A method for evaluating adaptability of load variation of a hydroelectric power station water turbine

Abstract

The application discloses a kind of water turbine load variation adaptability evaluation methods of hydropower station, it is related to the technical field of hydropower unit operation evaluation, the method obtains the active power, speed deviation, guide vane opening, pressure pulsation, vibration, head and speed regulation instruction data of water turbine in the process of load variation, identifies load variation event and establishes event window, after time synchronization, abnormal point correction, resampling and normalization preprocessing, construct incentive quantity, coordination quantity, disturbance quantity and recovery quantity, combined with current head segmentation, load segmentation and guide vane opening segmentation determine working condition weight, generate comprehensive quantity and memory quantity, output adaptability level, recommended load change rate upper limit, recommended adjustment interval and confidence identification, for subsequent load regulation constraint and conservative regulation mode switching, it is favorable to improve the reliability of load variation scene unit adaptability evaluation and regulation access determination.

Description

Technical Field

[0001] This invention relates to the field of hydropower unit operation evaluation technology, specifically a method for evaluating the adaptability of hydropower turbine load variations. Background Technology

[0002] Hydropower station units frequently operate under various conditions, including load increases, load decreases, load shedding, and continuous small-step adjustments, during grid peak shaving, primary frequency regulation, and continuous load distribution. During load fluctuations, the unit's active power, guide vane opening, speed deviation, pressure pulsation, and vibration response change synchronously. Judging the unit's condition solely based on steady-state indicators at a single moment often fails to accurately reflect the unit's capacity to withstand current load changes, its disturbance absorption capacity, and its ability to restore stability.

[0003] In the aforementioned application scenarios, existing technologies typically assess the operating status of generating units using unit health assessment models, transient characteristic analysis methods, or comprehensive risk assessment frameworks. However, these methods have limitations. They tend to focus on single-condition descriptions, post-event dynamic analysis, or system-level risk assessments, making it difficult to provide event-level adaptive criteria for subsequent adjustment actions when a specific load change occurs. The direct problem arising from this is that while the dispatching or control side can obtain some monitoring conclusions, there is still a lack of unified technical basis for determining whether the current load request is appropriate for execution, whether the load change rate needs to be tightened, and whether the adjustment interval needs to be extended.

[0004] In existing technologies, one type of approach focuses on assessing the health status of the generating unit. For example, Chinese patent application CN108375476A discloses a method for assessing the health of a hydropower unit. This method establishes a health standard model by collecting online monitoring data such as active power, operating head, and vibration, and obtains the unit's health status based on the deviation under current operating conditions. This type of approach can reflect the unit's operational health under given operating conditions, but it mainly focuses on static or quasi-static condition evaluation. It lacks specificity for the continuous dynamic processes before and after load changes, and it is difficult to directly answer whether a target load change is suitable for execution and what adjustment constraints should be adopted after execution.

[0005] Another approach focuses on the dynamic characteristics analysis during load abrupt changes. For example, Chinese patent CN109271684B discloses a method for analyzing the dynamic characteristics of a pump-turbine during load shedding, focusing on pressure signals, speed fluctuations, and flow evolution. The paper "Transient safety assessment and risk mitigation of a hydroelectric generation system" provides a comprehensive safety assessment of the transient process at the system level. This type of approach can reveal the characteristics of pressure pulsation, vibration, or risk changes during transient processes, but its main purpose is mechanism analysis, risk assessment, or system-level safety evaluation. It typically does not unify guide vane response, power ramp-up, speed recovery, and the impact of continuous events into adaptive quantitative results for a single load change, nor does it directly generate subsequent control constraints such as recommended upper limits for load change rates and recommended adjustment intervals.

[0006] Existing technologies typically assess the operating status of generating units through health assessment models, dynamic characteristic analysis methods, or comprehensive risk assessment frameworks. However, these technologies have certain limitations. For example, many solutions rely primarily on single-condition health status or single-transient analysis conclusions, lacking a unified evaluation caliber that uses load change events as the analysis unit. Although some solutions have addressed information such as vibration, pressure pulsation, or speed regulation response, they do not adequately utilize the coupling relationship between guide vane opening response, power ramp-up characteristics, speed deviation attenuation, and recovery processes, making it difficult to form event-level comprehensive criteria suitable for peak-shaving operation. In scenarios involving multiple consecutive load adjustments, existing solutions typically do not fully consider the cumulative impact of previous events on the adaptive state of subsequent events. Fourthly, when sensor data is missing, timestamps drift, or the proportion of anomalies is high, existing solutions often lack a degradation assessment mechanism to maintain output availability. In scenarios with frequent load changes in hydropower turbines, it remains difficult to simultaneously consider event-level dynamic response characterization, continuous disturbance adaptive identification, and subsequent load regulation constraint output.

[0007] Therefore, there is an urgent need for a method to assess the adaptability of hydropower turbines to load changes, so as to uniformly process and quantify the dynamic operating data of the units from multiple sources in response to load change events, obtain adaptive results that can reflect the unit's ability to withstand target load changes, its recovery ability and disturbance risk, and provide a reliable basis for subsequent load regulation access, load change rate constraints and regulation interval control. Summary of the Invention

[0008] To address the shortcomings of existing technologies that rely on single steady-state indicators, local vibration phenomena, or isolated transient analysis results to assess turbine status in hydropower station load variation scenarios, which fail to simultaneously reflect load change drivers, coordinated execution responses, hydraulic-mechanical coupling disturbances, stability recovery capabilities, and the cumulative impact of continuous load events, this invention provides a method for assessing the adaptability of hydropower station turbines to load variations. This method aims to continuously identify and uniformly assess load variation events, generating adaptive results that directly serve subsequent load regulation access, load change rate constraints, and regulation interval control.

[0009] This invention uses load change events as the basic analysis object, incorporating the dynamic response of the turbine before, during, and after the change into the same processing chain. Around a single load change request, it first acquires operational data such as active power, speed deviation, guide vane opening, pressure pulsation, vibration, head, and speed control commands. Then, it identifies the event trigger time and establishes an event window covering the baseline, execution, and recovery phases. Preprocessing is completed under a unified time reference, enabling data from different sources to be evaluated using the same criteria.

[0010] After establishing a unified data foundation, this invention constructs excitation quantities characterizing the intensity of load disturbances, coordination quantities characterizing the tracking relationship of the execution chain, disturbance quantities characterizing the degree of hydraulic mechanical coupling fluctuations, and recovery quantities characterizing the unit's recovery capability. These are then combined with the operating condition weights corresponding to the current head, current load, and current guide vane opening to generate a comprehensive quantity. Furthermore, the comprehensive quantity of the current event is linked to the state of the previous event to generate a memory quantity reflecting the cumulative impact of continuous load events. This ensures that the adaptive results not only reflect the capacity to withstand a single load change but also the gradually changing state of adaptability under continuous peak-shaving conditions.

[0011] Based on the above concept, this invention directly outputs the evaluation results to the unit monitoring host, the governor upper control module, and the load allocation decision module for judging, constraining, and controlling subsequent adjustment requests. When situations such as missing sensor data, excessive timestamp drift, or excessive anomaly percentage occur, this invention can also trigger an anomaly handling chain to generate results with credibility indicators while maintaining the continuity of the main evaluation logic, thereby improving the feasibility of engineering operation.

[0012] To achieve the above objectives, the present invention provides a method for assessing the adaptability of hydropower station turbines to load variations, focusing on event-level dynamic assessment. This method first acquires active power data, speed deviation data, guide vane opening data, pressure pulsation data, vibration data, head data, and speed control command data of the hydropower station turbine during load variations. Based on the correspondence between changes in speed control commands and changes in active power, the starting point of the load variation event is identified, generating an analysis window corresponding to the current event. Around this analysis window, time synchronization, outlier correction, resampling, and normalization preprocessing are performed on various operating data to form dynamic response data that can be directly used in the assessment.

