Dynamic regulation method and system for hydraulic generator based on environmental changes

Abstract

The present invention relates to the technical field of hydraulic generator regulation, and particularly to a dynamic regulation method and system for a hydraulic generator based on environmental changes, which improves its operating efficiency and stability in complex and variable environments, and at the same time enhances safety and economy; the method includes: continuously obtaining environmental data information of the location where the hydraulic generator is located according to a set acquisition frequency; using a pre-trained key feature extraction model to extract features from the environmental data information collected each time to obtain an environmental key feature vector; performing fluctuation analysis on a plurality of consecutive environmental key feature vectors to obtain an environmental fluctuation index; comparing the environmental fluctuation index with a preset fluctuation threshold; in response to the environmental fluctuation index exceeding the preset fluctuation threshold, inputting the latest obtained environmental key feature vector into a hydraulic generator environmental impact analysis model to obtain a hydraulic generator environmental impact index.

Description

Technical Field

[0001] The present invention relates to the technical field of hydraulic generator regulation, and particularly to a dynamic regulation method and system for hydraulic generators based on environmental changes. Background Art

[0002] In the field of renewable energy, as the core equipment of hydropower generation, the operating efficiency and stability of hydraulic generators directly affect the power supply quality and economic benefits of the power system; however, the operating environment of hydraulic generators is complex and changeable, including various natural factors such as water flow velocity, water level height, water temperature, air humidity, temperature, and wind speed. These natural factors will all have a significant impact on the operating efficiency and safety of hydraulic generators.

[0003] Existing hydraulic generator operation regulation methods often rely on fixed operating parameter settings or simple adjustments based on manual experience, and it is difficult to accurately respond to complex and changeable environmental changes in real time. For example, when the water flow velocity suddenly increases, if the rotation speed or blade angle of the hydraulic generator is not adjusted in time, it may cause the unit to operate overloaded and even lead to mechanical failures; conversely, if the water flow velocity decreases and the output is not reduced in time, it will cause energy waste. Summary of the Invention

[0004] To solve the above technical problems, the present invention provides a dynamic regulation method and system for hydraulic generators based on environmental changes, which improves its operating efficiency and stability in complex and changeable environments, and at the same time enhances safety and economy.

[0005] In the first aspect, the present invention provides a dynamic regulation method for hydraulic generators based on environmental changes, and the method includes:

[0006] Continuously obtain the environmental data information of the location of the hydraulic generator according to the set acquisition frequency;

[0007] Use a preset trained key feature extraction model to extract features from the environmental data information collected each time to obtain an environmental key feature vector;

[0008] Perform fluctuation analysis on a continuous plurality of environmental key feature vectors to obtain an environmental fluctuation index;

[0009] Compare the environmental fluctuation index with a preset fluctuation threshold;

[0010] In response to the environmental fluctuation index exceeding the preset fluctuation threshold, input the latest obtained environmental key feature vector into the hydraulic generator environmental impact analysis model to obtain a hydraulic generator environmental impact index;

[0011] Optimize the environmental impact index of the hydraulic generator within the preset operating mode mapping space to obtain the optimal operating mode of the hydraulic generator under the real-time environmental state; and adjust the operating parameters of the hydraulic generator according to the optimal operating mode;

[0012] In response to the environmental fluctuation index not exceeding the preset fluctuation threshold, keep the real-time operating mode of the hydraulic generator unchanged.

[0013] Further, the calculation formula of the environmental fluctuation index is:

[0014]

[0015] where FI represents the environmental fluctuation index, reflecting the average fluctuation degree; V i represents the environmental key feature vector at the i-th time point; ∥V i+1 -V i ∥ represents the Euclidean distance between V i+1 and V i to calculate the difference between two vectors; n represents the number of data points within the time window.

[0016] Further, the construction method of the key feature extraction model includes:

[0017] Collect data on the operating status of the hydraulic generator under different environmental conditions;

[0018] Clean the collected data to remove outliers, missing values, and noise data;

[0019] Select a deep learning model as the basic architecture of the key feature extraction model; the deep learning model includes a convolutional neural network, a recurrent neural network, a support vector machine, and an autoencoder;

[0020] Use the processed data as input to train the key feature extraction model;

[0021] Use an independent test set to evaluate the performance of the model;

[0022] Tune the model according to the evaluation results;

[0023] Deploy the trained feature extraction model to the dynamic regulation system of the hydraulic generator.

[0024] Further, the influencing factors for setting the preset fluctuation threshold include the physical characteristics of the hydraulic generator, the change range and frequency of environmental factors, the requirements and stability of the power system, and the safety performance and reliability.

[0025] Further, the construction method of the environmental impact analysis model of the hydraulic generator includes:

[0026] Collect a historical dataset containing environmental data and the operating data of the hydraulic generator;

[0027] Preprocess the collected data, including removing outliers, filling in missing values, data standardization, and normalization;

[0028] Select a machine learning model as the infrastructure of the hydraulic generator environmental impact analysis model; the machine learning model includes linear regression, random forest regression, gradient boosting tree, multi-layer perceptron, convolutional neural network, and recurrent neural network;

[0029] Use the preprocessed dataset to train the selected model and adjust the hyperparameters of the model;

[0030] Validate the model using an independent test set and evaluate its generalization ability on unknown data;

[0031] Optimize the model according to the validation results;

[0032] Deploy the model to the actual operation control system of the hydraulic generator.

