Optimization control method, device and equipment based on limited operation area in hydropower station AGC system and storage medium

Abstract

The invention relates to the technical field of power system automation, and discloses an optimal control method, device and equipment based on a limited operation area in a hydropower station AGC system and a storage medium, and the method comprises the steps: obtaining the operation parameters of the hydropower station AGC system; inputting the operation parameters of the hydropower station AGC system into a preset optimization algorithm, and determining an optimal control strategy of the hydropower station AGC system in the limited operation area; acquiring real-time data containing actual operating parameters of the hydropower station and processing the real-time data into control vectors; inputting the control vector into a preset optimization algorithm; calculating an initial control parameter according to the dynamically adjusted running state of the hydropower station AGC system; monitoring the real-time operation state of the hydropower station AGC system; after the running state of the hydropower station AGC system is adjusted, the real-time running state is continuously monitored, and it is ensured that the system stably runs in the limited running area. The operation efficiency and stability of the hydropower station can be improved, and safe operation of the system is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of power system automation, and in particular to an optimization control method, device, equipment and storage medium based on a restricted operation area in an AGC system of a hydropower station. Background Art

[0002] The existing automatic generation control system (AGC) of hydropower stations is mainly responsible for automatically adjusting the power generation of hydropower stations according to the needs and operating conditions of the power grid to achieve stable operation and economic dispatch of the power grid. These systems usually rely on preset control strategies and algorithms to monitor and adjust the operating parameters of hydropower stations in real time. However, with the continuous expansion of the scale of the power grid and the development of the power market, the existing AGC system faces new challenges and demands.

[0003] First, the increased complexity of grid operation requires the AGC system to be able to respond to the dynamic changes of the grid more flexibly and accurately. Second, traditional control strategies may not fully take into account the operating constraints of hydropower stations, such as reservoir water level, unit operating status, etc., which may lead to unsatisfactory control effects or increased system operation risks. In addition, with the large-scale access of renewable energy, the volatility and uncertainty of the grid have further increased, which has put forward higher requirements for the adaptability and intelligence level of the AGC system.

[0004] In the process of implementing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the prior art: the existing AGC system often lacks sufficient flexibility and adaptability when dealing with complex grid operating conditions and changeable power market environments; when faced with the constraints on the operation of hydropower stations, the existing control strategy may not be able to achieve the optimal control effect, and sometimes even lead to reduced system operating efficiency or impaired stability; in addition, the existing system still needs to be improved in terms of intelligence and automation levels, especially in real-time data processing and control strategy optimization. Summary of the invention

[0005] The embodiments of the present invention aim to provide an optimization control method, device, equipment and storage medium based on a restricted operation area in an AGC system of a hydropower station, so as to solve the technical problems raised in the prior art.

[0006] The embodiment of the present invention solves the technical problem by adopting the following technical solution:

[0007] In a first aspect, an optimization control method based on a restricted operation area in an AGC system of a hydropower station is provided, comprising:

[0008] Obtain the operating parameters of the hydropower station AGC system;

[0009] Inputting the operating parameters of the hydropower station AGC system into a preset optimization algorithm to determine the optimal control strategy of the hydropower station AGC system within the restricted operation area;

[0010] Acquire real-time data containing actual operating parameters of the hydropower station and process them into control vectors;

[0011] The control vector is input into a preset optimization algorithm, the optimization algorithm outputs a control result, the control result is matched with the restricted operation area to form an optimal control instruction, and the operation of the hydropower station AGC system is dynamically adjusted according to the optimal control instruction;

[0012] Calculate initial control parameters based on the dynamically adjusted operating status of the hydropower station AGC system;

[0013] Monitor the real-time operation status of the hydropower station AGC system. If the operation status exceeds the preset restricted operation area, adjust the control parameters; if the operation status is within the restricted operation area, maintain the current control parameters;

[0014] After the operating status of the hydropower station AGC system is adjusted, continue to monitor the real-time operating status to ensure that the system operates stably within the restricted operating area.

