Hydroelectric generating set shutdown rule optimization method considering rotation speed rising extreme value and water head fluctuation extreme value

Abstract

The invention provides a hydroelectric generating set shutdown rule optimization method considering a rotating speed rising extreme value and a water head fluctuation extreme value. The method comprises the following steps of S1, obtaining water turbine modeling data; s2, constructing a high-precision water turbine model based on a BP neural network; s3, constructing a high-precision servo system model; s4, building a water turbine adjusting system simulation model based on an Octave tool; s5, constructing a comprehensive objective function considering the rotation speed rise extreme value, the water head fluctuation extreme value and the standard ITAE index; and S6, optimizing the closing inflection point and the closing speed of the guide vane of the hydroelectric generating set in combination with a snow-melting optimization algorithm, the water turbine regulating system simulation model and the comprehensive objective function. According to the hydroelectric generating set shutdown rule optimization method considering the rotation speed rising extreme value and the water head fluctuation extreme value, the influence of the shutdown rule on the rotation speed rising extreme value and the water head fluctuation extreme value can be fully considered, and safe and stable operation of the hydroelectric generating set is effectively guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of safe and stable operation of hydropower units, and in particular to a method for optimizing shutdown rules of hydropower units considering maximum speed increase and maximum head fluctuation. Background Art

[0002] Hydropower generators play a vital role in modern power systems, providing a stable and renewable energy source. However, the operating environment of hydropower generators is complex and dynamic, characterized by significant nonlinearity, time-varying characteristics, and strong coupling. Achieving safe, efficient, and smooth operation during the operation and shutdown of hydropower generators is a key issue. In particular, improper shutdown can cause drastic fluctuations in speed and head, leading to equipment damage and system instability. Therefore, optimizing the shutdown schedule is crucial for ensuring the safe operation of hydropower generators and increasing their service life.

[0003] Currently, the shutdown process for hydropower units mostly uses traditional methods, which rely primarily on experience and simple control strategies and have certain limitations. Traditional shutdown methods often ignore the maximum speed increase during the shutdown process. During rapid shutdown, the turbine speed may increase instantaneously, exceeding safety limits, causing equipment wear and even damage. Head fluctuation is a significant factor affecting the stability of hydropower units. Traditional shutdown methods fail to fully consider the maximum head fluctuation when performing shutdown operations, which may cause water hammer and damage to pipes and equipment. Furthermore, traditional shutdown methods are mostly static strategies that fail to dynamically adjust to actual operating conditions, making it impossible to achieve optimal shutdown results. In particular, traditional shutdown operations rely heavily on operator experience and lack systematic optimization strategies, making it difficult to ensure optimal shutdown results in complex and changing operating environments.

[0004] To overcome the limitations of traditional shutdown methods, modern control theory and optimization algorithms have been introduced into the study of hydropower unit shutdown processes. These methods, by considering the system's dynamic characteristics and various constraints, can achieve more optimal shutdown schedules. Commonly used optimization methods include genetic algorithms (GAs), particle swarm optimization (PSO), and the Grey Wolf Optimizer (GWO).

[0005] The Snow Melt Optimization Algorithm (SMOA) is an emerging intelligent optimization algorithm inspired by the physical phenomena of snow melting. It simulates the position and state changes of snowflakes during snowmelt and, through iterative optimization, gradually approaches the optimal solution. The SMOA offers strong global search capabilities, fast convergence, and ease of implementation, making it suitable for solving complex nonlinear optimization problems.

[0006] Given the limitations of traditional hydropower unit shutdown methods, which often fail to account for maximum speed rise and head fluctuation, it is necessary to propose a new optimization method for the guide vane closing inflection point and closing speed of hydropower units, which comprehensively considers factors such as maximum speed rise and head fluctuation. This optimization method will significantly improve the operational safety and economic efficiency of hydropower units and reduce equipment damage and system instability caused by speed and head fluctuations. Summary of the Invention

[0007] Aiming at the limitation of traditional hydropower unit shutdown method that does not take into account the maximum speed rise and the maximum head fluctuation, the present invention proposes a hydropower unit shutdown rule optimization method that takes into account the maximum speed rise and the maximum head fluctuation.

