Risk assessment method and device for phase modulation operation of conventional hydro-generator

Abstract

The invention relates to the technical field of hydro-generator operation risk analysis, in particular to a conventional hydro-generator phase modulation operation risk assessment method and device, and the method comprises the steps: collecting risk parameters of a unit through a monitoring system server, and constructing a risk assessment index of the phase modulation operation of a hydro-generator; a risk evaluation index calculation model is constructed, risks of phase modulation operation under different working conditions are evaluated, then weights of risk evaluation index characteristic quantities are quantified, and weak links of unit phase modulation operation are identified; and on the basis of the obtained risk evaluation index evaluation model, a hydro-generator phase modulation operation risk evaluation prediction model is constructed in combination with a Monte Carlo simulation method. According to the method, a conventional hydro-generator phase modulation operation risk analysis model is established to quantify unit real-time and future risk parameter weights, so that a reliable quantitative reference basis is provided for safe operation of a power station and key overhaul of high-risk parts.

Description

Technical Field

[0001] This application relates to the field of risk analysis technology for hydro-generator operation, and in particular to a method and apparatus for assessing the risk of phase-shifting operation of a conventional hydro-generator. Background Technology

[0002] With the large-scale grid connection of new energy units and power electronic equipment, the problem of transient overvoltage in the power system is becoming increasingly prominent. New energy sources and DC transmission inherently lack dynamic reactive power support capabilities, directly leading to the following issues: 1. Insufficient voltage regulation capability when new energy is transmitted via DC; 2. The reactive power surplus generated after commutation failure will cause overvoltage in the transmitting system, which seriously hinders the efficient consumption of new energy.

[0003] Currently, related technologies can significantly improve reactive power regulation capabilities by optimizing their operation modes and control strategies, thereby providing reliable reactive power support to the power grid at a lower cost. Against this backdrop, conventional hydro-generator phase-shifting operation has become a highly favored solution in areas rich in renewable energy.

[0004] However, when a conventional hydro-generator switches from power generation to phase-shifting operation, it undergoes a transient process involving intense interaction between water, air, and the turbine runner. During this process, the pressure pulsation of the turbine and the stress fluctuation of the runner can be dozens of times greater than during power generation, directly causing significant changes in key parameters such as unit swing and vibration. This not only poses potential hazards to the normal operation of the unit but can also, in severe cases, affect its normal operation.

[0005] Furthermore, traditional dynamic balance analysis methods for conventional hydro-turbine generator units have significant limitations. They not only ignore the coupling relationship between various vibration and sway data of the unit, but also fail to consider the actual contribution of these data to the risk level of the unit. They are difficult to analyze the potential risk patterns behind the complex fluctuations of multiple parameters during the phase-shifting operation transition of the unit, thus failing to accurately screen out high-risk parts of the unit and effectively identify its weak links, which urgently needs to be addressed. Summary of the Invention

[0006] This application provides a method and apparatus for risk assessment of conventional hydro-generator phase-shifting operation, in order to solve the problems that existing technologies cannot analyze the potential risk patterns behind the complex fluctuations of multiple index parameters during the phase-shifting operation transition process of the unit, and cannot accurately screen out the high-risk parts of the unit.

[0007] The first aspect of this application provides a risk assessment device for the phase-shifting operation of a conventional hydro-generator, comprising the following steps: collecting multiple unit risk parameters of a target conventional hydro-generator during phase-shifting operation, and calculating a risk assessment index of the target conventional hydro-generator during phase-shifting operation based on the unit risk parameters and a pre-constructed risk assessment index calculation model; assessing the risk of the target conventional hydro-generator during phase-shifting operation under different operating conditions using the risk assessment index calculation model and the risk assessment index to obtain corresponding risk assessment values, and quantifying the weights of risk assessment index feature quantities based on the risk assessment values; constructing a risk assessment prediction model for the target conventional hydro-generator during phase-shifting operation based on the risk assessment index calculation model and a preset Monte Carlo simulation strategy, and conducting a risk assessment of the target conventional hydro-generator during phase-shifting operation using the risk assessment prediction model to obtain corresponding risk assessment results.

[0008] Based on the above-mentioned technical means, this application embodiment establishes a risk analysis model for the phase-shifting operation of conventional hydro-generators to quantify the weights of real-time and future risk parameters of the unit, thereby providing a reliable quantitative reference for the safe operation of the power plant and the key maintenance of high-risk components.

[0009] Optionally, in one embodiment of this application, the step of collecting multiple unit risk parameters of the target conventional hydro-generator during phase-shifting operation and calculating the risk assessment index of the target conventional hydro-generator during phase-shifting operation based on the unit risk parameters and a pre-constructed risk assessment index calculation model includes: collecting multiple unit risk parameters of the target conventional hydro-generator during phase-shifting operation based on a preset monitoring system server or dynamic balancing experiment strategy, wherein the multiple unit risk parameters include at least one of the following: upper guide bearing swing, thrust bearing swing, main shaft swing, water guide bearing swing, top cover vibration, water guide bearing housing vibration, stator core vibration, upper frame vibration, lower frame vibration, upper guide pressure, lower guide pressure, water guide pressure, thrust pressure, and main shaft seal outlet pressure; performing data cleaning on the multiple unit risk parameters based on a preset polynomial fitting strategy and the risk assessment index calculation model to obtain multiple unit risk standardized parameters; and determining the risk assessment index of the target conventional hydro-generator during phase-shifting operation based on the multiple unit risk standardized parameters.

[0010] Based on the above technical means, this application embodiment collects key parameters from multiple dimensions to cover core components and status, reduce risk blind spots, and ensures data accuracy and standardization through standardized collection and polynomial fitting processing. Furthermore, this application embodiment scientifically calculates risk indicators, thereby effectively helping to accurately identify hidden dangers and supporting proactive operation and maintenance.

[0011] Optionally, in one embodiment of this application, the step of using the risk assessment index calculation model and the risk assessment index to assess the risk of the target conventional hydro-generator operating under different conditions to obtain a corresponding risk assessment value, and quantifying the weight of the risk assessment index feature quantity according to the risk assessment value, includes: determining the risk assessment index and Lagrange basis function corresponding to each unit's risk parameter based on a preset Lagrange interpolation strategy, and calculating the corresponding Lagrange interpolation result based on the risk assessment index and the Lagrange basis function of each unit's risk parameter; and based on the Lagrange interpolation result... As a result, each risk indicator under phase-shifting operation is standardized to obtain a corresponding standardized risk indicator, and the standardized risk indicator is quantitatively evaluated to generate a risk assessment value under phase-shifting operation. The risk weight of the risk parameters of the target conventional hydro-generator under phase-shifting operation is determined, and the deterioration index of the standardized risk indicator under phase-shifting operation is calculated based on the risk weight and the risk assessment value. Based on the deterioration index, the entropy value of the characteristic quantity of each risk assessment indicator is calculated, and the weight of the corresponding risk assessment indicator characteristic quantity is calculated based on the entropy value of the characteristic quantity of each risk assessment indicator.

