Power generation equipment fault prediction method, system, equipment and medium

Abstract

The invention provides a power generation equipment fault prediction method and system, equipment and a medium, and belongs to the technical field of power generation equipment fault prediction. According to the method, to-be-predicted features with high relevancy with fault prediction are input into a trained random forest algorithm model and a gradient elevator algorithm model for power generation equipment fault prediction, and two power generation equipment fault prediction results are obtained. And combining the two obtained fault prediction results of the power generation equipment to obtain a final fault prediction result of the power generation equipment. The characteristic with high fault relevancy refers to the operation data of the power generation equipment with high fault occurrence probability. Compared with the prior art, the power generation equipment fault prediction method effectively solves the problem that complex power generation equipment faults are difficult to accurately predict due to a single prediction model in the prior art.

Description

Technical Field

[0001] The present invention belongs to the technical field of power generation equipment fault prediction, and specifically relates to a power generation equipment fault prediction method, system, equipment and medium. Background Art

[0002] As the demand for new energy continues to grow, the stable operation of power generation equipment is crucial to ensuring power supply. However, during the long-term operation of power generation equipment, various factors (such as wear, overheating and electrical problems) may cause equipment failure, which in turn affects the continuity and safety of power production.

[0003] The existing power generation equipment fault prediction methods are usually single prediction models, which are difficult to accurately predict complex power generation equipment faults. Summary of the invention

[0004] The purpose of the present invention is to provide a method, system, device and medium for predicting power generation equipment failures, which are used to solve the problem that a single prediction model in the prior art is difficult to accurately predict complex power generation equipment failures.

[0005] In order to achieve the above object, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for predicting faults of power generation equipment, comprising the following steps: Acquire the operating data of the power generation equipment, and perform default value and abnormal value supplementation and normalization preprocessing on the obtained operating data of the power generation equipment; Extracting features with high correlation with fault prediction from the preprocessed operating data of the power generation equipment, wherein the features with high correlation with fault refer to operating data of the power generation equipment with high probability of fault occurrence; Use features that are highly relevant to fault prediction to construct training sets, validation sets, and test sets; Construct a random forest algorithm model and a gradient boosting machine algorithm model, train the random forest algorithm model and the gradient boosting machine algorithm model based on the obtained training set, evaluate the performance of the random forest algorithm model and the gradient boosting machine algorithm model based on the obtained validation set, test the training results of the random forest algorithm model and the gradient boosting machine algorithm model based on the constructed test set, and obtain the trained random forest algorithm model and the gradient boosting machine algorithm model; The features to be predicted that are highly correlated with fault prediction are input into the trained random forest algorithm model and gradient boosting algorithm model to predict the faults of power generation equipment, and the fault prediction results of two types of power generation equipment are obtained; The two power generation equipment fault prediction results obtained are combined to obtain the final power generation equipment fault prediction result.

[0006] A further improvement of the present invention is that the operating data of the power generation equipment includes temperature values, pressure values, vibration values, current values ​​and voltage values.

[0007] A further improvement of the present invention is that the obtained operating data of the power generation equipment is first supplemented with default values ​​and abnormal values, and then normalized after the default values ​​and abnormal values ​​are fully supplemented.

[0008] A further improvement of the present invention is that a correlation analysis method or a recursive feature elimination method is used to extract features with a high correlation with fault prediction from the pre-processed operating data of the power generation equipment.

[0009] A further improvement of the present invention is that the features with high relevance to fault prediction are divided into a training set, a validation set and a test set in a ratio of 14:3:3.

[0010] A further improvement of the present invention is that the two obtained power generation equipment fault prediction results are combined using a weighted average method to obtain a final power generation equipment fault prediction result.

[0011] A further improvement of the present invention is that after obtaining the final power generation equipment fault prediction result, the final power generation equipment fault prediction result can also be displayed and notified.