[0013] After preprocessing, this invention extracts guide vane opening change characteristics, power ramp-up characteristics, load change amplitude characteristics, speed deviation attenuation characteristics, pressure pulsation fluctuation characteristics, and vibration envelope fluctuation characteristics from the dynamic response data, and constructs excitation, coordination, disturbance, and recovery quantities based on these characteristics. Specifically, the excitation quantity characterizes the disturbance input intensity formed by the combined amplitude and rate of load change; the coordination quantity characterizes the tracking relationship between guide vane action, power response, and speed convergence; the disturbance quantity characterizes the degree of coupling fluctuation between pressure pulsation, vibration, and speed deviation; and the recovery quantity characterizes the unit's ability to absorb disturbances and restore stable operation.

[0014] After the four core quantities are formed, this invention determines the operating condition weights based on the current head segment, current load segment, and current guide vane opening segment. These operating condition weights are then used to couple the coordination, recovery, disturbance, and excitation quantities to generate a comprehensive quantity for the current event. Subsequently, the comprehensive quantity of the current event is linked with the comprehensive state of the previous load change event and the interval between adjacent events to generate a memory quantity for the current event. An adaptive result is then generated under the combined effect of the comprehensive quantity and the memory quantity. This adaptive result includes at least an adaptive level, a recommended upper limit for the load change rate, a recommended adjustment interval, and a confidence indicator. It is used both to output the current event assessment conclusion and to constrain subsequent adjustment processes.

[0015] This invention directly outputs the adaptive results to the unit monitoring host, the governor upper control module, and the load allocation decision module, enabling subsequent load adjustment requests to be judged based on the current assessment results before execution. When the adaptive level is low or the reliability is insufficient, the corresponding module can control the load change rate, extend the adjustment interval, or restrict high-risk adjustment actions accordingly, thereby forming a closed-loop connection between the assessment chain and the control chain.

[0016] Furthermore, load change event identification is performed by combining the timing of speed control command changes with the starting point of active power changes. The event window covers the baseline phase before the change, the execution phase of the change, and the recovery phase after the change, enabling event identification and window establishment to stably handle different types of load increases, decreases, and continuous adjustment scenarios. Correspondingly, the preprocessing phase continuously performs time synchronization, anomaly correction, resampling, and dimensionless transformation, ensuring that subsequent construction and generation processes are based on a unified data foundation.

[0017] Furthermore, the coordination quantity is constructed based on the deviation relationship between the guide vane opening response sequence, power ramp response sequence, and speed deviation convergence sequence and the corresponding baseline response; the disturbance quantity is constructed based on the pressure pulsation fluctuation characteristics, vibration envelope fluctuation characteristics, and maximum speed deviation; and the recovery quantity is constructed based on the power regression process, speed deviation decay process, and stable fall-off process. By constructing different types of key responses separately and then generating a unified comprehensive quantity, the coordination degree of the execution chain, the level of coupled disturbances, and the recovery capability can be reflected under the same evaluation framework.

[0018] Furthermore, the operating condition weights are formed offline based on historical stable operating samples, segmented by head, load, and guide vane opening, and are invoked online according to the current operating condition when a current load change event occurs. The memory data is updated based on the current event's aggregated value, the previous event's aggregated value, the previous event's memory status, and the interval between adjacent events. Through this further scheme, the evaluation results not only take into account the differences in the current operating condition but also identify the cumulative impact of continuous events, thereby improving the robustness of adaptive judgments in continuous peak-shaving scenarios.

[0019] In cases involving output objects and abnormal operating conditions, this invention can also output the adaptive synthesis results to the unit monitoring host, the governor upper control module, and the load allocation decision module, respectively. It triggers a dimensionality reduction evaluation mode when it detects missing sensor data, excessive timestamp drift, or anomaly percentage exceeding limits. During anomaly handling, the weights corresponding to the missing data are adjusted, and usable feature chains are retained to continue generating adaptive results with confidence indicators. When the confidence level is insufficient, relevant modules control the switching to a conservative adjustment mode or restrict subsequent adjustment requests accordingly.

[0020] The beneficial effects of this invention are: By incorporating load change event identification, event window establishment, time synchronization, outlier correction, resampling, and normalization preprocessing into a unified processing chain, a consistent input basis is established for dynamic data of hydropower turbines in load increase, load decrease, and continuous regulation scenarios. Existing technologies often focus more on judging single steady-state operating conditions or observing single transient phenomena, making it difficult to include the continuous response process before and after speed control command changes in the same evaluation caliber. The unified event window simultaneously incorporates the baseline phase, execution phase, and recovery phase into the analysis, aligning the temporal relationships between active power, speed deviation, guide vane opening, pressure pulsation, and vibration data. This improves the stability of data processing under load change scenarios and provides more favorable conditions for maintaining the comparability of subsequent adaptive assessment results.

[0021] By constructing an excitation quantity based on the characteristics of load change amplitude and power ramp-up, load regulation requests of different amplitudes and speeds are uniformly transformed into comparable disturbance input intensities. In existing technologies, if the unit status is judged solely based on the target power value or a single change amplitude, the differences in hydraulic and mechanical responses caused by different ramp-up speeds at the same amplitude are easily overlooked. After coupling the load change amplitude and power ramp-up intensity together, the excitation quantity can map the intensity of the load request itself to the front end of the evaluation chain in advance. This improves the ability to quantify the strength of load changes and facilitates more targeted judgments on subsequent regulation access.

[0022] By constructing a coordination quantity based on guide vane opening response, power ramp-up response, and speed deviation convergence response, the tracking relationship between actuator actions and unit dynamic response is incorporated into the same evaluation object. Existing technologies, when dealing with load regulation problems, often observe guide vane action, power changes, and speed recovery separately, which has limitations in timely identification of action disconnections, tracking lags, or convergence mismatches. The coordination quantity unifies the characterization of the three types of response deviations—guide vane, power, and speed—ensuring that mismatches in any key link of the execution chain are reflected in the comprehensive evaluation results. This improves the granularity of closed-loop analysis of the speed regulation execution chain and enhances the reliability of control coordination evaluation during load changes.

[0023] By constructing disturbance quantities based on pressure pulsation characteristics, vibration envelope pulsation characteristics, and maximum speed deviation, and recovery quantities based on power regression process, speed deviation decay process, and stable return process, the coupled disturbance level and disturbance absorption capacity during load changes are simultaneously incorporated into the evaluation. Existing technologies often focus only on vibration, pressure pulsation, or recovery time, which has limitations in simultaneously reflecting hydraulic mechanical coupled fluctuations and stability recovery capacity. The disturbance quantity characterizes the degree of transient disturbance amplification, while the recovery quantity characterizes the ability to return to a stable state after the disturbance. The combination of the two can form a more complete dynamic state profile. This improves the differentiation between unit withstand capacity and recovery capacity and is conducive to improving the engineering feasibility of load change adaptability assessment.

[0024] By determining the weights of operating conditions based on the current head segment, current load segment, and current guide vane opening segment, and combining the comprehensive quantity and the memory quantity to generate adaptive results, the assessment results can simultaneously reflect the differences in current operating conditions and the cumulative impact of continuous load events. Existing technologies typically use fixed weights or single-event conclusions, which have limitations in reflecting the differences in boundary operating conditions and the gradual deterioration trend of adaptability after continuous disturbances. The operating condition weights allow the contribution of each core quantity under different operating conditions to be adjusted according to the current segment, while the memory quantity brings the comprehensive state and event interval of the previous load change event into the current result. This improves the robustness of adaptive assessment in continuous peak-shaving scenarios and can improve the rationality of subsequent recommended upper limits for load change rates and recommended adjustment intervals.

[0025] By outputting the adaptability level, recommended upper limit of load change rate, recommended adjustment interval, and confidence indicator, and sending these to the unit monitoring host, governor upper control module, and load allocation decision module, the evaluation results can be directly transformed into subsequent control constraint information. In existing technologies, many evaluation or diagnostic schemes only remain at the level of status description, risk warning, or mechanism analysis, which has limitations in directly serving subsequent load adjustment actions. After the adaptability results are bound to specific output destinations, the monitoring, speed control, and load allocation links can execute access, limiting, and interval control actions around the same result. This improves the efficiency of the connection between evaluation results and control actions and helps to improve the closed-loop reliability of the unit load adjustment process.