[0008] In order to achieve the above technical features, the purpose of the present invention is achieved as follows: a method for optimizing the shutdown rule of a hydropower unit considering the maximum speed increase and the maximum head fluctuation, comprising the following steps: S1, obtain turbine modeling data; S2, building a high-precision turbine model based on BP neural network; S3, building a high-precision servo system model; S4, build a simulation model of the turbine regulation system based on Octave tool; S5, construct a comprehensive objective function considering the maximum speed rise, maximum head fluctuation and standard ITAE index; S6, combines the snowmelt optimization algorithm, the turbine regulation system simulation model and the comprehensive objective function to optimize the closing inflection point and closing speed of the guide vanes of the hydropower unit.

[0009] Preferably, the step S1 includes the following steps: Building a high-precision turbine model requires detailed data support. This data comes from the actual operating turbine system. The process of obtaining turbine modeling data includes data collection, data preprocessing, and data verification steps. S11, data collection: Collect operational and experimental data of the turbine system; S12, data preprocessing: Pre-process the collected data to ensure data quality and consistency; S13, data verification: The pre-processed data is verified to confirm the accuracy and reliability of the data.

[0010] Preferably, the operating data in S11 includes: Input parameters: water head, flow rate, guide vane opening; Output parameters: speed, output power, torque; The experimental data include: Laboratory test data: Conduct performance tests on turbines to obtain accurate input-output relationship data; Field test data: Conduct field tests in the actual operating environment to obtain real operating data.

[0011] Preferably, the data preprocessing in S12 specifically includes: S121, data cleaning: Outlier processing: detect and remove obvious outliers; Missing value processing: use data completion methods to fill missing data; S122, data conversion: Normalization: scaling the data to the same range to speed up model training and improve accuracy; Feature extraction: Extract useful features from the raw data: unit speed, guide vane opening, unit flow rate and unit torque; S123, data segmentation: Training set: the main data set used for model training, accounting for 70%-80% of the total data; Validation set: used for model tuning and selection, accounting for 10%-15% of the total data; Test set: used for final evaluation of model performance, accounting for 10%-15% of the total data.

[0012] Preferably, the data verification in S13 specifically includes: S131, Statistical analysis: Descriptive statistics: Analyze the basic statistical characteristics of the data, including variance, root mean square error, and standard deviation; Correlation analysis: Analyze the correlation between each input parameter and output parameter, and select the parameters with higher correlation for modeling; S132, Visual Analysis: Scatter plot: Draw a scatter plot between input and output parameters to intuitively display data distribution and relationships; Time series graph: draw a time series graph to observe the time variation and periodicity of the data; S133, data consistency check: Check the consistency of data in different time periods and different working conditions to ensure data reliability.

[0013] Preferably, the step S2 is specifically as follows: Based on the BP neural network, a flow characteristic neural network model and a torque characteristic neural network model are constructed with unit speed and guide vane opening as input and turbine flow and torque as output respectively.

[0014] Preferably, the step S3 is specifically as follows: Construct a high-precision servo system model. The servo system contains nonlinear links such as delay, saturation, speed limit, and dead zone. The transfer function of its linear part is: ; Where, K y is the comprehensive amplifier coefficient; T y1 and T y are the reaction time constants of the intermediate servomotor and the main servomotor, respectively. For the nonlinear part of the servo system, the focus is on the guide vane opening closing inflection point and closing speed.

[0015] Preferably, the step S4 is specifically as follows: A hydraulic turbine regulation system simulation model was built based on the Octave tool. The simulation model includes a water diversion system model, a hydraulic turbine model, a generator and power grid model, and a speed governor and servo system model. (1) Water diversion system model: The water diversion system adopts a rigid water hammer model, which is mathematically described as: ; Where: T w is the water flow inertia time constant; (2) Generator and grid model: Based on the infinite power grid, the generator and load transfer functions are: ; Where: T a is the unit inertia time constant, e g is the generator load self-regulation coefficient; (3) Controller model: The transfer function of the linear part of the PID controller is: ; Where: T 1v is the time constant of the differential link; u is the controller output; e is the tracking error; K P 、 K I and K D They are proportional, integral and differential gains respectively; considering the actual situation, when the unit is running in speed or power mode, K D=0, in no-load operation mode K D These are the original parameters of the power plant.

[0016] Preferably, the step S5 is specifically as follows: Construct a system that considers the maximum speed increase, maximum head fluctuation and standard ITAE index J ITAE The comprehensive objective function of J ITAE It is a commonly used objective function for PID parameter optimization, as shown in the following formula: ; Where, t is the actual time; t s is the total simulation time; e ( t ) is the integral of the turbine control signal error; Comprehensive objective function including maximum speed rise, maximum head fluctuation, and standard ITAE index J 综合 As shown in the following formula: ; Where, f 1 is the maximum value of speed increase under the current shutdown rule; f 2 is the maximum water head fluctuation under the current shutdown rule.

