An optimization control method and system for an AGC system of a hydropower station

Abstract

The present invention discloses an optimization control method and system for an AGC system of a hydropower station, which relates to the field of automatic control technology for hydropower stations. The method includes collecting and monitoring power system operation data, constructing a monitoring model to monitor the power system operation status; determining the operation area according to the operation characteristics of the hydropower station and the power system requirements; dynamically adjusting the unit operation status based on the operation status and power system load requirements, and generating optimization control instructions; receiving and executing the optimization control instructions, and adjusting the unit output power and operation status. The present invention maximizes the power generation efficiency of the hydropower station and reduces operating costs by dynamically adjusting the unit operation status. It avoids unstable vibrations that may be caused by the unit operating in the vibration zone, improves the stability and reliability of the power system, flexibly adjusts the unit operation status, and enhances the operational flexibility and adaptability of the hydropower station.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic control of hydropower stations, and in particular to an optimization control method and system for an AGC system of a hydropower station. Background Art

[0002] As the global energy mix shifts toward optimization, hydropower stations, as a crucial component of renewable energy, are playing an increasingly prominent role in power systems. Automatic generation control systems (AGC), the core system for hydropower station operations and management, are crucial for ensuring the safe and stable operation of power systems. However, in actual operation, AGC systems face numerous challenges and shortcomings that urgently need to be addressed.

[0003] During operation, hydropower station units are restricted by vibration zones and operating zones, which greatly affects the operating efficiency and stability of the units. The existence of vibration zones may not only cause damage to unit equipment, but may also have adverse effects on the power system, such as voltage fluctuations and frequency deviations. Traditional AGC systems often lack effective identification and avoidance of vibration zones, causing units to operate under poor operating conditions, thereby increasing operational risks. Existing technologies for optimizing the operation of hydropower stations mainly focus on aspects such as unit combination and load distribution, while the optimization control of restricted operating zones is relatively insufficient. This results in the inability of hydropower station units to fully utilize their optimal operating areas during operation, thereby affecting the overall operating efficiency of the hydropower station. In addition, the existing technologies for the automation and intelligence levels of hydropower stations need to be improved, especially when faced with complex and changing operating environments, and lack flexible and effective response strategies. Summary of the Invention

[0004] The present invention is proposed in view of the problems existing in the existing optimization control method of the AGC system of a hydropower station. Therefore, the problem to be solved by the present invention is how to provide an optimization control method and system for the AGC system of a hydropower station.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] In a first aspect, the present invention provides an optimization control method for an AGC system of a hydropower station, which includes collecting and monitoring power system operation data, constructing a monitoring model to monitor the operation status of the power system; determining an operation area according to the operation characteristics of the hydropower station and the power system requirements; dynamically adjusting the unit operation status based on the operation status and power system load requirements, and generating optimization control instructions; receiving and executing the optimization control instructions, and adjusting the unit output power and operation status.

[0007] As a preferred solution of the optimization control method of the hydropower station AGC system of the present invention, the power system operation data includes real-time operation data of the hydropower station, power system network data, power system load demand and fault data.

[0008] As a preferred solution of the optimization control method of the hydropower station AGC system of the present invention, the construction of the monitoring model includes the following steps: collecting the operating data of the hydropower station power system, cleaning the collected data, and preprocessing the collected data; extracting features from the preprocessed data, selecting key features for subsequent analysis, and constructing the monitoring model based on the extracted features. The relevant expression of the monitoring model is as follows:

[0009]

[0010] Where M(t) is the evaluation value of the power system operation status, t is the power system operation time; H(t) is the real-time operation data function of the hydropower station, N(t) is the power system network data function, F(t) is the fault data function, and is the weight parameter;

[0011]

[0012] Among them, a i , b i , c i is the characteristic coefficient of the hydropower station, α max is the maximum opening of the valve, α is the opening of the flow control valve, λ is the attenuation constant, d j , e j , f j are the network characteristic coefficients, m is the number of key features, γ(tt q ) is the unit impulse function, h q is the weight of the fault event, t q is the time when the failure occurs, and r is the total number of failure events; use the validation set to evaluate the constructed monitoring model, optimize the model, and deploy the optimized monitoring model to the actual environment for use; continuously monitor the model, check the performance and stability of the model, and iterate and improve the monitoring model.

