Intelligent operation and maintenance decision support system for photovoltaic power station

Abstract

The invention discloses an intelligent operation and maintenance decision support system for a photovoltaic power station. The system comprises a data acquisition layer, a data processing layer, a data analysis layer, a decision support layer and a user interaction layer. The data acquisition layer collects operation data of equipment, the data processing layer preprocesses and integrates the data, the data analysis layer performs deep analysis on the processed data, including feature extraction and fault prediction, and the decision support layer generates and optimizes a maintenance strategy according to a data analysis result. And the user interaction layer displays the data analysis result and the maintenance strategy to a user and receives input and feedback of the user. In this way, the operation efficiency of the photovoltaic power station can be effectively improved, the service life of equipment can be effectively prolonged, the maintenance cost is reduced, and the economic benefits of the photovoltaic power station are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of photovoltaic power generation, and in particular relates to an intelligent operation and maintenance decision support system for a photovoltaic power station. Background Art

[0002] Photovoltaic power generation is a technology that uses solar energy to convert into electrical energy through the photovoltaic effect. It is a clean, renewable energy source that does not pollute the environment and has broad application prospects. Photovoltaic power stations are the main application form of photovoltaic power generation, and their operating status directly affects the power generation efficiency and economic benefits of the power station.

[0003] However, due to the large number of photovoltaic power station equipment and complex operating environment, it is difficult to accurately obtain and understand the operating status, performance trends, failure modes and other information of the equipment, which brings great difficulties to the operation and maintenance of the power station. In addition, how to formulate the best maintenance strategy based on the operating status, performance trends, failure modes and other information of the equipment to achieve the lowest maintenance cost and the shortest downtime is also a difficult problem.

[0004] Therefore, existing photovoltaic power stations are in urgent need of an intelligent operation and maintenance decision support system that can conduct in-depth analysis of the equipment's operating data, identify the equipment's operating status, performance trends, and failure modes, generate and optimize maintenance strategies, and provide intelligent maintenance decision support. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a photovoltaic power station intelligent operation and maintenance decision support system for solving the technical problems of photovoltaic power station intelligent operation and maintenance in view of the deficiencies in the above-mentioned prior art.

[0006] The present invention adopts the following technical solutions: A photovoltaic power station intelligent operation and maintenance decision support system, characterized by comprising: The data collection layer is used to collect the operating data of photovoltaic power station equipment; Data processing layer, used to pre-process and integrate operational data; The data analysis layer is used to conduct in-depth analysis on the preprocessed and integrated operation data to obtain analysis results, including feature extraction and fault prediction, to identify the operation status, performance trend, and failure mode of the equipment, and transmit the analysis results to the decision support layer; A decision support layer for generating and optimizing maintenance strategies based on analysis results; The user interaction layer is used to display analysis results and optimize maintenance strategies, and receive user input and feedback.

[0007] Preferably, the data processing layer includes: The data cleaning module pre-processes the collected data through the data quality checking unit, the data missing processing unit and the data abnormality processing unit; The data integration module integrates the cleaned data to form a data set suitable for subsequent analysis.

[0008] More preferably, the data quality checking unit is used to check the completeness, consistency, accuracy and timeliness of the data; Missing data processing unit, used to process missing data by deletion, interpolation, and prediction methods; The data anomaly processing unit is used to detect and process abnormal data through statistical methods, clustering methods, and classification methods.

[0009] More preferably, the data integration module integrates data from different devices, different times and different parameters through data alignment, data conversion and data fusion to form a unified data set.

[0010] Preferably, the data analysis layer includes: The feature extraction module extracts features that have a significant impact on the equipment's operating status, performance trends, and failure modes from the data transmitted by the data processing layer through the principal component analysis method; The fault prediction module uses a deep learning model to predict the operating status, performance trend, and failure mode of the equipment based on the extracted features, and transmits the analysis results to the decision support layer.

[0011] Preferably, the data analysis layer includes: The feature extraction module extracts the equipment operation status, performance trend and failure mode from the data transmitted by the data processing layer through the principal component analysis method.

[0012] The fault prediction module uses a deep learning model to predict the operating status, performance trend, and failure mode of the equipment based on the extracted features.

[0013] Preferably, the decision support layer includes: The maintenance strategy generation module generates maintenance strategies based on the equipment operation status, performance trends, failure modes, equipment operation history, and equipment importance transmitted by the data analysis layer; The strategy optimization module uses genetic algorithms to optimize the generated maintenance strategies and transmits the maintenance strategies to the user interaction layer.

