A heat recovery system and method for the lubricating oil system of a power plant steam turbine unit.

Abstract

This application discloses a heat recovery system and method for a power plant turbine lubricating oil system, relating to the field of steam turbine power generation equipment technology. The method includes: real-time acquisition of real-time operating data and historical operating logs from multiple systems in a target scenario; dynamic performance baseline identification of the heat exchanger between the lubricating oil system and the heat transfer medium recovery system to obtain a real-time performance baseline characterizing the current heat transfer performance; prediction of the thermal state change trend of the lubricating oil system within a preset future time period; calculation of feedforward control quantities for adjusting the flow rate of the heat transfer medium; generation of a composite control command for the flow rate of the heat transfer medium; and execution of heat recovery control. This application solves the technical problems of low heat recovery efficiency, high energy consumption of cooling equipment, and lag in lubricating oil temperature control in existing power plant turbine lubricating oil systems.

Description

Technical Field

[0001] This application relates to the field of steam turbine power generation equipment technology, specifically to a heat recovery system and method for the lubricating oil system of a power plant steam turbine unit. Background Technology

[0002] As power plant technologies evolve towards higher parameters and larger capacities, the thermal management of turbine lubrication systems is becoming increasingly prominent. Traditional lubrication systems typically employ water or air cooling for direct heat dissipation, leading to heat waste. Simultaneously, the energy consumption of cooling equipment continues to rise, hindering heat recovery efficiency and increasing unit operation and maintenance costs. Even with simple PID automatic control technology for heat recovery, the technical problem of lubrication oil temperature control lag persists. Summary of the Invention

[0003] This application provides a heat recovery system and method for the lubricating oil system of a power plant turbine unit, which solves the technical problems of low heat recovery efficiency, high energy consumption of cooling equipment, and lagging lubricating oil temperature control in existing lubricating oil systems.

[0004] The technical solution to the above-mentioned technical problems in this application is as follows:

[0005] In a first aspect, this application provides a heat recovery system for a power plant turbine unit lubricating oil system, comprising:

[0006] The data acquisition unit is used to collect real-time operating data and historical operating logs from multiple systems in the target scenario.

[0007] The performance baseline update unit is used to identify the dynamic performance baseline of the heat exchanger between the lubricating oil system and the heat transfer medium recovery system based on the historical operation log, and obtain the real-time performance baseline characterizing the current heat transfer performance.

[0008] The thermal dynamic prediction unit is used to predict the trend of thermal state change of the lubricating oil system within a future preset period based on the real-time operating data.

[0009] The feedforward control calculation unit is used to combine the real-time performance baseline with the thermal state change trend to calculate the feedforward control quantity for adjusting the flow rate of the heat transfer medium with the goal of maintaining the stable temperature in front of the lubricating oil tank.

[0010] The composite control output unit is used to generate a composite control command for the flow rate of the heat transfer medium based on the feedforward control quantity, and to execute heat recovery control.

[0011] Secondly, this application provides a method for heat recovery in a power plant turbine unit lubricating oil system, the method comprising:

[0012] Real-time collection of real-time operational data and historical operational logs from multiple systems in the target scenario;

[0013] Based on the historical operation log, dynamic performance baseline identification is performed on the heat exchanger between the lubricating oil system and the heat transfer medium recovery system to obtain a real-time performance baseline characterizing the current heat transfer performance.

[0014] Based on the real-time operating data, predict the trend of thermal state change of the lubricating oil system within a future preset time period;

[0015] Based on the real-time performance baseline and the thermal state change trend, with the goal of maintaining a stable temperature in front of the lubricating oil tank, a feedforward control quantity for adjusting the flow rate of the heat transfer medium is calculated.

[0016] Based on the feedforward control quantity, a composite control command for the heat transfer medium flow rate is generated to execute heat recovery control.

[0017] This application provides one or more technical solutions, which have at least the following technical effects or advantages:

[0018] This application provides a heat recovery system and method for a power plant turbine unit's lubricating oil system. First, a data acquisition unit collects real-time operating data and historical operating logs of the lubricating oil system, heat transfer medium recovery system, and main unit under a target scenario. Second, a performance baseline update unit, based on historical operating logs, dynamically identifies the performance baseline of the heat exchanger, obtaining a real-time performance baseline characterizing the current heat transfer performance and reflecting the actual heat transfer capacity of the heat exchanger under different operating conditions. Third, a thermal dynamic prediction unit uses real-time operating data to predict the thermal state change trend of the lubricating oil system within a preset time period, enabling the system to anticipate potential fluctuations in lubricating oil temperature and shift from passive control to proactive prevention. Then, a feedforward control calculation unit combines the real-time performance baseline and thermal state change trend to calculate a feedforward control quantity with the goal of maintaining a stable temperature before the lubricating oil tank, pre-adjusting the heat transfer medium flow rate from the source to reduce the possibility of temperature fluctuations. Finally, a composite control output unit generates composite control commands based on the feedforward control quantity to execute heat recovery control, considering not only predicted thermal state changes but also actual temperature deviations during operation for feedback adjustment, thereby improving heat recovery efficiency.

[0019] Through the above technical solution, this application reduces the energy consumption of the cooling equipment and effectively improves the problem of lag in lubricating oil temperature control, achieving the dual goals of efficient heat recovery and temperature control of the lubricating oil system. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of the structure of a heat recovery system for a power plant turbine unit lubrication oil system provided in an embodiment of this application;

[0022] Figure 2 This is a schematic flowchart of a heat recovery method for a power plant turbine unit lubrication oil system provided in an embodiment of this application.

[0023] The components represented by each number in the attached diagram are explained below:

[0024] Data acquisition unit 11, performance baseline update unit 12, thermal dynamic prediction unit 13, feedforward control calculation unit 14, composite control output unit 15. Detailed Implementation

[0025] This application provides a heat recovery system and method for the lubricating oil system of a power plant turbine unit, which addresses the technical problems of low heat recovery efficiency, high energy consumption of cooling equipment, and lag in lubricating oil temperature control in existing lubricating oil systems.