[0013] As a preferred solution of the optimization control method of the hydropower station AGC system described in the present invention, the feature processing includes the following steps: calculating the basic statistics of the features in the data sample and the correlation coefficient between the features and the target variable; using the key indicator calculation formula to calculate, determining the key feature threshold, and determining the key feature data based on the comparison relationship between the key feature threshold and the correlation coefficient; determining the key feature data, when the correlation coefficient is above the key feature threshold, it is considered that the monitoring correlation importance of the feature is high and it is regarded as a key feature, for the selected key feature, constructing a feature vector, and performing feature processing to obtain the processed key feature; when the correlation coefficient is less than the key feature threshold, it is considered that the monitoring correlation importance of the feature is low and it is regarded as a non-key feature, and the data sample is discarded in the subsequent monitoring process; obtaining the mean vector of the key feature, calculating the covariance matrix of the key feature, and calculating the eigenvalues ​​and corresponding eigenvectors of the covariance matrix; selecting and retaining the eigenvector corresponding to the largest eigenvalue in the covariance matrix, sorting the eigenvalues ​​and selecting the top K largest eigenvalues, using the selected principal component eigenvectors, projecting the original data into a new feature space to obtain the standardized key feature.

[0014] As a preferred solution of the optimization control method of the hydropower station AGC system of the present invention, wherein: the unit operating state includes a vibration zone, an operating zone and a restricted operating zone; the calculation formula for the unit operating state division limit is as follows:

[0015]

[0016] Where M t The limit value for the unit operation status is divided into M j is the power system operation status evaluation value calculated in the jth monitoring, w j is the weight factor, giving higher weight to more recent data, R1 is the number of monitoring times; when the power system operation status assessment value M(t) is less than 0.9M t When the power system operation status is in the vibration zone, the unit is not suitable for long-term operation and corresponding measures need to be taken; when the power system operation status evaluation value M(t) is between 0.9M t and 1.1M t When the power system operation status evaluation value M(t) is greater than 1.1M, the current power system operation status belongs to the operation area and the unit is in the normal operation range; when the power system operation status evaluation value M(t) is greater than 1.1M t , the current state belongs to the restricted operation area. The unit can operate, but the operation time in the area needs to be avoided or reduced.

[0017] As a preferred solution of the optimization control method of the hydropower station AGC system of the present invention, the dynamic adjustment of the unit operating status includes the following steps: real-time monitoring of the load demand of the power system to understand the current power supply and demand situation, and collecting the operating status data of each unit in the hydropower station; analyzing the trend and change characteristics of the load demand to determine whether the unit operating status needs to be adjusted; and formulating the adjustment decision of the unit operating status based on the real-time monitored unit operating status data and load demand. The relevant expression is as follows:

[0018]

[0019] Where D(t) is the load demand of the power system, w i is the time weight factor for the i-th hour, L h (i) is the historical average load in the ith hour, p j is the periodic weight factor of day j, L w (j) is the historical average load on day j, a k is the polynomial trend coefficient, t k is the time variable, n is the order of the polynomial, and m is the number of seasonal components. The optimization control algorithm is called to calculate the optimal unit output power and operating status based on the adjustment decision and real-time monitoring data to maximize the power generation efficiency of the hydropower station. That is, maximizing the unit output power is the optimization goal, and the unit output power and operating status are dynamically adjusted. The relevant expressions are as follows:

[0020]

[0021] Where, P o is the output power of the hydroelectric generating unit, M(t) is the operating status parameter, ρ is the density of water, g is the acceleration of gravity, H is the water level difference, α1, β1, γ1 and δ1 are empirical coefficients, which are calibrated according to the specific hydropower station and generating unit. According to the calculation results of the optimization control algorithm, the output power and operating status of the unit are adjusted in real time. After the adjustment is completed, the load demand of the power system and the operating status of the hydropower station continue to be monitored, the adjusted unit operating data are collected, and fine-tuning is performed according to the actual situation.