[0026] By switching to a dimensionality reduction evaluation mode when missing sensor data, excessive timestamp drift, or excessive outlier ratios are detected, and outputting adaptive results with confidence level indicators, the evaluation chain can remain continuously available under anomaly monitoring conditions. Existing technologies are prone to two limitations when data quality deteriorates: continuing to use the complete model leads to amplified misjudgments, and directly abandoning the output results in a lack of reference data for the control side. By reallocating the weights corresponding to missing data and using confidence level indicators to differentiate the usability of results, the impact of outlier data is limited to a controllable range. This improves the reliability of evaluations in anomaly scenarios and enhances the security of switching to conservative adjustment modes and intercepting adjustment requests. Attached Figure Description

[0027] Figure 1 The process of this invention Figure 1 ; Figure 2 The process of this invention Figure 2 ; Figure 3 The process of this invention Figure 3 . Detailed Implementation

[0028] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] Example 1 like Figure 1 As shown, this embodiment illustrates the complete main scheme of a method for assessing the adaptability of hydropower station turbines to load variations. This method is deployed in an assessment environment comprised of the power station monitoring host, the governor's upper-level control module, and the load allocation decision module. The active power data of the unit comes from the unit power acquisition channel; the speed deviation data comes from the speed detection channel of the speed control system; the guide vane opening data comes from the guide vane displacement detection channel; the pressure pulsation data comes from the volute pressure measurement point and the tailrace pressure measurement point; the vibration data comes from the unit swing or vibration monitoring channel; the head data comes from the upstream water level, downstream water level, and the head conversion module; and the speed control command data comes from the load regulation command recording channel. To ensure that data from different sources can enter a unified processing chain, this embodiment resamples each sampling sequence according to a unified reference period. Each record is further enhanced with a unit identifier field, an event number field, a timestamp field, and a data version field. The unit identifier field serves as a shared key field across modules. The event number field is generated using a combination of the unit identifier and the trigger time. The timestamp field records the sampling point under a unified baseline period. The data version field records the update batch to which the current data belongs.

[0030] In this embodiment, all variables are sourced from the sensor sampling sequence, speed control command record, unit rated parameter table, operating condition weight table, and previous event result object within the event window. The difference, standard deviation, envelope, mean deviation, and recovery time derived from the original sampling are all calculated within the current event window and are not generated in reverse from the manually entered fitness level.

[0031] In this embodiment, load variation events are first identified based on speed control command records and active power change records. If the magnitude of a speed control command change at a certain moment exceeds the 90th percentile of the absolute value distribution of command changes in the most recent batch of stable operation records, and the direction of active power change in the three consecutive sampling periods following that moment is consistent with the direction of the speed control command, then that moment is determined as the event trigger moment. .around An event window is established, consisting of a baseline phase, an execution phase, and a recovery phase. The duration of the baseline phase is denoted as [missing information]. The duration of the recovery phase is recorded as , and All parameters are stored as parameters to be calibrated in the window configuration object. The key fields of the window configuration object include the unit identifier field and the operating condition segment field, while the value fields include the baseline duration, recovery duration, update time, and version field. An update is triggered when the median recovery time of the most recent thirty stable operating events of the same type drifts by more than 10%. When two consecutive batches of update results conflict, the previous valid version is frozen. The parameter gap list is as follows: Parameters to be calibrated include the baseline duration. and recovery time The missing field is the stable value range of different units under different head segments. It is recommended to fill it in with the actual peak-shaving operation records and speed regulation system event logs of the past 30 days. The calibration period is once a week. The effective condition is that the number of valid events in the same segment is not less than 30 and the recovery time dispersion is lower than the preset threshold.

[0032] Preprocessing is performed on the active power, speed deviation, guide vane opening, pressure pulsation, vibration, and head data entering the event window. Preprocessing first aligns the data according to a uniform timestamp, then uses a sliding median filter with a length of five sampling points to remove isolated spikes, and finally bases the data on rated power. Rated speed and reference head The original quantities are transformed into dimensionless quantities. When a sampling point is missing, it is only allowed to be filled in once using a linear interpolation between two adjacent valid sampling points. Sampling points that are still missing after more than one interpolation are directly marked as invalid and their weights are deducted in subsequent confidence calculations. After preprocessing, the following features are obtained: power change amplitude feature, power ramp feature, guide vane opening change feature, speed deviation attenuation feature, pressure pulsation fluctuation feature, and vibration envelope fluctuation feature. The power ramp feature is calculated using the power difference between two adjacent unified sampling periods, and the vibration envelope fluctuation feature is calculated using the sliding envelope of the vibration absolute value sequence within the current window.

[0033] After preprocessing, the load excitation is first constructed. The load excitation is used to quantify the comprehensive disturbance intensity exerted on the unit by a primary load change, and can be written as formula (1): in, Indicates the first The stimulus amount for the next load change event. This indicates the active power corresponding to the start of the event window. This indicates the active power at the end of the event execution phase. Indicates the rated power. This represents the power increment between adjacent sampling periods. Indicates a uniform sampling period. Indicates the first Secondary event window, and This indicates the incentive weight. and The range of values ​​is arrive And satisfy Its initial value is obtained by normalizing the correlation coefficients of the power change amplitude and power ramp value on the pressure pulsation in the last thirty stable operating events. The output is the excitation quantity. Its output goes to the comprehensive quantity calculation module, and its technical function is to unify the load change amplitude and power ramp-up speed into the disturbance input intensity used in subsequent evaluation.

[0034] Subsequently, a coordination quantity is constructed to characterize the degree of tracking consistency between guide vane action, power response, and speed convergence, which can be written as formula (2): in, Indicates the first The amount of coordination for this event. This represents the average deviation between the guide vane opening response and the baseline expected response. This represents the average deviation between the power response and the target response. This represents the average deviation of the speed deviation convergence process relative to the baseline convergence process. , and This represents the weights of the three types of deviations. The guide vane opening response comes from the guide vane opening sequence, the power response comes from the active power sequence, and the speed deviation convergence process comes from the speed deviation sequence. All three types of deviations are averaged within the event window using a uniform sampling period. , and The initial value is determined by the proportion of the three types of deviations affecting the adjustment success rate in the most recent batch of stable operation records, and is stored in the coordination quantity configuration object. The output is the coordination quantity. Its output goes to the comprehensive quantity calculation module, and its technical function is to reflect whether there is a disconnect in the execution chain, tracking lag or convergence mismatch.

[0035] After the coordination quantity, the disturbance quantity is constructed. The disturbance quantity is used to quantify the degree of coupling fluctuation of hydraulic pulsation, mechanical vibration and speed deviation, and can be written as formula (3): in, Indicates the first The amount of disturbance in this event. This represents the standard deviation of the pressure pulsation sequence within the event window. This represents the standard deviation of reference steady-state pressure pulsation. This represents the standard deviation of the vibration envelope sequence within the event window. This represents the standard deviation of the reference steady-state vibration envelope. Indicates time Speed ​​deviation, This indicates the upper limit of the allowable speed deviation. , and This indicates the weight of the disturbance component. The reference steady-state standard deviation is taken from the steady-state statistical results of the most recent thirty stable operating events, and the upper limit of the allowable speed deviation is taken from the unit speed regulation safety constraint configuration object. The output result is the disturbance quantity. Its output goes to the comprehensive quantity calculation module, and its technical function is to identify the degree of coupling amplification between the hydraulic system and the mechanical system after load changes.

[0036] After the disturbance, a recovery quantity is constructed. The recovery quantity is used to quantify the unit's ability to absorb disturbances and restore a stable operating state, which can be written as formula (4): in, Indicates the first The recovery amount of this incident. This represents the effective recovery amount within the target range of power regression. This indicates the time required for power to recover to the target range. This indicates the amount of speed deviation attenuation. This indicates the duration of the speed deviation decay. This indicates the time required for pressure pulsations and vibrations to collectively return to a stable threshold range. , and This indicates the recovery component weights. , and The statistical results of the recovery segments derived from power, speed deviation, pressure pulsation, and vibration data are output as the recovery amount. Its output goes to the comprehensive quantity calculation module, and its technical function is to map the recovery speed and the degree of recovery completion into a comparable recovery capability metric.