[0012] In a second aspect, the present invention provides a power generation equipment fault prediction system, including a data acquisition module, a data preprocessing module, a feature extraction module, a data set construction module, a model training module, a power generation equipment fault prediction module and a power generation equipment fault prediction result combination module; The data acquisition module is used to acquire the operating data of the power generation equipment; The data preprocessing module is used to perform default value and abnormal value supplement and normalization preprocessing on the obtained operating data of the power generation equipment; The feature extraction module is used to extract features with high correlation with fault prediction from the preprocessed operating data of the power generation equipment, wherein the features with high correlation with faults refer to operating data of the power generation equipment with high probability of fault occurrence; The data set construction module is used to construct a training set, a validation set and a test set using features highly relevant to fault prediction; The power generation equipment fault prediction module is used to construct a random forest algorithm model and a gradient boosting machine algorithm model, train the random forest algorithm model and the gradient boosting machine algorithm model based on the obtained training set, evaluate the performance of the random forest algorithm model and the gradient boosting machine algorithm model based on the obtained validation set, test the training results of the random forest algorithm model and the gradient boosting machine algorithm model based on the constructed test set, and obtain the trained random forest algorithm model and the gradient boosting machine algorithm model; The power generation equipment fault prediction module is used to input the features to be predicted that are highly correlated with fault prediction into the trained random forest algorithm model and gradient boosting algorithm model to perform power generation equipment fault prediction, and obtain two power generation equipment fault prediction results; The power generation equipment fault prediction result combination module is used to combine the two obtained power generation equipment fault prediction results to obtain a final power generation equipment fault prediction result.

[0013] In a third aspect, the present invention provides an electronic device including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the power generation equipment fault prediction method introduced above when executing the computer program.

[0014] In a fourth aspect, the present invention provides a storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the power generation equipment fault prediction method introduced above.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention belongs to an improved invention. Compared with the existing power generation equipment fault prediction method, the power generation equipment fault prediction method proposed by the present invention inputs the features to be predicted with high correlation with fault prediction into the trained random forest algorithm model and gradient boosting algorithm model to predict the power generation equipment fault, and obtains two power generation equipment fault prediction results. The two power generation equipment fault prediction results obtained are combined to obtain the final power generation equipment fault prediction result. It can be seen that the present invention adopts the random forest algorithm model and the gradient boosting algorithm model to obtain two power generation equipment fault prediction results. On the one hand, the present invention adopts the combination of the random forest algorithm model and the gradient boosting algorithm model, which not only reduces the risk of overfitting, but also improves the generalization ability of the model. On the other hand, the present invention makes full use of the respective advantages of the random forest algorithm model and the gradient boosting algorithm model, and makes up for the respective deficiencies of the random forest algorithm model and the gradient boosting algorithm model, thereby greatly improving the accuracy of fault prediction, thereby effectively solving the problem that the single prediction model in the prior art is difficult to accurately predict complex power generation equipment faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of the method for predicting faults of power generation equipment of the present invention; Figure 2 It is a schematic diagram of the power generation equipment fault prediction system of the present invention; Figure 3 It is a schematic structural diagram of the electronic device of the present invention. DETAILED DESCRIPTION

[0017] In order to further understand the content of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the embodiments are only for explaining the present invention and are not intended to limit it.

[0018] The power generation equipment fault prediction method proposed in the present invention inputs the features to be predicted that are highly correlated with fault prediction into the trained random forest algorithm model and gradient boosting algorithm model to predict the power generation equipment fault, and obtains two power generation equipment fault prediction results. The two power generation equipment fault prediction results obtained are combined to obtain the final power generation equipment fault prediction result. Compared with the prior art, the present invention effectively solves the problem that the single prediction model in the prior art is difficult to accurately predict complex power generation equipment faults.

[0019] Embodiment 1: The flowchart of the method for predicting faults of power generation equipment of the present invention is as follows: Figure 1 As shown, the power generation equipment fault prediction method of the present invention comprises the following steps: S1. Obtaining the operating data of the power generation equipment, and performing default value and abnormal value supplementation and normalization preprocessing on the obtained operating data of the power generation equipment.

[0020] S2. Extracting features with high correlation with fault prediction from the preprocessed operating data of the power generation equipment, wherein the features with high correlation with faults refer to operating data of the power generation equipment with a high probability of fault occurrence.

[0021] S3. Use features that are highly relevant to fault prediction to construct training sets, validation sets, and test sets.

[0022] S4. Construct a random forest algorithm model and a gradient boosting machine algorithm model, train the random forest algorithm model and the gradient boosting machine algorithm model based on the obtained training set, evaluate the performance of the random forest algorithm model and the gradient boosting machine algorithm model based on the obtained validation set, test the training results of the random forest algorithm model and the gradient boosting machine algorithm model based on the constructed test set, and obtain the trained random forest algorithm model and the gradient boosting machine algorithm model.