[0022] As a preferred solution of the optimization control method of the hydropower station AGC system described in the present invention, the optimization control instructions include: when the power system load demand is higher than the historical power system load demand, the units are placed in the operating area first to ensure efficient and stable power generation; if all units are already in the operating area and still cannot meet the load demand, some units are placed in the restricted operating area for a short time to meet the load demand, and the unit operating status is monitored. If the unit is found to enter the vibration zone, the output power is immediately adjusted to be reduced; when the power system load demand is lower than the historical power system load demand, the units are kept in the operating area first to maintain power generation efficiency; if the load demand is lower than the total power generation capacity of the hydropower station, some units are placed in low power consumption mode or standby state to reduce energy loss and equipment wear. On the premise of ensuring the safe and stable operation of the hydropower station, the restricted operating area is skipped and the units are directly adjusted to low power consumption mode or standby state.

[0023] In the second aspect, the present invention provides an optimization control system of an AGC system of a hydropower station, which includes: an AGC controller for monitoring the power system status, collecting data, and generating control instructions according to a preset control strategy; an operating area identification module for determining the optimal operating area and the restricted operating area according to the operating characteristics of the hydropower station and the power system requirements; an optimization control module for dynamically adjusting the unit operating status based on the operating area identification and the power system load requirements to maximize the power generation efficiency of the hydropower station; and a unit control module for receiving and executing optimization control instructions to adjust the unit output power and operating status.

[0024] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, the steps of the optimization control method of the hydropower station AGC system are implemented.

[0025] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, the steps of the optimization control method of the hydropower station AGC system are implemented.

[0026] The present invention has the beneficial effect of maximizing the hydropower station's power generation efficiency and reducing operating costs by dynamically adjusting the unit's operating status. It also avoids unstable vibrations that may result from unit operation in a vibration zone, improving the stability and reliability of the power system. By flexibly adjusting the unit's operating status based on power system requirements and the hydropower station's operating characteristics, the hydropower station's operational flexibility and adaptability are enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0028] Figure 1 Flowchart of the optimization control method for the AGC system of a hydropower station.

[0029] Figure 2 This is the structural diagram of the optimization control method of the hydropower station AGC system. DETAILED DESCRIPTION

[0030] To make the above-mentioned objects, features, and advantages of the present invention more easily understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0031] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0032] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0033] Example 1

[0034] Reference Figure 1 , which is the first embodiment of the present invention, provides an optimization control method for an AGC system of a hydropower station, comprising:

[0035] S1: Collect and monitor power system operation data and build a monitoring model to monitor the power system operation status.

[0036] Specifically, the power system operation data includes real-time operation data of hydropower stations, power system network data, power system load demand and fault data.

[0037] Real-time hydropower station operating data: Unit output: reflects the actual generating capacity of the hydropower station's generator units. Speed: reflects the rotational speed of the turbine, which is related to power generation efficiency and water flow conditions. Flow: indicates the amount of water flowing through the turbine, which affects power generation. Head: refers to the difference in water level upstream and downstream of the turbine, a key factor in power generation efficiency. Temperature: includes unit cooling water temperature, oil temperature, etc., which affects unit operating stability and lifespan.

[0038] Power system network data: Voltage: reflects the power quality of the power system and is crucial for the safe operation of equipment. Current: reflects the scale and direction of power transmission within the power system. Frequency: determines the basis for synchronous operation of the power system and is closely related to power quality. Power: includes active power and reactive power and is a core parameter for power system operation and adjustment.