[0037] In obtaining incentive amount Coordination volume Disturbance and recovery amount Then, the comprehensive quantity, memory quantity, and adaptive comprehensive value are generated, which can be written as formula (5): in, Indicates the first The total amount of this event , , and The weights for different operating conditions are read from the operating condition weight table according to the current head segment, current load segment, and current guide vane opening segment, and the values ​​must be within the specified range. arrive The weights are ; Indicates the first The amount of memory for this event. This indicates the amount of memory for the previous event. This indicates the total amount of the previous event. This indicates the time interval between two consecutive load change events. Indicates the memory decay coefficient; Indicates the overall fitness score. This represents the memory correction weights. The key fields of the operating condition weight table object include the unit identifier field, head segment field, load segment field, and guide vane opening segment field. The value fields include four operating condition weights, a version field, and an update time field. The update trigger condition is that the deviation between the comprehensive quantity and the actual regulation stability in the last thirty stable operating events exceeds a preset threshold. The freeze condition is that two consecutive updates cause a reverse fluctuation in the adaptability level. The output results are, in order, the comprehensive quantity... Memory capacity and overall adaptability Its output goes to the adaptive level division module and the adjustment request admission module. Its technical function is to integrate the current event state and the cumulative state of previous events into the main evaluation chain.

[0038] The working process is as follows: After receiving a new speed control command record, the evaluation module immediately listens to the active power change record. When the event trigger time is identified... Event numbers are generated and event windows are established. Within the event window, power, speed deviation, guide vane opening, pressure pulsation, vibration, and head data are continuously collected under a unified time reference. After synchronizing, correcting outliers, resampling, and normalizing the raw data, the excitation quantity is calculated sequentially. Coordination volume Disturbance Recovery amount Comprehensive quantity Memory capacity and overall adaptability Subsequently, the adaptive comprehensive value and the level classification threshold were used as the basis for classification. , The comparative relationships are used to classify high-adaptation levels, medium-adaptation levels, and low-adaptation levels, among which... and The initial values ​​are taken from the 70th and 30th percentiles of the distribution of the composite values ​​of the most recent 30 stable operating events, and updated in a rolling batch once a week; when The system generates an execution permission flag and outputs a suggested upper limit for the load change rate and a suggested adjustment interval. When a limit execution flag is generated, Conservative adjustment flags and adjustment request interception flags are generated in real time. The key fields of the adaptive result object include the unit identifier field and the event number field, and the value fields include the adaptive comprehensive value, adaptive level, recommended upper limit of load change rate, recommended adjustment interval, confidence flag, and execution flag. The version field records the current output batch, and the update time field records the result release time. When a new valid sampling record enters within the same event window, the updated value field is overwritten with the same key field. When the confidence flag is lower than the minimum confidence threshold, the object is frozen and prohibited from being used for new adjustment request admission judgment.

[0039] To enable the above evaluation chain to be recalculated in the engineering records, this embodiment also sets up an event recalculation record object. The event recalculation record object uses the unit identification field and the event number field as the joint key fields. The value fields include event trigger time, window start point, window end point, rated power, reference head, normalized value of power change amplitude, normalized value of maximum power ramp, average deviation of guide vane response, average deviation of power response, average deviation of speed convergence, ratio of standard deviation of pressure pulsation, ratio of standard deviation of vibration envelope, ratio of maximum speed deviation, power recovery amount, power recovery time, speed deviation attenuation amount, speed attenuation time, stabilization and fallback time, weight of four operating conditions, comprehensive amount of the previous event, memory amount of the previous event, and interval between adjacent events. All of the above fields are from the statistical results of the current event window, the unit rated parameter table, the operating condition weight table, and the result object of the previous event, and the adaptability level is not directly generated from the manually filled results. During recalculation, the system first substitutes the normalized value of power change amplitude and the normalized value of maximum power ramp into formula (1) to obtain Then, by substituting the three types of average deviations into formula (2), we obtain... Substituting the ratio of pressure pulsation standard deviation, the ratio of vibration envelope standard deviation, and the ratio of maximum speed deviation into formula (3) yields the following result: Substituting the recovery amount and recovery time fields into formula (4) yields the following result. Finally, by substituting the working condition weight, the total amount of the previous event, and the interval of the previous event into formula (5), we obtain... , and The recalculation record object retains the generation batch and data version for each intermediate quantity. When the version of any input field changes, the record will be updated for the same event number. , , , , , and All are regenerated in the same window to avoid only saving the final level and making it impossible to trace the source of the calculation.

[0040] In one specific recording method, the adaptability level and recommended output are not directly entered manually, but generated by a level mapping table. The key fields of the level mapping table include the unit identifier field, head segment field, load segment field, and adaptability level field; the value fields include the lower limit of the adaptive composite value, the upper limit of the adaptive composite value, the rule for generating the upper limit of the recommended load change rate, the rule for generating the recommended adjustment interval, and the rule for generating the execution identifier; the version field records the threshold batch; and the update time field records the time of the most recent rolling update. When falling into the high adaptability range, it is recommended to call the upper limit of the load change rate to the normal upper limit under the current operating conditions, and it is recommended to call the normal adjustment interval; when When the load change rate falls within the moderately suitable range, it is recommended to lower the upper limit of the load change rate by one segment step from the conventional upper limit, and to extend the adjustment interval by one segment step; when When the load falls into the low adaptability range, a conservative adjustment flag is output, and the recommended upper limit of the load change rate is limited to the conservative upper limit, and the recommended adjustment interval is switched to the conservative interval. The threshold of this mapping table is derived from the comprehensive value distribution of the last thirty stable operating events and the speed regulation safety constraint configuration object. The freezing condition is that two consecutive threshold updates in the same operating condition segment cause the same historical event to span more than two adaptability levels.

[0041] In this embodiment, the basic anomaly handling only addresses the basic availability issues required by the main path. The anomaly triggering criteria are any of the following: two or more consecutive missing sampling points, timestamp misalignment exceeding two unified sampling periods, or a sustained saturation value in the pressure pulsation sampling sequence. The thresholds are derived from the unified sampling period configuration, the time synchronization error statistics of the last thirty stable running events, and the pressure measurement point range upper limit configuration, respectively. The priority is executed in the order of timestamp misalignment anomaly, consecutive missing points anomaly, and saturation value anomaly. When a single sampling point is missing, interpolation is performed once using adjacent valid sampling points. When any of the above anomalies is triggered, the current event is marked as a basic reliability insufficient event, new comprehensive quantities and memory quantities are stopped from being updated, and the previous batch of valid adaptive result objects remains frozen. The recovery condition is that the subsequent five consecutive unified sampling periods return to valid sampling, the timestamps are realigned, and the pressure pulsation sampling value falls back to within the range upper limit. After the recovery condition is met, the frozen state is lifted, and the calculation of the current event window is restarted. This basic anomaly handling does not enter the complete dimensionality reduction evaluation; it is only used to ensure that the main path of Embodiment 1 has a minimum executable closed loop in engineering.

[0042] Whether a load variation event can be safely executed depends not on a single vibration value, a single power deviation value, or a single pressure pulsation value, but on the combined relationship between the load disturbance intensity, the consistency of the execution chain tracking, the degree of hydraulic-mechanical coupling fluctuation, and the recovery capability. Excitation quantity Unifying load change amplitude and power ramp rate into disturbance input intensity allows commands with different amplitudes and rates to be compared under the same evaluation caliber; coordination quantity This reflects the matching of guide vane movement, power response, and speed convergence. When there is lag or local mismatch in the execution chain, even if the final power reaches the target value, it will be reflected as a decrease in the coordination quantity; disturbance quantity. It captures the coupled amplification behavior of pressure pulsations, vibrations, and speed deviations, and can distinguish between transient acceptable fluctuations and persistent instability signs; recovery quantity This unifies the power recovery, speed decay, and stabilization process into a single recovery capability metric, ensuring that the assessment results consider not only the peak value at the time of disturbance but also the speed at which the unit returns to a stable state. (Comprehensive metric) After applying condition-sensitive weighting to the aforementioned four types of quantities, the differences under different head ranges, load ranges, and guide vane opening ranges can be incorporated into the same main chain; memory quantity Then, the cumulative effect of adjacent events is introduced into the adaptive comprehensive value. This avoids misjudging the unit's sustainable adaptation status based on a single event.