[0012] Based on the above technical means, the embodiments of this application improve the accuracy of risk indicator calculation and ensure the consistency of assessment by using Lagrange interpolation and standardization processing strategies. Furthermore, by combining risk weights to calculate the deterioration index and using entropy values ​​to determine the weights of characteristic quantities, the risk assessment becomes more accurate and objective, providing a reliable basis for risk management and control of hydro-generator phase-shifting operation.

[0013] Optionally, in one embodiment of this application, the step of constructing a risk assessment and prediction model for the target conventional hydro-generator during phase-shifting operation based on the risk assessment index calculation model and a preset Monte Carlo simulation strategy, and conducting a risk assessment of the target conventional hydro-generator during phase-shifting operation using the risk assessment and prediction model to obtain the corresponding risk assessment result, includes: acquiring the system load curve, unit output curve, and new energy unit output curve of the target conventional hydro-generator; performing Monte Carlo simulation based on the system load curve, the unit output curve, and the new energy unit output curve to obtain system operation data; and constructing a corresponding training dataset based on the system operation data; constructing the risk assessment and prediction model based on a preset input layer, hidden layer, output layer, and activation function, and iteratively training the risk assessment and prediction model using the training dataset; evaluating the model performance of the risk assessment and prediction model after each iteration of training to obtain a risk assessment and prediction model that meets preset performance requirements; acquiring the current operation data of the target conventional hydro-generator, and inputting the current operation data into the trained risk assessment and prediction model to generate the risk assessment result corresponding to the target conventional hydro-generator.

[0014] Based on the above technical means, the embodiments of this application obtain system data by combining multi-curve simulation, provide reliable data support for model training, and iteratively train and evaluate the model to ensure performance, thereby efficiently obtaining risk assessment results and improving the accuracy and efficiency of risk prediction for hydro-generator phase-shifting operation.

[0015] A second aspect of this application provides a risk assessment device for phase-shifting operation of a conventional hydro-generator, comprising: a data acquisition module for acquiring multiple unit risk parameters of a target conventional hydro-generator during phase-shifting operation, and calculating a risk assessment index of the target conventional hydro-generator during phase-shifting operation based on the unit risk parameters and a pre-constructed risk assessment index calculation model; a quantification module for assessing the risk of the target conventional hydro-generator during phase-shifting operation under different operating conditions using the risk assessment index calculation model and the risk assessment index, to obtain corresponding risk assessment values, and quantifying the weights of risk assessment index feature quantities based on the risk assessment values; and a risk assessment module for constructing a risk assessment prediction model of the target conventional hydro-generator during phase-shifting operation based on the risk assessment index calculation model and a preset Monte Carlo simulation strategy, and performing a risk assessment on the target conventional hydro-generator during phase-shifting operation using the risk assessment prediction model to obtain corresponding risk assessment results.

[0016] Optionally, in one embodiment of this application, the acquisition module includes: an acquisition unit, used to acquire multiple unit risk parameters of the target conventional hydro-generator during phase-shifting operation based on a preset monitoring system server or dynamic balancing experiment strategy, wherein the multiple unit risk parameters include at least one of the following: upper guide bearing runout, thrust bearing runout, main shaft runout, water guide bearing runout, top cover vibration, water guide bearing housing vibration, stator core vibration, upper frame vibration, lower frame vibration, upper guide pressure, lower guide pressure, water guide pressure, thrust pressure, and main shaft seal outlet pressure; a data cleaning unit, used to perform data cleaning on the multiple unit risk parameters based on a preset polynomial fitting strategy and the risk assessment index calculation model to obtain multiple unit risk standardized parameters; and a determination unit, used to determine the risk assessment index of the target conventional hydro-generator during phase-shifting operation based on the multiple unit risk standardized parameters.

[0017] Optionally, in one embodiment of this application, the quantization module includes: a first calculation unit, configured to determine the risk assessment index and Lagrange basis function corresponding to each unit risk parameter based on a preset Lagrange interpolation strategy, and calculate the corresponding Lagrange interpolation result based on the risk assessment index and Lagrange basis function of each unit risk parameter; a standardization unit, configured to standardize each risk index under phase-shifting operation based on the Lagrange interpolation result to obtain the corresponding standardized risk index, and quantify and evaluate the standardized risk index to generate a risk assessment value under phase-shifting operation; a second calculation unit, configured to determine the risk weight of the risk parameter of the target conventional hydro-generator under phase-shifting operation, and calculate the deterioration index of the standardized risk index under phase-shifting operation based on the risk weight and the risk assessment value; and a third calculation unit, configured to calculate the entropy value of each risk assessment index feature based on the deterioration index, and calculate the weight of the corresponding risk assessment index feature based on the entropy value of each risk assessment index feature.

[0018] Optionally, in one embodiment of this application, the risk assessment module includes: a construction unit, configured to acquire the system load curve, unit output curve, and new energy unit output curve of the target conventional hydro-generator, and perform Monte Carlo simulation based on the system load curve, the unit output curve, and the new energy unit output curve to obtain system operating data, and construct a corresponding training dataset based on the system operating data; a modeling unit, configured to construct the risk assessment prediction model based on a preset input layer, hidden layer, output layer, and activation function, and iteratively train the risk assessment prediction model using the training dataset; a performance evaluation unit, configured to evaluate the model performance of the risk assessment prediction model after each iteration of training to obtain a risk assessment prediction model that meets preset performance requirements; and a generation unit, configured to acquire the current operating data of the target conventional hydro-generator, and input the current operating data into the trained risk assessment prediction model to generate the risk assessment result corresponding to the target conventional hydro-generator.

[0019] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the conventional hydro-generator phase-shifting operation risk assessment method as described in the above embodiments.

[0020] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described conventional hydro-generator phasing operation risk assessment method.

[0021] A fifth aspect of this application provides a computer program product, including a computer program that is executed to implement the above-described conventional hydro-generator phase-shifting operation risk assessment method.

