sealing performance index can be understood as a quantitative value characterizing the degradation of the main shaft sealing function. It can be achieved by analyzing the differential change rate of the sealing pressure historical data and the current value. Its purpose is to warn of early signs of sealing failure. The shaft misalignment index is an indicator for quantifying the geometric deviation of the unit shaft. It can be achieved by vector synthesis calculation based on the shaft tilt sensor data. Its purpose is to detect the evolution of mechanical alignment problems.

[0064] In detail, a more refined set of failure risk indicators can be calculated based on preprocessed data. These indicators include: a vibration anomaly index reflecting mechanical loosening or impact; a temperature anomaly index reflecting cooling or lubrication failure; an erosion rate index that directly quantifies the wear process; a sealing performance index that assesses sealing effectiveness; and an axis misalignment index that characterizes changes in the mounting foundation.

[0065] Next, these five dimensions of indicators are input into a pre-trained fault diagnosis model. This model is a hybrid intelligent system. First, it uses a deep learning network (such as a deep belief network) to learn the complex nonlinear relationship between each indicator and fault type from historical fault data, extracting deep features. Then, it uses a fuzzy inference system to handle the uncertainty of expert knowledge and rules, interpreting and fusing the network output. Finally, the model outputs a comprehensive fault risk score between 0 and 1, with a higher score indicating a greater overall risk of system failure.

[0066] Then, the score is compared with a preset risk threshold. Once the threshold is exceeded, detailed fault warning information is immediately generated. This information not only includes the overall risk level (e.g., "high risk"), but also indicates the most likely fault location (e.g., "suggest checking the water guide bearing"), and provides recommended maintenance actions (e.g., "suggest scheduling a shutdown to check the seal within 72 hours"), thus providing clear decision support for maintenance personnel and enabling predictive maintenance.

[0067] Based on the same general inventive concept, this invention also protects a turbine operation optimization control system. The turbine operation optimization control system provided by this invention will be described below. The turbine operation optimization control system described below can be referred to in correspondence with the turbine operation optimization control method described above.

[0068] Figure 2 This is a schematic diagram of the structure of the turbine operation optimization control system provided in an embodiment of the present invention, as shown below. Figure 2 As shown, the turbine operation optimization control system of this embodiment includes a preprocessing module 21, a determination module 22, an optimization module 23, and a control module 24.

[0069] The preprocessing module 21 is used to acquire the operating parameters of the turbine unit and preprocess the operating parameters to obtain preprocessed data. The preprocessed data includes inflow rate, sediment particle size distribution, sediment hardness, head, unit vibration, shaft inclination, main shaft sealing status, water guide bearing temperature, runner blade erosion status, load demand, frequency, and guide vane opening. The determination module 22 is used to determine the abrasion risk index and cavitation probability based on the preprocessed data; Optimization module 23 is used to generate an optimized control strategy based on the abrasion risk index and the cavitation probability using an intelligent control algorithm; The control module 24 is used to control the operation of the turbine unit according to the optimized control strategy.

[0070] Figure 3This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. The electronic device may include: a processor 310, a communication interface 320, a memory 330, and a communication bus 340. The processor 310, communication interface 320, and memory 330 communicate with each other via the communication bus 340. The processor 310 can call logical instructions stored in the memory 330 to execute a turbine operation optimization control method.

[0071] Furthermore, the logical instructions in the aforementioned memory 330 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0072] It should be noted that all relevant information that may be involved in the various embodiments of the present invention is processed in strict accordance with the requirements of laws and regulations, following the principles of legality, legitimacy, and necessity, based on the reasonable purpose of the business scenario, and is information that users actively provide or generate during the use of the product / service, as well as information obtained with user authorization.

[0073] The information processed by this invention may vary depending on the specific product / service scenario and should be based on the specific scenario in which the user uses the product / service. This may involve user account information, device information, or other related information. This invention will handle the relevant information and its processing with the utmost diligence.

[0074] This invention places great importance on the security of related information and has adopted reasonable and feasible security protection measures that comply with industry standards to protect related information and prevent unauthorized access, public disclosure, use, modification, damage or loss of related information.

[0075] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0076] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the operation and control of a water turbine, characterized in that, include: The operating parameters of the turbine unit are acquired and preprocessed to obtain preprocessed data. The preprocessed data includes inflow rate, sediment particle size distribution, sediment hardness, head, unit vibration, shaft inclination, main shaft sealing status, water guide bearing temperature, runner blade erosion status, load demand, frequency, and guide vane opening. Based on the preprocessed data, the abrasion risk index and cavitation probability are determined; Based on the abrasion risk index and the cavitation probability, an optimized control strategy is generated through an intelligent control algorithm. The operation of the turbine unit is controlled according to the optimized control strategy.

2. The turbine operation optimization control method according to claim 1, characterized in that, Based on the preprocessed data, the abrasion risk index is determined, including: Based on the inflow rate, particle size distribution characteristics of sediment, and sediment hardness, the basic abrasion factor reflecting the abrasion capacity of water flow carrying sediment is calculated. Based on unit vibration, shaft tilt, main shaft sealing status, water guide bearing temperature, and impeller blade erosion status, a state adjustment coefficient reflecting the influence of the unit's current mechanical status on erosion is calculated. The final abrasion risk index is obtained by multiplying the basic abrasion factor, the state adjustment coefficient, and the preset time decay factor.