[0043] Example 2 like Figure 1 and Figure 2 As shown, this embodiment illustrates an enhanced scheme for evaluating the adaptability of hydropower station turbine load variations. It focuses on disclosing adaptive calling of load condition weights under multi-condition segmentation, multi-source dynamic response fusion, secondary verification chain, and an enhanced memory update mechanism for multiple consecutive load variation events. Similar to Embodiment 1, a unified sampling period is used. Event trigger time Baseline duration Recovery time Incentive amount Coordination volume Disturbance Recovery amount Comprehensive quantity Memory capacity and overall adaptability The definitions remain unchanged; the difference lies in that, based on the main processing chain of Example 1, this embodiment introduces segmented operating condition weight configuration data and continuous event history records from the historical stable operation sample library to enhance the processing of comprehensive quantities and memory quantities. The operating condition weight table object in the historical stable operation sample library uses the unit identifier field, head segment field, load segment field, and guide vane opening segment field as composite key fields, and the value fields include the basic weight. , , , Enhance the correction coefficient , , , The data includes fields for sample quantity, update time, and version. When the deviation rate between the fitness level and the actual execution result in the most recent 30 events in the same segment exceeds 15%, the weight table is updated. When two consecutive updates are in opposite directions and the absolute deviation does not decrease, the previous batch of versions is frozen.

[0044] In this embodiment, the sources of each variable are inherited from the event recalculation record object in Embodiment 1, and further from the working condition weight table object, calibration record object, verification model version object, and continuous event history record object; among them, the segment center value, segment span, basic weight, and enhancement correction coefficient are read from the working condition weight table object, and the low-adaptation event indicator and continuous event observation window are read from the continuous event history record object.

[0045] To avoid the operating condition weights remaining merely as abstract calls, this embodiment further discloses the minimum field structure of the operating condition weight table object. The key fields of the operating condition weight table object include a unit identifier field, a head segment field, a load segment field, and a guide vane opening segment field; the value fields include a head segment center value, a head segment span, a load segment center value, a load segment span, a guide vane opening segment center value, a guide vane opening segment span, a coordination quantity base weight, a recovery quantity base weight, a disturbance quantity base weight, an excitation quantity base weight, four enhancement correction coefficients, a sample quantity field, a sample start and end time field, a calibration method field, a version field, and an update time field. The sum of the four base weights is... When any basic weight is less than the minimum weight lower limit, it is truncated according to the minimum weight lower limit and then renormalized; the initial value of the enhancement correction coefficient comes from the contribution ranking of each core quantity to the actual execution stability deviation in the historical stable operation samples within the same working condition segment. When the current event arrives, the system first matches the corresponding row according to the average head, average active power of the execution phase and average guide vane opening of the execution phase within the event window, and then reads the segment center value, segment span, basic weight and enhancement correction coefficient; if the current working condition falls between two adjacent segment boundaries, the segment row with more samples and more recent update time is called first, and the boundary call status is written to the review identifier field of the enhancement result object.

[0046] The calibration record object for operating condition weights is used to store the source of the weights. This object uses the unit identification field, the operating condition segment field, and the calibration batch field as joint key fields. The value fields include the number of events participating in calibration, the number of events removed, the ranking of coordination contribution, the ranking of recovery contribution, the ranking of disturbance contribution, the ranking of excitation contribution, the weight normalization result, the deviation rate, the reason for the update trigger, and the frozen status. The calibration batch only records the weight generation calibrator and does not write the adaptive conclusion. When the number of events participating in calibration is lower than the preset minimum sample size, the previous valid batch is continued to be used, and the insufficient sample flag is written in the operating condition weight table object. When the deviation rate decreases for two consecutive batches, the version can be updated. When the deviation rate does not decrease or the directions of the weight changes of two adjacent batches are opposite, the previous valid version is frozen. Through the above field structure, the segment center value, span, basic weight, and enhancement correction coefficient called by formulas (6) and (7) can all be traced back to the specific calibration batch.

[0047] In this embodiment, after the event identification, time synchronization, anomaly correction, resampling and normalization processing of Embodiment 1 are completed, the operating condition deviation coefficient is constructed using the average head corresponding to the current event, the average active power during the execution phase and the average guide vane opening during the execution phase, so as to reflect the degree of deviation of the current event relative to the current segment center operating condition. The operating condition deviation coefficient can be written as formula (6): in, Indicates the first Operating condition deviation coefficient for this event This represents the average head within the current event window. This indicates the reference center value of the current head segment. This indicates the effective span of the current head segment. This represents the average active power during the current event execution phase. This indicates the reference center value for the current load segment. Indicates the effective span of the current load segment. This indicates the average guide vane opening during the current event execution phase. This indicates the reference center value for the current guide vane opening segment. This indicates the effective span of the current guide vane opening segment. , and This represents the weights of the three types of deviation components. , , The corresponding spans are both derived from the working condition weight table object. , and The initial value is obtained by normalizing the contribution rates of the three types of deviation components to the fluctuations in the adaptive results from the historical stable operating samples. The output result is the operating condition deviation coefficient. Its output goes to the weight generation module, and its technical function is to convert the degree of deviation of different working conditions into the correction input for the basic weight.

[0048] Obtain the deviation coefficient of the working condition Then, adaptive correction is performed on the basic operating condition weights in Example 1 to generate the enhanced operating condition weights and enhanced comprehensive quantities used in the current event, which can be written as formula (7): in, Indicates the first In the second incident One enhanced operating condition weight, These correspond to the weights of the coordination quantity, recovery quantity, disturbance quantity, and incentive quantity, respectively. This indicates the basic weight corresponding to the current operating condition segment. Indicates the relationship with the first The correction coefficients corresponding to the basic weights. Indicates the first The enhanced overall quantity of this event. Basic weight. and correction factor Read from the working condition weight table object, working condition deviation coefficient The output comes from formula (6). The output result is the weighted weight of the enhanced working condition. and enhance overall performance Its output goes to the secondary verification module and the enhanced memory update module. Its technical function is to make the comprehensive quantity calculation under different working conditions with different deviations more closely reflect the real risk of the current event, while keeping the main structure of Embodiment 1 unchanged.

[0049] To avoid amplifying local fluctuations from a single sensor channel directly into the final adaptive result, this embodiment sets a consistency verification rule after enhancing the overall quantity. The consistency verification rule compares the coordinated quantity, disturbance quantity, and recovery quantity with the corresponding reference intervals in the historical stable operating samples of the same operating condition segment. When any core quantity exceeds the corresponding reference interval, and there are measurement point saturation, single-point jumps, or short-term missing measurement records within the same event window, the current event is marked as an enhancement verification event. The reference interval is taken as the 15th to 85th percentile of the distribution of the core quantities corresponding to the most recent thirty stable operating events in the same segment, and the verification result is written to the verification identifier field of the enhancement result object. This rule is only used to trigger recalculation or manual verification and does not participate in the main calculation of the adaptive overall value, thereby avoiding adding extra scoring quantities outside the creative axis.

[0050] In order to obtain enhanced comprehensive quantity Then, the memory update method in Example 1 is enhanced to form a combined value of enhanced memory and enhanced adaptability, which can be written as formula (8): in, Indicates the first The enhanced memory capacity of this event This indicates an enhanced memory of the previous event. This indicates the enhanced overall quantity of the previous event. This indicates the time interval between two consecutive load change events. Indicates the first Whether an event falls into a low-fitness level is indicated by the value when the event is determined to be in a low-fitness level. Otherwise, the value is , Indicates the length of the observation window for consecutive events. , and This indicates the combined weights that enhance memory capacity. Indicates the overall value of enhanced adaptability. This indicates that the memory correction weights are enhanced. The initial value is the rounded-up value of the average string length of continuous load variation events in the most recent batch of peak-shaving operation days. , , and It is jointly calibrated using historically stable operating samples and historical records of low-adaptation events, and stored in the enhanced memory object. The output is the amount of enhanced memory. and enhanced adaptability comprehensive value Its output is directed to the adaptability level division module, the suggested load change rate upper limit generation module, and the multi-unit load allocation reference module. Its technical function is to simultaneously consider the impact of the current event status and the cumulative effect of continuous events on the final result.