[0026] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0027] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0028] In the description of this application, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use this application. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that this application can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid unnecessarily obscuring the description of this application. Therefore, this application is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0029] Example 1, as Figure 1 As shown in the figure, this application provides a heat recovery system for the lubricating oil system of a power plant turbine unit, including:

[0030] Data acquisition unit 11 is used to collect real-time operating data and historical operating logs of multiple systems in the target scenario.

[0031] In this embodiment of the application, the lubricating oil temperature and lubricating oil volumetric flow rate are collected by temperature sensors and volumetric flow meters arranged at the main engine end outlet and in front of the lubricating oil tank of the turbine unit lubricating oil system.

[0032] Meanwhile, temperature sensors and medium volume flow meters are installed in the heat transfer medium recovery system before and after recovery to collect the temperature of the heat transfer medium before recovery, the temperature of the heat transfer medium after recovery, and the medium volume flow rate.

[0033] In addition, it communicates with the power plant's DCS system or other data storage servers to obtain recent and historical operation logs containing historical records of the above parameters, as well as host load parameters of the host system.

[0034] Specifically, the data acquisition unit 11 in the system includes:

[0035] Obtain the lubricating oil system's operating parameters and historical lubricating oil operating logs;

[0036] Obtain the operating parameters and historical operating logs of the heat transfer medium recovery system;

[0037] Obtain host system load parameters and historical host operation logs;

[0038] The lubricating oil operating parameters, the medium operating parameters, and the main unit load parameters are output as the real-time operating data;

[0039] The historical lubricating oil operation log, the historical media operation log, and the historical host operation log are output as the historical operation log.

[0040] In this embodiment of the application, firstly, a data connection is established with the sensor network of the lubricating oil system to obtain the lubricating oil operating parameters in real time, including the temperature of the lubricating oil at different key nodes, such as the outlet of the main bearing, the inlet and outlet of the cooler, the inlet of the lubricating oil tank, the real-time volumetric flow rate of the lubricating oil, the pressure of the lubricating oil, and the liquid level of the lubricating oil tank.

[0041] At the same time, historical records related to lubricating oil are retrieved and integrated from the historical database to form a historical lubricating oil operation log, which includes changes in the above-mentioned lubricating oil operation parameters in different time periods in the past, system start-up and shutdown records, maintenance records, and information on abnormal operating conditions that have occurred.

[0042] Secondly, for the heat transfer medium recovery system, temperature sensors, volumetric flow meters, and pressure sensors deployed on the system's pipelines are used to collect media operating parameters such as the inlet temperature of the heat transfer medium before entering the heat exchanger, the outlet temperature after flowing out of the heat exchanger, the real-time volumetric flow rate of the medium in the pipeline, and the operating pressure of the medium.

[0043] Accordingly, the historical media operation log containing information such as historical data of the aforementioned media operation parameters, media replenishment records, and historical operation status of related pump units is obtained from the historical storage module of the heat transfer medium recovery system itself.

[0044] Secondly, data exchange is conducted with the control center or DCS system of the power plant's main unit system to obtain real-time load parameters of the main unit system, such as the main unit's power generation, steam flow, turbine speed or shaft power, which can characterize the current operating load level of the main unit.

[0045] Meanwhile, the host system's historical database is used to extract host load parameter variation curves, host start-up and shutdown records, load adjustment command records, and the historical operating status of key auxiliary machines, which together form the historical host operation log.

[0046] Finally, the real-time collected lubricating oil operating parameters, medium operating parameters, and host load parameters are processed for data format standardization and timestamp alignment to ensure consistency and accuracy in the time dimension. Then, they are integrated, packaged, and output as real-time operating data required for subsequent processing.

[0047] Specifically, the historical lubricating oil operation log, historical media operation log, and historical host operation log are cleaned to remove invalid data and outliers. Missing data is interpolated or marked appropriately, and data is aggregated according to a preset time granularity, such as hourly or daily. Finally, the three types of historical logs are integrated and output as the historical operation log required by the system.

[0048] The performance baseline update unit 12 is used to identify the dynamic performance baseline of the heat exchanger between the lubricating oil system and the heat transfer medium recovery system based on the historical operation log, and obtain the real-time performance baseline characterizing the current heat transfer performance.

[0049] The lubricating oil system and the heat transfer medium recovery system exchange heat through a heat exchange component located in front of the lubricating oil tank.

[0050] In this embodiment, firstly, based on the historical operating logs obtained through the above integration, sample data of the heat exchanger under normal operating conditions are selected. An improved least-squares support vector machine algorithm is then used to train the selected sample data, constructing a mapping model between the heat exchanger's heat transfer performance and various influencing factors.

[0051] Then, based on the real-time collected lubricating oil operating parameters and medium operating parameters, the logarithmic mean temperature difference and actual heat exchange rate under the current operating conditions are calculated and input into the trained mapping model to obtain the predicted value of the current heat transfer performance.

[0052] Simultaneously, by combining baseline heat transfer performance data under the same operating conditions from historical operation logs, and using the sliding window mean comparison method, the deviation correction coefficient of the predicted values ​​is dynamically adjusted to generate a real-time performance baseline that reflects the current actual heat transfer capacity of the heat exchanger. This real-time performance baseline is continuously updated as operating time and operating conditions change.

[0053] The heat exchange components utilize plate heat exchangers, with plates made of 316L stainless steel, which offers corrosion resistance and thermal conductivity, enabling them to withstand long-term contact between lubricating oil and the heat transfer medium. The plate heat exchanger features a herringbone corrugated design, enhancing fluid turbulence and improving the heat transfer coefficient. Compared to traditional shell-and-tube heat exchangers, this design increases heat transfer efficiency by 30%-40% for the same heat exchange area.

[0054] Meanwhile, the heat exchanger's sealing gaskets are made of EPDM rubber, with a temperature resistance range of -40℃ to 150℃, ensuring good sealing performance and service life within the normal operating temperature range of the lubricating oil system. Furthermore, the heat exchanger features a detachable structure, facilitating regular cleaning of oil and impurities from the plate surfaces to maintain stable heat transfer performance. When gaskets or plates need to be replaced, the entire equipment does not need to be disassembled, significantly reducing maintenance time.