[0015] Further, in the step of inputting the control vector into a preset optimization algorithm, the optimization algorithm is trained in the following manner: historical operation data of the AGC system of the hydropower station is obtained and converted into a control parameter set suitable for the optimization algorithm;

[0016] The control parameter set is used as input to train the optimization algorithm, and the operating parameters of the hydropower station AGC system are used as input to the optimization algorithm to determine the optimal control strategy; thus, a trained optimization algorithm is obtained.

[0017] Furthermore, the step of obtaining the historical operation data of the AGC system of the hydropower station and converting it into a control parameter set suitable for the optimization algorithm specifically includes the following: collecting the historical operation data of the AGC system of the hydropower station through a monitoring system;

[0018] Cleaning the historical operation data collected by the monitoring system to exclude abnormal data;

[0019] Based on the cleaned data, the system operation status is judged by analyzing the change trend of the operation parameters;

[0020] The system operating status characteristics are extracted and processed to obtain control parameters, and a control parameter set suitable for the optimization algorithm is constructed.

[0021] Furthermore, the step of using the control parameter set as input to train the optimization algorithm is specifically as follows: initializing the input parameters and output results of the optimization algorithm;

[0022] After the control vectors in the control parameter set are normalized, the control vectors are input into the optimization algorithm as training samples for training, and the training is cyclically performed until a predetermined number of training times is reached.

[0023] Furthermore, in the step of dynamically adjusting the operation of the hydropower station AGC system according to the optimal control instruction, the calculation formula of the optimal control instruction is as follows:

[0024]

[0025] Where C represents the control instruction set; P i,in and P i,out Respectively represent the input and output power of the i-th period; R limit and R current They represent the restricted operating area and the current operating area respectively; λ is the weight coefficient, which is used to balance the input-output power difference and the operating area deviation; n is the total number of time periods; arg min means finding C that minimizes the objective function.

[0026] Furthermore, in the step of calculating the initial control parameters according to the dynamically adjusted operating state of the hydropower station AGC system, the initial control parameter calculation formula is as follows:

[0027]

[0028] Among them, P init represents the initial control parameter; P min and P max They represent the minimum and maximum values ​​of the control parameters respectively; β is the initial control parameter adjustment coefficient; t0 is the time from system startup to the present; T init represents the time window for calculating the initial control parameters; the cosine function is used to generate min To P max Smooth transition to ensure that the system can smoothly enter the running state when it starts.

[0029] Furthermore, the control parameter adjustment is calculated as follows:

[0030]

[0031] Among them, P adj Represents the adjusted control parameter; P current Indicates the current control parameters; P max and P min Respectively represent the maximum and minimum values ​​of the control parameters; α is the adjustment amplitude coefficient; t is the current time; T cycle Represents the control parameter adjustment period; the sin function is used to generate a periodic adjustment signal to achieve smooth control parameter adjustment.

[0032] In a second aspect of the present invention, an optimization control device based on a restricted operation area in an AGC system of a hydropower station is provided, comprising:

[0033] The first acquisition module is used to obtain the operating parameters of the AGC system of the hydropower station;

[0034] A first input module, used to input the operating parameters of the hydropower station AGC system into a preset optimization algorithm to determine the optimal control strategy;

[0035] A second acquisition module is used to acquire real-time data including actual operating parameters of the hydropower station and process it into a control vector;

[0036] A second input module, used to input the control vector into a preset optimization algorithm and output an optimal control instruction;

[0037] A calculation module is used to dynamically adjust the operation of the hydropower station AGC system according to the optimal control instructions;

[0038] The first monitoring module is used to monitor the real-time operation status of the hydropower station AGC system and adjust the control parameters;

[0039] The second monitoring module is used to continue monitoring the operating status of the hydropower station AGC system after the control parameters are adjusted to ensure stable operation of the system.

[0040] In a third aspect of the present invention, an electronic device is provided, comprising: at least one processor, a memory and an input-output unit; wherein the memory is used to store a computer program, and the processor is used to call the computer program stored in the memory to execute any one of the methods described in the first aspect.

[0041] In a fourth aspect of the present invention, a computer-readable storage medium is provided, which includes instructions, and when the instructions are executed on a computer, the computer executes any one of the methods in the first aspect.

