Machine learning-based intelligent operation and maintenance management system and method for hydropower plant

Abstract

The application discloses a kind of water and electricity plant intelligent operation and maintenance management system and method based on machine learning, belong to water and electricity plant operation and maintenance technical field.The application collects water and electricity plant multidimensional data, carries out data preprocessing, storage enters data warehouse;Unified metadata label is defined, and data blood relationship tracking mechanism is established;Determine the starting point of tracing, based on storage location backtracking, trace data access history, according to data flow chart backtracking, integrated data traceability chain is obtained;Entity recognition and relationship extraction are carried out, and Neo4j is used to build associated graph;The relationship between power grid dispatching data and equipment operation is analyzed, and the associated graph is adjusted;Identify dominant abnormality and implicit abnormality, based on associated graph fusion, obtain water and electricity plant abnormal data;The trend of equipment operation data and sensor monitoring data is predicted, combined with residual analysis positioning deviation value, obtain data fluctuation mode, analyze correlation, obtain the source of water and electricity plant abnormal data.

Description

Technical Field

[0001] This invention relates to the field of hydropower plant operation and maintenance technology, specifically a machine learning-based intelligent operation and maintenance management system and method for hydropower plants. Background Technology

[0002] With the rapid development of the power industry, hydropower plants, as an important source of electricity supply, play a crucial role in ensuring the reliability of the power system through their safe and stable operation. Hydropower plants generate massive amounts of multi-dimensional data during operation. Under the trend of intelligent operation and maintenance, how to fully utilize this multi-dimensional data to achieve real-time monitoring, fault early warning, and precise maintenance of hydropower plant equipment has become a significant challenge for the hydropower industry. Traditional hydropower plant operation and maintenance management methods mainly rely on manual inspections and experience-based judgment, which are insufficient to meet the demands of modern hydropower plants for efficient and precise operation and maintenance.

[0003] Existing technologies often lack efficient data acquisition, preprocessing, and storage mechanisms when processing massive, multi-dimensional data from hydropower plants. Different types of data may originate from different systems and devices, with inconsistent data formats and interfaces, leading to difficulties in data integration and hindering the full realization of data value. Furthermore, existing technologies lack the ability to deeply mine and analyze the correlations between the multi-dimensional data from hydropower plants, making it difficult to grasp the overall operational status of the plant. Current anomaly detection and diagnosis methods are mainly based on single data features or simple threshold judgments, which are prone to false positives and false negatives. Summary of the Invention

[0004] The purpose of this invention is to provide a machine learning-based intelligent operation and maintenance management system and method for hydropower plants to solve the problems raised in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] In a first aspect, the present invention provides a machine learning-based intelligent operation and maintenance management method for hydropower plants, comprising the following steps:

[0007] Collect multi-dimensional data from hydropower plants, including equipment operation data, sensor monitoring data, and power grid dispatch data, perform data preprocessing, and store the data in a data warehouse; in the data warehouse, define unified metadata tags and establish a data lineage tracing mechanism; determine the starting point of the traceability, trace back based on the storage location, trace the data access history, trace back according to the data flow diagram, and integrate to obtain the data traceability chain;

[0008] Based on the data warehouse and data traceability chain, entity identification and relationship extraction are performed, and a relationship graph is constructed using Neo4j; the relationship between power grid dispatch data and equipment operation is analyzed, including command impact analysis and chain reaction analysis, and the relationship graph is adjusted accordingly;

[0009] Based on equipment operation data and sensor monitoring data, an isolated forest method is used to detect anomalies and obtain explicit anomalies. The data distribution is reconstructed through an autoencoder to identify implicit anomalies. The explicit and implicit anomalies are then fused based on a correlation map to obtain the hydropower plant's anomaly data.

[0010] By using the Prophet model to predict the trends of equipment operation data and sensor monitoring data, and combining residual analysis to locate deviation values, the data fluctuation patterns are obtained based on the deviation values. The correlation between abnormal data and data fluctuation patterns in hydropower plants is analyzed to identify the source of abnormal data in hydropower plants.

[0011] In conjunction with the first aspect, in the first implementation of the first aspect of this application, the step of collecting multi-dimensional data from the hydropower plant, including equipment operation data, sensor monitoring data, and power grid dispatch data, performing data preprocessing, and storing the data in a data warehouse includes:

[0012] According to the set acquisition frequency, the system collects equipment operation data, including hydro-generator sets, transformers, and switchgear, and transmits the data to the data acquisition server via fieldbus or wireless network; sensors monitor the equipment and environmental conditions in real time, and the collected sensor monitoring data is converted into digital signals for transmission; the sensors include environmental sensors, electrical sensors, and mechanical sensors; a data connection is established with the power grid dispatching system, and the latest power grid dispatching data, including dispatching instructions and power grid operation data, is obtained according to the update frequency of the power grid dispatching system.

[0013] The data preprocessing includes data cleaning and data transformation. The data warehouse is built using a data lake, specifically Delta Lake.

[0014] In conjunction with the first aspect, in the second implementation of the first aspect of this application, the step of defining unified metadata tags and establishing a data lineage tracing mechanism in the data warehouse includes:

[0015] Based on the sources of multi-dimensional data from hydropower plants, metadata is generated; naming rules are established, a hierarchical structure of metadata tags is constructed, and metadata tags are entered; the collection path of each type of metadata from the data source to the data warehouse is obtained, and the collection timestamp is marked.

[0016] Record the storage location of the multi-dimensional data of the hydropower plant in the data warehouse, specifically the file path in the data lake; record the access status of the multi-dimensional data of the hydropower plant, specifically which users or systems performed operations on which multi-dimensional data of the hydropower plant at what time; and draw a data flow diagram by analyzing the flow of the multi-dimensional data of the hydropower plant within the data warehouse and between it and external systems.

[0017] In conjunction with the first aspect, in the third implementation of the first aspect of this application, the step of determining the starting point of tracing, tracing back based on storage location, tracing back the data access history, tracing back according to the data flow graph, and integrating to obtain the data tracing chain includes:

[0018] The user inputs the data characteristics to be traced, and based on the data characteristics to be traced, the data type and identification information of the data characteristics specified by the user are determined.

[0019] Based on the determined data type and identification information, a search is performed in the file path records of the data lake. When the searched data is historical backup data of equipment operation data, the specific file storing the data is located according to the pre-recorded file naming rules and the structure of the file path in the data lake. In the log table that records the multi-dimensional data access of the hydropower plant, all access records of the data are queried using the storage location of the located data as an index. Based on the queried operation records, the impact of each operation on the data is analyzed.

[0020] Based on the data flow diagram, starting with the located data, the path of data flowing from the data source into the data warehouse is determined; along the data flow path, the data is traced back to the data collection stage.

[0021] All information obtained during the process of tracing back to the storage location, tracing back to the data access history, and tracing back based on the data flow diagram will be integrated to form a data traceability chain.