[0055] Specifically, the performance baseline update unit 12 in the system includes:

[0056] Based on a preset neighborhood time window, nearby data is extracted from the historical operation log to obtain the nearby operation log;

[0057] Based on the nearby operation log, the first lubricating oil temperature at the main unit outlet of the lubricating oil system, the second lubricating oil temperature in front of the lubricating oil tank, and the lubricating oil volume flow rate are obtained and output as the first real-time parameter set.

[0058] Based on the nearby operation log, the temperature before recovery, the temperature after recovery, and the volumetric flow rate of the heat transfer medium recovery system are obtained and output as a second real-time parameter set.

[0059] Based on the first real-time parameter set and the second real-time parameter set, and combined with a preset heat transfer basic model, an inversion operation is performed to obtain the real-time performance baseline.

[0060] In this embodiment, firstly, a preset neighborhood time window is set, such as the past 24 hours or the most recent 1000 sets of sampled data. The nearest operating log that is closest to the current operating condition is extracted from the historical operating log. A representative historical data segment is selected to ensure the accuracy and timeliness of subsequent performance baseline identification.

[0061] Secondly, from the extracted recent operation logs, the first lubricating oil temperature at the main unit outlet, the second lubricating oil temperature in front of the lubricating oil tank, and the lubricating oil volumetric flow rate are selected.

[0062] The first lubricating oil temperature is the initial temperature of the lubricating oil after it leaves the main bearing, and the second lubricating oil temperature is the temperature of the lubricating oil before it enters the oil tank. The first lubricating oil temperature, the second lubricating oil temperature and the lubricating oil volume flow rate are combined and output as the first real-time parameter set, which reflects the initial state, target state and flow conditions of the lubricating oil in the heat exchange process.

[0063] At the same time, the temperature before and after heat transfer medium recovery, as well as the volumetric flow rate of the heat transfer medium recovery system, are obtained from the nearby operation log.

[0064] Among them, the temperature before recovery is the temperature of the heat transfer medium before entering the heat exchanger, and the temperature after recovery is the temperature of the heat transfer medium after absorbing heat from the lubricating oil and leaving the heat exchanger. The temperature before recovery, the temperature after recovery, and the volume flow rate of the medium are combined and output as the second real-time parameter set, which reflects the state changes and flow conditions of the heat transfer medium during the heat exchange process.

[0065] Finally, the first and second real-time parameter sets are substituted into a preset heat transfer basic model, such as a heat exchanger heat transfer calculation model established based on the ε-NTU method or the logarithmic mean temperature difference method, and the key performance parameters such as the real-time heat transfer coefficient or the total heat transfer resistance of the heat exchanger are solved by inversion calculation.

[0066] Furthermore, based on the historical operation logs, dynamic performance baseline identification is performed on the heat exchanger between the lubricating oil system and the heat transfer medium recovery system to obtain a real-time performance baseline characterizing the current heat transfer performance, including:

[0067] Obtain the sequence of media-side operating parameters for the current time window;

[0068] Input the sequence of operating parameters on the medium side into the pre-constructed heat transfer performance degradation mapping model to obtain the heat transfer performance degradation factor;

[0069] The real-time performance baseline is calculated based on the performance degradation factor and the baseline heat transfer performance obtained from the design data.

[0070] In this embodiment of the application, firstly, the sequence of operating parameters of the heat transfer medium recovery system is extracted from the historical operation log corresponding to the current time window. This sequence includes continuous data points of parameters such as heat transfer medium volume flow rate, temperature before recovery, temperature after recovery, and medium operating pressure collected at fixed time intervals, such as every minute, within a preset time window, such as the past 1 hour.

[0071] Secondly, the extracted sequence of operating parameters on the medium side is input into a pre-constructed heat transfer performance degradation mapping model. This model is trained based on the performance degradation patterns of the heat exchanger during different operating periods from historical data. For example, it is constructed using methods such as neural networks or multiple regression by analyzing the correlation between changes in medium flow rate, temperature difference, and heat transfer coefficient degradation.

[0072] Furthermore, after receiving the sequence of operating parameters on the medium side, the model calculates the changing trend, fluctuation range, and deviation from historical benchmark data of each indicator in the parameter sequence, and outputs a heat transfer performance degradation factor that can quantify the degree of current heat transfer performance degradation of the heat exchanger. The value range of this factor is usually from 0 to 1. The closer it is to 1, the smaller the performance degradation and the closer the heat transfer effect is to the initial design state.

[0073] Finally, based on the reference heat transfer performance parameters of the heat exchanger under rated operating conditions provided in the design data, such as the design heat transfer coefficient and the design heat transfer capacity, the reference heat transfer performance is multiplied by (1 - heat transfer performance attenuation factor) to calculate the real-time performance baseline that takes into account the current performance attenuation. This baseline can accurately reflect the actual heat transfer capacity of the heat exchanger under the current operating conditions.

[0074] The construction of the attenuation mapping model includes:

[0075] Obtain historical media operation logs and extract the media-side operation parameter sequence, wherein the media-side operation parameter sequence includes the average flow rate and average inlet temperature of the heat transfer medium;

[0076] The historical average heat transfer coefficient, which is consistent with the time sequence of the medium-side operating parameters, is obtained from the historical operation log. The historical average heat transfer coefficient is calculated based on the inversion of a preset basic heat transfer model.

[0077] The heat transfer performance degradation mapping model is obtained by using the feature vector formed by the average flow rate and average inlet temperature of the heat transfer medium as the model input and the corresponding historical average heat transfer coefficient as the model output target, and supervising the training of the preset regression model.

[0078] In this embodiment, firstly, sample data containing complete operating cycles is selected from historical media operation logs, such as media operation parameter records for 24 consecutive hours each day over the past 6 months, to ensure the comprehensiveness and representativeness of the data. For each preset time sub-window, such as every hour, the average volumetric flow rate of the heat transfer medium within that time period is calculated as the average flow rate, and the average inlet temperature of the heat transfer medium before entering the heat exchanger is calculated as the average inlet temperature. The two average values ​​are combined to form a feature vector in the media-side operating parameter sequence.