[0033] Furthermore, the construction method of the preset operation mode mapping space includes:

[0034] Clarify the operation requirements and limitations of the hydraulic generator under different environmental conditions;

[0035] Collect the operation data of the hydraulic generator under different environmental conditions, including environmental parameters and corresponding operation parameters; preprocess the collected data to remove outliers and missing values;

[0036] According to the operation requirements and limitations of the hydraulic generator, divide the operation modes into different categories and define corresponding operation parameter settings for each mode;

[0037] Take the environmental key feature vector as the input space and the operation mode as the output space to construct the mapping relationship from the input space to the output space;

[0038] Establish the mapping relationship between the environmental key feature vector and the operation mode, and construct the operation mode mapping space.

[0039] Furthermore, the influencing factors for setting the acquisition frequency include the environmental change rate, system response requirements, data processing capabilities, cost-benefit, historical data analysis, and on-site conditions.

[0040] On the other hand, the present application also provides a dynamic control system for a hydraulic generator based on environmental changes, and the system includes:

[0041] The environmental data acquisition module continuously obtains the environmental data information of the location of the hydraulic generator according to the set acquisition frequency;

[0042] The feature extraction module uses a pre-trained key feature extraction model to extract features from the environmental data information collected each time, and obtains an environmental key feature vector;

[0043] The environmental fluctuation analysis module performs fluctuation analysis on multiple consecutive environmental key feature vectors to obtain an environmental fluctuation index;

[0044] The threshold comparison module compares the environmental fluctuation index with a preset fluctuation threshold;

[0045] The environmental impact analysis module, in response to the environmental fluctuation index exceeding the preset fluctuation threshold, inputs the latest obtained environmental key feature vector into the hydraulic generator environmental impact analysis model to obtain a hydraulic generator environmental impact index;

[0046] The operation mode optimization module optimizes the hydraulic generator environmental impact index in a preset operation mode mapping space to obtain the best operation mode of the hydraulic generator under the real-time environmental state; and adjusts the operation parameters of the hydraulic generator according to the best operation mode; in response to the environmental fluctuation index not exceeding the preset fluctuation threshold, the real-time operation mode of the hydraulic generator remains unchanged.

[0047] In a third aspect, the present application provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor. The transceiver, the memory, and the processor are connected through the bus. When the computer program is executed by the processor, the steps in any one of the above methods are implemented.

[0048] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps in any one of the above methods are implemented.

[0049] Compared with the prior art, the beneficial effects of the present invention are as follows: By setting the acquisition frequency, the method continuously obtains environmental data and immediately performs analysis and processing, enabling the system to quickly respond to environmental changes, timely adjust the operation state of the hydraulic generator, and effectively avoid problems such as overload, failure, and energy waste;

[0050] By using the key feature extraction model and the environmental impact analysis model, the intelligent analysis and processing of complex environmental factors are realized, the key features can be accurately extracted, and the impact of environmental changes on the hydraulic generator can be predicted, so as to make scientific decisions;

[0051] Automatically adjust the operating mode of the hydraulic generator according to the real-time environmental status, without manual intervention or simple adjustment based on fixed parameters; enable the hydraulic generator to maintain the optimal operating state under different environmental conditions, improving the operating efficiency and stability; ensure the unit operates under the optimal working conditions by precisely regulating the operating parameters of the hydraulic generator, reducing unnecessary energy waste and improving the power generation efficiency;

[0052] Respond to environmental changes in a timely manner, avoid mechanical failures and safety accidents caused by overloading operation, and improve the safety and reliability of the hydraulic generator; the intelligent and automated regulation method reduces the frequency of manual operation and intervention, lowering the operation and maintenance costs and human resource input;

[0053] In summary, this dynamic regulation method significantly improves the operating efficiency and stability of the hydraulic generator in a complex and changeable environment by regulating the operation of the hydraulic generator in real time, intelligently, and adaptively, while enhancing the safety and economy. Brief Description of the Drawings

[0054] Figure 1 is the flowchart of the present invention;

[0055] Figure 2 is the flowchart of the construction method of the key feature extraction model;

[0056] Figure 3 is the structure diagram of the dynamic regulation system of the hydraulic generator based on environmental changes. Detailed Embodiments

[0057] In the description of the present application, those skilled in the art should know that the present application can be implemented as a method, a device, an electronic device, and a computer-readable storage medium. Therefore, the present application can be specifically implemented in the following forms: complete hardware, complete software (including firmware, resident software, microcode, etc.), and a combination of hardware and software. In addition, in some embodiments, the present application can also be implemented in the form of a computer program product in one or more computer-readable storage media, which contains computer program code.

[0058] The above computer-readable storage medium may adopt any combination of one or more computer-readable storage media. Computer-readable storage media include: electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination of the above. More specific examples of computer-readable storage media include: portable computer disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, flash memories, optical fibers, compact disk read-only memories, optical storage devices, magnetic storage devices, or any combination of the above. In this application, the computer-readable storage medium may be any tangible medium that contains or stores a program, and this program may be used by or in conjunction with an instruction execution system, device, or component.

[0059] In the technical solution of this application, the acquisition, storage, use, processing, etc. of data all comply with the relevant provisions of national laws.

[0060] This application describes the provided method, device, and electronic device through flowcharts and / or block diagrams.