[0022] In conjunction with the first aspect, in the fourth implementation of the first aspect of this application, the step of performing entity identification and relationship extraction based on a data warehouse and data traceability chain, and constructing a relational graph using Neo4j, includes:

[0023] Extract data related to equipment, sensors, and failure modes from the data warehouse; identify equipment entities based on the unique identifiers of sensor monitoring data; extract relevant attributes for each equipment entity; identify sensor entities based on sensor monitoring data; record the attributes of each sensor entity; analyze the failure record data in the equipment operation data and sensor monitoring data to identify different failure mode entities; the attributes of each failure mode entity include failure name, failure phenomenon description, failure severity level, and failure frequency.

[0024] By analyzing the correlation between equipment operation data and sensor monitoring data, as well as the installation and configuration information of equipment and sensors, the relationships between equipment and sensors are extracted. Combining the equipment operation data and the fault record data, devices with fault modes exceeding a set threshold are extracted, thus obtaining the relationships between equipment and fault modes. The Neo4j graph database is installed and configured, and the identified equipment entities, sensor entities, and fault mode entities are created as nodes in Neo4j. When creating nodes, the extracted corresponding attributes are added to the nodes. Based on the extracted relationships between equipment and sensors, equipment and fault modes, and sensors and fault modes, corresponding edges are created in Neo4j to connect the nodes, with each edge labeled with the relationship type and related attributes.

[0025] In conjunction with the first aspect, in the fifth implementation of the first aspect of this application, the analysis of the relationship between power grid dispatch data and equipment operation includes command impact analysis and chain reaction analysis, and adjusting the correlation graph includes:

[0026] The methods for analyzing the impact of dispatch instructions include: using regression analysis algorithms to establish a mathematical model between power grid dispatch instructions and changes in equipment operating parameters; and determining the critical paths and key parameters that affect equipment operation through model analysis and verification with actual data.

[0027] The methods for chain reaction analysis include: obtaining the correlation between equipment based on the physical connection and operating logic of hydropower plant equipment; simulating the chain reaction process by combining the direct impact of power grid dispatch instructions on equipment operation; analyzing the direct impact of the power generation adjustment instruction on the generator when the power grid issues it, and gradually deriving how the resulting impact is transmitted to other equipment based on the correlation between equipment.

[0028] Based on the results of the instruction impact analysis and chain reaction analysis, new relationship nodes and edges related to power grid dispatch are added to the existing correlation graph. For the indirect relationships between devices involved in the chain reaction, corresponding edges are added to the correlation graph to connect them and label the relationship attributes. The relationship attributes are information used to describe the characteristics and properties of the relationships between nodes in the correlation graph.

[0029] In conjunction with the first aspect, in the sixth implementation of the first aspect of this application, the step of using isolated forest to detect anomalies based on device operation data and sensor monitoring data to obtain explicit anomalies, and then reconstructing the data distribution through an autoencoder to identify latent anomalies, includes:

[0030] The scikit-learn library in Python was selected as the Isolation Forest algorithm library to initialize the Isolation Forest model. Multi-dimensional data from the hydropower plant was input into the Isolation Forest model for training. Multiple isolation trees were constructed by randomly selecting subsets of data, with each tree dividing the data points until every data point was isolated. After training, the operating data of the equipment to be detected and the sensor monitoring data were input into the Isolation Forest model. The Isolation Forest model calculated an anomaly score for each data point. Based on a pre-set anomaly score threshold, it was determined whether a data point was an anomaly. When the anomaly score was higher than the threshold, the data point was marked as an explicit anomaly, and relevant information was recorded.

[0031] The model architecture of the autoencoder is designed, including an encoder and a decoder. A multilayer perceptron is used as the basic structure of the encoder and decoder, and the performance is optimized by adjusting the number of hidden layers and neurons. The autoencoder is trained using multi-dimensional data from a hydropower plant. During training, the parameters are adjusted by minimizing the reconstruction error so that the data reconstructed by the decoder is close to the original input data. After training, the operating data of the equipment to be detected and the sensor monitoring data are input into the autoencoder to reconstruct the data and calculate the reconstruction error. A reconstruction error threshold is set. When the reconstruction error of the data exceeds the threshold, the data is determined to have a latent anomaly.

[0032] In conjunction with the first aspect, in the seventh implementation of the first aspect of this application, the step of fusing explicit and implicit anomalies based on a correlation graph to obtain hydropower plant anomaly data includes:

[0033] Map the device entities associated with anomalies to device nodes in the association graph, map the sensor entities associated with anomalies to sensor nodes in the association graph, and add historical fault modes as predefined nodes to the graph; create explicit anomaly edges between device nodes and sensor nodes and explicit anomalies respectively, and label the anomaly score and type; create implicit anomaly edges between device nodes and sensor nodes and implicit anomalies respectively, and label the reconstruction error and trend characteristics.

[0034] Analyze overt and covert anomalies of the same device or sensor within the same time period. When time windows overlap and parameters are correlated, they are marked as composite anomalies. Calculate the similarity between overt and covert anomalies in vibration spectrum and temperature gradient, specifically by calculating cosine similarity. When the similarity is greater than a set threshold, it is determined to be different manifestations of the same fault event.

[0035] The anomaly propagation path is queried using Neo4j's Cypher language. Path weights are assigned based on the physical connection strength between devices to obtain high-probability propagation chains. Based on these high-probability propagation chains, when multiple explicit anomalies are associated with the same device, it is determined to be a high-priority fault. When an explicit anomaly is accompanied by a latent anomaly in the same device, it is determined to be a composite fault. When multiple latent anomalies in the same device form a closed loop in the association graph, it is determined to be a systemic degradation. Anomaly data is then integrated and dynamically prioritized to obtain the hydropower plant's anomaly data.

[0036] In conjunction with the first aspect, in the eighth implementation of the first aspect of this application, the step of predicting the trend of equipment operation data and sensor monitoring data through the Prophet model, locating deviation values ​​through residual analysis, obtaining data fluctuation patterns based on the deviation values, analyzing the correlation between abnormal hydropower plant data and data fluctuation patterns, and obtaining the source of abnormal hydropower plant data includes:

[0037] Configure the Prophet model and train it separately using the similarity of vibration spectrum and temperature gradient between explicit and implicit anomalies to generate trend features, seasonal features, and periodic features of the anomalous data. After training, use the trained Prophet model to predict equipment operation data and sensor monitoring data for a future period of time. The prediction results include trend values ​​of equipment operation data and sensor monitoring data.

[0038] The actual equipment operation data and sensor monitoring data are compared with the trend values ​​predicted by the Prophet model to calculate the residual. The residual is equal to the actual value minus the predicted value, reflecting the difference between the actual data and the model prediction results. Based on the calculated residual, the deviation value is located by setting a threshold. When the absolute value of the residual exceeds the threshold, the data point is determined to be a deviation value.

[0039] The located deviations are analyzed, and the data fluctuation patterns are classified according to the characteristics and distribution of the deviations, including periodic fluctuations, sudden fluctuations, and persistent deviations. Correlation analysis is performed between the abnormal data of the hydropower plant and the data fluctuation patterns. By calculating the correlation coefficient, the degree of association between the abnormal data and different fluctuation patterns is determined. Based on the degree of association and combined with the historical fault records of the hydropower plant equipment, the source of the abnormal data of the hydropower plant is inferred.