[0023] S5. Input the features to be predicted that are highly correlated with fault prediction into the trained random forest algorithm model and gradient boosting algorithm model to predict power generation equipment faults, and obtain two power generation equipment fault prediction results.

[0024] S6. Combining the two power generation equipment fault prediction results obtained to obtain a final power generation equipment fault prediction result.

[0025] Embodiment 2: The schematic diagram of the power generation equipment fault prediction system of the present invention is as follows Figure 2As shown, the power generation equipment fault prediction system of the present invention includes a data acquisition module, a data preprocessing module, a feature extraction module, a data set construction module, a model training module, a power generation equipment fault prediction module and a power generation equipment fault prediction result combination module.

[0026] The data acquisition module is used to acquire the operating data of the power generation equipment.

[0027] The data preprocessing module is used to perform default value and abnormal value supplement and normalization preprocessing on the obtained operating data of the power generation equipment.

[0028] The feature extraction module is used to extract features with high correlation with fault prediction from the preprocessed operating data of the power generation equipment. Features with high correlation with faults refer to operating data of power generation equipment with high probability of fault occurrence.

[0029] The dataset construction module is used to construct training sets, validation sets, and test sets using features that are highly relevant to fault prediction.

[0030] The power generation equipment fault prediction module is used to construct a random forest algorithm model and a gradient boosting machine algorithm model, train the random forest algorithm model and the gradient boosting machine algorithm model based on the obtained training set, evaluate the performance of the random forest algorithm model and the gradient boosting machine algorithm model based on the obtained validation set, and test the training results of the random forest algorithm model and the gradient boosting machine algorithm model based on the constructed test set to obtain the trained random forest algorithm model and the gradient boosting machine algorithm model.

[0031] The power generation equipment fault prediction module is used to input the features to be predicted that are highly correlated with fault prediction into the trained random forest algorithm model and gradient boosting algorithm model to predict power generation equipment faults, and obtain two power generation equipment fault prediction results.

[0032] The power generation equipment fault prediction result combination module is used to combine the two power generation equipment fault prediction results obtained to obtain the final power generation equipment fault prediction result.

[0033] Embodiment 3: S1. Obtaining the operating data of the power generation equipment, and performing default value and abnormal value supplementation and normalization preprocessing on the obtained operating data of the power generation equipment.

[0034] First, the operating data of the power generation equipment is obtained (stored in the form of time series) through sensors, monitoring systems or SCADA (Supervisory Control And Data Acquisition) systems, and the obtained operating data of the power generation equipment is preprocessed.

[0035] The operating data of the power generation equipment includes temperature, pressure, vibration, current and voltage.

[0036] Specifically, in this step, the default values ​​and abnormal values ​​are first supplemented for the obtained operating data of the power generation equipment, and then normalized (eliminating the deviation caused by the dimension effect and the numerical value) after the default values ​​and abnormal values ​​are supplemented.

[0037] S2. Extract features with high correlation with fault prediction from the preprocessed operating data of the power generation equipment.

[0038] A correlation analysis method or a recursive feature elimination method is used to extract features with high correlation with fault prediction from the preprocessed operating data of the power generation equipment, wherein the features with high correlation with faults refer to the operating data of the power generation equipment with a high probability of fault occurrence.

[0039] S3. Use features that are highly relevant to fault prediction to construct training sets, validation sets, and test sets.

[0040] The features with high correlation with fault prediction are used to construct training set, validation set and test set. The features with high correlation with fault prediction are divided into training set, validation set and test set in the ratio of 14:3:3.

[0041] S4. Construct a random forest algorithm model and a gradient boosting machine algorithm model, train the random forest algorithm model and the gradient boosting machine algorithm model based on the obtained training set, evaluate the performance of the random forest algorithm model and the gradient boosting machine algorithm model based on the obtained validation set, test the training results of the random forest algorithm model and the gradient boosting machine algorithm model based on the constructed test set, and obtain the trained random forest algorithm model and the gradient boosting machine algorithm model.

[0042] Construct a random forest algorithm model and a gradient boosting machine algorithm model, train the random forest algorithm model and the gradient boosting machine algorithm model based on the obtained training set, and evaluate the performance of the random forest algorithm model and the gradient boosting machine algorithm model based on the obtained validation set (the indicators for evaluating the performance of the random forest algorithm model and the gradient boosting machine algorithm model include classification indicators and regression indicators, classification indicators: accuracy, recall rate and F1 score, regression indicators: mean square error, root mean square error and mean absolute error), test the training results of the random forest algorithm model and the gradient boosting machine algorithm model based on the constructed test set, and obtain the trained random forest algorithm model and the gradient boosting machine algorithm model.