[0039] Historical operating data: used to analyze long-term unit operating trends and performance changes. Failure data: helps predict unit failures and conduct preventive maintenance in advance. Maintenance data: records the unit maintenance process and results, providing guidance for optimizing maintenance strategies.

[0040] Collect real-time hydropower station operation data, power system network data, load demand data, historical operation data, fault data, and maintenance data. This data is the basis for building a monitoring model, and its accuracy and completeness must be ensured.

[0041] Clean the collected data to remove outliers, noise, and duplicate data. Preprocess the data, such as standardization, normalization, or discretization, to better suit subsequent mathematical operations and model building.

[0042] Extract the corresponding features from the cleaned and preprocessed data, and select key features for subsequent analysis based on the characteristics of the problem and the nature of the data.

[0043] Calculate the basic statistics of the features in the data sample. The relevant calculation formula is as follows:

[0044]

[0045] Where μ i is the mean of the i-th feature in the data sample, x i is the i-th feature in the data sample, N is the number of features in the data sample, σ i is the standard deviation of the i-th feature in the data sample;

[0046] Calculate the correlation coefficient between the feature and the target variable. The relevant calculation formula is as follows:

[0047]

[0048] Where r xyis the correlation coefficient, y i is the target variable of the i-th feature in the data sample, is the mean of the i-th feature in the data sample, is the mean of the target variable;

[0049] Use the key indicator calculation formula to calculate and determine the key feature threshold. The relevant calculation formula is as follows:

[0050]

[0051] Where C(x) is the key feature threshold, and x is the input data feature vector;

[0052] Determine key feature data based on the comparison relationship between key feature thresholds and correlation coefficients;

[0053] Determine the key characteristic data, when the correlation coefficient r xy When the value is greater than or equal to the key feature threshold C(x), the monitoring relevance of the feature is considered to be of high importance and is regarded as a key feature. For the selected key feature, a feature vector is constructed and feature processing is performed to obtain the processed key feature.

[0054] When the correlation coefficient r xy When the value is less than the critical feature threshold C(x), the monitoring relevance of the feature is considered to be low and it is considered as a non-critical feature, and the data sample is discarded in the subsequent monitoring process;

[0055] Assuming there are m key features, the feature vector V is expressed as V = [f1, f2, ..., f m ], where f i Indicates key features;

[0056] Get the mean vector of the key features and calculate the covariance matrix of the key features. The relevant calculation formula is:

[0057]

[0058] Where C ij Create an m×m covariance matrix for the covariance between the i-th key feature and the j-th key feature;

[0059] Calculate the eigenvalues ​​λ of the covariance matrix i and the corresponding eigenvector w i , the relevant calculation formula is:

[0060] Cw i =λ i w i

[0061] In the formula, the eigenvalue represents the variance in the covariance matrix, and the eigenvector represents the principal component in the covariance matrix;

[0062] Select the eigenvector corresponding to the largest eigenvalue in the retained covariance matrix, sort the eigenvalues ​​and select the top K largest eigenvalues, use the selected principal component eigenvectors to project the original data into the new feature space, and obtain the standardized key features. The relevant expression is:

[0063]

[0064] Z=[Z1,Z2,…,Z m ]

[0065] Among them, Z i is the value of the jth feature of the i-th sample in the new feature space after standardization.

[0066] S2: Determine the operating area based on the operating characteristics of the hydropower station and the power system requirements;

[0067] Specifically, we build a monitoring model based on the extracted features. During the model building process, we should pay attention to the selection and adjustment of parameters. The relevant expressions of the monitoring model are as follows:

[0068]

[0069] Where M(t) is the power system operating status assessment value, t is the power system operating time; H(t) is the real-time operating data function of the hydropower station, N(t) is the power system network data function, F(t) is the fault data function, and γ is a weight parameter determined according to the system characteristics, which is used to adjust the influence of different data items on the monitoring model.