[0022] Therefore, the embodiments of this application have the following beneficial effects: The embodiments of this application collect multiple unit risk parameters of a target conventional hydro-generator during phase-shifting operation, and calculate the risk assessment index of the target conventional hydro-generator during phase-shifting operation based on the unit risk parameters and a pre-constructed risk assessment index calculation model. The risk assessment index calculation model and risk assessment index are used to evaluate the risk of the target conventional hydro-generator during phase-shifting operation under different operating conditions to obtain corresponding risk assessment values, and the weights of the risk assessment index characteristic quantities are quantified based on the risk assessment values. Based on the risk assessment index calculation model and a preset Monte Carlo simulation strategy, a risk assessment prediction model for the target conventional hydro-generator during phase-shifting operation is constructed, and the risk assessment prediction model is used to assess the risk of the target conventional hydro-generator during phase-shifting operation to obtain the corresponding risk assessment results. This application establishes a risk analysis model for the phase-shifting operation of conventional hydro-generators to quantify the weights of real-time and future risk parameters of the unit, thereby providing a reliable quantitative reference for the safe operation of the power plant and the key maintenance of high-risk components. This solves the problems of existing technologies, such as the difficulty in analyzing the potential risk patterns behind the complex fluctuations of multiple index parameters during the phase-shifting operation transition process, and the inability to accurately screen out high-risk parts of the unit.

[0023] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0024] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a conventional hydro-generator phase-shifting operation risk assessment method provided in an embodiment of this application; Figure 2 A schematic diagram of a conventional hydro-generator phase-shifting operation risk assessment process is provided for embodiments of this application; Figure 3 A schematic diagram of a process for generating sample data based on a Monte Carlo simulation strategy is provided for an embodiment of this application; Figure 4 This is a schematic diagram of the structure of a risk assessment and prediction model provided in an embodiment of this application; Figure 5 This is an example diagram of a conventional hydro-generator phase-shifting operation risk assessment device according to an embodiment of this application; Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0025] Among them, 10-Conventional hydro-generator phase-adjustment operation risk assessment device; 100-Acquisition module, 200-Quantization module, 300-Risk assessment module; 601-Memory, 602-Processor, 603-Communication interface. Detailed Implementation

[0026] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0027] The following describes a method and apparatus for risk assessment of phase-shifting operation of a conventional hydro-generator according to embodiments of this application, with reference to the accompanying drawings. Addressing the problems mentioned in the background section, this application provides a method for risk assessment of phase-shifting operation of a conventional hydro-generator. In this method, multiple unit risk parameters of the target conventional hydro-generator during phase-shifting operation are collected, and risk assessment indicators of the target conventional hydro-generator during phase-shifting operation are calculated based on the unit risk parameters and a pre-constructed risk assessment index calculation model. The risk of phase-shifting operation of the target conventional hydro-generator under different operating conditions is assessed using the risk assessment index calculation model and the risk assessment indicators to obtain corresponding risk assessment values. The weights of the risk assessment index characteristic quantities are quantified based on the risk assessment values. Based on the risk assessment index calculation model and a preset Monte Carlo simulation strategy, a risk assessment prediction model for the target conventional hydro-generator during phase-shifting operation is constructed, and the risk assessment of the target conventional hydro-generator during phase-shifting operation is performed using the risk assessment prediction model to obtain corresponding risk assessment results. This application establishes a risk analysis model for phase-shifting operation of a conventional hydro-generator to quantify the weights of real-time and future risk parameters of the unit, thereby providing a reliable quantitative reference for the safe operation of the power plant and the key maintenance of high-risk components. This solves the problem that existing technologies are unable to analyze the potential risk patterns behind the complex fluctuations of multiple parameters during the phase-shifting operation transition of the unit, and are unable to accurately screen out the high-risk parts of the unit.

[0028] Specifically, Figure 1 This is a flowchart of a conventional hydro-generator phase-shifting operation risk assessment method provided in an embodiment of this application.

[0029] like Figure 1 As shown, the conventional hydro-generator phase-shifting operation risk assessment method includes the following steps: In step S101, multiple unit risk parameters of the target conventional hydro generator during phase-shifting operation are collected, and the risk assessment index of the target conventional hydro generator during phase-shifting operation is calculated based on the unit risk parameters and the pre-built risk assessment index calculation model.

[0030] like Figure 2 As shown, the embodiments of this application first utilize a monitoring system server to collect risk parameters of the unit, including information such as the upper guide bearing swing, thrust bearing swing, main shaft swing, water guide bearing swing, top cover vibration, water guide bearing housing vibration, stator core vibration, upper frame vibration, lower frame vibration, upper guide pressure, lower guide pressure, water guide pressure, thrust pressure, and main shaft seal outlet pressure (i.e., multiple unit risk parameters), thereby constructing a risk assessment index for the phase-adjusting operation of the hydro-generator. This risk assessment index is divided into three levels, I, II, and III, from light to heavy, including controllable risk, difficult-to-control risk, and uncontrollable risk.

[0031] Optionally, in one embodiment of this application, multiple unit risk parameters of the target conventional hydro-generator during phase-shifting operation are collected, and risk assessment indicators of the target conventional hydro-generator during phase-shifting operation are calculated based on the unit risk parameters and a pre-constructed risk assessment index calculation model. This includes: collecting multiple unit risk parameters of the target conventional hydro-generator during phase-shifting operation based on a preset monitoring system server or dynamic balancing experiment strategy, wherein the multiple unit risk parameters include at least one of the following: upper guide bearing swing, thrust bearing swing, main shaft swing, water guide bearing swing, top cover vibration, water guide bearing housing vibration, stator core vibration, upper frame vibration, lower frame vibration, upper guide pressure, lower guide pressure, water guide pressure, thrust pressure, and main shaft seal outlet pressure; performing data cleaning on the multiple unit risk parameters based on a preset polynomial fitting strategy and risk assessment index calculation model to obtain multiple unit risk standardized parameters; and determining the risk assessment indicators of the target conventional hydro-generator during phase-shifting operation based on the multiple unit risk standardized parameters.

[0032] In actual implementation, the process of calculating the risk assessment indicators for conventional hydro-generator phasing operation in the embodiments of this application is as follows: 1. Collect risk parameters of the unit using the monitoring system server, including information such as upper guide bearing runout, thrust bearing runout, spindle runout, water guide bearing runout, top cover vibration, water guide bearing housing vibration, stator core vibration, upper frame vibration, lower frame vibration, upper guide pressure, lower guide pressure, water guide pressure, thrust pressure, and spindle seal outlet pressure.

[0033] 2. Using the conventional hydro-generator phase-shifting operation risk assessment index calculation model, we obtain the risk assessment index for phase-shifting operation under different operating conditions, and then quantify the weight of the characteristic quantities of the risk assessment index to identify the weak links in the phase-shifting operation of the unit.