[0079] Secondly, for the historical operating data that corresponds completely to the above feature vectors in time, based on the preset heat transfer basic model, the actual heat transfer coefficient of the heat exchanger in that time period is calculated by using the lubricating oil operating parameters during that time period, including the first lubricating oil temperature, the second lubricating oil temperature, the lubricating oil volume flow rate, and the medium operating parameters, including the temperature before recovery, the temperature after recovery, and the medium volume flow rate, and this is used as the historical average heat transfer coefficient.

[0080] Subsequently, a support vector regression model was selected, and the training and validation sets were divided in a 7:3 ratio. The model was trained using supervised learning. All extracted feature vectors were used as the input sample set, i.e., average flow rate and average inlet temperature were used as inputs. The number of nodes in the input layer was equal to the dimension of the input features. For example, if there were two features in the input layer, then the input layer would contain two nodes. The corresponding historical average heat transfer coefficient was used as the output sample set. The output layer generally does not use an activation function. For example, if the output time has two nodes, a continuous value is directly output. One to three hidden layers were set, and the number of nodes in each layer was adjusted experimentally, such as 64 or 32. The ReLU activation function was selected.

[0081] During training, the hyperparameters of the model are continuously adjusted to minimize the prediction error on the validation set, such as the root mean square error. When the validation set loss does not decrease for five consecutive rounds, the training process is automatically terminated, resulting in a heat transfer performance degradation mapping model that can map the relationship between the operating parameters on the medium side and the heat transfer coefficient.

[0082] The thermal dynamic prediction unit 13 is used to predict the trend of thermal state change of the lubricating oil system within a future preset period based on the real-time operating data.

[0083] Furthermore, based on the real-time operating data, the trend of thermal state changes in the lubricating oil system within a preset time period is predicted, including:

[0084] Based on the historical operation logs, extract the lubricating oil temperature time-series data of the lubricating oil system and the host load parameter time-series data of the host system.

[0085] Trend analysis was performed on the time-series data of lubricating oil temperature and the time-series data of host load parameters to obtain the trends of lubricating oil temperature and host load.

[0086] With the goal of maximizing trend similarity, the lubricating oil temperature trend and the main engine load trend are trend aligned, and the typical lubricating oil-load delay is determined based on the trend alignment results;

[0087] Based on the typical lubricating oil-load delay, the lubricating oil temperature time-series data and the host load parameter time-series data are reorganized based on time-series adjustment to establish a delay-free mapping relationship.

[0088] Based on the data reconstruction results, a load-lubricating oil temperature mapping model based on regression analysis was established.

[0089] In this embodiment of the application, firstly, the lubricating oil temperature time series data of the lubricating oil system in multiple consecutive complete operating cycles is extracted from the historical operation log. For example, the time series of temperature parameters such as the lubricating oil tank inlet temperature and the main engine outlet oil temperature are collected every 10 seconds, as well as the corresponding main engine load parameter time series data of the main engine system in the same time period, such as the time series data of the turbine shaft power or generator output power recorded every 5 seconds.

[0090] Furthermore, the extracted lubricating oil temperature time-series data is preprocessed, including using a moving average method to remove high-frequency noise, using linear interpolation to fill in occasional short-term data gaps, and using the 3σ criterion to identify and remove anomalous jump values ​​to ensure the stationarity and continuity of the data sequence. Similarly, the same preprocessing procedure is performed on the host load parameter time-series data to eliminate data anomalies caused by sensor fluctuations or communication interference, resulting in purified lubricating oil temperature time-series and host load time-series data.

[0091] Secondly, trend analysis was performed on the preprocessed lubricating oil temperature time-series data and the main engine load parameter time-series data. A sliding window trend extraction algorithm was used, with a window size of 1 hour. The linear fitting slope of the lubricating oil temperature within each window was calculated as the temperature trend indicator. A positive slope indicates an upward temperature trend, and a larger absolute value of the slope indicates a more pronounced trend. Similarly, the linear fitting slope of the main engine load parameter time-series data within the corresponding window was calculated as the load trend indicator. Trend curves were plotted for all windows' temperature and load trend indicators. Comparative analysis revealed that changes in main engine load typically precede changes in lubricating oil temperature. For example, when the main engine load begins to rise at time t, the lubricating oil temperature only begins to show a significant upward trend at time t+Δt; this time difference represents the potential lubricating oil-load delay.

[0092] Then, aiming for the highest trend similarity, the lubricating oil temperature trend and the main engine load trend are aligned. A dynamic time warping algorithm is employed, using the main engine load trend curve as a reference sequence and the lubricating oil temperature trend curve as the sequence to be matched. By calculating the cumulative distance between the two curves, a nonlinear time warping path that minimizes the distance is sought, determining the optimal matching position for trend feature points in the two sequences. Based on the matching results, delay times under different operating conditions are statistically analyzed. For example, the average delay duration is calculated during the load rise, steady-state, and falling phases. Finally, the delay duration with the highest frequency is determined as the typical lubricating oil-load delay. Verification with actual data shows that this typical delay is typically 8-12 minutes in the 300MW steam turbine generator set of this embodiment.

[0093] Subsequently, based on the determined typical lubricating oil-load delay Δt, the lubricating oil temperature time-series data and the main engine load parameter time-series data are reconstructed using time-series adjustments. The main engine load parameter time-series data is shifted forward by Δt time, so that the load data at time t corresponds to the lubricating oil temperature data at time t+Δt, eliminating the time-series misalignment caused by the lag in the heat transfer process.

[0094] For example, if the host load at t=10:00 in the original data corresponds to the lubricating oil temperature at t=10:10, then after adjustment, the load data at 10:00 and the temperature data at 10:10 are combined into a set of associated samples. After performing the above adjustment on all time-series data, a dataset containing paired samples of "host load-lubricating oil temperature" is constructed, where each sample contains the adjusted host load value, current ambient temperature, lubricating oil flow rate and other auxiliary parameters, as well as the corresponding lubricating oil temperature value, establishing a delay-free mapping relationship to eliminate the influence of time delay.