[0040] Secondly, the present invention provides a machine learning-based intelligent operation and maintenance management system for hydropower plants, comprising:

[0041] The data acquisition and data backtracking module includes a data acquisition unit, a data lineage tracing unit, and a data backtracking unit. The data acquisition unit collects multi-dimensional data from the hydropower plant, including equipment operation data, sensor monitoring data, and power grid dispatching data, performs data preprocessing, and stores the data in a data warehouse. The data lineage tracing unit defines unified metadata tags and establishes a data lineage tracing mechanism within the data warehouse. The data backtracking unit determines the starting point for tracing, backtracks based on storage location, traces data access history, and integrates the data traceability chain according to the data flow diagram.

[0042] The association graph generation module includes an association graph construction unit and an association graph adjustment unit. The association graph construction unit performs entity identification and relationship extraction based on the data warehouse and data traceability chain, and uses Neo4j to build the association graph. The association graph adjustment unit analyzes the relationship between power grid dispatch data and equipment operation, including command impact analysis and chain reaction analysis, and adjusts the association graph.

[0043] The hydropower plant anomaly detection module includes: a visible anomaly analysis unit, a hidden anomaly analysis unit, and a hydropower plant anomaly data generation unit. The visible anomaly analysis unit uses isolated forest detection based on equipment operation data and sensor monitoring data to identify visible anomalies. The hidden anomaly analysis unit reconstructs the data distribution using an autoencoder to identify hidden anomalies. The hydropower plant anomaly data generation unit fuses visible and hidden anomalies based on a correlation graph to obtain hydropower plant anomaly data.

[0044] The hydropower plant abnormal data source analysis module includes a deviation value location unit and a hydropower plant abnormal data source analysis unit. The deviation value location unit predicts the trend of equipment operation data and sensor monitoring data through the Prophet model and locates the deviation value by combining residual analysis. The hydropower plant abnormal data source analysis unit obtains the data fluctuation pattern based on the deviation value, analyzes the correlation between the hydropower plant abnormal data and the data fluctuation pattern, and obtains the source of the hydropower plant abnormal data.

[0045] Compared with the prior art, the beneficial effects of the present invention are:

[0046] 1. This invention establishes a unified data warehouse by collecting multi-dimensional data from hydropower plants and performing efficient data preprocessing and storage; at the same time, it defines unified metadata tags and establishes a data lineage tracing mechanism, which enables comprehensive management and traceability of data.

[0047] 2. Based on a data warehouse and data traceability chain, this invention performs entity recognition and relationship extraction, uses Neo4j to build an association graph, and deeply analyzes the relationship between power grid dispatch data and equipment operation. This enables us to grasp the intrinsic connections between multi-dimensional data of hydropower plants as a whole.

[0048] 3. This invention employs isolated forests and autoencoders to detect explicit and implicit anomalies respectively, and fuses them based on correlation maps to obtain comprehensive and accurate anomaly data for hydropower plants. Simultaneously, it uses the Prophet model to predict the trends of equipment operation data and sensor monitoring data, combines residual analysis to locate deviation values, obtains data fluctuation patterns based on deviation values, and analyzes the correlation between abnormal data and data fluctuation patterns, which can accurately determine the source of abnormal data. Attached Figure Description

[0049] Figure 1 This is a schematic diagram illustrating the steps of a machine learning-based intelligent operation and maintenance management method for hydropower plants according to the present invention.

[0050] Figure 2 This is a system architecture diagram of a machine learning-based intelligent operation and maintenance management system for hydropower plants according to the present invention. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] Example: Figures 1-2 As shown, the present invention provides a technical solution.

[0053] like Figure 1 The schematic diagram illustrates the steps of a machine learning-based intelligent operation and maintenance management method for hydropower plants according to the present invention. The present invention provides a machine learning-based intelligent operation and maintenance management method for hydropower plants, comprising the following steps:

[0054] Step S100: Collect multi-dimensional data from the hydropower plant, including equipment operation data, sensor monitoring data, and power grid dispatch data; perform data preprocessing and store the data in a data warehouse; define unified metadata tags in the data warehouse and establish a data lineage tracing mechanism; determine the tracing starting point, trace back based on the storage location, trace the data access history, trace back according to the data flow diagram, and integrate to obtain the data tracing chain;

[0055] Specifically, according to the set acquisition frequency, equipment operation data is collected. The equipment includes hydro-generator sets, transformers, and switchgear. The data is transmitted to the data acquisition server via fieldbus or wireless network. Sensors monitor the equipment and environmental status in real time. The collected sensor monitoring data is converted into digital signals and then transmitted. The sensors include environmental sensors, electrical sensors, and mechanical sensors. A data connection is established with the power grid dispatching system. According to the update frequency of the power grid dispatching system, the latest power grid dispatching data, including dispatching instructions and power grid operation data, is obtained.

[0056] The data preprocessing includes data cleaning and data transformation. The data warehouse is built using a data lake, specifically Delta Lake.

[0057] Furthermore, based on the sources of multi-dimensional data from hydropower plants, metadata is generated; naming rules are established, a hierarchical structure of metadata tags is constructed, and metadata tags are entered; the collection path of each type of metadata from the data source to the data warehouse is obtained, and the collection timestamp is marked.

[0058] Record the storage location of the multi-dimensional data of the hydropower plant in the data warehouse, specifically the file path in the data lake; record the access status of the multi-dimensional data of the hydropower plant, specifically which users or systems performed operations on which multi-dimensional data of the hydropower plant at what time; and draw a data flow diagram by analyzing the flow of the multi-dimensional data of the hydropower plant within the data warehouse and between it and external systems.

[0059] Furthermore, the user inputs the data characteristics to be traced, and based on the data characteristics to be traced, the data type and identification information of the data characteristics specified by the user are determined;

[0060] Based on the determined data type and identification information, a search is performed in the file path records of the data lake. When the searched data is historical backup data of equipment operation data, the specific file storing the data is located according to the pre-recorded file naming rules and the structure of the file path in the data lake. In the log table that records the multi-dimensional data access of the hydropower plant, all access records of the data are queried using the storage location of the located data as an index. Based on the queried operation records, the impact of each operation on the data is analyzed.

[0061] Based on the data flow diagram, starting with the located data, the path of data flowing from the data source into the data warehouse is determined; along the data flow path, the data is traced back to the data collection stage.

[0062] All information obtained during the process of tracing back to the storage location, tracing back to the data access history, and tracing back based on the data flow diagram will be integrated to form a data traceability chain.