[0034] Subsequently, embodiments of this application can establish optimization strategies for the phasing operation of conventional hydro-generators. Specifically, embodiments of this application can construct a risk assessment and prediction model for the phasing operation of hydro-generators based on the acquired risk assessment index model and combined with the Monte Carlo simulation method. This model quantifies the contribution of future risk parameters of the unit to the risk assessment index, providing a quantitative reference for the safe operation of the power plant and the key maintenance of high-risk components.

[0035] In the specific implementation process, the risk assessment index calculation process of this application embodiment is as follows: 1. The risk parameters of the data acquisition unit are shown in Table 1: Table 1

[0036] As shown in Table 1, the unit risk parameters (X1~X) in the embodiments of this application are... 26 The information, including the upper guide bearing runout, thrust bearing runout, main shaft runout, water guide bearing runout, top cover vibration, water guide bearing housing vibration, stator core vibration, upper frame vibration, lower frame vibration, upper guide pressure, lower guide pressure, water guide pressure, thrust pressure, and main shaft seal outlet pressure, can accurately reflect the unit's operating performance.

[0037] 2. If some data is not connected to the monitoring system server or the power plant does not have corresponding sensors installed, the embodiments of this application can obtain the data through dynamic balancing experiments. The deployed sensors include a PSTA-H type vibration tester, a TTS216 type dynamic signal tester, a CWY type eddy current displacement sensor, a DP type low-frequency vibration sensor, and a KYB type pressure transmitter. The main testing unit consists of a PSTA-H type vibration tester and a TTS216 type dynamic signal tester; the eddy current displacement sensor measures the horizontal runout of the upper guide bearing, lower guide bearing, and water guide bearing of the unit; the low-frequency vibration sensor monitors the vibration status of the upper frame, lower frame, stator, top cover, and tailrace; and the pressure transmitter measures the water pressure.

[0038] It should be noted that the vibration, swing, and water pressure values ​​output by the sensors are all presented in peak-to-peak value form. All peak-to-peak value data collected by the sensors will be centrally transmitted to the test host for unified analysis and processing.

[0039] 3. The embodiments of this application can utilize mature polynomial fitting technology to remove or correct outliers in sensor data (such as vibration, sway, water pressure), thereby improving data reliability.

[0040] Secondly, in the process of constructing the risk assessment index for the phase-shifting operation of the hydro-generator under different operating conditions in the embodiments of this application, the risk assessment index for the phase-shifting operation of the hydro-generator can be divided into three levels from light to heavy: I, II, and III, including three categories: controllable risk, difficult-to-control risk, and uncontrollable risk. The definitions of the three types of risks are shown in Table 2.

[0041] Table 2

[0042] Among them, controllable risks refer to the stability problems such as vibration, sway, and water pressure pulsation that may occur to a certain extent under the current phase-shifting operation conditions, but these problems will not lead to accidents or component failures; uncontrollable risks refer to the stability problems such as severe vibration, sway, and water pressure pulsation that may occur under the current phase-shifting operation conditions, and if these problems are not effectively controlled, they are very likely to cause component failures; uncontrollable risks refer to the risk that the unit has a very high probability of encountering a major safety accident under the current phase-shifting operation conditions.

[0043] Therefore, the embodiments of this application collect key parameters from multiple dimensions to cover core components and status, reduce risk blind spots, and ensure data accuracy and achieve standardization through standardized collection and polynomial fitting processing. Furthermore, the embodiments of this application can effectively help to accurately identify hidden dangers and support proactive operation and maintenance by scientifically calculating risk indicators.

[0044] In step S102, the risk of phase adjustment operation of the target conventional hydro generator under different operating conditions is assessed using the risk assessment index calculation model and risk assessment index, so as to obtain the corresponding risk assessment value, and the weight of the characteristic quantity of the risk assessment index is quantified according to the risk assessment value.

[0045] Furthermore, embodiments of this application can construct a risk assessment index calculation model to evaluate the risks of phase-shifting operation under different operating conditions, thereby quantifying the weights of risk assessment index characteristic quantities and identifying weak links in the phase-shifting operation of the unit.

[0046] Optionally, in one embodiment of this application, the risk of a target conventional hydro-generator operating under phase-shifting conditions is assessed using a risk assessment index calculation model and risk assessment indices to obtain corresponding risk assessment values. The weights of the risk assessment index feature quantities are then quantified based on the risk assessment values. This includes: determining the risk assessment index and Lagrange basis function corresponding to each unit's risk parameter based on a preset Lagrange interpolation strategy; calculating the corresponding Lagrange interpolation result based on the risk assessment index and Lagrange basis function for each unit's risk parameter; standardizing each risk index under phase-shifting operation based on the Lagrange interpolation result to obtain a corresponding standardized risk index; quantifying and assessing the standardized risk index to generate a risk assessment value under phase-shifting operation conditions; determining the risk weights of the target conventional hydro-generator's risk parameters under phase-shifting operation conditions; calculating the degradation index of the standardized risk index under phase-shifting operation conditions based on the risk weights and risk assessment values; calculating the entropy value of each risk assessment index feature quantity based on the degradation index; and calculating the weight of the corresponding risk assessment index feature quantity based on the entropy value of each risk assessment index feature quantity.

[0047] It should be noted that, in order to reduce errors caused by human factors, the embodiments of this application also need to standardize the risk assessment indicators for phase adjustment operation under various working conditions.

[0048] As one possible approach, embodiments of this application may employ the Lagrange interpolation method to accurately quantify the risk level of risk assessment indicators. Specifically, embodiments of this application may define deduced values. n +1 feature points are ( x 0, d ( x 0)), ..., ( x n , d ( x n Then the corresponding n The result of the second Lagrange interpolation is: (1) In the formula, The first state characteristic quantity j One risk parameter; For the first j Risk assessment indicators for each risk parameter; For the ( j +1) risk assessment indicators for risk parameters; For the first j The Lagrange basis function of each risk parameter is calculated using the following formula: (2) In the formula, The first state characteristic quantity i Risk parameters.

[0049] When there is n When each risk level is reached, m The risk assessment indicators are applied under the unit's phase-shifting operation (0MW to maximum leading phase value). p The standardized calculation formula is as follows: (3) In the formula, i For the first i One risk assessment indicator, i =1, 2, 3, ... m (X1~X 26 ); j For the first j Each risk level j =1, 2, 3; For the unit in phase-shifting operation condition p Next i The first risk assessment indicator j A standardized set of risk assessment indicators for each risk level; Phase-shifting operation of the unit p The measured values ​​of the risk assessment indicators; and These are the unit's phase-changing operation conditions. p The maximum and minimum values ​​of the risk assessment indicators in each risk level range.