[0051] The working process is as follows: After the basic event identification and preprocessing of Example 1 are completed, this example reads the average head, average load and average guide vane opening under the same event number, and matches the current segment center value and basic weight from the working condition weight table object, and calculates the working condition deviation coefficient using formula (6). Then, formula (7) is called to generate the enhanced operating condition weights. and enhance overall performance Then call formula (8) to update and enhance memory capacity. And generate an enhanced adaptive comprehensive value. Subsequently, the overall enhancement fitness score was used relative to the enhancement level threshold. and The positional relationship output includes the adaptability level of each operating condition, the upper limit of the recommended load change rate after operating condition constraints, and the recommended adjustment interval after operating condition constraints. If the consistency review rule marks the current event as an enhanced review event, the unified sampling period, window boundary, and basic feature definition are maintained as in Example 1. Only the abnormal measurement points of the current event are replaced with redundant channel values ​​or the most recent valid sample interpolation values ​​under the same timestamp. Then, the enhanced comprehensive quantity and enhanced memory quantity calculation are repeated. If, after recalculation, there are still core quantities exceeding the reference range and accompanied by abnormal measurement points, the adaptability level of this output is reduced by one level. The key fields of the enhanced result object include the unit identification field and the event number field. The value fields include the enhanced adaptability comprehensive value, the adaptability level of each operating condition, the upper limit of the recommended load change rate after operating condition constraints, the recommended adjustment interval after operating condition constraints, and the review identifier. The version field records the version of the operating condition weight table and the version of the review rule used. When it is detected that three consecutive events under the same operating condition segment are marked as low adaptability levels and have not triggered enhanced review events, the risk warning weight is increased. The next event in the same segment is then adjusted. and Adjust each step by a preset increment, and write the update time field back to the working condition weight table object.

[0052] The enhanced anomaly handling in this embodiment differs from the basic anomaly handling in Embodiment 1. Its focus is on identifying feature conflicts and the cumulative risk of consecutive events. The anomaly triggering criteria are based on whether the core quantity exceeds the reference interval for the same segment, whether it is accompanied by measurement point anomalies, and whether the average event indicator value exceeds the event density threshold. The thresholds are derived from the core quantity distribution of the last thirty stable operating events and the statistical results of the low-adaptation event density of the most recent batch of peak-shaving operating days. Priority is given to consistency verification anomalies, followed by consecutive low-adaptation event density anomalies. When a single feature quantity conflicts with other feature quantities, the consistency verification rules are used to determine whether to enter the enhanced verification process. When two consecutive events are marked as enhanced verification events, online updates to the operating condition weight table are suspended; only the generation of enhanced adaptive comprehensive values ​​is allowed without modifying the weights. When three consecutive events exhibit low-adaptation levels and the event indicator values... When the average value exceeds the preset event density threshold, the risk warning weight of the next event is increased and the upper limit of the suggested adjustment interval is shortened. The recovery condition is that the subsequent four consecutive events are all restored to the medium-adaptation level or above and no longer trigger enhanced review events. After the recovery condition is met, the enhanced review status is lifted and the online weight update is restored. The above-mentioned enhanced anomaly handling only works within the enhancement chain and does not replace the complete dimensionality reduction assessment and credibility identifier generation in Example 3.

[0053] Example 1 can complete the full assessment of a single load change event. However, under conditions of frequent switching between multiple operating conditions, dense continuous peak-shaving disturbances, and interference at local measuring points, using only fixed segment weights and single-event memory can easily lead to two types of biases. First, when the current operating condition deviates significantly from the segment center value, the basic weights cannot promptly reflect the true contribution of different characteristic quantities to stability. Second, anomalies at a single measuring point can directly transmit local distortions of coordination quantities, disturbance quantities, or recovery quantities to the final result. This example utilizes the operating condition deviation coefficient. Adaptive adjustment of the base weights to enhance the weights of the enhanced operating conditions. It can automatically adjust according to the current head, load, and guide vane opening deviation, thus avoiding the continued use of segmented center weights under boundary conditions of high head and high load or low head and low load; it can identify local noise amplification, short-term drift of measuring points, and single-feature anomalies using consistency verification rules, thereby suppressing false anomalies from entering the final output; and it utilizes enhanced memory capacity. The density of consecutive low adaptive events and the rate of change of the augmentation summation of adjacent events are jointly incorporated into the augmentation adaptive summation value. This approach can map the cumulative deterioration trend under continuous peak-shaving scenarios into stricter recommended upper limits for load change rates and recommended adjustment intervals. The sub-condition adaptability levels obtained based on this enhancement chain not only reflect the risk of the current event itself, but also the current condition location, the cumulative effect of continuous events, and the consistency of multi-source verification. Therefore, it is more suitable as a reference result for load allocation under multi-unit coordinated regulation and condition constraints.

[0054] Example 3 like Figures 1 to 3 As shown, this embodiment illustrates the boundary and anomaly schemes of a hydropower station turbine load variation adaptability assessment method. It focuses on disclosing the triggering rules for missing sensor data, excessive timestamp drift, and excessive anomaly percentage, the dimensionality reduction assessment mode switching path, the generation of credibility identifiers, and the switching to conservative regulation mode at low adaptability levels, as well as the subsequent regulation request interception chain. The inputs of this embodiment include active power data, speed deviation data, guide vane opening data, pressure pulsation data, vibration data, head data, and speed control command data, as in Embodiment 1, and the enhanced comprehensive quantity, as in Embodiment 2. Enhance memory capacity and enhanced adaptability comprehensive value In addition, data validity indicators, time synchronization offset detection results, and outlier percentage detection results are added as inputs for anomaly handling. Similar to Example 1, a unified sampling period is used. Event trigger time Baseline duration Recovery time The definitions remain unchanged; similar to Example 2, the working condition weight table objects and enhanced comprehensive quantities... Enhance memory capacity and enhanced adaptability comprehensive value The field structure and update boundaries remain unchanged. The difference lies in that this embodiment adds data validity identifier, time synchronization offset detection result, and outlier percentage detection result to the main chain, and switches the main evaluation chain to dimensionality reduction evaluation mode after an anomaly is detected. The anomaly management object uses the unit identifier field and event number field as the joint key field. The value field includes missing data ratio, timestamp offset, outlier percentage, anomaly priority, dimensionality reduction mode identifier, credibility identifier, conservative adjustment identifier, and interception identifier. The version field records the anomaly criterion version, and the update time field records the latest anomaly state generation time. When any anomaly crosses the preset threshold boundary, it is updated immediately. When two consecutive sampling cycles fall back to within the threshold, the original state is retained for one sampling cycle before being released to prevent frequent switching caused by boundary jitter.

[0055] In this embodiment, the sources of each variable include the event recalculation record object of embodiment 1, the enhanced result object of embodiment 2, the anomaly management object, and the anomaly mapping record object; among them, the proportion of missing data, timestamp offset, and anomaly point proportion come from the anomaly management object, the set of available core quantity identifiers comes from the anomaly priority judgment result, the enhanced comprehensive quantity and enhanced memory quantity come from the enhanced result object of embodiment 2, and the credibility level and interception identifier are written into the anomaly mapping record object.

[0056] In this embodiment, based on the unified preprocessing results completed in Embodiments 1 and 2, three types of basic abnormal quantities for the current event are calculated: missing data ratio, timestamp offset, and outlier percentage. The missing data ratio represents the proportion of invalid sampling points within the event window to the total number of expected sampling points. The timestamp offset represents the maximum offset sampling period between the current data stream and the unified time reference. The outlier percentage represents the proportion of sampling points still marked as out-of-bounds anomalies after amplitude limiting correction to the total number of sampling points in the current event window. After obtaining these three types of basic abnormal quantities, an anomaly triggering criterion is constructed. This criterion is used to determine whether the current event has entered the dimensionality reduction evaluation mode, and can be written as formula (9): in, Indicates the first The number of abnormal triggers for this event. This indicates the percentage of missing data for the current event. Indicates the threshold for the proportion of missing data. This represents the timestamp offset of the current event. This represents the timestamp offset threshold. This indicates the percentage of outliers in the current event. This indicates the threshold representing the percentage of outliers. , and This represents the weights of the three types of outlier components. Determined by the 80th percentile of the distribution of missing data proportions in the most recent 30 stable operating events. Take the maximum number of synchronization offset sampling periods allowed under a uniform sampling period. The thresholds are determined by the 85th percentile of the distribution of anomalies in the last 30 stable running events, and the sources of the above thresholds are all written into the version field of the anomaly management object. , and The initial value is obtained by normalizing the contribution rates of the three types of anomalous components in historical anomalous events that led to misjudgments. If or , , If any component exceeds its threshold, the current event is set as a candidate event for dimensionality reduction. The output is the anomaly trigger quantity. Its output goes to the exception priority judgment module and the dimension reduction evaluation switching module. Its technical function is to uniformly map different types of exception sources to comparable exception trigger strengths.