[0070]

[0071] Among them, a i , b i , c i is a coefficient determined according to the characteristics of the hydropower station, α max is the maximum opening of the valve, α is the opening of the flow control valve, λ is the attenuation constant, d j , e j , f j , are coefficients determined according to network characteristics, m is the number of key features, used to simulate the dynamic changes of network data; α and β are demand parameters determined according to historical data; γ(t) is the unit impulse function, h q is the weight of the fault event, t q is the time when the failure occurs, and r is the total number of failure events.

[0072] Use the validation set or test set to evaluate the constructed monitoring model. Evaluation metrics include accuracy, sensitivity, specificity, etc., which should be selected based on specific needs.

[0073] Optimize the model based on the evaluation results. This involves adjusting parameters, adding or removing features, improving algorithms, etc. to improve model performance.

[0074] Deploy the optimized monitoring model to the actual environment for use. Ensure that the model can receive and process data in real time and output accurate monitoring results.

[0075] Continuously monitor the model and regularly check its performance and stability. If you find any performance degradation or anomalies, make adjustments and optimizations in a timely manner.

[0076] As data changes and new requirements emerge, the monitoring model should be iterated and improved regularly. This includes updating data, re-extracting features, adjusting parameters, etc. to adapt to the new environment and requirements.

[0077] The unit operating status includes vibration zone, operating zone and restricted operating zone; the calculation formula for the unit operating status division limit is as follows:

[0078]

[0079] Where M t The limit value for the unit operation status is divided into M j is the power system operation status evaluation value calculated in the jth monitoring, w j is the weight factor, giving higher weight to more recent data, and R1 is the number of monitoring times;

[0080] When the power system operation status evaluation value M(t) is less than 0.9M t When the current power system operating state is determined to be in the vibration zone, the unit is not suitable for long-term operation and corresponding measures need to be taken;

[0081] When the power system operation status evaluation value M(t) is between 0.9M t and 1.1M t When the current power system operating state is between , the unit is in the normal operating range;

[0082] When the power system operation status evaluation value M(t) is greater than 1.1M t , the current state belongs to the restricted operation area. The unit can operate, but the operation time in the area needs to be avoided or reduced.

[0083] S3: Dynamically adjust the unit operating status and generate optimized control instructions based on the operating status and power system load demand;

[0084] Specifically, it monitors the load demand of the power system in real time, understands the current power supply and demand situation, and collects the operating status data of each unit in the hydropower station;

[0085] Analyze the trend and changing characteristics of load demand to determine whether the unit operating status needs to be adjusted;

[0086] According to the real-time monitored unit operating status data and load demand, the adjustment decision of the unit operating status is made. The relevant expression is as follows:

[0087]

[0088] Where D(t) is the load demand of the power system, w i is the time weight factor for the i-th hour, L h (i) is the historical average load in the ith hour, p j is the periodic weight factor of day j, L w (j) is the historical average load on day j, a k is the polynomial trend coefficient, t k is the time variable, n is the order of the polynomial, and m is the number of seasonal components;

[0089] The optimization control algorithm is called to calculate the optimal unit output power and operating status based on the adjustment decision and real-time monitoring data to maximize the power generation efficiency of the hydropower station. In other words, maximizing the unit output power is the optimization goal, and the unit output power and operating status are dynamically adjusted. The relevant expressions are as follows:

[0090]

[0091] Where, P o is the output power of the hydroelectric generator set, M(t) is the operating state parameter, ρ is the density of water, g is the acceleration of gravity, H is the water level difference, α1, β1, γ1 and δ1 are empirical coefficients, which are calibrated according to the specific hydropower station and generator set;

[0092] According to the calculation results of the optimization control algorithm, the output power and operating status of the unit are adjusted in real time;

[0093] After the adjustment is completed, continue to monitor the load demand of the power system and the operating status of the hydropower station, collect the operating data of the adjusted units, and make fine adjustments based on actual conditions.