[0050] Since the goal is to minimize vibration, sway, and water pressure pulsation of the hydro-generator unit during phase-shifting operation, all risk assessment indicators listed in Table 2 are inverse indicators. The characteristics of inverse indicators are: the larger the value, the lower the unit's safety; the smaller the value, the higher the unit's safety, i.e., 0 represents the best operating condition and 1 represents the worst operating condition.

[0051] Secondly, the embodiments of this application can be calculated based on the degradation index (DI) of entropy weight. i Each risk assessment indicator under phase shifting operation condition p Entropy value under for: (4) Equation (4) exists Given the situation, we can obtain: (5) By multiplying the weights of the risk parameters by the degradation index, the risk of phase-shifting operation of the hydro-generator under different operating conditions can be determined. That is, the overall DI is the weighted value of all state characteristic factors, calculated using the following formula: (6) In the formula, Indicates the unit's phase-shifting operation condition p The risk value below; Indicates the unit's phase-shifting operation condition p The next i The weights of each risk parameter; m Indicates the unit's phase-shifting operation condition p The present invention specifies the number of risk assessment indicators, including the upper guide bearing runout, thrust bearing runout, spindle runout, water guide bearing runout, top cover vibration, water guide bearing housing vibration, stator core vibration, upper frame vibration, lower frame vibration, upper guide pressure, lower guide pressure, water guide pressure, thrust pressure, and spindle seal outlet pressure, etc. m Take 26.

[0052] Furthermore, in this embodiment of the application, the entropy weight method can be used to calculate the weights of the characteristic quantities of each risk assessment indicator.

[0053] Specifically, from equation (4), the entropy values ​​of the characteristic quantities of each risk assessment indicator are: (7) Subsequently, embodiments of this application can perform difference coefficient calculation to calculate a coefficient representing the degree of diversity. : (8) Furthermore, the embodiments of this application can calculate the weights of the characteristic quantities of each risk assessment indicator, as shown in the following formula: (9) Subsequently, in the embodiments of this application, the unit operates under phase-shifting conditions. p The set of entropy weights for all inverse indices under the given conditions can be denoted as: : (10) Therefore, the embodiments of this application improve the accuracy of risk index calculation and ensure assessment consistency through Lagrange interpolation and standardization processing strategies. Furthermore, by combining risk weights to calculate the degradation index and using entropy values ​​to determine the weights of characteristic quantities, the risk assessment becomes more accurate and objective, providing a reliable basis for risk management and control of hydro-generator phase-shifting operation.

[0054] In step S103, a risk assessment prediction model for the target conventional hydro-generator during phase-shifting operation is constructed based on the risk assessment index calculation model and the preset Monte Carlo simulation strategy. The risk assessment prediction model is then used to assess the risk of the target conventional hydro-generator during phase-shifting operation to obtain the corresponding risk assessment results.

[0055] Subsequently, based on the obtained risk assessment index evaluation model and combined with the Monte Carlo simulation method, this application embodiment can construct a risk assessment and prediction model for the phase-shifting operation of a hydro-generator.

[0056] Specifically, in this application embodiment, the evaluation mechanism model (i.e., the risk assessment index calculation model) can be used as a basis, combined with the Monte Carlo simulation method, to generate operational data to deeply explore the key influencing factors affecting the risk assessment index and provide training samples for the prediction model.

[0057] Secondly, those skilled in the art should understand that, because hydro-generator units are highly nonlinear systems, various operating indicators interact and constrain each other during phase-shifting operation; the coupling mechanism between indicators is complex, leading to some ambiguity in assessing the overall operational risk of the unit. Machine learning methods are often used for this type of mapping relationship. A backpropagation (BP) neural network is a multi-layer feedforward network trained using an error backpropagation algorithm. It can accurately describe nonlinear functions and possesses good learning capabilities. However, considering that a single BP neural network model structure may be too large, resulting in low computational efficiency...

[0058] Therefore, in the embodiments of this application, the output data of each energy supply unit and various forms of energy demand data can be trained separately with the BP neural networks corresponding to each risk parameter of the unit's phase-shifting operation, thereby constructing an M-BP neural network risk assessment index prediction model.

[0059] Therefore, the embodiments of this application can quantify the real-time risk probability of the unit and provide a risk assessment and prediction model for the phase-shifting operation of the hydro-generator, so as to provide a quantitative reference for the safe operation of the power plant and the key maintenance of high-risk components.

[0060] Optionally, in one embodiment of this application, a risk assessment and prediction model for the target conventional hydro-generator during phase-shifting operation is constructed based on a risk assessment index calculation model and a preset Monte Carlo simulation strategy. The risk assessment and prediction model is then used to assess the risk of the target conventional hydro-generator during phase-shifting operation to obtain the corresponding risk assessment result. This includes: acquiring the system load curve, unit output curve, and new energy unit output curve of the target conventional hydro-generator; performing Monte Carlo simulation based on the system load curve, unit output curve, and new energy unit output curve to obtain system operation data; and constructing a corresponding training dataset based on the system operation data; constructing a risk assessment and prediction model based on preset input layers, hidden layers, output layers, and activation functions; iteratively training the risk assessment and prediction model using the training dataset; evaluating the model performance after each iteration of training to obtain a risk assessment and prediction model that meets preset performance requirements; acquiring the current operation data of the target conventional hydro-generator and inputting the current operation data into the trained risk assessment and prediction model to generate the corresponding risk assessment result for the target conventional hydro-generator.

[0061] like Figure 3 As shown in the figure, the process of generating sample data based on the Monte Carlo simulation strategy in this application embodiment is as follows: 1. The embodiments of this application can use the Monte Carlo simulation method to obtain system operation data based on the system load curve, the unit output curve, and the output curve of the new energy unit; 2. Substitute the simulation data into the above evaluation mechanism model to obtain the unit's risk parameters (X1~X). 26 This includes information such as the upper guide bearing runout, thrust bearing runout, spindle runout, water guide bearing runout, top cover vibration, water guide bearing housing vibration, stator core vibration, upper frame vibration, lower frame vibration, upper guide pressure, lower guide pressure, water guide pressure, thrust pressure, and spindle seal outlet pressure, thereby quantifying risk assessment indicators. 3. By continuously sampling and simulating to obtain operational data, the weights of the characteristic quantities of each risk assessment indicator are evaluated, thereby obtaining a large amount of sample data, i.e., training data; in addition, in order to ensure the accuracy of the training model, after multiple experiments, the number of sampling times is set to 20,000.