3. The turbine operation optimization control method according to claim 2, characterized in that, Based on the inflow rate, sediment particle size distribution characteristics, and sediment hardness, a basic abrasion factor reflecting the sediment-carrying abrasion capacity of water flow is calculated, including: Based on the particle size distribution characteristics of the sediment, the sediment particles are divided into at least two particle size groups; For each particle size group, the group erosion contribution value is calculated based on the representative particle size of each particle size group, the sediment hardness, and the inflow rate; wherein, the group erosion contribution value is positively correlated with a specified power of the representative particle size and the sediment hardness. The initial erosion factor is obtained by weighted summing of the erosion contribution values ​​of each particle size group based on the mass proportion of each group. The initial abrasion factor is corrected based on the inflow rate to obtain the basic abrasion factor.

4. The turbine operation optimization control method according to claim 2, characterized in that, Based on unit vibration, shaft inclination, main shaft sealing condition, water guide bearing temperature, and impeller blade erosion condition, a condition adjustment coefficient reflecting the impact of the unit's current mechanical condition on erosion is calculated, including: Based on the unit vibration, the real-time vibration spectrum of the unit is obtained, and the vibration energy value of a specific frequency band related to the dynamic imbalance of the runner is extracted; The misalignment deviation of the axis is calculated based on the axis inclination. Based on historical and current data of the spindle sealing status, assess the rate of sealing performance degradation. The temperature of the water-guided bearing is compared with a preset safe operating threshold to obtain the temperature influence factor; Based on image recognition technology or acoustic monitoring technology, the erosion state of the rotor blades is quantitatively evaluated to obtain an erosion state index. The vibration energy value, shaft misalignment deviation, sealing performance degradation rate, temperature influence factor, and erosion state index are input into a pre-trained fuzzy inference system for analysis, and the state adjustment coefficient is output.

5. The turbine operation optimization control method according to claim 1, characterized in that, Based on the preprocessed data, the probability of cavitation erosion is determined, including: Calculate the actual cavitation number under the current operating conditions based on the water head, inflow rate, and guide vane opening. Based on the head and load requirements, query the pre-stored turbine characteristic data to obtain the critical cavitation number under the current operating conditions; Calculate the cavitation safety margin between the actual cavitation number and the critical cavitation number; Based on the cavitation safety margin, the current cavitation probability is determined through a preset probability mapping relationship.

6. The turbine operation optimization control method according to claim 5, characterized in that, Calculate the actual cavitation number under the current operating conditions based on the water head, inflow rate, and guide vane opening, including: Calculate the reference pressure at the turbine runner inlet based on the water head and the inflow rate; Based on the guide vane opening, the pressure loss value in the current flow channel is determined by using a pre-stored model showing the relationship between the guide vane opening and the flow channel pressure loss. Obtain the atmospheric pressure at the turbine installation location and the saturated steam pressure corresponding to the current water temperature; The actual cavitation number is calculated based on the reference pressure, the pressure loss value, the atmospheric pressure value, and the saturated vapor pressure value.

7. The turbine operation optimization control method according to claim 5, characterized in that, Calculating the cavitation erosion safety margin between the actual cavitation number and the critical cavitation number includes: Calculate the difference between the critical cavitation number and the actual cavitation number; The ratio of the difference to the critical cavitation number is determined as the current instantaneous cavitation safety margin. Obtain the cavitation safety margin sequence of the water turbine within a preset historical time period, and calculate the changing trend of the cavitation safety margin based on the sequence; The current instantaneous cavitation erosion safety margin is weighted and fused with the changing trend to obtain the cavitation erosion safety margin.

8. The turbine operation optimization control method according to claim 1, characterized in that, Based on the erosion risk index and the cavitation probability, an optimized control strategy is generated through an intelligent control algorithm, including: A multivariate optimization function is constructed with the objectives of unit operating efficiency, equipment life loss and operating stability as the objectives, where the equipment life loss term is a weighted sum of the erosion risk index and the cavitation probability; Based on the current load demand, power grid frequency and inflow water flow, the operating scenario is adaptively identified, and dynamic weights corresponding to the scenario are assigned to the abrasion risk index and cavitation probability. The abrasion risk index and cavitation probability are input into the fuzzy inference system to dynamically adjust the operating constraint boundary of the turbine. Based on the adjusted constraint boundary, the multivariate optimization function is solved using an optimization algorithm to generate the final optimized control strategy.

9. The turbine operation optimization control method according to any one of claims 1-8, characterized in that, Also includes: Based on the preprocessed data, multiple fault risk indicators are calculated; The failure risk indicators mentioned above include vibration anomaly index, temperature anomaly index, erosion rate index, sealing performance index, and shaft misalignment index; The fault risk indicators are input into a pre-trained fault diagnosis model to obtain a comprehensive fault risk score; wherein, the fault diagnosis model is built based on a deep learning network and a fuzzy inference system, and is used to fuse the multiple fault risk indicators and output the comprehensive fault risk score; The comprehensive fault risk score is compared with a preset risk threshold to generate fault warning information; wherein, the fault warning information includes the fault location, risk level and recommended maintenance action.

10. A turbine operation optimization control system, characterized in that, include: The preprocessing module is used to acquire the operating parameters of the turbine unit and preprocess the operating parameters to obtain preprocessed data; wherein, the preprocessed data includes inflow rate, sediment particle size distribution, sediment hardness, head, unit vibration, shaft inclination, main shaft sealing status, water guide bearing temperature, runner blade erosion status, load demand, frequency, and guide vane opening. The determination module is used to determine the abrasion risk index and cavitation probability based on the preprocessed data; The optimization module is used to generate an optimized control strategy based on the abrasion risk index and the cavitation probability using an intelligent control algorithm. The control module is used to control the operation of the turbine unit according to the optimized control strategy.

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