[0043] In this step, cross-validation and grid search methods are also used to find the optimal random forest algorithm model and gradient boosting machine algorithm model parameters.

[0044] Specifically, in this step, cross-validation and grid search methods are used to find the optimal random forest algorithm model parameters (such as the number of trees and the maximum depth of the trees), and cross-validation and grid search methods are used to find the optimal gradient boosting machine algorithm model parameters (such as the learning rate and the maximum depth of the trees).

[0045] S5. Input the features to be predicted that are highly correlated with fault prediction into the trained random forest algorithm model and gradient boosting algorithm model to predict power generation equipment faults, and obtain two power generation equipment fault prediction results.

[0046] The features to be predicted that are highly correlated with fault prediction (operating data of power generation equipment with a high probability of failure) are input into the trained random forest algorithm model and gradient boosting algorithm model to predict power generation equipment failures, and two types of power generation equipment failure prediction results are obtained.

[0047] S6. Combining the two power generation equipment fault prediction results obtained to obtain a final power generation equipment fault prediction result.

[0048] Finally, the two power generation equipment fault prediction results are combined using the weighted average method to obtain the final power generation equipment fault prediction result.

[0049] The calculation formula of the weighted average method is:

[0050] in, The final prediction result of power generation equipment failure is: and are the weights of the random forest algorithm model and the gradient boosting machine algorithm model, is the prediction result of the random forest algorithm model, This is the prediction result of the gradient boosting machine algorithm model.

[0051] Prediction results of the random forest algorithm model The calculation formula is:

[0052] in, is the prediction result of the random forest algorithm model, is the number of trees, For decision tree.

[0053] Prediction results of the gradient boosting machine algorithm model The calculation formula is:

[0054] in, is the prediction result of the gradient boosting machine algorithm model, is the mean value of the training set, is a decision tree, m=1,2,...,M, M is the number of decision trees, For the sample, is the learning rate.

[0055] The average value of the training set The calculation formula is:

[0056]

[0057] Where L is the loss function, For the i The target value of samples, For the i The characteristic value of the sample, is the learning rate.

[0058] After obtaining the final power generation equipment fault prediction result, the power generation equipment fault prediction method proposed in the present invention can also display and notify the final power generation equipment fault prediction result. This embodiment displays the final power generation equipment fault prediction result through a chart or a dashboard. This embodiment notifies the final power generation equipment fault prediction result to the operation and maintenance personnel through a variety of methods (such as text messages, telephone calls, or emails), and the operation and maintenance personnel can take corresponding maintenance measures based on the received final power generation equipment fault prediction result.

[0059] Embodiment 4: See also Figure 3 As shown, the present invention also provides an electronic device 100 for a method for predicting faults of power generation equipment; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0060] The memory 101 can be used to store the computer program 103, and the processor 102 implements the steps of the power generation equipment fault prediction method described in Example 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data (such as audio data) created according to the use of the electronic device 100, etc. In addition, the memory 101 can include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0061] The at least one processor 102 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor, etc. The processor 102 is the control center of the electronic device 100, and uses various interfaces and lines to connect various parts of the entire electronic device 100.

[0062] The memory 101 in the electronic device 100 stores a plurality of instructions to implement a method for predicting a fault of a power generation device, and the processor 102 can execute the plurality of instructions to implement: Acquire the operating data of the power generation equipment, and perform default value and abnormal value supplementation and normalization preprocessing on the obtained operating data of the power generation equipment; Extracting features with high correlation with fault prediction from the preprocessed operating data of the power generation equipment, wherein the features with high correlation with fault refer to operating data of the power generation equipment with high probability of fault occurrence; Use features that are highly relevant to fault prediction to construct training sets, validation sets, and test sets; Construct a random forest algorithm model and a gradient boosting machine algorithm model, train the random forest algorithm model and the gradient boosting machine algorithm model based on the obtained training set, evaluate the performance of the random forest algorithm model and the gradient boosting machine algorithm model based on the obtained validation set, test the training results of the random forest algorithm model and the gradient boosting machine algorithm model based on the constructed test set, and obtain the trained random forest algorithm model and the gradient boosting machine algorithm model; The features to be predicted that are highly correlated with fault prediction are input into the trained random forest algorithm model and gradient boosting algorithm model to predict the faults of power generation equipment, and the fault prediction results of two types of power generation equipment are obtained; The two power generation equipment fault prediction results obtained are combined to obtain the final power generation equipment fault prediction result.