[0014] More preferably, the generated maintenance strategy is optimized using a genetic algorithm as follows: Encode each possible maintenance action into a binary string, generate a set of binary strings as the initial population, and each binary string represents a possible maintenance strategy; Define a fitness function to evaluate the quality of each maintenance strategy; According to the fitness function, excellent individuals are selected for reproduction, and then new individuals are generated through crossover and mutation operations. The crossover operation refers to cutting the binary strings of two individuals at a certain point and then exchanging the latter half to generate two new individuals. The mutation operation refers to randomly changing a certain bit in the binary string. Define the termination condition. When the termination condition is met, the algorithm stops and outputs the current optimal maintenance strategy. The termination condition can be reaching the preset maximum number of iterations, or the fitness of the optimal individual has not improved significantly in several consecutive iterations.

[0015] More preferably, the fitness function for:

[0016] in, , and are the parameters of the maintenance strategy, , and is the weight parameter.

[0017] Preferably, the user interaction layer includes: Data visualization module, which displays data analysis results and maintenance strategies to users in the form of graphics or tables; The user interaction module receives the user's input and passes it to other layers.

[0018] Compared with the prior art, the present invention has at least the following beneficial effects: An intelligent operation and maintenance decision support system for photovoltaic power stations can obtain equipment operation data in a timely manner, including equipment status parameters and environmental parameters, through real-time monitoring of the data acquisition layer, providing an accurate data basis for subsequent data processing and analysis.

[0019] Furthermore, the data cleaning and integration functions of the data processing layer can effectively improve the quality of data, reduce data missing and anomalies, and make the data more suitable for subsequent analysis.

[0020] Furthermore, the feature extraction and fault prediction functions of the data analysis layer can extract features that have a significant impact on the equipment's operating status, performance trends, and failure modes from a large amount of data, thereby improving the accuracy of fault prediction, helping to promptly detect and prevent equipment failures, and improving the equipment's operating efficiency and service life.

[0021] Furthermore, the maintenance strategy generation and optimization function of the decision support layer can generate the optimal maintenance strategy based on the equipment's operating status, performance trends, failure modes, equipment operating history and equipment importance, thereby reducing maintenance costs, shortening equipment downtime and improving the economic benefits of photovoltaic power stations.

[0022] Furthermore, the data visualization and user interaction functions of the user interaction layer can display data analysis results and maintenance strategies to users in an intuitive way, making it easier for users to understand and operate. At the same time, it can also receive user input and feedback, providing data support for the system's self-learning and optimization.

[0023] In summary, the present invention can effectively improve the operating efficiency of the photovoltaic power station and the service life of the equipment, reduce the maintenance cost, and improve the economic benefits of the photovoltaic power station.

[0024] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a schematic diagram of the modules of the present invention. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0028] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0029] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0030] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0031] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0032] The present invention provides an intelligent operation and maintenance decision support system for photovoltaic power stations. The data acquisition layer collects the operating data of the equipment, the data processing layer preprocesses and integrates the data, and the data analysis layer performs in-depth analysis on the processed data, including feature extraction and fault prediction. The decision support layer generates and optimizes maintenance strategies based on the data analysis results, displays the data analysis results and maintenance strategies to users through the user interaction layer, and receives user input and feedback.

[0033] See also Figure 1 The present invention provides a photovoltaic power station intelligent operation and maintenance decision support system, including a data acquisition layer, a data processing layer, a data analysis layer, a decision support layer and a user interaction layer.

[0034] Data collection layer: It is used to collect the operating data of photovoltaic power station equipment, including equipment status parameters, environmental parameters, etc.; it obtains data in real time through sensors and monitoring equipment, and then sends it to the data processing layer through network transmission.

[0035] Data processing layer: Used to preprocess and integrate data transmitted by the data acquisition layer to form a data set suitable for subsequent analysis.

[0036] The data processing layer includes a data cleaning module and a data integration module.

[0037] The data cleaning module pre-processes the collected data through the data quality checking unit, the data missing processing unit and the data anomaly processing unit.

[0038] The data integration module integrates the cleaned data to form a data set suitable for subsequent analysis.

[0039] Data analysis layer block: Used to perform in-depth analysis of data transmitted by the data processing layer, including feature extraction and fault prediction, to identify the operating status, performance trends, and failure modes of the equipment.

[0040] The data analysis layer block includes a feature extraction module and a fault prediction module.

[0041] The feature extraction module extracts features that have a significant impact on the equipment's operating status, performance trends, and failure modes from the data transmitted by the data processing layer through the principal component analysis (PCA) method.