[0042] The above-mentioned embodiments of the present invention have at least the following beneficial effects: the optimization control method of the hydropower station AGC system of the present invention can determine the optimal control strategy within the restricted operation area by acquiring and processing the operating parameters of the hydropower station in real time and inputting them into the preset optimization algorithm. This method can effectively improve the operating efficiency of the hydropower station while ensuring the stability and safety of the system under various operating conditions. By dynamically adjusting the control instructions, the system can quickly respond to changes in grid demand and achieve more accurate and flexible power regulation. In addition, the optimization control method of the present invention also includes the analysis and utilization of historical operating data, and improves the accuracy and adaptability of the control strategy by training the optimization algorithm. This method can reduce dependence on manual intervention, improve the level of automation, and reduce operating costs. At the same time, by real-time monitoring and adjustment of control parameters, the system can better adapt to the fluctuations and uncertainties of the power grid, improve the adaptability of the hydropower station to fluctuations in renewable energy, and enhance the resilience and reliability of the entire power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] One or more embodiments are exemplarily described by the figures in the corresponding drawings, and these exemplifications do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings represent similar elements, and unless otherwise stated, the figures in the drawings do not constitute proportional limitations.

[0044] Figure 1 It is a flow chart of an optimization control method based on a restricted operation area in an AGC system of a hydropower station provided by the present invention;

[0045] Figure 2 It is a structural schematic diagram of the soil electrokinetic condition acquisition system based on the model provided by the present invention;

[0046] Figure 3 The schematic diagram schematically shows the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to facilitate the understanding of the present invention, the present invention is described in more detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that when an element is described as "connecting" another element, it can be directly on another element, or there can be one or more centered elements therebetween. The orientation or positional relationship indicated by the terms "upper", "lower", "left", "right", "upper end", "lower end", "top" and "bottom" used in this specification is based on the orientation or positional relationship shown in the accompanying drawings, only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention. In addition, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0048] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0049] Combine the following Figure 1 , the optimization control method 100 based on the restricted operation area in the hydropower station AGC system provided in the embodiment of the present application is described in detail through a specific embodiment.

[0050] Figure 1 The flowchart of the optimization control method based on the restricted operation area in the AGC system of the hydropower station provided by the present invention is shown in FIG. The optimization control method based on the restricted operation area in the AGC system of the hydropower station provided by one embodiment of the present invention comprises:

[0051] Step 101, obtaining the operating parameters of the hydropower station AGC system;

[0052] Step 102, inputting the operating parameters of the hydropower station AGC system into a preset optimization algorithm to determine the optimal control strategy of the hydropower station AGC system within the restricted operation area;

[0053] Step 103, obtaining real-time data including actual operating parameters of the hydropower station and processing it into a control vector;

[0054] Step 104, inputting the control vector into a preset optimization algorithm, the optimization algorithm outputting a control result, matching the control result with the restricted operation area to form an optimal control instruction, and dynamically adjusting the operation of the hydropower station AGC system according to the optimal control instruction;

[0055] Step 105, calculating initial control parameters according to the dynamically adjusted operating state of the hydropower station AGC system;

[0056] Step 106, monitoring the real-time operation status of the hydropower station AGC system, if the operation status exceeds the preset restricted operation area, adjusting the control parameters; if the operation status is within the restricted operation area, maintaining the current control parameters;

[0057] Step 107: After the operation status of the hydropower station AGC system is adjusted, the real-time operation status continues to be monitored to ensure that the system operates stably within the restricted operation area.

[0058] It should be noted that obtaining the operating parameters of the hydropower station AGC system involves comprehensive monitoring of the current state of the hydropower station AGC system, including but not limited to key parameters such as water level, flow, power generation, unit status, etc. These parameters are the basis for the subsequent optimization control strategy formulation.