[0062] Figure 4 This is a schematic diagram of the risk assessment and prediction model (i.e., the M-BP neural network risk assessment index prediction model). Figure 4 As shown, the M-BP neural network risk assessment index prediction model in this application embodiment specifically includes: 1. A backpropagation (BP) neural network consists of an input layer, hidden layers, and an output layer: In the embodiments of this application, the feature extraction process from the input layer to the hidden layer is as follows: (11) In the formula, h 1 represents the feature vector of the first hidden layer; For activation functions; W 1 represents the weight coefficient matrix of the first hidden layer; x The training samples are the energy supply-side and demand-side data in the system obtained during the sample data generation phase. b 1 represents the hidden layer bias vector.

[0063] 2. The activation function is generally the sigmoid function, as shown in the following formula: (12) 3. If there are X With a hidden layer, the predicted risk assessment index can be expressed as: (13) 4. Since the calculation of the output layer is exactly the same as that of the hidden layer, the output layer can be considered equivalent to the first hidden layer. X The calculation is performed at level +1. The error between the predicted and actual values ​​of the risk assessment indicators can be expressed as: (14) 5. Combining equations (13) and (14), the reverse derivation yields the first... n The error of the hidden layer is: (15) 6. The weight coefficient matrix of each layer after error update is as follows: (16) The above process is one iteration in the training process of the BP neural network. In this embodiment, the power supply and demand side data obtained during the sample data generation stage can be selected as input parameters, and their corresponding risk assessment indicators can be used as output parameters to construct training samples for the M-BP neural network, thereby revealing the mapping relationship between input and output and predicting risk assessment indicators.

[0064] 7. Input the predicted system operation data into the M-BP neural network model (i.e., the risk assessment prediction model) to obtain the predicted risk assessment indicators. Simultaneously, the predicted system operation data is input into the evaluation model to obtain the evaluation values ​​of the risk assessment indicators. In this application embodiment, the root mean square error (RMSE) can be selected to evaluate the quality of the prediction results, as shown in the following formula: (17) In the formula, This represents the root mean square error.

[0065] Therefore, the embodiments of this application combine multi-curve simulation to obtain system data, providing reliable data support for model training, and iteratively training and evaluating the model performance, thereby efficiently obtaining risk assessment results and improving the accuracy and efficiency of risk prediction for hydro-generator phase-shifting operation.

[0066] Subsequently, the embodiments of this application can verify the risk assessment and prediction model. Specifically, the embodiments of this application take data collected by the server of a conventional hydropower unit's phase-shifting operation monitoring system, including the upper guide bearing swing, thrust bearing swing, main shaft swing, water guide bearing swing, top cover vibration, water guide bearing box vibration, stator core vibration, upper frame vibration, lower frame vibration, upper guide pressure, lower guide pressure, water guide pressure, thrust pressure, and main shaft seal outlet pressure, as examples. Through numerical examples, the effectiveness of the data-model hybrid driven risk assessment and neural network risk evaluation index prediction method for conventional hydropower generator phase-shifting operation of the embodiments of this application is illustrated.

[0067] To facilitate quantitative analysis, based on the conventional hydro-generator phase-shifting operation risk assessment and prediction model of this application embodiment, the historical DI values ​​and risk assessment index feature weights under each operating condition are calculated and compared with the DI values ​​obtained based on the M-BP neural network prediction method, as shown in Table 3: Table 3

[0068] Table 3 provides a visual comparison of the accuracy of different methods, listing the root mean square error (RMSE) values ​​of the M-BP strategy proposed in this application's embodiments, as well as the RNN, ARIMA, and RNN methods. As shown in Table 3, compared to the RNN and ARIMA prediction methods, the RMSE values ​​of the proposed M-BP strategy are reduced by 60.08% and 68.85%, respectively. This indicates that the embodiments of this application demonstrate better performance in risk assessment and prediction for hydro-generator phasing operation.

[0069] The method for risk assessment of phase-shifting operation of conventional hydro-generators proposed in this application involves collecting multiple unit risk parameters of the target conventional hydro-generator during phase-shifting operation, and calculating risk assessment indicators for the target conventional hydro-generator during phase-shifting operation based on the unit risk parameters and a pre-constructed risk assessment index calculation model. The risk assessment index calculation model and risk assessment indicators are used to assess the risk of phase-shifting operation of the target conventional hydro-generator under different operating conditions to obtain corresponding risk assessment values. The weights of the risk assessment index characteristic quantities are quantified based on the risk assessment values. Based on the risk assessment index calculation model and a pre-set Monte Carlo simulation strategy, a risk assessment prediction model for the target conventional hydro-generator during phase-shifting operation is constructed, and the risk assessment prediction model is used to assess the risk of the target conventional hydro-generator during phase-shifting operation to obtain corresponding risk assessment results. This application establishes a risk analysis model for phase-shifting operation of conventional hydro-generators to quantify the weights of real-time and future risk parameters of the unit, thereby providing a reliable quantitative reference for the safe operation of the power plant and the key maintenance of high-risk components.

[0070] Secondly, with reference to the accompanying drawings, a risk assessment device for phasing operation of a conventional hydro-generator according to an embodiment of this application is described.

[0071] Figure 5 This is a block diagram of a conventional hydro-generator phase-shifting operation risk assessment device according to an embodiment of this application.

[0072] like Figure 5 As shown, the conventional hydro-generator phase-shifting operation risk assessment device 10 includes: a data acquisition module 100, a quantification module 200, and a risk assessment module 300.

[0073] The acquisition module 100 is used to acquire multiple unit risk parameters of the target conventional hydro generator during phase adjustment operation, and calculate the risk assessment index of the target conventional hydro generator during phase adjustment operation based on the unit risk parameters and the pre-built risk assessment index calculation model.

[0074] The quantification module 200 is used to assess the risk of phase-shifting operation of the target conventional hydro generator under different operating conditions using a risk assessment index calculation model and risk assessment indexes, so as to obtain the corresponding risk assessment value, and quantify the weight of the characteristic quantities of the risk assessment indexes based on the risk assessment value.

[0075] The risk assessment module 300 is used to construct a risk assessment and prediction model for the target conventional hydro-generator during phase-changing operation based on the risk assessment index calculation model and the preset Monte Carlo simulation strategy. The risk assessment and prediction model is used to conduct a risk assessment on the target conventional hydro-generator during phase-changing operation to obtain the corresponding risk assessment results.

[0076] Optionally, in one embodiment of this application, the acquisition module 100 includes: an acquisition unit, a data cleaning unit, and a determination unit.