[0063] In one specific embodiment, parameters such as the speed, power, voltage, and current of the hydro-generator unit are collected at a frequency of 5 minutes. For example, during monitoring on a certain day, the speed of hydro-generator unit A at 12:00 was 150 r / min, the power was 5000 kW, the voltage was 10 kV, and the current was 500 A. For transformers, data such as oil temperature, winding temperature, and load rate are also collected at 5-minute intervals. For example, at 13:00, the oil temperature of transformer T1 was 45℃, the winding temperature was 50℃, and the load rate was 70%. The data collected from the switchgear includes switch status, bus voltage, and branch current, and is updated every 5 minutes. For example, at 14:00, the switch of switchgear S3 was in the closed state, the bus voltage was 10.5 kV, and the branch current was 300 A. This data is transmitted to the data acquisition server via fieldbus.

[0064] Environmental sensors collect ambient temperature and humidity data every 10 minutes. On a certain day, at 15:00, the temperature sensor showed a temperature of 30℃, and the humidity sensor showed a humidity of 60%. Electrical sensors monitor the electrical parameters of equipment in real time, such as current transformers and voltage transformers, converting analog signals into digital signals for transmission. For example, an electrical sensor detected a current value of 450A at 16:00, with the converted digital signal value being 0x01C2. Mechanical sensors are used to monitor the mechanical state of equipment, such as vibration and displacement. For example, a vibration sensor collects data every 2 minutes. At 17:00, the vibration sensor of hydro-generator unit B collected a vibration amplitude of 0.5mm / s.

[0065] A data connection is established with the power grid dispatching system, and the latest data is obtained every 15 minutes. On a certain day, at 18:00, the dispatching instruction received was to increase the power generation of the hydroelectric generator unit to 6000kW. At the same time, the power grid operation data showed that the current power grid frequency was 50.1Hz and the voltage was 10.2kV.

[0066] Perform data cleaning and data transformation.

[0067] Metadata is generated based on multi-dimensional data sources from hydropower plants. For example, for the rotational speed data of hydro-generator units, the metadata includes information such as the data source being the rotational speed sensor of hydro-generator unit A, the data type being numerical, and the unit being r / min. Naming rules are established, such as using the format "equipment name_parameter name" for metadata tags of equipment operation data. A hierarchical structure is constructed, with the top layer being hydropower plant data, and the lower layers being equipment operation data, sensor monitoring data, grid dispatch data, etc. Metadata tags are entered, such as "hydro-generator unit A_rotational speed" and "ambient temperature sensor_temperature". The acquisition path for each type of metadata is obtained; for example, ambient temperature sensor data is transmitted wirelessly from the sensor, through a gateway to the data acquisition server, and finally stored in the data lake. Acquisition timestamps are marked; for example, data collected by the ambient temperature sensor at 15:00 is recorded as "2024-08-01 15:00:00". Data access information is recorded in the log table; for example, at 19:00, the data analysis system read the power data of hydro-generator unit A under that file path. Draw a data flow diagram to show the data flow from sensors and devices to the data acquisition server, then to the data lake, and between the data and external systems. For example, power grid dispatch data flows from the power grid dispatch system into the data lake through a secure interface. The data analysis system reads equipment operation data from the data lake, analyzes it, and then transmits the analysis results to the operation and maintenance management system.

[0068] The user-input data for tracing was characterized as "abnormal power data of hydro-generator unit A at 12:00 on August 1, 2024". Based on this characteristic, the data type was determined to be equipment operation data, identified as "hydro-generator unit A" and "2024-08-01 12:00". Searching the file path records in the data lake, the file storing this data, " / data / hydropower / equipment / 20240801 / water_turbine_generator_A.parquet", was located. Using this file path as an index in the log table, it was found that at 13:00, the data processing program performed a standardization operation on this data. Analysis of this operation record clarified the standardization algorithm and parameters, confirming that this operation would not cause data anomalies. Based on the data flow diagram, the path of the data from sensor acquisition to entry into the data lake was traced back. It was found that the power data was collected by the power sensor of hydro-generator unit A, transmitted via fieldbus to the data acquisition server, and then stored in the data lake. When tracing back to the data acquisition stage, the sensor's operating status record was checked, and it was found that the power sensor experienced brief signal interference between 11:50 and 12:10. However, the interference data has been processed after data cleaning, and the sensor malfunction causing the abnormality has been preliminarily ruled out.

[0069] Integrating all the above information forms a data traceability chain: data collected by the power sensor of the hydro-generator unit A → fieldbus transmission → data acquisition server reception → data cleaning (processing interference data) → standardization processing → storage in the data lake (file path: / data / hydropower / equipment / 20240801 / water_turbine_generator_A.parquet).

[0070] Step S200: Based on the data warehouse and data traceability chain, perform entity identification and relationship extraction, and use Neo4j to construct an association graph; analyze the relationship between power grid dispatch data and equipment operation, including instruction impact analysis and chain reaction analysis, and adjust the association graph;

[0071] Specifically, data related to equipment, sensors, and failure modes are extracted from the data warehouse; equipment entities are identified based on the unique identifiers of sensor monitoring data, and relevant attributes are extracted for each equipment entity; sensor entities are identified based on sensor monitoring data, and their attributes are recorded for each sensor entity; failure record data in equipment operation data and sensor monitoring data are analyzed to identify different failure mode entities, and the attributes of each failure mode entity include failure name, failure phenomenon description, failure severity level, and failure frequency.

[0072] By analyzing the correlation between equipment operation data and sensor monitoring data, as well as the installation and configuration information of equipment and sensors, the relationships between equipment and sensors are extracted. Combining the equipment operation data and the fault record data, devices with fault modes exceeding a set threshold are extracted, thus obtaining the relationships between equipment and fault modes. The Neo4j graph database is installed and configured, and the identified equipment entities, sensor entities, and fault mode entities are created as nodes in Neo4j. When creating nodes, the extracted corresponding attributes are added to the nodes. Based on the extracted relationships between equipment and sensors, equipment and fault modes, and sensors and fault modes, corresponding edges are created in Neo4j to connect the nodes, with each edge labeled with the relationship type and related attributes.

[0073] Furthermore, the methods for analyzing the impact of dispatch instructions include: using regression analysis algorithms to establish a mathematical model between power grid dispatch instructions and changes in equipment operating parameters; and determining the critical paths and key parameters that affect equipment operation through model analysis and verification with actual data.

[0074] The methods for chain reaction analysis include: obtaining the correlation between equipment based on the physical connection and operating logic of hydropower plant equipment; simulating the chain reaction process by combining the direct impact of power grid dispatch instructions on equipment operation; analyzing the direct impact of the power generation adjustment instruction on the generator when the power grid issues it, and gradually deriving how the resulting impact is transmitted to other equipment based on the correlation between equipment.

[0075] Based on the results of the instruction impact analysis and chain reaction analysis, new relationship nodes and edges related to power grid dispatch are added to the existing correlation graph. For the indirect relationships between devices involved in the chain reaction, corresponding edges are added to the correlation graph to connect them and label the relationship attributes. The relationship attributes are information used to describe the characteristics and properties of the relationships between nodes in the correlation graph.