[0094] S4: Receive and execute optimization control instructions to adjust the unit output power and operating status.

[0095] Specifically, when the power system load demand is higher than the historical power system load demand, the units are prioritized to be placed in the operating area to ensure efficient and stable power generation;

[0096] If all units are in the operating area and still cannot meet the load demand, some units will be placed in the restricted operating area for a short time to meet the load demand. The operating status of the units will be monitored. If a unit is found to have entered the vibration area, the output power will be immediately adjusted and reduced.

[0097] When the power system load demand is lower than the historical power system load demand, priority is given to keeping the units in the operating area to maintain power generation efficiency;

[0098] If the load demand is lower than the total power generation capacity of the hydropower station, some units will be placed in low-power mode or standby state to reduce energy loss and equipment wear. On the premise of ensuring the safe and stable operation of the hydropower station, the restricted operating area will be skipped and the units will be directly adjusted to low-power mode or standby state.

[0099] Furthermore, this embodiment also provides an optimization control system for the AGC system of a hydropower station, including: an AGC controller, used to monitor the power system status, collect data, and generate control instructions according to a preset control strategy; an operating area identification module, used to determine the optimal operating area and restricted operating area according to the operating characteristics of the hydropower station and the power system requirements; an optimization control module, used to dynamically adjust the unit operating status based on the operating area identification and the power system load requirements to maximize the power generation efficiency of the hydropower station; a unit control module, used to receive and execute optimization control instructions to adjust the unit output power and operating status.

[0100] This embodiment also provides a computer device, which is suitable for the optimization control method of the AGC system of a hydropower station, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement all or part of the steps of the method described in the embodiment of the present invention as proposed in the above embodiment.

[0101] This embodiment further provides a storage medium having a computer program stored thereon, which, when executed by a processor, performs the method of any optional implementation of the above embodiment. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0102] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. Technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0103] As can be seen from the above, this method maximizes the hydropower station's power generation efficiency and reduces operating costs by dynamically adjusting the unit's operating status. It also avoids unstable vibrations that may result from unit operation in a vibration zone, improving the stability and reliability of the power system. By flexibly adjusting the unit's operating status based on power system needs and the hydropower station's operating characteristics, it enhances the hydropower station's operational flexibility and adaptability.

[0104] Example 2

[0105] Reference Figure 2 This is the second embodiment of the present invention, which provides an optimization control method for the AGC system of a hydropower station. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0106] Table 1 Test data record table

[0107] Test subjects Voltage Current Active power Reactive power Operational efficiency Optimizing control instructions Hydropower Station A 11.5 500 120 30 92 Instruction 1 Hydropower Station A 11.7 520 125 32 93 Instruction 2 Hydropower Station A 11.3 480 118 28 91 Instruction 3 Hydropower Station A 11.8 540 130 35 94 Instruction 4 Hydropower Station A 11.6 510 122 31 92.5 Instruction 5 Hydropower Station A 11.4 490 119 29 91.8 Instruction 6

[0108] As can be seen from the table, the unit's operating efficiency has been significantly improved. After executing the optimized control instructions, the unit's operating efficiency has generally improved. This method dynamically adjusts the unit's operating status to precisely match the power system's load demand. This better matches the power system's load demand, improves power system stability, and helps reduce unnecessary energy waste. It has advantages in improving unit operating efficiency, matching power system load demand, and enhancing power system stability.