[0017] Preferably, the step S6 is specifically as follows: The snowmelt optimization algorithm, turbine regulation system simulation model and comprehensive objective function are combined to obtain the optimal combination of guide vane closing inflection point and closing speed during the shutdown process of the hydropower unit.

[0018] The present invention has the following beneficial effects: 1. The method for optimizing the shutdown rules of hydropower units designed in the present invention, which takes into account the maximum speed increase and the maximum head fluctuation, can fully consider the impact of the shutdown rules on the maximum speed increase and the maximum head fluctuation, and effectively ensure the safe and stable operation of the hydropower units. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The present invention will be further described below with reference to the accompanying drawings and examples.

[0020] Figure 1 Steps are designed for the method of the present invention.

[0021] Figure 2 This is a schematic diagram of the high-precision servo system model of the present invention.

[0022] Figure 3 This is a schematic diagram of the turbine regulation system model of the present invention.

[0023] Figure 4 This is the calculation process of the snowmelt optimization algorithm of the present invention. DETAILED DESCRIPTION

[0024] The embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0025] Example 1: Reference Figure 1-4 This embodiment provides a method for optimizing the shutdown rule of a hydropower unit that takes into account the maximum speed rise and the maximum head fluctuation, and includes three parts: constructing a high-precision numerical simulation model of a turbine regulation system, constructing a comprehensive objective function of a snowmelt optimization algorithm, and optimizing the closing inflection point and closing speed of the guide vanes of the hydropower unit. The implementation steps mainly include: obtaining turbine modeling data; constructing a high-precision turbine model based on a BP neural network; constructing a high-precision servo system model; building a turbine regulation system simulation model based on the Octave tool; constructing a comprehensive objective function that takes into account the maximum speed rise, the maximum head fluctuation, and the standard ITAE index; and optimizing the closing inflection point and closing speed of the guide vanes of the hydropower unit by combining the snowmelt optimization algorithm, the turbine regulation system simulation model, and the comprehensive objective function. The method for optimizing the shutdown rule of a hydropower unit that takes into account the maximum speed rise and the maximum head fluctuation can fully consider the impact of the shutdown rule on the maximum speed rise and the maximum head fluctuation, and effectively ensure the safe and stable operation of the hydropower unit.

[0026] To address the limitations of traditional hydropower unit shutdown methods, which often fail to consider maximum speed rise and head fluctuation, this paper proposes a method for optimizing hydropower unit shutdown patterns that comprehensively considers these factors. This method significantly improves the operational safety and economic efficiency of hydropower units, reducing equipment damage and system instability caused by speed and head fluctuations.

[0027] Example 2: This embodiment provides a method for optimizing the shutdown pattern of a hydropower unit taking into account the maximum speed increase and the maximum head fluctuation, including the following steps: (1) Obtain turbine modeling data.

[0028] Building a high-precision turbine model requires detailed data support, which is usually derived from actual turbine systems in operation. The process of obtaining turbine modeling data includes multiple steps such as data collection, data preprocessing, and data verification. The detailed steps are as follows: 1) Data Collection: Data collection is the basis of modeling, which mainly includes data in the following aspects: 1.1) Operational data: Input parameters: water head, flow rate, guide vane opening, etc.

[0029] Output parameters: speed, output power, torque, etc.

[0030] 1.2) Experimental data: Laboratory test data: Conduct performance tests on turbines to obtain accurate input-output relationship data.

[0031] Field test data: Conduct field tests in the actual operating environment to obtain real operating data.

[0032] 2) Data preprocessing: The collected data often needs to be preprocessed to ensure data quality and consistency. Data preprocessing includes the following steps: 2.1) Data cleaning: Outlier processing: Detect and remove obvious outliers to avoid negative impact on model training.

[0033] Missing value handling: Use interpolation, mean imputation or other data completion methods to fill missing data.

[0034] 2.2) Data conversion: Normalization: Scaling the data to the same range (such as [0, 1] or [-1, 1]) helps speed up model training and improve accuracy.

[0035] Feature extraction: Extract useful features from the raw data, such as unit speed, guide vane opening, unit flow rate, and unit torque.

[0036] 2.3) Data Segmentation: Training set: The main data set used for model training, accounting for 70%-80% of the total data.

[0037] Validation set: used for model tuning and selection, accounting for 10%-15% of the total data.