[0076] In one specific embodiment, detailed data on equipment such as hydro-generator sets, transformers, and switchgear are retrieved from the data warehouse. For example, a hydro-generator set is precisely located using the equipment number "WTG-003". Its attributes are rich and diverse: the equipment type is clearly a hydro-generator set; it belongs to the core part of the power generation system; its rated power is 12000kW; it was manufactured by XY Company; and it is installed in unit 3 of the plant. Furthermore, information such as the equipment's commissioning date being "2020-01-01" and its most recent maintenance date being "2024-02-15" is also extracted. These attributes are crucial for a comprehensive understanding of the equipment's status.

[0077] Based on the unique identifiers in the sensor monitoring data, various sensor entities were successfully identified. Taking the pressure sensor with the identifier "PS-012" as an example, its attributes cover multiple aspects. The sensor type is a pressure sensor, primarily used to monitor the water pressure at the turbine inlet, with a measurement range between 0-5 MPa and an accuracy of ±0.05 MPa. The installation location is near the valve on the turbine inlet pipe to ensure accurate detection of water pressure changes. The data transmission frequency is set to once every 3 minutes, and the data is stably transmitted to the data acquisition server via a wireless transmission module.

[0078] In-depth analysis of fault records in equipment operation data and sensor monitoring data successfully identified multiple fault mode entities. Taking "transformer winding short circuit fault" as an example, the attributes of this fault mode entity record detailed fault-related information. The fault name is transformer winding short circuit fault, and the fault phenomenon is described as a sharp rise in transformer oil temperature, accompanied by abnormal noises, while the output voltage and current exhibit unstable fluctuations. The fault severity level is rated as "extremely severe" because if this type of fault is not handled in time, it may lead to transformer burnout, seriously affecting the normal power generation of the hydropower plant. Through statistical analysis of historical data, this fault has occurred twice in the past two years.

[0079] By carefully analyzing the inherent correlation between equipment operating data and sensor monitoring data, and combining this with the equipment and sensor installation configuration drawings and actual recorded information, the close relationship between the equipment and sensors was successfully extracted. For example, there is an "installed" relationship between the WTG-003 hydro-generator unit and the PS-012 pressure sensor. This relationship clearly indicates that the WTG-003 hydro-generator unit is equipped with the PS-012 pressure sensor, the purpose of which is to monitor the water pressure at the turbine inlet in real time, providing critical data support for the stable operation of the equipment.

[0080] By comprehensively considering equipment operation data and fault record data, and by setting reasonable fault occurrence thresholds, the correlation between equipment and fault modes was accurately extracted. In-depth analysis of historical data revealed that the WTG-003 hydro-generator unit experienced "turbine blade cavitation fault" four times in the past two years, exceeding the set threshold of three times. Therefore, a "prone to failure" relationship was determined between equipment WTG-003 and the fault mode "turbine blade cavitation fault".

[0081] When creating the WTG-003 node for the hydro-generator unit, add all previously extracted attributes, such as equipment type, system, rated power, manufacturer, installation location, commissioning time, and last maintenance time, to the node. Similarly, when creating the PS-012 node for the pressure sensor, add its sensor type, measurement range, accuracy, installation location, and data transmission frequency to the node.

[0082] Based on the extracted relationships, corresponding edges are created in Neo4j to connect the relevant nodes. An edge indicating an "installed" relationship is created between the WTG-003 node of the hydro-generator unit and the PS-012 node of the pressure sensor, with detailed relationship attributes labeled. For example, the relationship confidence level is set to 100%, indicating that this installation relationship is unambiguous; the installation time can also be labeled as "2020-01-05" to record the sensor's installation time, providing a timeline for subsequent maintenance and troubleshooting. An edge indicating a "prone to failure" relationship is created between the WTG-003 node of the hydro-generator unit and the "hydro-turbine blade cavitation failure" node, labeled with a failure frequency of "4 times in the past two years," and adding relationship attributes such as the first failure occurrence time being "2023-05-10."

[0083] Data on power grid dispatch instructions from the past three months were collected, including power generation adjustment instructions and voltage adjustment instructions, as well as corresponding changes in equipment operating parameters, such as the speed, power, voltage, and current of the hydro-generator units. Using regression analysis, with the power generation adjustment instructions as the independent variable and the power change of the hydro-generator units as the dependent variable, a mathematical model was established: P = 0.75I + 1200, where P is the power of the hydro-generator units and I is the value of the power generation adjustment instruction. Through repeated training and precise fitting on a large amount of historical data, the model coefficients were obtained as a = 0.75 and b = 1200. Through model analysis and verification with actual data, it was determined that when the power generation adjustment instruction increases by 1000kW, the power of the hydro-generator units will increase by approximately 750kW within 6 minutes. Furthermore, through detailed process analysis and actual monitoring, the critical path was determined to be: the power grid dispatch center issues a power generation adjustment instruction → the communication network transmits the instruction to the hydropower plant control system → the turbine governor receives the instruction and adjusts the guide vane opening → the turbine speed changes accordingly → the generator output power changes accordingly. Key parameters include the power generation adjustment command value, turbine guide vane opening, turbine speed, and generator output power.

[0084] Based on detailed physical connection diagrams and in-depth operational logic analysis of the hydropower plant equipment, the complex interrelationships between the equipment were comprehensively identified. For example, the generator and transformer are closely connected via high-voltage transmission cables, and the transformer and switchgear are connected via busbars, forming a complete power transmission link. When the power grid issues a power generation adjustment command, the direct impact of this command on the generator is analyzed in detail. When the power grid issues a command to increase the power generation by 3000kW, the generator's output power rapidly increases by 2250kW within 6 minutes, and its speed increases by 6%.

[0085] Based on the interrelationships between the equipment, the chain reaction process is deduced step by step: The generator's output power increases by 2250kW, leading to an increase in its input torque and a 6% rise in speed. This change is transmitted to the turbine via the coupling, requiring the turbine to increase water flow to maintain the new speed, thus causing a change in the water pressure at the turbine's inlet. The changes in generator output power and voltage are transmitted to the transformer via the high-voltage transmission cable. The transformer's input power increases by 2250kW, and its input voltage rises by 4%. After the transformer's transformation, the output voltage decreases by 3%, and the output current increases by 12%. The changes in transformer output voltage and current are transmitted to the switchgear via the busbar. The switchgear's busbar voltage decreases by 3%, and the branch current increases by 12%, leading to an increase in the load on some switchgear and a slight increase in temperature.

[0086] Step S300: Based on equipment operation data and sensor monitoring data, an isolated forest is used to detect anomalies and obtain explicit anomalies. The data distribution is reconstructed through an autoencoder to identify implicit anomalies. The explicit and implicit anomalies are fused based on the correlation map to obtain the hydropower plant's abnormal data.

[0087] Specifically, the scikit-learn library in Python was selected as the isolated forest algorithm library to initialize the isolated forest model. Multi-dimensional data from the hydropower plant was input into the isolated forest model for training. Multiple isolated trees were constructed by randomly selecting subsets of data, and each tree partitioned the data points until each data point was isolated. After training, the operating data of the equipment to be detected and the sensor monitoring data were input into the isolated forest model. The isolated forest model calculated an anomaly score for each data point. Based on a pre-set anomaly score threshold, it was determined whether the data point was an anomaly. When the anomaly score was higher than the threshold, the data point was marked as an explicit anomaly, and relevant information was recorded.