[0095] Finally, based on the data reconstruction results, a load-lubricating oil temperature mapping model based on regression analysis was established. A gradient boosting regression tree was selected as the base model, which can capture nonlinear relationships and feature interactions. The host load parameters, ambient temperature, and lubricating oil circulation flow rate in the de-delayed mapping relationship were used as the model input features, and lubricating oil temperature as the output target variable. The dataset was divided into training and testing sets in an 8:2 ratio. The training set was used for model parameter learning, and the testing set was used to evaluate the model's generalization ability.

[0096] Furthermore, during model training, key hyperparameters are optimized using 5-fold cross-validation, such as setting the learning rate to 0.05, the maximum tree depth to 6, and the minimum number of samples per leaf node to 10, in order to balance the model's fitting ability and the risk of overfitting.

[0097] After training, the model performance is verified using test set data. If the root mean square error of the model is less than 0.5℃ and the mean absolute error is less than 0.3℃, the model is considered to have met the preset accuracy requirements. This load-lubricating oil temperature mapping model can be used to accurately predict the trend of lubricating oil temperature change based on the current and predicted changes in host load.

[0098] Specifically, the thermal dynamic prediction unit 13 in the system includes:

[0099] Based on the real-time operating data, the real-time host load of the host system is obtained, and the planned host load for a future preset time period is obtained simultaneously.

[0100] The real-time host load and the planned host load are combined and input into a pre-built load-lubricating oil temperature mapping model to obtain the predicted lubricating oil temperature sequence;

[0101] Curve fitting is performed on the predicted lubricating oil temperature sequence to obtain the trend of thermal state change.

[0102] In this embodiment, firstly, the real-time main load of the main engine system at the current moment is read from the power plant's real-time data acquisition and monitoring system, for example, the actual output power of the steam turbine is currently 280MW. Simultaneously, the planned main engine load for a future preset time period is obtained from the power plant's production scheduling management system. This preset time period can be set to the next 2 hours according to actual needs. The planned load sequence may include scheduling instructions that gradually increase from the current 280MW to the rated load of 300MW and maintain operation at 300MW.

[0103] Secondly, the real-time host load is used as the starting point of the prediction sequence, and it is concatenated with the planned host load within a preset future time period in chronological order to form a complete load change sequence. For example, the time step is 5 minutes, with a total of 25 data points, including 1 current real-time point and 24 planned future points. Simultaneously, the ambient temperature corresponding to this load sequence is extracted from the real-time operating data, such as the current ambient temperature of 25℃. Auxiliary input parameters, such as assuming the ambient temperature remains stable for the next 2 hours and the real-time lubricating oil volumetric flow rate, are also input along with the load change sequence into a pre-constructed load-lubricating oil temperature mapping model. The model uses an internal gradient boosting regression tree structure to perform hierarchical feature combination and nonlinear mapping calculations on the input load and auxiliary parameters, predicting the lubricating oil temperature at each time step. Finally, it outputs a predicted lubricating oil temperature sequence of the same length as the load sequence, for example, gradually rising from the current 55℃ to 58℃ and then stabilizing.

[0104] Finally, curve fitting is performed on the output predicted lubricating oil temperature sequence. Cubic spline interpolation is used, with time as the horizontal axis and predicted lubricating oil temperature as the vertical axis, to construct a smooth fitting curve between adjacent predicted points, eliminating possible jumps in discrete data points.

[0105] Furthermore, by analyzing the slope changes, extreme point distribution, and overall trend of the fitted curve, the thermal state change trend of the lubricating oil system within a preset time period can be obtained, such as determining the starting moment of temperature rise, the stage in which the maximum heating rate occurs, and the equilibrium value after the temperature stabilizes.

[0106] The feedforward control calculation unit 14 is used to combine the real-time performance baseline with the thermal state change trend to calculate the feedforward control quantity for adjusting the flow rate of the heat transfer medium with the goal of maintaining the stable temperature in front of the lubricating oil tank.

[0107] In this embodiment, maintaining the lubricating oil tank temperature at a set value is the control objective, for example, the target temperature is set to 45℃±1℃. The thermal state change trend output by the thermal dynamics prediction unit, i.e., the predicted lubricating oil temperature sequence and its fitting curve within a preset future time period, and the current real-time performance baseline of the heat exchanger, are used to calculate the required adjustment amount of the heat transfer medium flow rate to offset the predicted lubricating oil temperature fluctuations.

[0108] The composite control output unit 15 is used to generate a composite control command for the flow rate of the heat transfer medium based on the feedforward control quantity, and to perform heat recovery control.

[0109] In this embodiment, the feedforward control quantity is used as the dominant adjustment command, and a feedback correction mechanism is introduced. By comparing the deviation between the actual temperature in front of the current lubricating oil tank and the set target temperature, the feedforward control quantity is dynamically corrected.

[0110] Specifically, when the actual temperature is higher than the upper limit of the target temperature, the flow rate of the heat transfer medium is increased based on the original feedforward control quantity to enhance the heat exchange effect and accelerate the removal of heat; when the actual temperature is lower than the lower limit of the target temperature, the flow rate of the heat transfer medium is reduced to decrease the heat exchange intensity and avoid excessive drop in oil temperature.

[0111] At the same time, upper and lower limits of control quantities are set. For example, the adjustment range of the heat transfer medium flow rate is limited to 30% to 110% of the rated flow rate to prevent local overheating caused by the medium velocity in the heat exchanger being too low due to the flow rate being too small, or the flow rate being too large and exceeding the pipeline conveying capacity and pump unit operating range.

[0112] Finally, the modified and constrained composite control quantity is converted into a standard 4-20mA current signal and sent to the electric regulating valve on the heat transfer medium pipeline. The flow rate of the heat transfer medium is controlled by the continuous change of the valve opening, thereby dynamically matching the real-time thermal state changes of the lubricating oil system.