[0059] Specifically, the operating parameters include the real-time water level W(t), real-time flow Q(t), current output power P of the hydropower station out (t) and the unit operating status s(t), etc. These parameters can be obtained through sensors installed in key parts of the hydropower station, and the sensor data is transmitted to the control center in real time through the data acquisition system. The setting of parameters needs to be determined according to the specific conditions of the hydropower station and the requirements of the power grid, such as the monitoring accuracy of water level and flow, the adjustment range of power generation, etc.

[0060] Preferably, in the process of obtaining operating parameters, high-precision sensors and advanced data acquisition technology can be used to ensure the accuracy and real-time nature of the data. In addition, for the acquisition of operating parameters, in addition to real-time monitoring, historical data analysis can also be used to predict possible future operating states, so as to adjust the control strategy in advance. For example, the changing trend of reservoir water levels can be predicted based on seasonal rainfall patterns, thereby optimizing power generation plans.

[0061] In some embodiments, in the step of inputting the control vector into a preset optimization algorithm, the optimization algorithm is trained in the following manner: historical operation data of the hydropower station AGC system is obtained and converted into a control parameter set suitable for the optimization algorithm;

[0062] The control parameter set is used as input to train the optimization algorithm, and the operating parameters of the hydropower station AGC system are used as input to the optimization algorithm to determine the optimal control strategy; thus, a trained optimization algorithm is obtained.

[0063] It should be noted that the training method of the optimization algorithm involves using historical operating data to train a model that can predict the optimal control strategy based on the input control parameter set. This process is a key step in achieving optimal control of the hydropower station AGC system.

[0064] Specifically, the training of the optimization algorithm includes the following steps: first, collect historical data of the hydropower station AGC system under different operating conditions. These data may include water level, flow rate, power generation, etc. in different time periods; second, clean and preprocess the collected data to remove outliers and noise to ensure data quality; then, use these cleaned data to train the optimization algorithm. Possible methods include machine learning, deep learning or other advanced data analysis technologies.

[0065] Preferably, the training of the optimization algorithm can adopt the following detailed steps: first, define the optimization goal, such as minimizing the power generation cost or maximizing the power generation efficiency; second, select a suitable algorithm model, such as support vector machine, neural network or genetic algorithm, etc., according to the characteristics and needs of the hydropower station; third, set the training parameters, including learning rate, number of iterations, regularization coefficient, etc., to ensure the convergence and generalization ability of the algorithm; finally, perform cross-validation and model tuning to evaluate the performance of the algorithm and make necessary adjustments. In addition, the use of incremental learning or online learning methods can be considered to enable the optimization algorithm to adapt to the dynamic changes in the operating conditions of the hydropower station.

[0066] In some embodiments, the step of obtaining historical operation data of the AGC system of the hydropower station and converting it into a control parameter set suitable for the optimization algorithm specifically includes the following: collecting historical operation data of the AGC system of the hydropower station through a monitoring system;

[0067] Cleaning the historical operation data collected by the monitoring system to exclude abnormal data;

[0068] Based on the cleaned data, the system operation status is judged by analyzing the change trend of the operation parameters;

[0069] The system operating status characteristics are extracted and processed to obtain control parameters, and a control parameter set suitable for the optimization algorithm is constructed.

[0070] It should be noted that obtaining the historical operating data of the hydropower station AGC system and converting it into a control parameter set suitable for the optimization algorithm involves in-depth analysis and processing of the historical data in order to provide accurate input for the optimization algorithm.

[0071] Specifically, this step includes collecting the operating data of the hydropower station AGC system in different time periods, which may include historical water levels, flow rates, power generation, unit operating status, etc. Then, invalid or erroneous data points are removed through data cleaning, and then feature extraction is performed on the cleaned data to identify key parameters that have a significant impact on the system operating status. These parameters will be converted into control parameter sets to provide input for the training of the optimization algorithm.

[0072] Preferably, this step can be further refined into the following operation points: first, determine the time range and frequency of data collection to ensure the representativeness and continuity of the data; second, select appropriate data cleaning methods, such as outlier removal based on statistical thresholds or anomaly detection technology based on machine learning; then, apply feature engineering methods, such as principal component analysis (PCA) or autoencoders, to extract key features and reduce the dimension of the data; finally, cluster analysis and other methods can be used to classify similar operating states so that the optimization algorithm can better learn and predict. In addition, it is possible to consider introducing time series analysis technology to capture the temporal characteristics in the data and provide more abundant information for the optimization algorithm.