[0088] The model architecture of the autoencoder is designed, including an encoder and a decoder. A multilayer perceptron is used as the basic structure of the encoder and decoder, and the performance is optimized by adjusting the number of hidden layers and neurons. The autoencoder is trained using multi-dimensional data from a hydropower plant. During training, the parameters are adjusted by minimizing the reconstruction error so that the data reconstructed by the decoder is close to the original input data. After training, the operating data of the equipment to be detected and the sensor monitoring data are input into the autoencoder to reconstruct the data and calculate the reconstruction error. A reconstruction error threshold is set. When the reconstruction error of the data exceeds the threshold, the data is determined to have a latent anomaly.

[0089] Furthermore, the device entities associated with anomalies are mapped to device nodes in the association graph, and the sensor entities associated with anomalies are mapped to sensor nodes in the association graph. Historical fault modes are added to the graph as predefined nodes. Explicit anomaly edges are created between device nodes and sensor nodes and explicit anomalies, respectively, and the anomaly score and type are labeled. Implicit anomaly edges are created between device nodes and sensor nodes and implicit anomalies, respectively, and the reconstruction error and trend characteristics are labeled.

[0090] Analyze overt and covert anomalies of the same device or sensor within the same time period. When time windows overlap and parameters are correlated, they are marked as composite anomalies. Calculate the similarity between overt and covert anomalies in vibration spectrum and temperature gradient, specifically by calculating cosine similarity. When the similarity is greater than a set threshold, it is determined to be different manifestations of the same fault event.

[0091] The anomaly propagation path is queried using Neo4j's Cypher language. Path weights are assigned based on the physical connection strength between devices to obtain high-probability propagation chains. Based on these high-probability propagation chains, when multiple explicit anomalies are associated with the same device, it is determined to be a high-priority fault. When an explicit anomaly is accompanied by a latent anomaly in the same device, it is determined to be a composite fault. When multiple latent anomalies in the same device form a closed loop in the association graph, it is determined to be a systemic degradation. Anomaly data is then integrated and dynamically prioritized to obtain the hydropower plant's anomaly data.

[0092] In one specific embodiment, the Isolation Forest model is initialized using the scikit-learn library in Python, with the number of trees (n_estimators) set to 200 and the subsample size (max_samples) set to 0.8 times the total number of samples. Extracted multi-dimensional data from a hydropower plant is input into the Isolation Forest model for training. During training, the model randomly selects a subset of data to construct 200 isolation trees, and each tree recursively partitions the data points until every data point is isolated. After training, the data to be tested is input. For example, consider the operating data of a hydro-generator unit at a given moment: rotational speed 145 r / min (normal range 150 ± 5 r / min), power 4800 kW (normal range 5000 ± 200 kW), voltage 9.8 kV (normal range 10 ± 0.5 kV), current 490 A (normal range 500 ± 30 A), and corresponding temperature sensor measurements of 42℃ (normal range 40 ± 3℃) and vibration sensor measurements of 0.6 mm / s (normal range 0.5 ± 0.1 mm / s). The isolated forest model calculates outlier scores for these data points; for example, the outlier score for the rotational speed data point is 0.8, for the power data point it is 0.9, and for the temperature sensor data point it is 0.7. An anomaly score threshold of 0.75 is preset. When the anomaly scores of speed and power data points are higher than the threshold, they are marked as explicit anomalies, and relevant information is recorded, including the time of the anomaly occurrence "2024-08-05 10:00:00", the corresponding equipment is the hydro-generator set "WTG-001", and the sensors are the speed sensor "RS-001" and the power sensor "PS-001".

[0093] An autoencoder model was designed, with both the encoder and decoder employing a multilayer perceptron structure. The encoder contains three hidden layers with 128, 64, and 32 neurons respectively; the decoder also contains three hidden layers with 64 and 128 neurons, matching the dimensionality of the input data. The autoencoder was trained using multidimensional data from a hydropower plant, and the model parameters were adjusted by minimizing the reconstruction error (using the mean squared error loss function). After multiple rounds of training, the model was able to reconstruct the input data well. The data to be detected was then input into the autoencoder for reconstruction. For example, for the operating data of a hydropower generator unit at another time point, the speed was 152 r / min, the power was 5050 kW, the voltage was 10.1 kV, the current was 502 A, the temperature sensor measurement was 41℃, and the vibration sensor measurement was 0.52 mm / s. The reconstruction errors were calculated; for example, the reconstruction error for the speed data was 0.04, the reconstruction error for the power data was 0.03, and the reconstruction error for the temperature sensor data was 0.05. The reconstruction error threshold is set to 0.045. If the reconstruction error of the temperature sensor data exceeds the threshold, it is determined that there is a hidden anomaly in the data. The relevant information is recorded: the time of the anomaly is "2024-08-05 10:30:00", the corresponding device is the hydro-generator set "WTG-001", and the sensor is the temperature sensor "TS-001".

[0094] The device entity "WTG-001" associated with explicit anomalies is mapped to the turbine generator unit device node in the association map, and the associated speed sensor "RS-001" and power sensor "PS-001" are mapped to the sensor node in the map; the temperature sensor "TS-001" associated with implicit anomalies is mapped to the sensor node in the map. Simultaneously, historically occurring fault modes related to turbine generator units, such as "turbine generator unit blade wear fault," are added to the map as predefined nodes. Explicit anomaly edges are created between the turbine generator unit device node and explicit anomalies of speed and power, labeled with anomaly scores (speed anomaly score 0.8, power anomaly score 0.9) and types (abnormal equipment operating parameters); implicit anomaly edges are created between the device node and the implicit anomaly of the temperature sensor, labeled with reconstruction error (0.05) and trend characteristics (temperature data reconstruction error shows a gradually increasing trend).

[0095] This study analyzes the overt anomalies (speed and power anomalies) and covert anomalies (temperature anomalies) of the same equipment (hydro-generator unit "WTG-001") within a similar time period (2024-08-05 10:00:00-10:30:00). Due to partial overlap in the time windows and the correlation between speed, power, and temperature parameters during equipment operation (e.g., power changes may cause equipment heating, thus affecting temperature), these are labeled as composite anomalies. The similarity between overt and covert anomalies in vibration spectrum and temperature gradient is calculated. For example, vibration spectrum data and temperature gradient data within a certain period are selected, and cosine similarity is calculated. The cosine similarity of the vibration spectrum is 0.85, and the cosine similarity of the temperature gradient is 0.88. A similarity threshold of 0.8 is set to determine these as different manifestations of the same fault event.