[0113] Specifically, the composite control output unit 15 in the system includes:

[0114] Update and obtain the real-time lubricating oil outlet temperature of the lubrication system;

[0115] Calculate the temperature control deviation between the real-time lubricating oil outlet temperature and the target set temperature in front of the lubricating oil tank;

[0116] Based on the temperature control deviation and combined with the preset simplified heat transfer model, a feedback control quantity for the flow rate of the heat transfer medium is generated.

[0117] The feedback control quantity and the feedforward control quantity are superimposed to generate the composite control command, and the composite control command is sent to the flow regulation mechanism of the heat transfer medium to perform heat recovery control.

[0118] In this embodiment of the application, firstly, the real-time lubricating oil outlet temperature of the lubricating oil system, i.e. the temperature in front of the lubricating oil tank, is collected and updated in real time by a temperature sensor installed on the lubricating oil outlet heat exchanger pipe. The sampling frequency is consistent with the prediction time step of the thermal dynamic prediction unit, for example, once every 5 minutes.

[0119] Secondly, calculate the temperature control deviation between the real-time lubricating oil outlet temperature and the target set temperature before the lubricating oil tank. The target set temperature is determined according to the unit's operating procedures. For example, if it is set to 45℃, then the temperature control deviation ΔT = real-time lubricating oil outlet temperature - 45℃. When ΔT is positive, it indicates that the oil temperature is too high and heat exchange needs to be enhanced; when ΔT is negative, it indicates that the oil temperature is too low and heat exchange needs to be reduced.

[0120] Then, based on the temperature control deviation ΔT and a pre-defined simplified heat transfer model, a feedback control quantity for the heat transfer medium flow rate is generated. The simplified heat transfer model is based on the heat transfer rate equation Q=K×A×ΔTm, where Q is the heat exchange, K is the current heat transfer coefficient in the real-time performance baseline, A is the heat exchanger area, and ΔTm is the logarithmic mean temperature difference. Furthermore, by inversely calculating the formula, given the target heat exchange change ΔQ, the required adjustment amount of the heat transfer medium flow rate is determined.

[0121] Subsequently, the feedback control quantity and the feedforward control quantity are superimposed to generate a composite control command. The superposition method adopts a weighted summation, with the feedforward control quantity accounting for 70% of the weight and the feedback control quantity accounting for 30% of the weight, to highlight the dominant role of the feedforward control, while the feedback control corrects the deviation in real time.

[0122] For example, the feedforward control quantity is the medium flow rate that needs to be reduced from the current 80m³ / h. 3 / h increased to 90m 3 / h, the feedback control quantity needs to be increased by an additional 5m due to ΔT=+1℃. 3 / h, then the composite control command is 90+5=95m 3 / h.

[0123] Finally, the composite control command is sent to the flow regulation mechanism of the heat transfer medium, such as an electric regulating valve. After receiving the 4-20mA standard current signal, the regulating mechanism adjusts the valve opening according to the linear correspondence between the signal and the flow rate, such as 4mA corresponding to 30% of the rated flow rate and 20mA corresponding to 110% of the rated flow rate, thereby changing the flow rate of the heat transfer medium in real time and ensuring that the temperature before the lubricating oil tank remains stable within the target range.

[0124] In summary, compared with existing technologies, this application achieves accurate prediction of lubricating oil temperature change trends by constructing a load-lubricating oil temperature mapping model based on time alignment. Combined with a feedforward-feedback composite control strategy, it can actively adjust the flow rate of the heat transfer medium before the host load changes, thereby controlling the temperature in front of the lubricating oil tank within the set range. This effectively overcomes the response lag problem caused by heat transfer delay in traditional feedback control and improves the control accuracy and dynamic response performance of the heat recovery system.

[0125] In summary, the embodiments of this application have at least the following technical effects:

[0126] This application provides a heat recovery system for a power plant turbine unit's lubricating oil system. First, a data acquisition unit collects real-time operating data and historical operating logs of the lubricating oil system, heat transfer medium recovery system, and main unit under a target scenario. Second, a performance baseline update unit, based on historical operating logs, dynamically identifies the heat exchanger's performance baseline, obtaining a real-time performance baseline characterizing the current heat transfer performance and reflecting the heat exchanger's actual heat transfer capacity under different operating conditions. Third, a thermal dynamic prediction unit uses real-time operating data to predict the thermal state change trend of the lubricating oil system within a preset future time period, enabling the system to anticipate potential fluctuations in lubricating oil temperature and shift from passive control to proactive prevention. Then, a feedforward control calculation unit combines the real-time performance baseline and thermal state change trend to calculate a feedforward control quantity with the goal of maintaining a stable temperature before the lubricating oil tank, pre-adjusting the heat transfer medium flow rate from the source to reduce the possibility of temperature fluctuations. Finally, a composite control output unit generates composite control commands based on the feedforward control quantity to execute heat recovery control, considering not only predicted thermal state changes but also actual temperature deviations during operation for feedback adjustment, thereby improving heat recovery efficiency.

[0127] Through the above technical solution, this application reduces the energy consumption of the cooling equipment and effectively improves the problem of lag in lubricating oil temperature control, achieving the dual goals of efficient heat recovery and temperature control of the lubricating oil system.

[0128] Example 2, as Figure 2 As shown, based on the same inventive concept as the heat recovery system for a power plant turbine lubricating oil system provided in Embodiment 1, this application also provides a method for heat recovery in a power plant turbine lubricating oil system, including:

[0129] Real-time collection of real-time operational data and historical operational logs from multiple systems in the target scenario;

[0130] Based on the historical operation log, dynamic performance baseline identification is performed on the heat exchanger between the lubricating oil system and the heat transfer medium recovery system to obtain a real-time performance baseline characterizing the current heat transfer performance.

[0131] Based on the real-time operating data, predict the trend of thermal state change of the lubricating oil system within a future preset time period;

[0132] Based on the real-time performance baseline and the thermal state change trend, with the goal of maintaining a stable temperature in front of the lubricating oil tank, a feedforward control quantity for adjusting the flow rate of the heat transfer medium is calculated.