[0061] It should be understood that each block of the flowchart and / or block diagram, as well as the combination of blocks in the flowchart and / or block diagram, can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing devices, thereby producing a machine. These computer-readable program instructions are executed by a computer or other programmable data processing devices, resulting in a device that implements the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0062] These computer-readable program instructions can also be stored in a computer-readable storage medium that enables a computer or other programmable data processing device to work in a specific manner. In this way, the instructions stored in the computer-readable storage medium produce an instruction device product that includes the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0063] The computer-readable program instructions can also be loaded onto a computer, other programmable data processing device, or other device, such that a series of operation steps are executed on the computer, other programmable data processing device, or other device, resulting in a computer-implemented process. Thus, the instructions executed on the computer or other programmable data processing device can provide a process that implements the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0064] The following describes this application in conjunction with the accompanying drawings in this application.

[0065] Embodiment 1: As Figures 1 to 2 shown, the dynamic regulation method of a hydraulic generator based on environmental changes of the present invention specifically includes the following steps:

[0066] S1. Continuously obtain the environmental data information at the location of the hydro-generator according to the set acquisition frequency;

[0067] The environmental data information includes, but is not limited to, natural factors such as water flow velocity, water level height, water temperature, air humidity, temperature, and wind speed, etc. Deploy high-precision sensors around the hydro-generator to monitor key environmental indicators such as water flow velocity, water level height, water temperature, air humidity, temperature, and wind speed, etc. The sensors should have good reliability and anti-interference ability to ensure the accuracy of data acquisition;

[0068] Set an appropriate acquisition frequency according to the actual requirements and the requirements of the system design. The acquisition frequency should neither be too low to fail to capture environmental changes nor too high to cause unnecessary computational burden. The acquisition frequency needs to be determined through experimental verification to ensure that it can quickly respond to environmental changes without over-consuming resources;

[0069] The data collected by the sensors needs to be transmitted to the central control system through reliable communication means and stored in the database for subsequent processing. The security and integrity of the data should be ensured during the data transmission process;

[0070] The influencing factors for setting the acquisition frequency include:

[0071] Rate of environmental change: The rates of change of different environmental factors are different. The water flow velocity may change rapidly due to reasons such as rainfall and reservoir water release, while the water temperature, air humidity, etc. may change relatively slowly. Therefore, it is necessary to adjust the acquisition frequency according to the actual rates of change of each environmental factor. For environmental factors with rapid changes, a higher acquisition frequency should be set to ensure timely response. For environmental factors with slower changes, the acquisition frequency can be appropriately reduced to reduce the amount of data processing;

[0072] System response requirements: The response speed requirements of the hydro-generator operation control system are also important considerations for setting the acquisition frequency. If the system needs to quickly respond to environmental changes to adjust the operation state of the hydro-generator, then a higher acquisition frequency needs to be set. It is also necessary to consider the processing capacity and data transmission speed of the system to ensure the real-time and accuracy of the data under high-frequency acquisition;

[0073] Data processing capacity: After the data is collected, it needs to be processed and analyzed to extract key features and make decisions. The data processing and analysis ability of the system will limit the setting of the acquisition frequency. If the system processing capacity is limited, too high an acquisition frequency may lead to data processing delays or losses, affecting the control effect. Therefore, it is necessary to set a reasonable acquisition frequency according to the actual processing capacity of the system;

[0074] Cost - benefit: The setting of the acquisition frequency also needs to consider cost - benefit; high - frequency acquisition will increase the burden on sensors, data storage, and processing equipment, thus increasing costs; on the premise of meeting the system response requirements, the acquisition frequency should be minimized as much as possible to save costs; at the same time, the impact of the accuracy and reliability of data acquisition on the overall system benefit also needs to be considered;

[0075] Historical data analysis: By analyzing historical environmental data, the change rules and characteristics of each environmental factor can be understood, providing a reference for the setting of the acquisition frequency;

[0076] On - site conditions: On - site conditions such as the installation location, accuracy, and stability of sensors will also affect the setting of the acquisition frequency; the performance of sensors and other data acquisition equipment is also a factor to be considered when setting the acquisition frequency; high - performance equipment can support a higher acquisition frequency without affecting data quality.

[0077] In this step, by setting an appropriate acquisition frequency, it is possible to ensure that the system can timely capture the subtle changes in the environment around the hydro - generator, which helps to improve the stability and power supply quality of the power system; deploying high - precision sensors and ensuring their good reliability and anti - interference ability can significantly improve the accuracy of data acquisition; the setting of the acquisition frequency has been experimentally verified, neither too low to fail to capture environmental changes nor too high to cause unnecessary computational burden; it helps to balance the system response speed and resource consumption, improving the overall operation efficiency; reliable communication means are adopted during the data transmission process, and the security and integrity of the data are ensured to prevent data loss or tampering; the acquisition frequency is comprehensively set according to multiple factors such as the environmental change rate, system response requirements, data processing capacity, cost - benefit, and historical data analysis, enabling the system to flexibly respond to different operation scenarios and demand changes, which helps to improve the adaptability and scalability of the system; real - time and accurate environmental data provides strong decision - making support for the operation control of the hydro - generator; through data analysis, potential problems can be discovered in a timely manner, targeted control strategies can be formulated, and the operation mode and parameter settings of the hydro - generator can be optimized, thereby improving the power generation efficiency and economic benefits; this step provides a solid data foundation and technical support for the operation control of the hydro - generator by scientifically and reasonably setting the acquisition frequency and continuously obtaining environmental data information, which helps to enhance the overall performance and operation benefit of the power system.