[0038] Test set: used for final evaluation of model performance, accounting for 10%-15% of the total data.

[0039] 3) Data Verification: Data validation is an important step in ensuring data quality. Validation can confirm the accuracy and reliability of data.

[0040] 3.1) Statistical analysis: Descriptive statistics: Analyze the basic statistical characteristics of the data, such as variance, root mean square error, standard deviation, etc.

[0041] Correlation analysis: Analyze the correlation between each input parameter and output parameter, and select parameters with higher correlation for modeling.

[0042] 3.2) Visual Analysis: Scatter plot: Draw a scatter plot between input and output parameters to visually display data distribution and relationships.

[0043] Time series graph: Draw a time series graph to observe the time variation pattern and periodicity of the data.

[0044] 3.3) Data consistency check: Check the consistency of data in different time periods and different working conditions to ensure data reliability.

[0045] (2) To ensure the accuracy of the model, a flow characteristic neural network model (DCNN, Q 11 = Q 11 ( n 11 , Y )) and torque characteristic neural network model (TCNN, M 11 = M 11 ( n 11 , Y )).

[0046] (3) Construct a high-precision servo system model.

[0047] The servo system contains nonlinear links such as delay, saturation, speed limit, and dead zone. The transfer function of its linear part is: ; (1) in: K y is the comprehensive amplifier coefficient; T y1 and T y are the reaction time constants of the intermediate servo and the main servo, respectively. For the nonlinear part of the servo system, the focus is on the guide vane opening closing inflection point and closing speed.

[0048] (4) Build a simulation model of the turbine regulation system based on the Octave tool.

[0049] The simulation model includes a water diversion system model, a turbine model, a generator and power grid model, a speed regulator and a servo system model.

[0050] 1) Water diversion system model: For simplicity, the water diversion system can adopt a rigid water hammer model, which is mathematically described as follows: ; (2) In the formula T w is the water flow inertia time constant.

[0051] 2) Generator and grid model: Considering an infinite power grid, the generator and load transfer functions are: ; (3) In the formula T a is the unit inertia time constant, e g is the generator load self-regulation coefficient.

[0052] 3) Controller Model: The transfer function of the linear part of the PID controller is: ; (4) Where: T 1v is the time constant of the differential link; u is the controller output; e is the tracking error; K P 、 K I and K D Considering the actual situation, when the unit is running in speed or power mode, take K D =0, in no-load operation mode K D These are the original parameters of the power plant.

[0053] (5) Construct a model that considers the maximum speed increase, maximum head fluctuation and standard ITAE index ( J ITAE ) is the comprehensive objective function.

[0054] J ITAE It is a commonly used objective function for PID parameter optimization, as shown in the following formula: ; (5) Where, t is the actual time; t s is the total simulation time; e ( t ) is the integral of the turbine control signal error.

[0055] Comprehensive objective function including maximum speed rise, maximum head fluctuation, and standard ITAE index J 综合As shown in the following formula: ; (6) Where, f 1 is the maximum value of speed increase under the current shutdown rule; f 2 is the maximum water head fluctuation under the current shutdown rule.

[0056] (6) The snowmelt optimization algorithm, turbine regulation system simulation model and comprehensive objective function are combined to obtain the optimal combination of guide vane closing inflection point and closing speed during the shutdown process of the hydropower unit.

[0057] The Snow Ablation Optimizer (SAO) is a nature-inspired metaheuristic optimization algorithm that mimics the natural snow melting process to solve optimization problems. The algorithm consists of an initialization phase, an exploration phase, an exploitation phase, and a dual-population mechanism. The algorithm first initializes a set of solutions as snow piles and then updates them by simulating the natural snow melting process. This process calculates the melting rate of each solution, adjusts the solution's position accordingly, and repeatedly optimizes the solution using a fitness function to ultimately achieve the optimal solution.

[0058] The advantage of the SAO algorithm lies in its simulation of the snowmelt process, which allows the algorithm to achieve a good balance between exploration and exploitation, effectively avoiding premature convergence and improving solution accuracy. In addition, it demonstrates efficiency and robustness when handling complex optimization problems.

[0059] (1) Initialization phase: In SAO, the iterative process starts with a randomly generated population. As shown in Equation (7), the entire population is modeled as a matrix containing N rows and D columns, where N represents the size of the population and D represents the dimension of the solution space.

[0060] = ; (7) Where, Z is the initial population, and denote the lower and upper bounds of the solution space, respectively. Represents a random number in [0, 1].