[0133] Based on the feedforward control quantity, a composite control command for the heat transfer medium flow rate is generated to execute heat recovery control.

[0134] This includes real-time collection of operational data and historical logs from multiple systems in the target scenario, including:

[0135] Obtain the lubricating oil system's operating parameters and historical lubricating oil operating logs;

[0136] Obtain the operating parameters and historical operating logs of the heat transfer medium recovery system;

[0137] Obtain host system load parameters and historical host operation logs;

[0138] The lubricating oil operating parameters, the medium operating parameters, and the main unit load parameters are output as the real-time operating data;

[0139] The historical lubricating oil operation log, the historical media operation log, and the historical host operation log are output as the historical operation log.

[0140] Furthermore, based on the historical operation logs, dynamic performance baseline identification is performed on the heat exchanger between the lubricating oil system and the heat transfer medium recovery system to obtain a real-time performance baseline characterizing the current heat transfer performance, including:

[0141] Based on a preset neighborhood time window, nearby data is extracted from the historical operation log to obtain the nearby operation log;

[0142] Based on the nearby operation log, the first lubricating oil temperature at the main unit outlet of the lubricating oil system, the second lubricating oil temperature in front of the lubricating oil tank, and the lubricating oil volume flow rate are obtained and output as the first real-time parameter set.

[0143] Based on the nearby operation log, the temperature before recovery, the temperature after recovery, and the volumetric flow rate of the heat transfer medium recovery system are obtained and output as a second real-time parameter set.

[0144] Based on the first real-time parameter set and the second real-time parameter set, and combined with a preset heat transfer basic model, an inversion operation is performed to obtain the real-time performance baseline.

[0145] Specifically, in one embodiment of the application, based on the historical operating logs, dynamic performance baseline identification is performed on the heat exchanger between the lubricating oil system and the heat transfer medium recovery system to obtain a real-time performance baseline characterizing the current heat transfer performance, including:

[0146] Obtain the sequence of media-side operating parameters for the current time window;

[0147] Input the sequence of operating parameters on the medium side into the pre-constructed heat transfer performance degradation mapping model to obtain the heat transfer performance degradation factor;

[0148] The real-time performance baseline is calculated based on the performance degradation factor and the baseline heat transfer performance obtained from the design data.

[0149] The construction of the attenuation mapping model includes:

[0150] Obtain historical media operation logs and extract the media-side operation parameter sequence, wherein the media-side operation parameter sequence includes the average flow rate and average inlet temperature of the heat transfer medium;

[0151] The historical average heat transfer coefficient, which is consistent with the time sequence of the medium-side operating parameters, is obtained from the historical operation log. The historical average heat transfer coefficient is calculated based on the inversion of a preset basic heat transfer model.

[0152] The heat transfer performance degradation mapping model is obtained by using the feature vector formed by the average flow rate and average inlet temperature of the heat transfer medium as the model input and the corresponding historical average heat transfer coefficient as the model output target, and supervising the training of the preset regression model.

[0153] Furthermore, based on the real-time operating data, the trend of thermal state changes in the lubricating oil system within a preset time period is predicted, including:

[0154] Based on the historical operation logs, extract the lubricating oil temperature time-series data of the lubricating oil system and the host load parameter time-series data of the host system.

[0155] Trend analysis was performed on the time-series data of lubricating oil temperature and the time-series data of host load parameters to obtain the trends of lubricating oil temperature and host load.

[0156] With the goal of maximizing trend similarity, the lubricating oil temperature trend and the main engine load trend are trend aligned, and the typical lubricating oil-load delay is determined based on the trend alignment results;

[0157] Based on the typical lubricating oil-load delay, the lubricating oil temperature time-series data and the host load parameter time-series data are reorganized based on time-series adjustment to establish a delay-free mapping relationship.

[0158] Based on the data reconstruction results, a load-lubricating oil temperature mapping model based on regression analysis was established.

[0159] Among them, based on the real-time operating data, predicting the thermal state change trend of the lubricating oil system within a preset time period includes:

[0160] Based on the real-time operating data, the real-time host load of the host system is obtained, and the planned host load for a future preset time period is obtained simultaneously.

[0161] The real-time host load and the planned host load are combined and input into a pre-built load-lubricating oil temperature mapping model to obtain the predicted lubricating oil temperature sequence;

[0162] Curve fitting is performed on the predicted lubricating oil temperature sequence to obtain the trend of thermal state change.

[0163] Further, based on the feedforward control quantity, a composite control command for the heat transfer medium flow rate is generated to execute heat recovery control, including:

[0164] Update and obtain the real-time lubricating oil outlet temperature of the lubrication system;

[0165] Calculate the temperature control deviation between the real-time lubricating oil outlet temperature and the target set temperature in front of the lubricating oil tank;

[0166] Based on the temperature control deviation and combined with the preset simplified heat transfer model, a feedback control quantity for the flow rate of the heat transfer medium is generated.

[0167] The feedback control quantity and the feedforward control quantity are superimposed to generate the composite control command, and the composite control command is sent to the flow regulation mechanism of the heat transfer medium to perform heat recovery control.

[0168] Furthermore, the lubricating oil system and the heat transfer medium recovery system exchange heat through a heat exchange component located in front of the lubricating oil tank.