[0078] S2. Use the pre - trained key feature extraction model to extract features from the environmental data information collected each time, and obtain the environmental key feature vector;

[0079] The construction method of the key feature extraction model includes:

[0080] Collect a large amount of data on the operating status of hydro - generators under different environmental conditions;

[0081] Clean the collected data to remove outliers, missing values, and noisy data; then, normalize the data to ensure that data with different dimensions and ranges can be compared and analyzed on the same scale;

[0082] Select a deep learning model as the basic architecture of the key feature extraction model according to the data characteristics and task requirements; the deep learning model includes convolutional neural network, recurrent neural network, support vector machine, and autoencoder;

[0083] Use the processed data as input to train the key feature extraction model; during the training process, adjust the model parameters and optimization algorithms to enable the model to accurately extract key features from the input data;

[0084] Use an independent test set to evaluate the performance of the model and improve the accuracy and generalization ability of feature extraction; the evaluation metrics include accuracy, recall, and F1 score;

[0085] Tune the model according to the evaluation results, including adjusting the model structure, adding regularization terms to prevent overfitting, and using ensemble learning methods to improve stability;

[0086] Deploy the trained feature extraction model to the dynamic regulation system of the hydraulic generator to ensure that the system can call the model for feature extraction in real time.

[0087] In this step, through the automated feature extraction process, the possibility of manual intervention and errors is reduced, and the efficiency and accuracy of data processing are improved; the model can quickly extract key features from a large amount of complex environmental data, providing strong support for subsequent analysis and decision-making; since the model has been trained and deployed to the system, it can perform feature extraction on newly collected environmental data in real time, enabling the dynamic regulation system of the hydraulic generator to quickly respond to environmental changes, adjust operating parameters, and ensure the stability of power generation efficiency and safety; the key feature extraction model constructed by using advanced technologies such as deep learning has strong feature learning ability and generalization ability, and can automatically learn and extract key features that have a significant impact on the system operation from the data; enabling the system to more accurately understand the environmental state and make reasonable regulation decisions accordingly; by monitoring environmental changes in real time and accurately and adjusting the operating parameters of the hydraulic generator, it can ensure that the unit operates in an optimal state and avoid energy waste and mechanical failures caused by overload or inefficient operation; helps to optimize resource utilization and improve economic and environmental benefits; the key feature extraction model has been fully optimized and tuned during the training process and can cope with complex and changing environmental changes; at the same time, the stability of the model is improved through methods such as ensemble learning, enabling the system to still maintain good performance in the face of extreme or abnormal situations and ensuring the safe and stable operation of the hydraulic generator.

[0088] S3. Perform fluctuation analysis on consecutive multiple environmental key feature vectors to obtain an environmental fluctuation index;

[0089] The method for obtaining the environmental fluctuation index includes:

[0090] Collect environmental key feature vectors within a certain time window;

[0091] For each environmental key feature vector, calculate the difference between its adjacent vectors. The result of the difference is used to characterize the change in environmental conditions between adjacent time points. The difference result refers to the difference between environmental key feature vectors at adjacent time points, which is represented by calculating the Euclidean distance between two vectors. The difference result reflects the change in environmental conditions between adjacent time points.

[0092] To measure the amplitude of the fluctuation, calculate the sum of the absolute values of all difference results and the mean of the sum of squares. Define the environmental fluctuation index according to the calculation result of the fluctuation amplitude. Normalize the calculated fluctuation amplitude so that it falls within a fixed range for subsequent threshold comparison. The formula for the environmental fluctuation index is:

[0093]

[0094] where FI represents the environmental fluctuation index, reflecting the average degree of fluctuation; V i represents the environmental key feature vector at the i-th time point; ∥V i+1 - V i ∥ represents the Euclidean distance between V i+1 and V i and is used to calculate the difference magnitude between two vectors; n represents the number of data points within the time window.

[0095] In this step, by collecting the key environmental feature vectors within a certain time window, the dynamic changes of environmental conditions can be monitored in real time; by calculating the differences between adjacent vectors and further calculating the mean of the sum of the absolute values and the sum of squares of these differences, the quantification of the fluctuation amplitude of environmental conditions is achieved; this quantification method enables the degree of environmental change to be measured specifically and objectively, providing strong support for subsequent decision-making analysis; normalizing the fluctuation amplitude makes the fluctuation index fall within a fixed range, greatly improving the comparability and practicality of the fluctuation index; the normalized fluctuation index is not only convenient for threshold comparison but also enables horizontal and vertical comparative analysis under different times and different environmental conditions; by defining the environmental fluctuation index and substituting the measurement results of the fluctuation amplitude for calculation, a comprehensive assessment of the environmental condition fluctuations is achieved; the finally obtained environmental fluctuation index can be used as an important basis for decision-making support; in the fields of environmental monitoring, disaster warning, resource management, etc., the level of the environmental fluctuation index can directly reflect the stability and risk degree of environmental conditions, providing timely and effective information support for relevant decision-makers.

[0096] S4. Compare the environmental fluctuation index with a preset fluctuation threshold.

[0097] The influencing factors for setting the preset fluctuation threshold include:

[0098] Physical characteristics of the hydro-generator: Hydro-generators with different capacities and powers have different sensitivities and tolerances to environmental changes; large-capacity and high-power hydro-generators need to set lower fluctuation thresholds to identify and respond to environmental changes earlier to prevent overload or mechanical failures; the design and manufacturing standards of hydro-generators also affect their operating stability and adaptability to environmental changes; hydro-generators meeting higher standards may have better self-regulating capabilities, so the fluctuation threshold can be appropriately increased.