[0077] The acquisition unit is used to collect multiple unit risk parameters of the target conventional hydro-generator during phase adjustment operation based on a preset monitoring system server or dynamic balancing experiment strategy. The multiple unit risk parameters include at least one of the following: upper guide bearing runout, thrust bearing runout, main shaft runout, water guide bearing runout, top cover vibration, water guide bearing box vibration, stator core vibration, upper frame vibration, lower frame vibration, upper guide pressure, lower guide pressure, water guide pressure, thrust pressure, and main shaft seal outlet pressure.

[0078] The data cleaning unit is used to clean the risk parameters of multiple units based on a preset polynomial fitting strategy and risk assessment index calculation model, so as to obtain standardized risk parameters of multiple units.

[0079] The determination unit is used to determine the risk assessment indicators of the target conventional hydro generator during phase-changing operation based on the risk standardization parameters of multiple units.

[0080] Optionally, in one embodiment of this application, the quantization module 200 includes: a first calculation unit, a standardization unit, a second calculation unit, and a third calculation unit.

[0081] The first calculation unit is used to determine the risk assessment index and Lagrange basis function corresponding to the risk parameters of each unit based on a preset Lagrange interpolation strategy, and to calculate the corresponding Lagrange interpolation result based on the risk assessment index and Lagrange basis function of the risk parameters of each unit.

[0082] The standardization unit is used to standardize each risk indicator under phase-shifting operation based on the Lagrange interpolation results to obtain the corresponding standardized risk indicator, and to quantitatively evaluate the standardized risk indicator to generate the risk assessment value under phase-shifting operation.

[0083] The second calculation unit is used to determine the risk weight of the risk parameters of the target conventional hydro generator under phase-shifting operation conditions, and to calculate the deterioration index of the standardized risk index under phase-shifting operation conditions based on the risk weight and risk assessment value.

[0084] The third calculation unit is used to calculate the entropy value of each risk assessment indicator feature quantity based on the deterioration index, and to calculate the weight of the corresponding risk assessment indicator feature quantity based on the entropy value of each risk assessment indicator feature quantity.

[0085] Optionally, in one embodiment of this application, the risk assessment module 300 includes: a construction unit, a modeling unit, a performance evaluation unit, and a generation unit.

[0086] The construction unit is used to acquire the system load curve, unit output curve, and new energy unit output curve of the target conventional hydro-generator. Based on the system load curve, unit output curve, and new energy unit output curve, Monte Carlo simulation is performed to obtain system operation data. Based on the system operation data, the corresponding training dataset is constructed.

[0087] The modeling unit is used to construct a risk assessment and prediction model based on a preset input layer, hidden layer, output layer, and activation function, and to iteratively train the risk assessment and prediction model using a training dataset.

[0088] The performance evaluation unit is used to evaluate the model performance of the risk assessment prediction model after each iteration of training, so as to obtain a risk assessment prediction model that meets the preset performance requirements.

[0089] The generation unit is used to acquire the current operating data of the target conventional hydro generator and input the current operating data into the trained risk assessment prediction model to generate the risk assessment result corresponding to the target conventional hydro generator.

[0090] It should be noted that the foregoing explanation of the embodiment of the risk assessment method for phase-shifting operation of conventional hydro-generators also applies to the risk assessment device for phase-shifting operation of conventional hydro-generators in this embodiment, and will not be repeated here.

[0091] The conventional hydro-generator phase-shifting operation risk assessment device proposed in this application, applied to the offline training phase, includes a data acquisition module 100, used to acquire multiple unit risk parameters of the target conventional hydro-generator during phase-shifting operation, and calculate the risk assessment index of the target conventional hydro-generator during phase-shifting operation based on the unit risk parameters and a pre-constructed risk assessment index calculation model; a quantification module 200, used to assess the risk of the target conventional hydro-generator during phase-shifting operation under different operating conditions using the risk assessment index calculation model and the risk assessment index, to obtain the corresponding risk assessment value, and quantify the weight of the risk assessment index feature quantity based on the risk assessment value; and a risk assessment module 300, used to construct a risk assessment prediction model of the target conventional hydro-generator during phase-shifting operation based on the risk assessment index calculation model and a preset Monte Carlo simulation strategy, and to conduct a risk assessment of the target conventional hydro-generator during phase-shifting operation through the risk assessment prediction model, to obtain the corresponding risk assessment result. This application establishes a conventional hydro-generator phase-shifting operation risk analysis model to quantify the weight of real-time and future risk parameters of the unit, thereby providing a reliable quantitative reference for the safe operation of the power plant and the key maintenance of high-risk components.

[0092] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 601, the processor 602, and the computer program stored on the memory 601 and capable of running on the processor 602.

[0093] When the processor 602 executes the program, it implements the conventional hydro-generator phase-shifting operation risk assessment method provided in the above embodiments.

[0094] Furthermore, electronic devices also include: Communication interface 603 is used for communication between memory 601 and processor 602.

[0095] The memory 601 is used to store computer programs that can run on the processor 602.

[0096] The memory 601 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0097] If the memory 601, processor 602, and communication interface 603 are implemented independently, then the communication interface 603, memory 601, and processor 602 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 6 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0098] Optionally, in a specific implementation, if the memory 601, processor 602, and communication interface 603 are integrated on a single chip, then the memory 601, processor 602, and communication interface 603 can communicate with each other through an internal interface.

[0099] The processor 602 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0100] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described conventional hydro-generator phase-shifting operation risk assessment method.

[0101] This application also provides a computer program product, including a computer program, which, when executed, is used to implement the above-described conventional hydro-generator phase-shifting operation risk assessment method.

[0102] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0103] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0104] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0105] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0106] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0107] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it includes one or a combination of the steps of the method embodiments.

[0108] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0109] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for risk assessment of conventional hydro-generator phase-shifting operation, characterized in that, Includes the following steps: Multiple unit risk parameters of the target conventional hydro generator during phase-changing operation are collected, and the risk assessment index of the target conventional hydro generator during phase-changing operation is calculated based on the unit risk parameters and the pre-constructed risk assessment index calculation model. The risk assessment index calculation model and the risk assessment index are used to assess the risk of the target conventional hydro generator operating under different working conditions to obtain the corresponding risk assessment value, and the weight of the risk assessment index feature quantity is quantified according to the risk assessment value. Based on the risk assessment index calculation model and the preset Monte Carlo simulation strategy, a risk assessment and prediction model for the target conventional hydro-generator during phase-changing operation is constructed. The risk assessment and prediction model is then used to assess the risk of the target conventional hydro-generator during phase-changing operation to obtain the corresponding risk assessment results.