[0042] The fault prediction module uses deep learning models, such as convolutional neural networks (CNN) or long short-term memory networks (LSTM), to predict the operating status, performance trends, and failure modes of equipment based on the extracted features.

[0043] Decision support layer: Used to generate and optimize maintenance strategies based on the analysis results of the data analysis layer to achieve goals such as lowest cost and shortest downtime.

[0044] The decision support layer includes a maintenance strategy generation module and a strategy optimization module.

[0045] The maintenance strategy generation module generates a maintenance strategy based on the equipment operating status, performance trends, failure modes, equipment operating history and equipment importance transmitted by the data analysis layer.

[0046] The strategy optimization module uses genetic algorithms to optimize the generated maintenance strategies to achieve goals such as minimum cost and shortest downtime.

[0047] User interaction layer: It is used to present data analysis results and maintenance strategies to users in the form of graphics or tables, and receive user input, such as equipment parameters, operation and maintenance requirements, and user feedback, such as the execution results of maintenance strategies, so as to facilitate self-learning and optimization of the system.

[0048] The user interaction layer includes data visualization module and user interaction module.

[0049] The data visualization module is used to present data analysis results and maintenance strategies to users in the form of graphics or tables.

[0050] The user interaction module is used to receive user input and pass the user input to other layers.

[0051] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0052] A photovoltaic power station has multiple photovoltaic power generation equipment. Each device is equipped with various sensors that can monitor the operating status and environmental parameters of the equipment in real time. These data are collected by the data acquisition layer and transmitted to the data processing layer.

[0053] At the data processing layer, the data cleaning module first checks the integrity, consistency, accuracy and timeliness of the data through the data quality inspection unit. Then, the missing data processing unit processes the missing data through interpolation or prediction methods. Finally, the data anomaly processing unit detects and processes abnormal data through statistical methods, clustering methods, and classification methods. The data integration module integrates the cleaned data to form a unified data set.

[0054] In the data analysis layer, the feature extraction module uses the principal component analysis (PCA) method to standardize the data transmitted from the data processing layer, calculate the covariance matrix, calculate the eigenvalues ​​and eigenvectors of the covariance matrix, select the principal components, project the data transmitted from the data processing layer to the new feature space, and extract features that have a significant impact on the equipment's operating status, performance trends, and failure modes from the data transmitted from the data processing layer. The fault prediction module uses deep learning models, such as convolutional neural networks (CNN) or long short-term memory networks (LSTM), to learn the equipment's operating data and then predict the equipment's operating status, performance trends, and failure modes.

[0055] In the decision support layer, the maintenance strategy generation module generates maintenance strategies based on the equipment operating status, performance trends, failure modes, equipment operating history and equipment importance transmitted by the data analysis layer. The strategy optimization module uses a genetic algorithm to optimize the generated maintenance strategy to achieve goals such as minimum cost and shortest downtime. The specific steps include encoding each possible maintenance action into a binary string, generating a set of binary strings as the initial population, each binary string represents a possible maintenance strategy; defining a fitness function to evaluate the pros and cons of each maintenance strategy, and the fitness function is defined as:

[0056] in, is the fitness function, , and are the parameters of the maintenance strategy, such as maintenance costs, equipment downtime, and expected equipment life, , and is a weight parameter used to adjust the importance of each parameter in the fitness function. The lower the cost of the maintenance strategy, the higher its fitness. According to the fitness function, excellent individuals are selected for reproduction, and then new individuals are generated through crossover and mutation operations. The crossover operation refers to cutting the binary strings of two individuals at a certain point, and then exchanging the second half to generate two new individuals. The mutation operation refers to randomly changing a certain bit in the binary string. The termination condition is defined. When the termination condition is met, the algorithm stops and outputs the current optimal maintenance strategy. The termination condition can be reaching the preset maximum number of iterations, or the fitness of the optimal individual has not been significantly improved in several consecutive iterations.

[0057] Finally, in the user interaction layer, the data visualization module presents the data analysis results and maintenance strategies to the user in the form of graphics or tables. The user interaction module receives user input, such as equipment parameters and operation and maintenance requirements, and passes the user input to other layers. In addition, the user interaction module also receives user feedback, such as the execution results of the maintenance strategy, so that the system can self-learn and optimize.

[0058] For example, if the system predicts that a piece of equipment may fail in the future, the maintenance strategy generation module will generate a series of possible maintenance strategies based on the equipment's operating status, performance trends, failure modes, as well as the equipment's operating history and importance; Then, the strategy optimization module uses genetic algorithms to optimize these maintenance strategies. The optimization goal is to minimize maintenance costs and equipment downtime; Finally, the user interaction layer presents the optimized maintenance strategy to the user in the form of graphics or tables. The user can select the most suitable maintenance strategy to execute based on this information.