[0073] In some embodiments, the step of using the control parameter set as input to train the optimization algorithm is specifically as follows: initializing the input parameters and output results of the optimization algorithm;

[0074] After the control vectors in the control parameter set are normalized, the control vectors are input into the optimization algorithm as training samples for training, and the training is cyclically performed until a predetermined number of training times is reached.

[0075] It should be noted that using the control parameter set as input to train the optimization algorithm involves using the processed and extracted key control parameters to train an algorithm model that can output the optimal control strategy.

[0076] Specifically, this step includes initializing the parameters of the optimization algorithm, such as learning rate, number of iterations, network structure, etc., and inputting the normalized control vector into the algorithm as a training sample. Normalization is to ensure that the algorithm can more effectively process data of different magnitudes. During the training process, the algorithm will continuously adjust its own parameters to minimize the difference between the predictive control strategy and the actual optimal strategy.

[0077] Preferably, this step can further include the following operation points: first, select a suitable initialization method to set the initial parameters of the optimization algorithm to promote the rapid convergence of the algorithm; second, use normalization techniques such as minimum-maximum normalization or Z-score normalization to process the control vector to ensure that the data is on the same scale; then, according to the characteristics of the algorithm and the characteristics of the training data, select a suitable loss function and optimizer, such as the mean square error loss function and the Adam optimizer; finally, implement early stopping or regularization techniques to prevent the model from overfitting, and use cross-validation to evaluate the generalization ability of the model. In addition, you can consider using ensemble learning methods, such as random forests or gradient boosting machines, to improve the stability and prediction accuracy of the model.

[0078] In some embodiments, in the step of dynamically adjusting the operation of the hydropower station AGC system according to the optimal control instruction, the calculation formula of the optimal control instruction is as follows:

[0079]

[0080] Where C represents the control instruction set; P i,in and P i,out Respectively represent the input and output power of the i-th period; R limit and R current They represent the restricted operating area and the current operating area respectively; λ is the weight coefficient, which is used to balance the input-output power difference and the operating area deviation; n is the total number of time periods; arg min means finding C that minimizes the objective function.

[0081] It should be noted that the calculation formula of the optimal control instruction is the core algorithm for realizing the optimal control of the AGC system of the hydropower station, which is used to determine how to adjust the control parameters within a specific period of time to achieve the optimal power generation efficiency and meet the needs of the power grid.

[0082] Specifically, the calculation formula involves multiple parameters and variables, including the difference between input and output power within a time period, operating area restrictions, weight coefficients, etc. For example, P in,t and P out,t Represent the input and output power of the tth period, R min and R max Represent the minimum and maximum values ​​of the restricted operation area, respectively. The weight coefficient λ is used to balance the importance of different objectives. The total number of time periods T represents the total number of time periods that the optimization algorithm needs to consider, and x * Represents the optimal solution found by the optimization algorithm.

[0083] Preferably, the implementation of the calculation formula can be further refined into the following operating points: first, determine the value of the weight coefficient λ, which is usually set based on the specific requirements and priorities of the system operation; second, dynamically adjust R according to real-time data and historical data. min and R max The value of is used to reflect the current grid status and the operating constraints of the hydropower station. Then, numerical optimization methods such as linear programming, dynamic programming or gradient descent are used to solve the optimal control instruction x * ;Finally, machine learning techniques can be introduced to predict future power demand and supply conditions, thereby optimizing the calculation process of control instructions. In addition, multi-objective optimization methods can be considered to simultaneously consider multiple performance indicators such as cost, efficiency, and environmental impact.

[0084] In some embodiments, in the step of calculating the initial control parameters according to the dynamically adjusted operating state of the hydropower station AGC system, the initial control parameter calculation formula is as follows:

[0085]

[0086] Among them, P init represents the initial control parameter; P min and P max They represent the minimum and maximum values ​​of the control parameters respectively; β is the initial control parameter adjustment coefficient; t0 is the time from system startup to the present; T init represents the time window for calculating the initial control parameters; the cosine function is used to generate min To P max Smooth transition to ensure that the system can smoothly enter the running state when it starts.