[0099] Change range and frequency of environmental factors: By analyzing historical environmental data and understanding the change range and frequency of various natural factors, it helps to determine a reasonable fluctuation threshold; if the water flow velocity in a certain area changes frequently and significantly, a lower fluctuation threshold may need to be set to ensure timely response; considering the impact of extreme weather conditions on the operation of hydro-generators, environmental fluctuations may be more intense under these conditions, and special attention needs to be paid and the fluctuation threshold should be adjusted appropriately.

[0100] Requirements and Stability of Power System: The power system has strict requirements for power supply quality, including voltage stability, frequency stability, etc.; as an important power generation equipment, the stability of the operation state of the hydraulic generator directly affects the power supply quality of the power system; therefore, when setting the fluctuation threshold, it is necessary to consider how to ensure the stable operation of the hydraulic generator to meet the requirements of the power system; the operation efficiency of the hydraulic generator directly affects the economic benefits; too high a fluctuation threshold will cause the hydraulic generator to be unable to adjust the operation parameters in time when the environment changes, resulting in energy waste; while too low a fluctuation threshold will increase the regulation frequency and cost; therefore, when setting the fluctuation threshold, it is necessary to balance the relationship between economic benefits and operation stability;

[0101] Safety Performance and Reliability: The hydraulic generator will face the risk of mechanical failure when operating under extreme environmental conditions; in order to avoid mechanical failures caused by environmental changes, it is necessary to set a reasonable fluctuation threshold to take timely control measures; considering the redundant design of the hydraulic generator system can improve the reliability and fault tolerance of the system to a certain extent, thus providing a certain safety margin when setting the fluctuation threshold.

[0102] In this step, by setting a reasonable fluctuation threshold, the system can quickly identify the significance of environmental changes; setting a reasonable fluctuation threshold helps to maintain the stable operation of the hydraulic generator; in the case of small environmental fluctuations, the system keeps the current operation mode unchanged, avoiding the interference of unnecessary parameter adjustments on the system stability; while when the environmental fluctuations are significant, the system can quickly adjust the operation parameters to ensure that the hydraulic generator operates in the optimal state, thus improving the power supply quality and stability of the entire power system; by balancing the relationship between economic benefits and operation stability to set the fluctuation threshold, it is possible to maximize the energy utilization efficiency while ensuring the stable operation of the hydraulic generator; avoiding energy waste caused by too high a fluctuation threshold and increased regulation costs caused by too low a fluctuation threshold makes the operation of the hydraulic generator more economical and efficient; setting a reasonable fluctuation threshold also helps to improve the safety performance of the hydraulic generator; under extreme environmental conditions, the system can identify potential risk factors earlier and take corresponding control measures to avoid the occurrence of mechanical failures; at the same time, considering the redundant design of the hydraulic generator system, a certain safety margin is provided when setting the fluctuation threshold, further enhancing the reliability and fault tolerance of the system.

[0103] S5. In response to the environmental fluctuation index exceeding the preset fluctuation threshold, input the latest obtained environmental key feature vector into the hydraulic generator environmental impact analysis model to obtain the hydraulic generator environmental impact index;

[0104] The mathematical framework of the hydraulic generator environmental impact analysis model includes:

[0105] Input layer: The input is the environmental key feature vector, representing different environmental parameters;

[0106] Hidden layer: The hidden layer contains multiple neurons. Each neuron receives weighted inputs from the input layer or the previous hidden layer and applies a non-linear activation function to calculate the output;

[0107] Output layer: The output layer usually contains one or more neurons for outputting the environmental impact index of the hydraulic generator; this index may be a single value that combines multiple environmental factors, or it may be a multi-dimensional vector representing environmental impacts in different aspects;

[0108] The method for constructing the environmental impact analysis model of the hydraulic generator includes:

[0109] Collect a historical dataset containing environmental data and hydraulic generator operation data; these data should cover different environmental conditions and operating conditions to ensure the generalization ability of the model;

[0110] Preprocess the collected data, including removing outliers, filling in missing values, data standardization or normalization, etc., to improve the efficiency and accuracy of model training;

[0111] Select a machine learning model as the basic architecture of the environmental impact analysis model of the hydraulic generator; the machine learning model includes linear regression, random forest regression, gradient boosting tree, multi-layer perceptron, convolutional neural network, and recurrent neural network;

[0112] Use the preprocessed dataset to train the selected model; during the training process, it is necessary to adjust the hyperparameters of the model to optimize the performance of the model;

[0113] Use an independent test set to verify the model and evaluate its generalization ability on unknown data;

[0114] Tune the model according to the verification results, including adjusting the model structure, replacing with a more suitable algorithm, or further optimizing feature selection;

[0115] Deploy the model to the actual hydraulic generator operation control system to achieve dynamic control based on environmental changes.