[0059] In summary, the photovoltaic power station intelligent operation and maintenance decision support system of the present invention can effectively improve the operation efficiency of the photovoltaic power station and the service life of the equipment, reduce the maintenance cost, and improve the economic benefits of the photovoltaic power station.

[0060] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A photovoltaic power station intelligent operation and maintenance decision support system, characterized in that: include: The data collection layer is used to collect the operating data of photovoltaic power station equipment; Data processing layer, used to pre-process and integrate operational data; The data analysis layer is used to conduct in-depth analysis on the preprocessed and integrated operation data to obtain analysis results, including feature extraction and fault prediction, to identify the operation status, performance trend, and failure mode of the equipment, and transmit the analysis results to the decision support layer; A decision support layer for generating and optimizing maintenance strategies based on analysis results; The user interaction layer is used to display analysis results and optimize maintenance strategies, and receive user input and feedback.

2. The photovoltaic power station intelligent operation and maintenance decision support system according to claim 1, characterized in that: The data processing layer includes: The data cleaning module pre-processes the collected data through the data quality checking unit, the data missing processing unit and the data abnormality processing unit; The data integration module integrates the cleaned data to form a data set suitable for subsequent analysis.

3. The photovoltaic power station intelligent operation and maintenance decision support system according to claim 2 is characterized in that: Data quality check unit, used to check the completeness, consistency, accuracy and timeliness of data; Missing data processing unit, used to process missing data by deletion, interpolation, and prediction methods; The data anomaly processing unit is used to detect and process abnormal data through statistical methods, clustering methods, and classification methods.

4. The photovoltaic power station intelligent operation and maintenance decision support system according to claim 2, characterized in that: The data integration module integrates data from different devices, different times and different parameters through data alignment, data conversion and data fusion to form a unified data set.

5. The photovoltaic power station intelligent operation and maintenance decision support system according to claim 1, characterized in that: The data analysis layer includes: The feature extraction module extracts features that have a significant impact on the equipment's operating status, performance trends, and failure modes from the data transmitted by the data processing layer through the principal component analysis method; The fault prediction module uses a deep learning model to predict the operating status, performance trend, and failure mode of the equipment based on the extracted features, and transmits the analysis results to the decision support layer.

6. The photovoltaic power station intelligent operation and maintenance decision support system according to claim 1, characterized in that: The data analysis layer includes: The feature extraction module extracts the equipment operation status, performance trend, and failure mode from the data transmitted by the data processing layer through the principal component analysis method; The fault prediction module uses a deep learning model to predict the operating status, performance trend, and failure mode of the equipment based on the extracted features.

7. The photovoltaic power station intelligent operation and maintenance decision support system according to claim 1, characterized in that: The decision support layer includes: The maintenance strategy generation module generates maintenance strategies based on the equipment operation status, performance trends, failure modes, equipment operation history, and equipment importance transmitted by the data analysis layer; The strategy optimization module uses genetic algorithms to optimize the generated maintenance strategies and transmits the maintenance strategies to the user interaction layer.

8. The photovoltaic power station intelligent operation and maintenance decision support system according to claim 7, characterized in that: The genetic algorithm is used to optimize the generated maintenance strategy as follows: Encode each possible maintenance action into a binary string, generate a set of binary strings as the initial population, and each binary string represents a possible maintenance strategy; Define a fitness function to evaluate the quality of each maintenance strategy; According to the fitness function, excellent individuals are selected for reproduction, and then new individuals are generated through crossover and mutation operations. The crossover operation refers to cutting the binary strings of two individuals at a certain point and then exchanging the latter half to generate two new individuals. The mutation operation refers to randomly changing a certain bit in the binary string. Define the termination condition. When the termination condition is met, the algorithm stops and outputs the current optimal maintenance strategy. The termination condition can be reaching the preset maximum number of iterations, or the fitness of the optimal individual has not improved significantly in several consecutive iterations.

9. The photovoltaic power station intelligent operation and maintenance decision support system according to claim 8, characterized in that: Fitness function for: in, , and are the parameters of the maintenance strategy, , and is the weight parameter.

10. The photovoltaic power station intelligent operation and maintenance decision support system according to claim 1, characterized in that: The user interaction layer includes: Data visualization module, which displays data analysis results and maintenance strategies to users in the form of graphics or tables; The user interaction module receives the user's input and passes it to other layers.

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