2. The method for risk assessment of conventional hydro-generator phase-shifting operation according to claim 1, characterized in that, The method involves collecting multiple unit risk parameters of the target conventional hydro-generator during phase-shifting operation, and calculating the risk assessment index of the target conventional hydro-generator during phase-shifting operation based on the unit risk parameters and a pre-built risk assessment index calculation model, including: Based on a preset monitoring system server or dynamic balancing experiment strategy, multiple unit risk parameters of the target conventional hydro-generator are collected during phase adjustment operation. Among them, the multiple unit risk parameters include at least one of the following: upper guide bearing runout, thrust bearing runout, main shaft runout, water guide bearing runout, top cover vibration, water guide bearing housing vibration, stator core vibration, upper frame vibration, lower frame vibration, upper guide pressure, lower guide pressure, water guide pressure, thrust pressure, and main shaft seal outlet pressure. Based on the preset polynomial fitting strategy and the risk assessment index calculation model, the risk parameters of the multiple units are cleaned to obtain the standardized risk parameters of the multiple units. The risk assessment indicators for the target conventional hydro-generator during phase-changing operation are determined based on the risk standardization parameters of the multiple units.

3. The method for risk assessment of conventional hydro-generator phasing operation according to claim 1, characterized in that, The process involves using the risk assessment index calculation model and the risk assessment index to evaluate the risk of the target conventional hydro generator operating under different conditions to obtain a corresponding risk assessment value, and quantifying the weights of the risk assessment index feature quantities based on the risk assessment value, including: Based on a preset Lagrange interpolation strategy, the risk assessment index and Lagrange basis function corresponding to the risk parameters of each unit are determined, and the corresponding Lagrange interpolation result is calculated according to the risk assessment index and Lagrange basis function of the risk parameters of each unit. Based on the Lagrange interpolation results, each risk index under phase-shifting operation is standardized to obtain the corresponding standardized risk index, and the standardized risk index is quantitatively evaluated to generate a risk assessment value under phase-shifting operation. Determine the risk weights of the risk parameters of the target conventional hydro generator under phase-shifting operation conditions, and calculate the deterioration index of the standardized risk index under phase-shifting operation conditions based on the risk weights and the risk assessment values. Based on the degradation index, the entropy value of each risk assessment indicator feature quantity is calculated, and the weight of the corresponding risk assessment indicator feature quantity is calculated according to the entropy value of each risk assessment indicator feature quantity.

4. The method for risk assessment of conventional hydro-generator phasing operation according to claim 3, characterized in that, Based on the risk assessment index calculation model and the preset Monte Carlo simulation strategy, a risk assessment and prediction model is constructed for the target conventional hydro-generator during phase-changing operation. The risk assessment and prediction model is then used to assess the risk of the target conventional hydro-generator during phase-changing operation to obtain the corresponding risk assessment results, including: The system load curve, unit output curve, and new energy unit output curve of the target conventional hydro-generator are obtained. Based on the system load curve, the unit output curve, and the new energy unit output curve, Monte Carlo simulation is performed to obtain system operation data. Based on the system operation data, a corresponding training dataset is constructed. The risk assessment and prediction model is constructed based on the preset input layer, hidden layer, output layer and activation function, and the risk assessment and prediction model is iteratively trained using the training dataset. The performance of the risk assessment prediction model after each iteration of training is evaluated to obtain a risk assessment prediction model that meets the preset performance requirements. The current operating data of the target conventional hydro-generator is obtained and input into the trained risk assessment prediction model to generate the risk assessment result corresponding to the target conventional hydro-generator.

5. A risk assessment device for the phase-shifting operation of a conventional hydro-generator, characterized in that, include: The data acquisition module is used to collect multiple unit risk parameters of the target conventional hydro generator during phase adjustment operation, and to calculate the risk assessment index of the target conventional hydro generator during phase adjustment operation based on the unit risk parameters and a pre-built risk assessment index calculation model. The quantification module is used to assess the risk of the target conventional hydro generator operating under different working conditions using the risk assessment index calculation model and the risk assessment index, so as to obtain the corresponding risk assessment value, and quantify the weight of the risk assessment index feature quantity according to the risk assessment value. The risk assessment module is used to construct a risk assessment prediction model for the target conventional hydro-generator during phase-changing operation based on the risk assessment index calculation model and the preset Monte Carlo simulation strategy, and to conduct a risk assessment on the target conventional hydro-generator during phase-changing operation through the risk assessment prediction model to obtain the corresponding risk assessment results.

6. The conventional hydro-generator phase-shifting operation risk assessment device according to claim 5, characterized in that, The acquisition module includes: The acquisition unit is used to collect multiple unit risk parameters of the target conventional hydro-generator during phase adjustment operation based on a preset monitoring system server or dynamic balancing experiment strategy. The multiple unit risk parameters include at least one of the following: upper guide bearing runout, thrust bearing runout, main shaft runout, water guide bearing runout, top cover vibration, water guide bearing housing vibration, stator core vibration, upper frame vibration, lower frame vibration, upper guide pressure, lower guide pressure, water guide pressure, thrust pressure, and main shaft seal outlet pressure. The data cleaning unit is used to clean the data of the multiple unit risk parameters based on the preset polynomial fitting strategy and the risk assessment index calculation model, so as to obtain multiple unit risk standardized parameters. The determination unit is used to determine the risk assessment index of the target conventional hydro generator during phase adjustment operation based on the risk standardization parameters of the multiple units.

7. The conventional hydro-generator phase-shifting operation risk assessment device according to claim 5, characterized in that, The quantization module includes: The first calculation unit is used to determine the risk assessment index and Lagrange basis function corresponding to each unit risk parameter based on a preset Lagrange interpolation strategy, and to calculate the corresponding Lagrange interpolation result according to the risk assessment index and Lagrange basis function of each unit risk parameter. The standardization unit is used to standardize each risk index under phase-shifting operation based on the Lagrange interpolation result to obtain the corresponding standardized risk index, and to quantitatively evaluate the standardized risk index to generate a risk assessment value under phase-shifting operation. The second calculation unit is used to determine the risk weight of the risk parameters of the target conventional hydro generator under phase-shifting operation conditions, and to calculate the deterioration index of the standardized risk index under phase-shifting operation conditions based on the risk weight and the risk assessment value. The third calculation unit is used to calculate the entropy value of each risk assessment indicator feature quantity based on the degradation index, and to calculate the weight of the corresponding risk assessment indicator feature quantity according to the entropy value of each risk assessment indicator feature quantity.

8. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, the processor executing the program to implement the conventional hydro-generator phase-shifting operation risk assessment method as described in any one of claims 1-4.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the conventional hydro-generator phasing operation risk assessment method as described in any one of claims 1-4.

10. A computer program product, comprising a computer program, characterized in that, The computer program is executed to implement the conventional hydro-generator phase-shifting operation risk assessment method as described in any one of claims 1-4.

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