[0057] After identifying the current event as a candidate event for dimensionality reduction, it is necessary to further determine the anomaly priority and the set of retainable features. This embodiment divides the anomaly priority into three categories: synchronization anomalies (first priority), missing data anomalies (second priority), and anomaly exceeding limits (third priority). When the timestamp offset exceeds a threshold, synchronization anomalies are processed first; when the timestamp offset does not exceed the threshold but the proportion of missing data exceeds the threshold, missing data anomalies are processed first; anomaly exceeding limits is only determined based on the proportion of anomalies when neither of the above two types of anomalies is triggered. To ensure that the dimensionality reduction evaluation mode still retains the computability of the main chain, this embodiment uses a binary usability identifier to represent whether the four core quantities are retained, where... Indicates the first In the second incident Whether a core variable is available; when the corresponding core variable is available, the value is [value]. When unavailable, the value is [value]. The four core quantities correspond to incentive quantities, coordination quantities, disturbance quantities, and recovery quantities, respectively. If the original data upon which a core quantity depends is completely removed in the current event due to exception priority handling, then the corresponding... Values The identifier can be used to directly enter the calculation of the dimensionality reduction comprehensive quantity, without having to construct an intermediate weight formula separately.

[0058] After obtaining the set of retainable features, a dimension-reduced synthesis is constructed. The dimension-reduced synthesis is used to ensure that the event-level adaptive evaluation is still computable when some feature chains fail, and can be written as formula (10): in, Indicates the first The dimensionality reduction comprehensive quantity for this event has the same meaning as in Examples 1 and 2. If only three of the four core quantities are retained, the denominator is the sum of the weights of the three available core quantities; if only two are retained, the denominator is the sum of the weights of the two available core quantities; when there are fewer than two available core quantities, the current dimensionality reduction comprehensive quantity calculation is terminated and an unreliable evaluation flag is output. The output result is the dimensionality reduction comprehensive quantity. Its output goes to the credibility identifier generation module and the conservative adjustment mode switching module. Its technical function is to reuse the main evaluation criteria of Implementation 1 and Implementation 2 without introducing new creative logic, so as to ensure that adaptive evaluation results with engineering significance can still be obtained under abnormal scenarios.

[0059] After dimensionality reduction and comprehensive analysis, a confidence indicator and a dimensionality reduction adaptive result are constructed. The confidence indicator is used to represent the degree of usability of the current result in subsequent control, and the dimensionality reduction adaptive result is used to trigger the conservative adjustment mode and the adjustment request interception, which can be written as formula (11): in, Indicates the first The credibility value of this event. , , and This indicates that the credibility factor is reduced by a certain weight. This represents the enhanced overall quantity obtained in the same way as in Example 2. This represents the smallest positive integers whose denominators are zero. Indicates the first The comprehensive value of dimensionality reduction adaptability for this event. This indicates the weights adjusted for dimensionality reduction and memory. This indicates that the previous event enhances memory capacity. Indicates the interception flag for adjustment requests. This indicates an indicator function that takes the value when the condition within the parentheses is true. Otherwise, the value is , Indicates the lower limit of credibility. This indicates the same low-adaptation level threshold used in Example 2. The 30th percentile of the confidence value distribution of the most recent 30 stable operating events is taken, and the threshold is derived from the statistical results of confidence values ​​in the abnormal event review database. , , and The initial value is obtained by normalizing the contribution rate of each anomalous component in historical anomalies to the false positive rate. The output is the confidence value. Dimensionality reduction adaptive comprehensive value and adjustment request interception flag Its output destinations are the unit monitoring host, the speed governor upper control module, and the load allocation decision module, respectively. Its technical function is to transform the degree of anomaly, the dimensionality reduction deviation, and the previous memory state into executable conservative control constraints.

[0060] This embodiment also sets up an anomaly mapping record object to store the source of credibility and action switching. The anomaly mapping record object uses the unit identifier field, event number field, and anomaly criterion version field as a joint key field. Value fields include missing data ratio, timestamp offset, anomaly point ratio, available core quantity identifier set, dimensionality reduction comprehensive quantity, enhanced comprehensive quantity, dimensionality reduction deviation ratio, credibility value, credibility level, dimensionality reduction adaptive comprehensive value, conservative adjustment flag, and adjustment request interception flag. The credibility level is divided according to the relationship between the credibility value and the credibility lower limit: when the credibility value is not lower than the credibility lower limit and the dimensionality reduction adaptive comprehensive value is not lower than the low adaptation level threshold, the credibility level is set to "adoptable," and only a suggested reduction in the upper limit of the load change rate is triggered; when the credibility value is not lower than the credibility lower limit but the dimensionality reduction adaptive comprehensive value is lower than the low adaptation level threshold, the credibility level is set to "conservative adoption required," and a conservative adjustment flag is triggered; when the credibility value is lower than the credibility lower limit, the credibility level is set to "unreliable adoption," and an adjustment request interception flag is triggered. This object does not add new evaluation formulas, but only saves the input, output and action mapping results of formulas (9), (10) and (11) for subsequent tracing of why abnormal events enter the conservative adjustment or interception process.

[0061] The working process is as follows: After Example 1 and Example 2 have completed the calculation of the basic feature quantity, enhanced comprehensive quantity and enhanced memory quantity of the current event, this example first reads the number of invalid sampling points, the maximum synchronization offset sampling period number and the number of abnormal points of the current event, and calculates the abnormal trigger quantity using formula (9). ;like Furthermore, if none of the three types of abnormal basic quantities exceed the limit individually, the current event retains the output result of Example 2 and does not enter the dimensionality reduction evaluation mode; if If any single abnormal basic quantity exceeds the limit, the current abnormal priority is determined according to the order of synchronous abnormality priority, missing abnormality second priority, and abnormal point exceeding the limit third priority, and a set of retainable feature quantities is generated. The dimensionality reduction comprehensive quantity is then calculated using formula (10). Then, formula (11) is used to obtain the confidence value. Dimensionality reduction adaptive comprehensive value and adjustment request interception flag And execute according to the following action chain: When and When the current adjustment request remains executable, the upper limit of the suggested load change rate will only be lowered by a preset step; when but When this happens, switch to conservative adjustment mode and extend the suggested adjustment interval by a preset step; when At that time, set the adjustment request interception flag. for The system stops current load increase requests, writes a frozen state to the governor's upper control module, and outputs an unreliable assessment prompt to the unit monitoring host. When the proportion of missing data, timestamp offset, and anomaly point proportion all fall back to within the threshold in the subsequent five consecutive unified sampling periods, the frozen state is lifted and the enhanced output of Example 2 is restored. The anomaly management object updates the version field and update time field each time the state changes. The key field of the conservative adjustment mode object is the unit identifier field, and the value fields include the conservative mode identifier, trigger reason field, recovery condition field, and most recent update time field. When the trigger reason disappears and the confidence value recovers to above the threshold, the system writes back to the normal mode.