[0109] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An optimization control method for an AGC system of a hydropower station, characterized by: include, Collect and monitor power system operation data, and build monitoring models to monitor the power system operation status; Determine the operating area based on the operating characteristics of the hydropower station and the power system requirements; Based on the operating status and power system load demand, dynamically adjust the unit operating status and generate optimized control instructions; Receive and execute optimization control instructions to adjust the unit output power and operating status; The power system operation data includes real-time operation data of hydropower stations, power system network data, power system load demand and fault data; The construction of the monitoring model includes the following steps: collecting the operating data of the power system of the hydropower station, cleaning the collected data, and preprocessing the data; Feature extraction is performed from the preprocessed data, key features are selected for subsequent analysis, and a monitoring model is constructed based on the extracted features. The relevant expressions of the monitoring model are as follows: Where M(t) is the evaluation value of the power system operation status, t is the power system operation time; H(t) is the real-time operation data function of the hydropower station, N(t) is the power system network data function, F(t) is the fault data function, and γ is the weight parameter; Among them, a i , b i , c i is the characteristic coefficient of the hydropower station, α max is the maximum opening of the valve, α is the opening of the flow control valve, λ is the attenuation constant, d j , e j , f j are the network characteristic coefficients, m is the number of key features, γ(tt q ) is the unit impulse function, h q is the weight of the fault event, t q is the time when the fault occurs, r is the total number of fault events; Use the validation set to evaluate the built monitoring model, optimize the model, and deploy the optimized monitoring model in the actual environment for use; Continuously monitor the model, check the performance and stability of the model, and iterate and improve the monitoring model.

2. The optimization control method of the hydropower station AGC system according to claim 1, characterized in that: Also includes feature processing, The following steps are included: Calculate basic statistics of features in the data sample and the correlation coefficients between features and target variables; Use the key indicator calculation formula to calculate and determine the key feature threshold, and determine the key feature data based on the comparison relationship between the key feature threshold and the correlation coefficient; Determine the key feature data. When the correlation coefficient is above the key feature threshold, the feature is considered to have a high degree of monitoring relevance and is considered a key feature. For the selected key feature, construct a feature vector and perform feature processing to obtain the processed key feature. When the correlation coefficient is less than the critical feature threshold, the monitoring relevance of the feature is considered to be low and it is considered a non-critical feature, and the data sample is discarded in the subsequent monitoring process; Obtain the mean vector of the key features, calculate the covariance matrix of the key features, and calculate the eigenvalues ​​and corresponding eigenvectors of the covariance matrix; Select the eigenvector corresponding to the largest eigenvalue in the retained covariance matrix, sort the eigenvalues ​​and select the top K largest eigenvalues, use the selected principal component eigenvectors to project the original data into the new feature space, and obtain the standardized key features.

3. The optimization control method of the hydropower station AGC system according to claim 2, characterized in that: The unit operating state includes vibration zone, operating zone and restricted operating zone; the calculation formula for the unit operating state division limit is as follows: Where M t The limit value for the unit operation status is divided into M j is the power system operation status evaluation value calculated in the jth monitoring, w j is the weight factor, giving higher weight to more recent data, and R1 is the number of monitoring times; When the power system operation status evaluation value M(t) is less than 0.9M t When the current power system operating state is determined to be in the vibration zone, the unit is not suitable for long-term operation and corresponding measures need to be taken; When the power system operation status evaluation value M(t) is between 0.9M t and 1.1M t When the current power system operating state is between , the unit is in the normal operating range; When the power system operation status evaluation value M(t) is greater than 1.1M t , the current state belongs to the restricted operation area. The unit can operate, but the operation time in the area needs to be avoided or reduced.

4. An optimization control system for an AGC system of a hydropower station, based on the optimization control method for an AGC system of a hydropower station according to any one of claims 1 to 3, characterized in that: include, AGC controller, which monitors the power system status, collects data, and generates control instructions based on preset control strategies; An operating area identification module is used to determine the optimal operating area and restricted operating area based on the operating characteristics of the hydropower station and the power system requirements; An optimization control module, which dynamically adjusts the unit's operating status based on operating area identification and power system load demand to maximize the hydropower station's power generation efficiency; The unit control module is used to receive and execute optimization control instructions to adjust the unit output power and operating status.

5. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the optimization control method of the hydropower station AGC system according to any one of claims 1 to 3 are implemented.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the optimization control method of the hydropower station AGC system according to any one of claims 1 to 3 are implemented.

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