[0096] The anomaly propagation path is queried using Neo4j's Cypher language. For example, to query the anomaly propagation path of the hydro-generator unit "WTG-001", path weights are assigned based on the physical connection strength between equipment (e.g., the generator and transformer are connected via a high-voltage cable, with a connection strength of 0.9; the transformer and switchgear are connected via a busbar, with a connection strength of 0.8). The high-probability propagation chain is: hydro-generator unit "WTG-001" → generator → transformer → switchgear. Based on the high-probability propagation chain, since the hydro-generator unit "WTG-001" has multiple explicit anomalies (speed and power anomalies) associated with it, it is determined to be a high-priority fault; at the same time, the equipment has explicit anomalies (speed and power anomalies) accompanied by implicit anomalies (temperature anomalies), and is determined to be a composite fault; if subsequent monitoring finds that multiple implicit anomalies of the same equipment form a closed loop in the correlation graph (e.g., implicit anomalies of multiple sensors such as temperature, pressure, and vibration form a closed loop), it is determined to be a systemic degradation.

[0097] All detected abnormal data are integrated and dynamically prioritized according to high-priority faults, complex faults, systemic degradation, and other common anomalies to obtain hydropower plant anomaly data. For example, at a certain moment, the abnormal data of the turbine generator unit "WTG-001" is ranked at the top of the anomaly data list due to its high-priority and complex fault characteristics, making it convenient for maintenance personnel to handle it first.

[0098] Step S400: Predict the trends of equipment operation data and sensor monitoring data using the Prophet model, locate deviation values ​​using residual analysis, obtain data fluctuation patterns based on deviation values, analyze the correlation between abnormal data and data fluctuation patterns of the hydropower plant, and obtain the source of abnormal data of the hydropower plant.

[0099] Specifically, the Prophet model is configured, and the Prophet model is trained using the similarity of the vibration spectrum and temperature gradient between the explicit and implicit anomalies to generate trend features, seasonal features, and periodic features of the anomalous data. After training, the trained Prophet model is used to predict the equipment operation data and sensor monitoring data for a future period of time. The prediction results include the trend values ​​of the equipment operation data and sensor monitoring data.

[0100] The actual equipment operation data and sensor monitoring data are compared with the trend values ​​predicted by the Prophet model to calculate the residual. The residual is equal to the actual value minus the predicted value, reflecting the difference between the actual data and the model prediction results. Based on the calculated residual, the deviation value is located by setting a threshold. When the absolute value of the residual exceeds the threshold, the data point is determined to be a deviation value.

[0101] The located deviations are analyzed, and the data fluctuation patterns are classified according to the characteristics and distribution of the deviations, including periodic fluctuations, sudden fluctuations, and persistent deviations. Correlation analysis is performed between the abnormal data of the hydropower plant and the data fluctuation patterns. By calculating the correlation coefficient, the degree of association between the abnormal data and different fluctuation patterns is determined. Based on the degree of association and combined with the historical fault records of the hydropower plant equipment, the source of the abnormal data of the hydropower plant is inferred.

[0102] In one specific embodiment, the Prophet model is configured using the fbprophet library in Python. For explicit anomalous data, the seasonality period is set to 7 days per week and 30 days per month to capture possible weekly and monthly variation patterns; the smoothing parameter for trend changes is set to 0.1 to make the model more sensitive to trend changes. For implicit anomalous data, based on its characteristics, the seasonality period is set to 24 hours per day, and changepoint_prior_scale is set to 0.05 to capture potential trend changes more precisely. The similarity features of vibration spectrum and temperature gradient of explicit anomalous data are input into the Prophet model for training. During training, the model learns the trend characteristics of explicit anomalous data over time series, such as the gradual upward trend of the frequency band where the energy concentration of the vibration spectrum is concentrated when there is a power anomaly; seasonal characteristics, such as the relatively large amplitude of temperature gradient anomaly changes at the weekend of each week; and periodic characteristics, such as the repetition of anomalous patterns in the vibration spectrum to a certain extent every 10 days. Similarly, by training on latent anomalous data, the model learns the unique characteristics of latent anomalous data in time series, such as the relatively obvious periodic changes in the reconstruction error of temperature sensor data between 2 and 4 a.m. every day.

[0103] The trained Prophet model was used to predict equipment operation data and sensor monitoring data for the coming week. For example, regarding the rotational speed of the hydro-generator unit, the model predicted that the trend value of the rotational speed would fluctuate slightly within a range of around 150 r / min over the coming week, with the daytime rotational speed slightly higher than the nighttime speed from Monday to Friday, exhibiting a certain seasonal variation. For temperature sensor data, the model predicted that the temperature value would fluctuate with changes in ambient temperature over the coming week. Considering the heat generated by the equipment operation, the temperature would rise during periods of high equipment load.

[0104] The actual equipment operating data and sensor monitoring data are compared with the trend values ​​predicted by the Prophet model to calculate the residuals. For example, at 14:00 on a certain day, the actual turbine generator speed is 148 r / min, the model predicts a trend value of 150 r / min, and the residual is 148 - 150 = -2 r / min; the actual temperature sensor measurement is 43℃, the model predicts a trend value of 41℃, and the residual is 43 - 41 = 2℃. A threshold of 1.5 is set for the absolute value of the residuals. For speed data, if the absolute value of the residual |-2| > 1.5, the data point is considered a deviation; for temperature data, if the absolute value of the residual |2| > 1.5, it is also considered a deviation. Relevant information for these deviations is recorded, including the time, the corresponding equipment, and the sensor.

[0105] The identified deviations are analyzed. For example, a series of deviations exhibits a regular 7-day cycle, indicating a periodic fluctuation pattern; other deviations appear suddenly at a certain moment with significant differences from preceding and following data, indicating a sudden fluctuation pattern; still others consistently exceed or fall below the normal range for a period of time, exhibiting a persistent deviation pattern. For periodic fluctuation patterns, features such as a 7-day cycle and an amplitude of ±2 r / min (taking rotational speed as an example) are extracted; for sudden fluctuation patterns, the sudden time point is recorded as "2024-08-10 16:00:00", and the deviation degree is 20% above the normal range (taking data from a certain sensor as an example); for persistent deviation patterns, the starting time of the deviation is determined to be "2024-08-05 08:00:00", the duration has reached 5 days, and the direction of deviation is consistently above the normal range.

[0106] Correlation analysis was performed on existing abnormal data from the hydropower plant and its fluctuation patterns. For example, for a certain type of power anomaly data from a turbine generator unit, the correlation coefficient with a periodic fluctuation pattern was calculated to be 0.85, with a sudden fluctuation pattern to be 0.2, and with a persistent deviation pattern to be 0.1. This indicates a strong correlation between this type of power anomaly data and a periodic fluctuation pattern. Combining this with historical fault records of the hydropower plant equipment, it was found that when the turbine generator unit's blades showed slight wear, its operating data exhibited a periodic fluctuation pattern, similar to the fluctuation pattern of the currently detected power anomaly data. Further examination of the equipment maintenance records revealed that the turbine generator unit underwent a blade inspection six months prior, after which the power anomaly gradually appeared. Based on this comprehensive assessment, it was inferred that the source of this power anomaly data was the gradual wear of the turbine generator unit's blades.