[0062] This embodiment emphasizes quantified triggering, threshold sources, priority handling, and the minimum executable action chain in its anomaly and recovery path. The anomaly chain is prioritized according to the order of timestamp offset anomaly, missing data anomaly, and anomaly exceeding limits. The threshold sources are the unified sampling period configuration, the statistical results of the proportion of missing data in stable operating samples, and the statistical results of the proportion of anomalies in stable operating samples, respectively. When the timestamp offset exceeds the threshold within two consecutive unified sampling periods, resynchronization detection is prioritized. If the offset is not recovered after resynchronization, the dimensionality reduction evaluation mode is entered, and updating the working condition weight table object is prohibited. When the proportion of missing data exceeds the threshold but the timestamp offset does not exceed the limit, the weight corresponding to the missing data is reduced, and the dimensionality reduction comprehensive quantity is recalculated. If the confidence value is still lower than the threshold after recalculation, the conservative adjustment mode is entered. When the proportion of anomalies exceeds the threshold and the first two types of anomalies are not triggered, the anomaly segment is blocked, and the dimensionality reduction comprehensive quantity is recalculated. If the recalculation results in an adjustment request interception flag, the threshold is adjusted accordingly. Still If the current load increase request is blocked, the reason for the block is written to the exception management object. Recovery requires at least two conditions to be met simultaneously: the base exception quantity must continuously fall below the threshold and the confidence value must recover to above the threshold, thus avoiding frequent unlocking near the boundary state. If three consecutive events are blocked due to low confidence values, the current operating condition segment of the unit is marked as a high-risk segment, and this mark is written to the risk segment object in the load allocation decision module. The key fields of the risk segment object include the unit identifier field and the operating condition segment field, and the value fields include the risk level field, the number of consecutive block times field, the update time field, and the failure time field. When four subsequent consecutive events in the same segment recover to an executable state, the high-risk segment mark is cleared.

[0063] In the scenario of assessing turbine load variations in hydropower stations, abnormal data does not necessarily mean that all assessments must be stopped. Instead, it is necessary to determine whether usable assessment results can continue to be obtained based on the type and intensity of the anomaly and the retainable characteristic chain. Anomaly trigger quantity. By unifying the proportion of missing data, timestamp offset, and outlier percentage into a single anomaly trigger criterion, comparable priorities can be established between different anomaly types; dimensionality reduction and comprehensive analysis. Through available identifiers The system retains computable core quantities and automatically excludes weights corresponding to unusable core quantities, ensuring that the main evaluation logic of Examples 1 and 2 continues even when some core quantities fail, instead of simply discarding the entire event; confidence value. By incorporating the degree of anomaly, the deviation between the dimensionality reduction and enhancement results, and the memory state of preceding events into the final judgment, the admission of anomaly events for continued participation in adjustment requests is no longer determined solely by a single threshold. Through this causal chain, when the anomaly is minor and the main chain can still be preserved, the system can output a comprehensive dimensionality reduction adaptive value and provide adjustment suggestions with conservative constraints. When the anomaly is severe, the dimensionality reduction deviation is too large, or the confidence value is too low, the system can execute the action chain of freezing, switching to conservative adjustment mode, and intercepting adjustment requests, thereby ensuring that the evaluation results in anomaly scenarios still maintain engineering executability and control security.

[0064] The above description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be modified within the scope of the concept described herein by means of the above teachings or the technology or knowledge in related fields.

Claims

1. A method for assessing the adaptability of hydropower station turbines to load variations, characterized in that, include: The system acquires active power data, speed deviation data, guide vane opening data, pressure pulsation data, vibration data, head data, and speed control command data of the hydropower station turbine during load changes. Based on the speed control command data and active power data, it identifies the starting point of the load change event and establishes an event window covering the baseline stage before the change, the execution stage of the change, and the recovery stage after the change. It performs time synchronization, anomaly correction, resampling, and normalization preprocessing on each data item in the event window to obtain dynamic response data corresponding to the current load change event. It then extracts guide vane opening change characteristics, power ramp-up characteristics, load change amplitude characteristics, speed deviation attenuation characteristics, pressure pulsation fluctuation characteristics, and vibration envelope fluctuation characteristics from the dynamic response data. Based on the dynamic response data, an excitation quantity characterizing the load disturbance intensity, a coordination quantity characterizing the execution chain tracking relationship, a disturbance quantity characterizing the degree of hydraulic mechanical coupling fluctuation, and a recovery quantity characterizing the unit's recovery speed and degree of recovery completion are constructed. The excitation quantity is determined based on the load change amplitude characteristics and power ramp characteristics; the coordination quantity is determined based on the deviation relationship between the guide vane opening response, power ramp response, and speed deviation convergence response relative to the baseline response; the disturbance quantity is determined based on pressure pulsation fluctuation characteristics, vibration envelope fluctuation characteristics, and maximum speed deviation; and the recovery quantity is based on power recovery... The homing process, speed deviation decay process, and stabilization and decline process are determined; based on the current head segment, current load segment, and current guide vane opening segment, the operating condition weights corresponding to the excitation quantity, coordination quantity, disturbance quantity, and recovery quantity are determined; weighted coupling processing is performed on the coordination quantity, recovery quantity, disturbance quantity, and excitation quantity to obtain the comprehensive quantity of the current event; based on the comprehensive quantity of the current event, the comprehensive quantity of the previous load change event, the memory quantity of the previous load change event, and the interval between adjacent load change events, the memory quantity of the current event is updated; and the adaptive result of the current event is generated based on the comprehensive quantity and the memory quantity. Based on the adaptability results, the system is divided into high adaptability, medium adaptability, and low adaptability levels. It outputs the adaptability level, the upper limit of the recommended load change rate, the recommended adjustment interval, and the confidence index. The adaptability level, the upper limit of the recommended load change rate, the recommended adjustment interval, and the confidence index are sent to the unit monitoring host, the governor upper control module, and the load allocation decision module to constrain the subsequent load adjustment process and the conservative adjustment mode switching process, and to perform access judgment on subsequent adjustment requests.

2. The method for assessing the adaptability of hydropower station turbine load variations according to claim 1, characterized in that, Event identification and window establishment include: determining the event trigger time based on the time of speed control command change and the starting point of active power change; determining the window start time based on the baseline duration before the event trigger time; and determining the window end time based on the recovery duration after the event trigger time.

3. The method for assessing the adaptability of hydropower station turbine load variations according to claim 1, characterized in that, The normalization preprocessing includes: performing synchronization alignment on the active power data, speed deviation data, guide vane opening data, pressure pulsation data, vibration data, head data, and speed control command data based on a unified timestamp; removing isolated outliers based on sliding median processing and amplitude limiting correction; and performing dimensionless transformation on the preprocessed data based on rated power, rated speed, and reference head.

4. The method for assessing the adaptability of hydropower station turbine load variations according to claim 1, characterized in that, The coordination quantity is determined based on the deviation relationship between the guide vane opening response sequence, power ramp response sequence, and speed deviation convergence sequence and the corresponding baseline expected response sequence, and is used to characterize the degree of tracking consistency between the actuator action and the unit dynamic response.

5. The method for assessing the adaptability of hydropower station turbine load variations according to claim 1, characterized in that, The disturbance amount is determined based on the pressure pulsation fluctuation characteristics, vibration envelope fluctuation characteristics, and maximum speed deviation within the event window, and is used to characterize the degree of coupling amplification of hydraulic pulsation and mechanical response caused by load change events.

6. The method for assessing the adaptability of hydropower station turbine load variations according to claim 1, characterized in that, The recovery amount is determined jointly based on the recovery process of the power return target range, the speed deviation decay process, and the stabilization process of pressure pulsation and vibration falling back to the stable threshold range. It is used to characterize the unit's ability to absorb disturbances and restore a stable operating state.

7. The method for assessing the adaptability of hydropower station turbine load variations according to claim 1, characterized in that, The operating condition weights are formed offline based on historical stable operating samples, segmented by head, load, and guide vane opening, and are invoked according to the current operating condition range when a current load change event occurs.

8. The method for assessing the adaptability of hydropower station turbine load variations according to claim 1, characterized in that, The memory value is updated based on the changing trend of the current event's aggregate value and the aggregate value of the previous event, as well as the interval between adjacent events, and is used to characterize the adaptive cumulative change state during continuous load changes.

9. The method for assessing the adaptability of hydropower station turbine load variations according to claim 1, characterized in that, The adaptive results include at least the adaptive composite value, adaptive level, recommended upper limit of load change rate, recommended adjustment interval, and confidence level. The unit monitoring host receives the adaptive composite value and adaptive level, the governor upper control module receives the recommended upper limit of load change rate and recommended adjustment interval, and the load allocation decision module receives the confidence level and adaptive level.

10. The method for assessing the adaptability of hydropower station turbine load variations according to claim 1, characterized in that, When any of the following situations are detected: missing sensor data, timestamp drift exceeding the limit, or the proportion of outliers in the event window exceeding the limit, the weight of the missing data is reduced and the system switches to dimensionality reduction evaluation mode. Based on the dimensionality reduction evaluation mode, an adaptive result with a confidence label is output.

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