[0087] It should be noted that the initial control parameter calculation formula is used to determine the initial control settings of the hydropower station AGC system when it is started or reset to ensure that the system can smoothly transition to normal operation.

[0088] Specifically, the calculation formula involves the initial value, maximum value, and minimum value of the control parameter, as well as an adjustment coefficient and time-related parameters. init represents the initial control parameter, C min and C max represent the minimum and maximum values ​​of the control parameters respectively, α is the initial control parameter adjustment coefficient, t0 is the time from system startup to the present, τ represents the time window for initial control parameter calculation, and f trans The function is used to generate a smooth transition signal.

[0089] Preferably, the implementation of the calculation formula can be further refined into the following operating points: first, according to the design and operation requirements of the system, determine the maximum and minimum values ​​of the control parameters; second, select or design a suitable transition function f trans , which can be an exponential function, Sigmoid function or other types of smooth curves to achieve a smooth transition from the minimum to the maximum value; then, according to the specific conditions when the system starts, adjust the initial control parameter adjustment coefficient α to adapt to different startup scenarios; finally, set the length of the time window τ to ensure that the system can smoothly reach the preset operating state within the time window. In addition, it is possible to consider introducing an adaptive control strategy to dynamically adjust the initial control parameters according to real-time feedback to cope with the uncertainty and changes in system operation.

[0090] In some embodiments, the control parameter adjustment is calculated as follows:

[0091]

[0092] Among them, P adjRepresents the adjusted control parameter; P current Indicates the current control parameters; P max and P min Respectively represent the maximum and minimum values ​​of the control parameters; α is the adjustment amplitude coefficient; t is the current time; T cycle Represents the control parameter adjustment period; the sin function is used to generate a periodic adjustment signal to achieve smooth control parameter adjustment.

[0093] It should be noted that the calculation method for adjusting the control parameters refers to a method for dynamically adjusting the control parameters according to real-time monitoring data during the operation of the AGC system of a hydropower station to ensure that the system operates in the optimal state.

[0094] Specifically, this calculation method involves the current control parameter, the maximum and minimum values ​​of the control parameter, the adjustment amplitude coefficient, the current time, the adjustment period, and the generation of the periodic adjustment signal. adj represents the adjusted control parameter, C curr Indicates the current control parameters, C max and C min Represent the maximum and minimum values ​​of the control parameters respectively, β is the adjustment amplitude coefficient, t represents the current time, T adj represents the control parameter adjustment period, and f sig The function is used to generate a periodic adjustment signal.

[0095] Preferably, the implementation of this calculation method can be further refined into the following operating points: first, according to the actual operation of the system and historical data, the maximum and minimum values ​​of the control parameters are set; second, the adjustment amplitude coefficient β is determined, and this coefficient can be adjusted according to the sensitivity of the system to the change of the control parameters; then, according to the stability and response speed of the system operation, the adjustment period T is set. adj The length of the signal generation function f is then selected to adjust the periodicity appropriately. sig , such as sine wave, square wave or triangle wave, to achieve smooth adjustment of control parameters; finally, an adaptive adjustment mechanism can be introduced to dynamically adjust β and T according to real-time feedback signals adj , in order to improve the responsiveness and adaptability of the system. In addition, the use of predictive control technology can be considered to adjust the control parameters in advance based on the prediction of future operating conditions to optimize the overall performance of the system.