[0169] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0170] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0171] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A heat recovery system for a power plant steam turbine unit lubricating oil system, characterized by, include: The data acquisition unit is used to collect real-time operating data and historical operating logs from multiple systems in the target scenario. The performance baseline update unit is used to identify the dynamic performance baseline of the heat exchanger between the lubricating oil system and the heat transfer medium recovery system based on the historical operation log, and obtain the real-time performance baseline characterizing the current heat transfer performance. The lubricating oil system and the heat transfer medium recovery system exchange heat through a heat exchanger set in front of the lubricating oil tank. The thermal dynamic prediction unit is used to predict the trend of thermal state change of the lubricating oil system within a future preset period based on the real-time operating data. The feedforward control calculation unit is used to combine the real-time performance baseline with the thermal state change trend to calculate the feedforward control quantity for adjusting the flow rate of the heat transfer medium with the goal of maintaining the stable temperature in front of the lubricating oil tank. The composite control output unit is used to generate a composite control command for the flow rate of the heat transfer medium based on the feedforward control quantity, and to execute heat recovery control. This includes real-time collection of operational data and historical logs from multiple systems in the target scenario, including: Obtain the lubricating oil system's operating parameters and historical lubricating oil operating logs; Obtain the operating parameters and historical operating logs of the heat transfer medium recovery system; Obtain host system load parameters and historical host operation logs; The lubricating oil operating parameters, the medium operating parameters, and the main unit load parameters are output as the real-time operating data; The historical lubricating oil operation log, the historical media operation log, and the historical host operation log are output as the historical operation log; Among them, based on the real-time operating data, predicting the thermal state change trend of the lubricating oil system within a preset time period includes: Based on the historical operation logs, extract the lubricating oil temperature time-series data of the lubricating oil system and the host load parameter time-series data of the host system. Trend analysis was performed on the time-series data of lubricating oil temperature and the time-series data of host load parameters to obtain the trends of lubricating oil temperature and host load. With the goal of maximizing trend similarity, the lubricating oil temperature trend and the main engine load trend are trend aligned, and the typical lubricating oil-load delay is determined based on the trend alignment results; Based on the typical lubricating oil-load delay, the lubricating oil temperature time-series data and the host load parameter time-series data are reorganized based on time-series adjustment to establish a delay-free mapping relationship. Based on the data reconstruction results, a load-lubricating oil temperature mapping model based on regression analysis was established; The method of predicting the thermal state change trend of the lubricating oil system within a preset time period based on the real-time operating data also includes: Based on the real-time operating data, the real-time host load of the host system is obtained, and the planned host load for a future preset time period is obtained simultaneously. The real-time host load and the planned host load are combined and input into a pre-built load-lubricating oil temperature mapping model to obtain the predicted lubricating oil temperature sequence; Curve fitting is performed on the predicted lubricating oil temperature sequence to obtain the trend of thermal state change.

2. A heat recovery system for a lubricating oil system of a steam turbine unit of a power plant as set forth in claim 1, characterized in that, Based on the historical operation logs, dynamic performance baseline identification is performed on the heat exchanger between the lubricating oil system and the heat transfer medium recovery system to obtain a real-time performance baseline characterizing the current heat transfer performance, including: Based on a preset neighborhood time window, nearby data is extracted from the historical operation log to obtain the nearby operation log; Based on the nearby operation log, the first lubricating oil temperature at the main unit outlet of the lubricating oil system, the second lubricating oil temperature in front of the lubricating oil tank, and the lubricating oil volume flow rate are obtained and output as the first real-time parameter set. Based on the nearby operation log, the temperature before recovery, the temperature after recovery, and the volumetric flow rate of the heat transfer medium recovery system are obtained and output as a second real-time parameter set. Based on the first real-time parameter set and the second real-time parameter set, and combined with a preset heat transfer basic model, an inversion operation is performed to obtain the real-time performance baseline.

3. A heat recovery system for a lubricating oil system of a steam turbine unit of a power plant as set forth in claim 1, characterized in that, Based on the historical operation logs, dynamic performance baseline identification is performed on the heat exchanger between the lubricating oil system and the heat transfer medium recovery system to obtain a real-time performance baseline characterizing the current heat transfer performance, including: Obtain the sequence of media-side operating parameters for the current time window; Input the sequence of operating parameters on the medium side into the pre-constructed heat transfer performance degradation mapping model to obtain the heat transfer performance degradation factor; The real-time performance baseline is calculated based on the performance degradation factor and the baseline heat transfer performance obtained from the design data.

4. The heat recovery system for the lubricating oil system of a power plant turbine unit as described in claim 3, characterized in that, The construction of the attenuation mapping model includes: Obtain historical media operation logs and extract the media-side operation parameter sequence, wherein the media-side operation parameter sequence includes the average flow rate and average inlet temperature of the heat transfer medium; The historical average heat transfer coefficient, which is consistent with the time sequence of the medium-side operating parameters, is obtained from the historical operation log. The historical average heat transfer coefficient is calculated based on the inversion of a preset basic heat transfer model. The heat transfer performance degradation mapping model is obtained by using the feature vector formed by the average flow rate and average inlet temperature of the heat transfer medium as the model input and the corresponding historical average heat transfer coefficient as the model output target, and supervising the training of the preset regression model.

5. A heat recovery system for a power plant turbine unit lubricating oil system as described in claim 1, characterized in that, Based on the feedforward control quantity, a composite control command for the heat transfer medium flow rate is generated to execute heat recovery control, including: Update and obtain the real-time lubricating oil outlet temperature of the lubrication system; Calculate the temperature control deviation between the real-time lubricating oil outlet temperature and the target set temperature in front of the lubricating oil tank; Based on the temperature control deviation and combined with the preset simplified heat transfer model, a feedback control quantity for the flow rate of the heat transfer medium is generated. The feedback control quantity and the feedforward control quantity are superimposed to generate the composite control command, and the composite control command is sent to the flow regulation mechanism of the heat transfer medium to perform heat recovery control.

6. A method for heat recovery in the lubricating oil system of a power plant turbine unit, characterized in that, A heat recovery system for a power plant turbine unit lubricating oil system according to any one of claims 1-5, comprising: Real-time collection of real-time operational data and historical operational logs from multiple systems in the target scenario; Based on the historical operation log, dynamic performance baseline identification is performed on the heat exchanger between the lubricating oil system and the heat transfer medium recovery system to obtain a real-time performance baseline characterizing the current heat transfer performance. Based on the real-time operating data, predict the trend of thermal state change of the lubricating oil system within a future preset time period; Based on the real-time performance baseline and the thermal state change trend, with the goal of maintaining a stable temperature in front of the lubricating oil tank, a feedforward control quantity for adjusting the flow rate of the heat transfer medium is calculated. Based on the feedforward control quantity, a composite control command for the heat transfer medium flow rate is generated to execute heat recovery control.

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