[0116] In this step, it is possible to analyze the key environmental feature vectors in real time and accurately predict the impact of environmental changes on the operating efficiency of the hydro-generator; by timely adjusting the operating parameters, it is ensured that the hydro-generator can maintain the optimal operating state under different environmental conditions, thereby improving the overall power generation efficiency; through predicting the environmental impact index, the model can identify potential safety risks in advance and trigger corresponding control measures to avoid the occurrence of overloading operation or mechanical failure of the unit, thus enhancing the operating safety of the hydro-generator; the model can intelligently adjust the output of the hydro-generator according to environmental fluctuations to avoid energy waste caused by excessive power generation under adverse environmental conditions; at the same time, when the environmental conditions are favorable, by optimizing the operating parameters, the maximum energy utilization is achieved and the economic benefits are improved; deploying the model into the actual operating control system of the hydro-generator realizes the automated control based on environmental changes; this not only reduces the burden of manual monitoring and adjustment, but also improves the accuracy and timeliness of the control, making the operation of the hydro-generator more intelligent and efficient; through real-time analysis and control, the model helps to maintain the stability of the operating state of the hydro-generator; when the environmental fluctuations are large, it can quickly adjust the operating parameters to cope with the changes, reduce the system fluctuations and unstable factors caused by environmental changes, and improve the stability and reliability of the entire power system; by constructing and applying the environmental impact analysis model, the efficient, safe and stable operation of the hydro-generator is realized, providing strong support for the wide application and sustainable development of renewable energy.

[0117] S6. Optimize the environmental impact index of the hydro-generator within the preset operating mode mapping space to obtain the best operating mode of the hydro-generator under the real-time environmental state; and adjust the operating parameters of the hydro-generator according to the best operating mode.

[0118] The best operating mode refers to the operating mode in which the hydro-generator can operate in the most efficient and safest state under the current environmental conditions. The construction method of the preset operating mode mapping space includes:

[0119] Clarify the operating requirements and limitations of the hydro-generator under different environmental conditions; including improving the operating efficiency of the hydro-generator within the allowable range, ensuring the safety guarantee of the hydro-generator under various environmental conditions, and the economic benefits of the power market.

[0120] Collect the operating data of the hydro-generator under different environmental conditions, including environmental parameters and corresponding operating parameters; preprocess the collected data to remove outliers and missing values to ensure the accuracy and integrity of the data; extract the key features that have a significant impact on the operation of the hydro-generator from the original data.

[0121] Divide the operating modes into different categories according to the operating requirements and limitations of the hydro-generator.

[0122] Define corresponding operating parameter settings for each mode to ensure that the hydraulic generator can operate stably in this mode and meet the operating requirements;

[0123] Take the environmental key feature vector as the input space and the operating mode as the output space to construct the mapping relationship from the input space to the output space;

[0124] Use machine learning methods to train historical data and establish the mapping relationship between the environmental key feature vector and the operating mode;

[0125] Verify and optimize the model through methods such as cross-validation to ensure that the model can accurately map the environmental key feature vector to the corresponding operating mode.

[0126] In this step, by analyzing the environmental key feature vector in real time and optimizing within the preset operating mode mapping space, it can ensure that the hydraulic generator always operates in the best mode under complex and changeable environmental conditions; it helps to maximize the operating efficiency of the hydraulic generator and reduce energy waste; the construction of the operating mode mapping space fully considers the operating limitations and safety requirements of the hydraulic generator; during the real-time regulation process, the system can automatically avoid potential risks such as overload, vibration, and overheating to ensure the safe operation of the hydraulic generator under various environmental conditions; combined with the real-time electricity price in the power market and the maintenance cost of the equipment, the operating mode mapping space can optimize the operating strategy of the hydraulic generator to achieve the maximization of economic benefits; by reducing unnecessary downtime and maintenance costs while maximizing power generation, it improves the overall economic benefits of power production; using machine learning methods to establish the mapping relationship between the environmental key feature vector and the operating mode realizes the intelligent regulation of the hydraulic generator; it can automatically adapt to environmental changes, reduce manual intervention, and improve the accuracy and real-time performance of regulation; through methods such as cross-validation to verify and optimize the model, it ensures the accuracy and robustness of the mapping model; this enables the system to maintain stable regulation performance in the face of abnormal or extreme environmental conditions and avoid system collapse or out-of-control; through dynamic regulation methods, it can maximize the utilization of hydraulic resources, reduce dependence on traditional energy, and promote the optimization of the energy structure and the sustainable development of the environment.

[0127] More specifically, the classification and definition of the operating mode are based on the operating requirements and limitations of the hydraulic generator under different environmental conditions; specific classifications include but are not limited to:

[0128] High-efficiency operation mode: Maximize the power generation efficiency when environmental conditions permit;

[0129] Safe operation mode: Ensure the safe operation of the hydraulic generator when environmental conditions are unstable or there are potential risks;

[0130] Economic operation mode: Combine the real-time electricity price in the power market and the maintenance cost of the equipment to optimize the operation strategy and maximize the economic benefits;

[0131] Low-load operation mode: When the environmental conditions are poor or the demand is low, reduce the output to avoid unnecessary energy waste.

[0132] S7. If the environmental fluctuation index does not exceed the preset fluctuation threshold, keep the real-time operation mode of the hydraulic generator unchanged;

[0133] If the environmental fluctuation index is lower than or equal to the preset fluctuation threshold, the system determines that the current environment is in a relatively stable state and will not have a significant impact on the operation efficiency and safety of the hydraulic generator;

[0134] After confirming the environmental stability, the system will not trigger the adjustment mechanism for the operation parameters of the hydraulic generator; it will maintain the current real-time operation mode of the hydraulic generator unchanged to ensure that the generator continues to operate in a stable state;

[0135] Although the current environment is stable, the system does not stop collecting and monitoring environmental data; the system continues to obtain the environmental data information of the location of the hydraulic generator at the set collection frequency and is ready to respond in a timely manner when the environment changes;

[0136] By reducing unnecessary regulation operations, step S7 helps to optimize the utilization of resources; during the environmental stability period, the hydraulic generator can maintain a high-efficiency and stable operation state, reduce the energy loss and mechanical wear caused by frequent adjustments, and thus improve the overall economic benefits and the service life of the equipment;

[0137] Although the current environment is stable, the system always remains highly vigilant and is ready to respond quickly when the environmental fluctuation index exceeds the preset threshold; this ensures that the system can make rapid adjustments in the face of sudden environmental changes and guarantees the operation efficiency and safety of the hydraulic generator.