[0096] The above-mentioned embodiments of the present invention have the following beneficial effects: The optimization control method of the hydropower station AGC system of the present invention can significantly improve the operating efficiency and response speed of the system by real-time monitoring and dynamic adjustment of control parameters. It can make adjustments quickly according to the power grid demand and the actual operating status of the hydropower station to ensure that the power generation process not only meets the stability requirements of the power grid, but also reaches the optimal state of economic operation. In addition, the method analyzes and learns historical data through an optimization algorithm, can predict and adapt to the fluctuations of the power grid, and reduce the energy waste caused by untimely response. Further, the implementation of the invention can enhance the adaptive ability of the hydropower station AGC system, reduce the dependence on manual operation through intelligent control strategies, and reduce the possibility of human error. At the same time, the stability and reliability of the system are enhanced because the real-time monitoring and automatic adjustment mechanism can respond to various emergencies in a timely manner to avoid the system operation beyond the safety limit. This method also helps to extend the service life of the equipment and reduce the wear and tear of the hydropower station equipment by avoiding extreme operating conditions.

[0097] Please also read Figure 2 , Figure 2 It is a structural schematic diagram of an optimization control device based on a restricted operation area in the AGC system of a hydropower station provided by the present invention, comprising:

[0098] The first acquisition module 201 is used to acquire the operating parameters of the AGC system of the hydropower station;

[0099] The first input module 202 is used to input the operating parameters of the hydropower station AGC system into a preset optimization algorithm to determine the optimal control strategy;

[0100] The second acquisition module 203 is used to acquire real-time data including actual operating parameters of the hydropower station and process it into a control vector;

[0101] A second input module 204 is used to input the control vector into a preset optimization algorithm and output an optimal control instruction;

[0102] The calculation module 205 is used to dynamically adjust the operation of the hydropower station AGC system according to the optimal control instructions;

[0103] The first monitoring module 206 is used to monitor the real-time operation status of the hydropower station AGC system and adjust the control parameters;

[0104] The second monitoring module 207 is used to continue monitoring the operating status of the AGC system of the hydropower station after the control parameters are adjusted to ensure stable operation of the system.

[0105] It should be particularly noted that the optimization control device 200 based on the restricted operating area in the AGC system of the hydropower station provided in the embodiment of the present invention only shows the part related to the technical problem to be solved by the embodiment of the present invention. It can be understood that the optimization control device 200 based on the restricted operating area in the AGC system of the hydropower station provided in the embodiment of the present invention also includes other structures for realizing the functions of the optimization control device 200 based on the restricted operating area in the AGC system of the hydropower station, which will not be described one by one here.

[0106] Reference below Figure 3 , which shows a schematic diagram of a structure 300 of an electronic device suitable for implementing some embodiments of the present invention. The electronic devices in some embodiments of the present invention may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The terminal device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0107] like Figure 3 As shown, the electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0108] Typically, the following devices may be connected to the I / O interface 305: input devices 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 308 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 309. The communication devices 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0109] Furthermore, the storage medium of the embodiment of the present application stores program instructions that can implement all the above methods, wherein the program instructions can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0110] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them. Under the concept of the present invention, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other changes in different aspects of the present invention as above, which are not provided in detail for the sake of simplicity. Although the present invention is described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An optimization control method based on restricted operation area in a hydropower station AGC system, characterized in that: The method comprises the following steps: obtaining the operating parameters of the AGC system of the hydropower station; Inputting the operating parameters of the hydropower station AGC system into a preset optimization algorithm to determine the optimal control strategy of the hydropower station AGC system within the restricted operation area; Acquire real-time data containing actual operating parameters of the hydropower station and process them into control vectors; The control vector is input into a preset optimization algorithm, the optimization algorithm outputs a control result, the control result is matched with the restricted operation area to form an optimal control instruction, and the operation of the hydropower station AGC system is dynamically adjusted according to the optimal control instruction; Calculate initial control parameters based on the dynamically adjusted operating status of the hydropower station AGC system; Monitor the real-time operation status of the hydropower station AGC system. If the operation status exceeds the preset restricted operation area, adjust the control parameters; if the operation status is within the restricted operation area, maintain the current control parameters; After the operating status of the hydropower station AGC system is adjusted, continue to monitor the real-time operating status to ensure that the system operates stably within the restricted operating area.