[0061] (2) Exploration stage: When snow, or liquid water converted from snow, is transformed into steam, the search agent exhibits highly decentralized characteristics due to irregular motion. Brownian motion is used to simulate this situation. Brownian motion, as a random process, is widely used to simulate animal foraging behavior. For standard Brownian motion, the step size is obtained from the probability density function based on a normal distribution with mean zero and variance 1. The relevant mathematical expression is shown in Equation (8).

[0062] ; (8) The exploration phase uses Gaussian Brownian motion to simulate the highly dispersed characteristics that occur when snow or meltwater from snow turns to steam. Based on this process, the exploration position is updated to expand the search range. The position update formula is as follows: ; (9) Where, for t In the iteration i The position of the particle, A random individual from the elite collection. is a random number between 0 and 1. Random numbers generated by Gaussian distribution representing Brownian motion, is the center of mass of the entire particle. The corresponding expression formula is as follows: ; (10) ; (11) Where, and They represent the second-best and third-best individuals in the current population respectively. Indicates the centroid position of the individuals with the top 50% fitness values.

[0063] ; (12) in Represents the number of leaders, which is equal to half of the size of the entire group. Therefore, in each iteration, Elite (t) is randomly selected from the set consisting of the current optimal solution, the second-best individual, the third-best individual and the leader's centroid position.

[0064] (3) Mining stage: The mining phase uses the snowmelt model (degree-day method) to develop around the current optimal solution to obtain a better solution instead of further expanding its highly decentralized functions. The position update formula in this phase is as follows: ; (13) Where, is a random number in [-1,1], M is the snowmelt rate, which is calculated as follows: ; (14) Where: is the current iteration number; is the maximum number of iterations.

[0065] (4) Dual population mechanism The SAO algorithm uses a dual-population mechanism to balance the needs of the exploration and exploitation phases. It randomly splits the entire particle population into two equal-sized subpopulations, each responsible for exploration and exploitation. As the iteration process progresses, the size of the exploration subpopulation gradually decreases, while the size of the exploitation subpopulation increases accordingly, ensuring that the algorithm maintains an effective balance between exploring new solutions and refining known solutions.

[0066] To summarize, the complete position update equation of the SAO algorithm is as follows: ; (15) The shutdown rule optimization method for hydropower units designed in the present invention, which takes into account the maximum speed rise and the maximum head fluctuation, can fully consider the impact of the shutdown rule on the maximum speed rise and the maximum head fluctuation, and effectively ensure the safe and stable operation of the hydropower units.

[0067] The above detailed description of the specific embodiment of the present invention is intended to be only one embodiment, and the present invention is not limited to the specific embodiment described above. For those skilled in the art, any equivalent modifications and substitutions to the present invention are also within the scope of the present invention. Therefore, any equivalent changes and modifications made without departing from the spirit and scope of the present invention should be included within the scope of the present invention.

Claims

1. A method for optimizing the shutdown rules of a hydropower unit considering the maximum speed increase and the maximum head fluctuation, characterized in that: The following steps are involved: S1, obtain turbine modeling data; S2, building a high-precision turbine model based on BP neural network; S3, building a high-precision servo system model; S4, build a simulation model of the turbine regulation system based on Octave tool; S5, construct a comprehensive objective function considering the maximum speed rise, maximum head fluctuation and standard ITAE index; S6, combines the snowmelt optimization algorithm, the turbine regulation system simulation model and the comprehensive objective function to optimize the closing inflection point and closing speed of the guide vanes of the hydropower unit.

2. The method for optimizing shutdown rules of a hydropower unit according to claim 1, which takes into account the maximum speed increase and the maximum head fluctuation, is characterized in that: The step S1 comprises the following steps: Building a high-precision turbine model requires detailed data support. This data comes from the actual operating turbine system. The process of obtaining turbine modeling data includes data collection, data preprocessing, and data verification steps. S11, data collection: Collect operational and experimental data of the turbine system; S12, data preprocessing: Pre-process the collected data to ensure data quality and consistency; S13, data verification: The pre-processed data is verified to confirm the accuracy and reliability of the data.

3. The method for optimizing shutdown rules of a hydropower unit according to claim 2, which takes into account the maximum speed increase and the maximum head fluctuation, is characterized in that: The operating data in S11 includes: Input parameters: water head, flow rate, guide vane opening; Output parameters: speed, output power, torque; The experimental data include: Laboratory test data: Conduct performance tests on turbines to obtain accurate input-output relationship data; Field test data: Conduct field tests in the actual operating environment to obtain real operating data.