[0138] In this step, when the environmental fluctuation index does not exceed the preset threshold, that is, when the environment is relatively stable, the system keeps the real-time operation mode of the hydraulic generator unchanged, avoiding unnecessary adjustment of operation parameters; reducing the energy loss and mechanical wear that may be caused by frequent adjustment of operation parameters, thus optimizing resource utilization, improving power generation efficiency and economy; maintaining the continuous operation of the hydraulic generator in a stable state helps to reduce the equipment stress caused by frequent operation, reduce the risk of mechanical failure, and further extend the service life of the equipment; continuous monitoring ensures that the system can grasp the environmental dynamics in real time. Once the environmental fluctuation index exceeds the preset threshold, the system can quickly respond and adjust the operation parameters of the hydraulic generator, thus ensuring the operation efficiency and safety of the generator; by reducing unnecessary regulation operations, reducing energy consumption, and extending the service life of the equipment, the implementation of this step helps to improve the overall economic benefits of the hydropower station; this step reflects the application of intelligent management in hydropower production. Through automatic monitoring and intelligent decision-making, precise control and optimization adjustment of the operation state of the power generation equipment are realized; this helps to promote the digital transformation and intelligent upgrading of the hydropower industry.

[0139] Embodiment 2: As Figure 3 shown, the dynamic regulation system of the hydraulic generator based on environmental changes of the present invention specifically includes the following modules;

[0140] The environmental data acquisition module continuously obtains the environmental data information of the location of the hydraulic generator according to the set acquisition frequency;

[0141] The feature extraction module uses a preset trained key feature extraction model to extract features from the environmental data information collected each time, and obtains an environmental key feature vector;

[0142] The environmental fluctuation analysis module performs fluctuation analysis on a continuous plurality of environmental key feature vectors to obtain an environmental fluctuation index;

[0143] The threshold comparison module compares the environmental fluctuation index with the preset fluctuation threshold;

[0144] The environmental impact analysis module, in response to the environmental fluctuation index exceeding the preset fluctuation threshold, inputs the latest obtained environmental key feature vector into the hydraulic generator environmental impact analysis model to obtain a hydraulic generator environmental impact index;

[0145] The operation mode optimization module optimizes the hydraulic generator environmental impact index in the preset operation mode mapping space to obtain the best operation mode of the hydraulic generator in the real-time environmental state; and adjusts the operation parameters of the hydraulic generator according to the best operation mode; in response to the environmental fluctuation index not exceeding the preset fluctuation threshold, the real-time operation mode of the hydraulic generator remains unchanged.

[0146] Through continuous environmental data collection and real-time analysis, the system can quickly respond to environmental changes, timely adjust the operation mode of the hydraulic generator, and effectively avoid the problems of unit overload or energy waste caused by environmental changes;

[0147] By using advanced technologies such as machine learning or deep learning for feature extraction and environmental fluctuation analysis, the intelligent processing of complex environmental factors is realized, the accuracy and scientificity of regulation are improved, and the uncertainty brought by manual intervention and empirical judgment is reduced;

[0148] The system enhances the adaptability and flexibility of the equipment by dynamically adjusting the operation mode of the hydraulic generator to adapt to different environmental states, ensuring efficient and stable operation under different environmental conditions; by analyzing environmental fluctuations in advance and predicting their impact on the hydraulic generator, the system can take preventive measures to avoid the occurrence of potential failures, improving the reliability and safety of the equipment;

[0149] When the water flow velocity decreases, the output is automatically reduced, avoiding unnecessary energy waste and improving energy utilization efficiency; the entire regulation process is highly automated, reducing the complexity and error rate of manual operation, lowering the operation and maintenance costs, and improving the management efficiency;

[0150] In summary, through real-time, intelligent, and adaptive regulation of the operation mode of the hydraulic generator, the dynamic regulation system significantly improves its operation efficiency and stability in complex and changeable environments.

[0151] All the various change methods and specific embodiments of the dynamic regulation method of the hydraulic generator based on environmental changes in the foregoing Embodiment 1 are equally applicable to the dynamic regulation system of the hydraulic generator based on environmental changes in this embodiment. Through the foregoing detailed description of the dynamic regulation method of the hydraulic generator based on environmental changes, those skilled in the art can clearly know the implementation method of the dynamic regulation system of the hydraulic generator based on environmental changes in this embodiment. Therefore, for the sake of simplicity of the specification, it will not be elaborated here.

[0152] In addition, the present application also provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor. The transceiver, the memory, and the processor are respectively connected through the bus. When the computer program is executed by the processor, it realizes each process of the method embodiment for controlling the output data, and can achieve the same technical effect. To avoid repetition, it will not be elaborated here.