2. The optimization control method based on the restricted operation area in the AGC system of a hydropower station according to claim 1 is characterized in that: In the step of inputting the control vector into a preset optimization algorithm, the optimization algorithm is trained in the following manner: historical operation data of the AGC system of the hydropower station is obtained and converted into a control parameter set suitable for the optimization algorithm; The control parameter set is used as input to train the optimization algorithm, and the operating parameters of the hydropower station AGC system are used as input to the optimization algorithm to determine the optimal control strategy; thus, a trained optimization algorithm is obtained.

3. The optimization control method based on the restricted operation area in the AGC system of a hydropower station according to claim 2 is characterized in that: The step of obtaining the historical operation data of the AGC system of the hydropower station and converting it into a control parameter set suitable for the optimization algorithm specifically includes the following: collecting the historical operation data of the AGC system of the hydropower station through a monitoring system; Cleaning the historical operation data collected by the monitoring system to exclude abnormal data; Based on the cleaned data, the system operation status is judged by analyzing the change trend of the operation parameters; The system operating status characteristics are extracted and processed to obtain control parameters, and a control parameter set suitable for the optimization algorithm is constructed.

4. The optimization control method based on the restricted operation area in the AGC system of a hydropower station according to claim 2 is characterized in that: The step of using the control parameter set as input to train the optimization algorithm is specifically as follows: initializing the input parameters and output results of the optimization algorithm; After the control vectors in the control parameter set are normalized, the control vectors are input into the optimization algorithm as training samples for training, and the training is cyclically performed until a predetermined number of training times is reached.

5. The optimization control method based on restricted operation area in the AGC system of a hydropower station according to claim 1 is characterized in that: In the step of dynamically adjusting the operation of the hydropower station AGC system according to the optimal control instruction, the calculation formula of the optimal control instruction is as follows: Where C represents the control instruction set; P i,in and P i,out Respectively represent the input and output power of the i-th period; R limit and R current They represent the restricted operating area and the current operating area respectively; λ is the weight coefficient, which is used to balance the input-output power difference and the operating area deviation; n is the total number of time periods; arg min means finding C that minimizes the objective function.

6. The optimization control method based on restricted operation area in the AGC system of a hydropower station according to claim 1 is characterized in that: In the step of calculating the initial control parameters according to the dynamically adjusted operating state of the hydropower station AGC system, the initial control parameter calculation formula is as follows: Among them, P init represents the initial control parameter; P min and P max They represent the minimum and maximum values ​​of the control parameters respectively; β is the initial control parameter adjustment coefficient; t0 is the time from system startup to the present; T init represents the time window for calculating the initial control parameters; the cosine function is used to generate min To P max Smooth transition to ensure that the system can smoothly enter the running state when it starts.

7. The optimization control method based on restricted operation area in the AGC system of a hydropower station according to claim 1 is characterized in that: The control parameter adjustment is calculated as follows: Among them, P adj represents the adjusted control parameter; P current Indicates the current control parameters; P max and P min Respectively represent the maximum and minimum values ​​of the control parameters; α is the adjustment amplitude coefficient; t is the current time; T cycle Represents the control parameter adjustment period; the sin function is used to generate a periodic adjustment signal to achieve smooth control parameter adjustment.

8. An optimization control device based on restricted operation area in the AGC system of a hydropower station, characterized in that: include: The first acquisition module is used to obtain the operating parameters of the AGC system of the hydropower station; A first input module, used to input the operating parameters of the hydropower station AGC system into a preset optimization algorithm to determine the optimal control strategy; A second acquisition module is used to acquire real-time data including actual operating parameters of the hydropower station and process it into a control vector; A second input module, used to input the control vector into a preset optimization algorithm and output an optimal control instruction; A calculation module is used to dynamically adjust the operation of the hydropower station AGC system according to the optimal control instructions; The first monitoring module is used to monitor the real-time operation status of the hydropower station AGC system and adjust the control parameters; The second monitoring module is used to continue monitoring the operating status of the hydropower station AGC system after the control parameters are adjusted to ensure stable operation of the system.

9. An electronic device, characterized in that: It comprises a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the optimization control method based on the restricted operation area in the AGC system of a hydropower station as claimed in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by the processor, the optimization control method based on the restricted operation area in the AGC system of the hydropower station as described in any one of claims 1 to 8 is implemented.

Images