4. The method for optimizing shutdown rules of a hydropower unit according to claim 3, which takes into account the maximum speed increase and the maximum head fluctuation, is characterized in that: The data preprocessing in S12 specifically includes: S121, data cleaning: Outlier processing: detect and remove obvious outliers; Missing value processing: use data completion methods to fill missing data; S122, data conversion: Normalization: scaling the data to the same range to speed up model training and improve accuracy; Feature extraction: Extract useful features from the raw data: unit speed, guide vane opening, unit flow rate and unit torque; S123, data segmentation: Training set: the main data set used for model training, accounting for 70%-80% of the total data; Validation set: used for model tuning and selection, accounting for 10%-15% of the total data; Test set: used for final evaluation of model performance, accounting for 10%-15% of the total data.

5. The method for optimizing shutdown rules of a hydropower unit according to claim 4, which takes into account the maximum speed increase and the maximum head fluctuation, is characterized in that: The data verification in S13 specifically includes: S131, Statistical analysis: Descriptive statistics: Analyze the basic statistical characteristics of the data, including variance, root mean square error, and standard deviation; Correlation analysis: Analyze the correlation between each input parameter and output parameter, and select the parameters with higher correlation for modeling; S132, Visual Analysis: Scatter plot: Draw a scatter plot between input and output parameters to intuitively display data distribution and relationships; Time series graph: draw a time series graph to observe the time variation and periodicity of the data; S133, data consistency check: Check the consistency of data in different time periods and different working conditions to ensure data reliability.

6. The method for optimizing shutdown rules of a hydropower unit according to claim 1, which takes into account the maximum speed increase and the maximum head fluctuation, is characterized in that: The step S2 is specifically as follows: Based on the BP neural network, a flow characteristic neural network model and a torque characteristic neural network model are constructed with unit speed and guide vane opening as input and turbine flow and torque as output respectively.

7. The method for optimizing shutdown rules of a hydropower unit according to claim 1, wherein: The step S3 is specifically as follows: Construct a high-precision servo system model. The servo system contains nonlinear links such as delay, saturation, speed limit, and dead zone. The transfer function of its linear part is: ； Where, K y is the comprehensive amplifier coefficient; T y1 and T y are the reaction time constants of the intermediate servomotor and the main servomotor, respectively. For the nonlinear part of the servo system, the focus is on the guide vane opening closing inflection point and closing speed.

8. The method for optimizing shutdown rules of a hydropower unit according to claim 1, which takes into account the maximum speed increase and the maximum head fluctuation, is characterized in that: The step S4 is specifically as follows: A hydraulic turbine regulation system simulation model was built based on the Octave tool. The simulation model includes a water diversion system model, a hydraulic turbine model, a generator and power grid model, and a speed governor and servo system model. (1) Water diversion system model: The water diversion system adopts a rigid water hammer model, which is mathematically described as: ； Where: T w is the water flow inertia time constant; (2) Generator and grid model: Based on the infinite power grid, the generator and load transfer functions are: ； Where: T a is the unit inertia time constant, e g is the generator load self-regulation coefficient; (3) Controller model: The transfer function of the linear part of the PID controller is: ； Where: T 1v is the time constant of the differential link; u is the controller output; e is the tracking error; K P 、 K I and K D They are proportional, integral and differential gains respectively; considering the actual situation, when the unit is running in speed or power mode, K D =0, in no-load operation mode K D These are the original parameters of the power plant.

9. The method for optimizing shutdown rules of a hydropower unit according to claim 1, wherein: The step S5 is specifically as follows: Construct a system that considers the maximum speed increase, maximum head fluctuation and standard ITAE index J ITAE The comprehensive objective function of in, J ITAE It is a commonly used objective function for PID parameter optimization, as shown in the following formula: ； Where, t is the actual time; t s is the total simulation time; e ( t ) is the integral of the turbine control signal error; Comprehensive objective function including maximum speed rise, maximum head fluctuation, and standard ITAE index J 综合 As shown in the following formula: ； Where, f 1 is the maximum value of speed increase under the current shutdown rule; f 2 is the maximum water head fluctuation under the current shutdown rule.

10. The method for optimizing shutdown rules of a hydropower unit according to claim 1, wherein: The step S6 is specifically as follows: The snowmelt optimization algorithm, turbine regulation system simulation model and comprehensive objective function are combined to obtain the optimal combination of guide vane closing inflection point and closing speed during the shutdown process of the hydropower unit.

Images