[0153] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and modifications can still be made, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. A dynamic regulation method for a hydraulic generator based on environmental changes, characterized in that, The method includes: Continuously obtain the environmental data information at the location of the hydraulic generator according to the set acquisition frequency, including water flow velocity, water level height, water temperature, air humidity, temperature, and wind speed; Use a pre-trained key feature extraction model to extract features from the environmental data information collected each time to obtain an environmental key feature vector; Conduct fluctuation analysis on multiple consecutive environmental key feature vectors to obtain an environmental fluctuation index; Compare the environmental fluctuation index with a preset fluctuation threshold; In response to the environmental fluctuation index exceeding the preset fluctuation threshold, input the latest obtained environmental key feature vector into the hydraulic generator environmental impact analysis model to obtain a hydraulic generator environmental impact index; Optimize the latest obtained environmental key feature vector in a preset operation mode mapping space to obtain the optimal operation mode of the hydraulic generator under the real-time environmental state; and adjust the operation parameters of the hydraulic generator according to the optimal operation mode; In response to the environmental fluctuation index not exceeding the preset fluctuation threshold, keep the real-time operation mode of the hydraulic generator unchanged; The calculation formula of the environmental fluctuation index is: Among them, FI represents the environmental fluctuation index, reflecting the average fluctuation degree; V i represents the environmental key feature vector at the i-th time point; ∥V i+1 -V i ∥ represents the Euclidean distance between V i+1 and V i , which is used to calculate the difference between two vectors; n represents the number of data points within the time window.

2. The dynamic regulation method of a hydraulic generator based on environmental changes according to claim 1, wherein The construction method of the key feature extraction model includes: Collect data on the operation status of the hydraulic generator under different environmental conditions; Clean the collected data to remove outliers, missing values, and noise data; Select a deep learning model as the basic architecture of the key feature extraction model; the deep learning model includes convolutional neural network, recurrent neural network, support vector machine, and autoencoder; Use the processed data as input to train the key feature extraction model; Evaluate the performance of the model using an independent test set; Optimize the model according to the evaluation results; Deploy the trained feature extraction model to the hydraulic generator dynamic regulation system.

3. The dynamic regulation method of a hydraulic generator based on environmental changes according to claim 1, characterized in that The influencing factors for setting the preset fluctuation threshold include the physical characteristics of the hydraulic generator, the change range and frequency of environmental factors, the requirements and stability of the power system, and safety performance and reliability.

4. The dynamic regulation method of a hydraulic generator based on environmental changes according to claim 1, characterized in that The construction method of the hydraulic generator environmental impact analysis model includes: Collect a historical data set containing environmental data and hydraulic generator operation data; Preprocess the collected data, including removing outliers, filling missing values, data standardization, and normalization; Select a machine learning model as the basic architecture of the hydraulic generator environmental impact analysis model; the machine learning model includes linear regression, random forest regression, gradient boosting tree, multi-layer perceptron, convolutional neural network, and recurrent neural network; Use the preprocessed data set to train the selected model and adjust the hyperparameters of the model; Verify the model using an independent test set and evaluate its generalization ability on unknown data; Optimize the model according to the verification results; Deploy the model to the actual hydraulic generator operation regulation system.

5. The dynamic regulation method of a hydraulic generator based on environmental changes according to claim 1, characterized in that, The construction method of the preset operation mode mapping space includes: Clarify the operation requirements and limitations of the hydraulic generator under different environmental conditions; Collect the operation data of the hydraulic generator under different environmental conditions, including environmental parameters and corresponding operation parameters; preprocess the collected data to remove outliers and missing values; According to the operation requirements and limitations of the hydro-generator, the operation modes are divided into different categories, and the corresponding operation parameter settings are defined for each mode. Taking the environmental key feature vector as the input space and the operation mode as the output space, a mapping relationship from the input space to the output space is constructed. A mapping relationship between the environmental key feature vector and the operation mode is established to construct an operation mode mapping space.

6. The dynamic regulation method of a hydraulic generator based on environmental changes according to claim 1, characterized in that The influencing factors for setting the acquisition frequency include the environmental change rate, system response requirements, data processing capacity, cost-benefit, historical data analysis, and on-site conditions.

7. A dynamic regulation system for a hydraulic generator based on environmental changes, the system being applied to the dynamic regulation method for a hydraulic generator based on environmental changes as described in claim 1, characterized in that, The system includes: An environmental data acquisition module that continuously obtains the environmental data information at the location of the hydro-generator according to the set acquisition frequency. A feature extraction module that uses a pre-trained key feature extraction model to extract features from the environmental data information collected each time to obtain an environmental key feature vector. An environmental fluctuation analysis module that performs fluctuation analysis on multiple consecutive environmental key feature vectors to obtain an environmental fluctuation index. A threshold comparison module that compares the environmental fluctuation index with a preset fluctuation threshold. An environmental impact analysis module that, in response to the environmental fluctuation index exceeding the preset fluctuation threshold, inputs the latest obtained environmental key feature vector into the hydro-generator environmental impact analysis model to obtain a hydro-generator environmental impact index. An operation mode optimization module that optimizes the latest obtained environmental key feature vector within the preset operation mode mapping space to obtain the best operation mode of the hydro-generator under the real-time environmental state; and adjusts the operation parameters of the hydro-generator according to the best operation mode; in response to the environmental fluctuation index not exceeding the preset fluctuation threshold, the real-time operation mode of the hydro-generator remains unchanged.

8. A dynamic regulation electronic device for a hydraulic generator based on environmental changes, comprising a bus, a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected through the bus, and is characterized in that, When the computer program is executed by the processor, it implements the steps in the method described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps in the method described in any one of claims 1-6.

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