[0063] Embodiment 5: If the module / unit integrated in the electronic device 100 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).

[0064] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0065] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0066] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0067] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0068] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for predicting faults of power generation equipment, characterized in that: The following steps are involved: Acquire the operating data of the power generation equipment, and perform default value and abnormal value supplementation and normalization preprocessing on the obtained operating data of the power generation equipment; Extracting features with high correlation with fault prediction from the preprocessed operating data of the power generation equipment, wherein the features with high correlation with fault refer to operating data of the power generation equipment with high probability of fault occurrence; Use features that are highly relevant to fault prediction to construct training sets, validation sets, and test sets; Construct a random forest algorithm model and a gradient boosting machine algorithm model, train the random forest algorithm model and the gradient boosting machine algorithm model based on the obtained training set, evaluate the performance of the random forest algorithm model and the gradient boosting machine algorithm model based on the obtained validation set, test the training results of the random forest algorithm model and the gradient boosting machine algorithm model based on the constructed test set, and obtain the trained random forest algorithm model and the gradient boosting machine algorithm model; The features to be predicted that are highly correlated with fault prediction are input into the trained random forest algorithm model and gradient boosting algorithm model to predict the faults of power generation equipment, and the fault prediction results of two types of power generation equipment are obtained; The two power generation equipment fault prediction results obtained are combined to obtain the final power generation equipment fault prediction result.

2. The method for predicting power generation equipment failure according to claim 1, characterized in that: The operating data of the power generation equipment includes temperature value, pressure value, vibration value, current value and voltage value.

3. The method for predicting power generation equipment failure according to claim 1, characterized in that: The obtained operating data of the power generation equipment are first supplemented with default values ​​and abnormal values, and then normalized.

4. The method for predicting power generation equipment failure according to claim 1, characterized in that: The features with high correlation with fault prediction are extracted from the preprocessed operating data of the power generation equipment by using the correlation analysis method or the recursive feature elimination method.

5. The method for predicting power generation equipment failure according to claim 1, characterized in that: The features with high correlation with fault prediction are divided into training set, validation set and test set in the ratio of 14:3:

3.

6. The method for predicting power generation equipment failure according to claim 1, characterized in that: The two power generation equipment fault prediction results obtained are combined using the weighted average method to obtain the final power generation equipment fault prediction result.

7. The method for predicting power generation equipment failure according to claim 1, characterized in that: After obtaining the final power generation equipment failure prediction result, the final power generation equipment failure prediction result can also be displayed and notified.

8. A power generation equipment fault prediction system, characterized in that: It includes a data acquisition module, a data preprocessing module, a feature extraction module, a data set construction module, a model training module, a power generation equipment fault prediction module and a power generation equipment fault prediction result combination module; The data acquisition module is used to acquire the operating data of the power generation equipment; The data preprocessing module is used to perform default value and abnormal value supplement and normalization preprocessing on the obtained operating data of the power generation equipment; The feature extraction module is used to extract features with high correlation with fault prediction from the preprocessed operating data of the power generation equipment, wherein the features with high correlation with faults refer to the operating data of the power generation equipment with high probability of fault occurrence; The data set construction module is used to construct a training set, a validation set and a test set using features highly relevant to fault prediction; The power generation equipment fault prediction module is used to construct a random forest algorithm model and a gradient boosting machine algorithm model, train the random forest algorithm model and the gradient boosting machine algorithm model based on the obtained training set, evaluate the performance of the random forest algorithm model and the gradient boosting machine algorithm model based on the obtained validation set, test the training results of the random forest algorithm model and the gradient boosting machine algorithm model based on the constructed test set, and obtain the trained random forest algorithm model and the gradient boosting machine algorithm model; The power generation equipment fault prediction module is used to input the features to be predicted that are highly correlated with fault prediction into the trained random forest algorithm model and gradient boosting algorithm model to perform power generation equipment fault prediction, and obtain two power generation equipment fault prediction results; The power generation equipment fault prediction result combination module is used to combine the two obtained power generation equipment fault prediction results to obtain a final power generation equipment fault prediction result.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for predicting faults of power generation equipment according to any one of claims 1 to 7 are implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for predicting faults of power generation equipment according to any one of claims 1 to 7 are implemented.

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