[0107] like Figure 2 The system architecture diagram of the intelligent operation and maintenance management system for hydropower plants based on machine learning is shown in the present invention. The present invention provides an intelligent operation and maintenance management system for hydropower plants based on machine learning, comprising:

[0108] The data acquisition and data backtracking module includes a data acquisition unit, a data lineage tracing unit, and a data backtracking unit. The data acquisition unit collects multi-dimensional data from the hydropower plant, including equipment operation data, sensor monitoring data, and power grid dispatching data, performs data preprocessing, and stores the data in a data warehouse. The data lineage tracing unit defines unified metadata tags and establishes a data lineage tracing mechanism within the data warehouse. The data backtracking unit determines the starting point for tracing, backtracks based on storage location, traces data access history, and integrates the data traceability chain according to the data flow diagram.

[0109] The association graph generation module includes an association graph construction unit and an association graph adjustment unit. The association graph construction unit performs entity identification and relationship extraction based on the data warehouse and data traceability chain, and uses Neo4j to build the association graph. The association graph adjustment unit analyzes the relationship between power grid dispatch data and equipment operation, including command impact analysis and chain reaction analysis, and adjusts the association graph.

[0110] The hydropower plant anomaly detection module includes: a visible anomaly analysis unit, a hidden anomaly analysis unit, and a hydropower plant anomaly data generation unit. The visible anomaly analysis unit uses isolated forest detection based on equipment operation data and sensor monitoring data to identify visible anomalies. The hidden anomaly analysis unit reconstructs the data distribution using an autoencoder to identify hidden anomalies. The hydropower plant anomaly data generation unit fuses visible and hidden anomalies based on a correlation graph to obtain hydropower plant anomaly data.

[0111] The hydropower plant abnormal data source analysis module includes a deviation value location unit and a hydropower plant abnormal data source analysis unit. The deviation value location unit predicts the trend of equipment operation data and sensor monitoring data through the Prophet model and locates the deviation value by combining residual analysis. The hydropower plant abnormal data source analysis unit obtains the data fluctuation pattern based on the deviation value, analyzes the correlation between the hydropower plant abnormal data and the data fluctuation pattern, and obtains the source of the hydropower plant abnormal data.

[0112] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A machine learning-based intelligent operation and maintenance management method for hydropower plants, characterized in that, Includes the following steps: Collect multi-dimensional data from hydropower plants, including equipment operation data, sensor monitoring data, and power grid dispatch data, perform data preprocessing, and store the data in a data warehouse; in the data warehouse, define unified metadata tags and establish a data lineage tracing mechanism; determine the starting point of the traceability, trace back based on the storage location, trace the data access history, trace back according to the data flow diagram, and integrate to obtain the data traceability chain; Based on the data warehouse and data traceability chain, entity identification and relationship extraction are performed, and a relationship graph is constructed using Neo4j; the relationship between power grid dispatch data and equipment operation is analyzed, including command impact analysis and chain reaction analysis, and the relationship graph is adjusted accordingly; The process of entity identification and relationship extraction based on a data warehouse and data traceability chain, and the construction of a relational graph using Neo4j, includes: Extract data related to equipment, sensors, and failure modes from the data warehouse; identify equipment entities based on the unique identifiers of sensor monitoring data; extract relevant attributes for each equipment entity; identify sensor entities based on sensor monitoring data; record the attributes of each sensor entity; analyze the failure record data in the equipment operation data and sensor monitoring data to identify different failure mode entities; the attributes of each failure mode entity include failure name, failure phenomenon description, failure severity level, and failure frequency. By analyzing the correlation between equipment operation data and sensor monitoring data, as well as the installation and configuration information of equipment and sensors, the relationships between equipment and sensors are extracted. Combining the equipment operation data and the fault record data, devices with fault modes exceeding a set threshold are extracted, thus obtaining the relationships between equipment and fault modes. The Neo4j graph database is installed and configured, and the identified equipment entities, sensor entities, and fault mode entities are created as nodes in Neo4j. When creating nodes, the extracted corresponding attributes are added to the nodes. Based on the extracted relationships between equipment and sensors, equipment and fault modes, and sensors and fault modes, corresponding edges are created in Neo4j to connect the nodes, with each edge labeled with the relationship type and related attributes. Based on equipment operation data and sensor monitoring data, an isolated forest method is used to detect anomalies and obtain explicit anomalies. The data distribution is reconstructed through an autoencoder to identify implicit anomalies. The explicit and implicit anomalies are then fused based on a correlation map to obtain the hydropower plant's anomaly data. The process of fusing explicit and implicit anomalies based on correlation maps to obtain hydropower plant anomaly data includes: Map the device entities associated with anomalies to device nodes in the association graph, map the sensor entities associated with anomalies to sensor nodes in the association graph, and add historical fault modes as predefined nodes to the graph; create explicit anomaly edges between device nodes and sensor nodes and explicit anomalies respectively, and label the anomaly score and type; create implicit anomaly edges between device nodes and sensor nodes and implicit anomalies respectively, and label the reconstruction error and trend characteristics. Analyze overt and covert anomalies of the same device or sensor within the same time period. When time windows overlap and parameters are correlated, they are marked as composite anomalies. Calculate the similarity between overt and covert anomalies in vibration spectrum and temperature gradient, specifically by calculating cosine similarity. When the similarity is greater than a set threshold, it is determined to be different manifestations of the same fault event. The anomaly propagation path is queried using Neo4j's Cypher language. Path weights are assigned based on the physical connection strength between devices to obtain high-probability propagation chains. Based on these high-probability propagation chains, when multiple explicit anomalies are associated with the same device, it is determined to be a high-priority fault; when explicit anomalies are accompanied by latent anomalies in the same device, it is determined to be a composite fault; when multiple latent anomalies in the same device form a closed loop in the association graph, it is determined to be a systemic degradation. Anomaly data is then integrated and dynamically prioritized to obtain the hydropower plant's anomaly data. By using the Prophet model to predict trends in equipment operation data and sensor monitoring data, and combining residual analysis to locate deviations, data fluctuation patterns are obtained based on these deviations. The correlation between abnormal data and data fluctuation patterns in hydropower plants is analyzed to identify the sources of abnormal data, including: Configure the Prophet model and train it separately using the similarity of vibration spectrum and temperature gradient between explicit and implicit anomalies to generate trend features, seasonal features, and periodic features of the anomalous data. After training, use the trained Prophet model to predict equipment operation data and sensor monitoring data for a future period of time. The prediction results include trend values ​​of equipment operation data and sensor monitoring data. The actual equipment operation data and sensor monitoring data are compared with the trend values ​​predicted by the Prophet model to calculate the residual. The residual is equal to the actual value minus the predicted value, reflecting the difference between the actual data and the model prediction results. Based on the calculated residual, the deviation value is located by setting a threshold. When the absolute value of the residual exceeds the threshold, the data point is determined to be a deviation value. The located deviations are analyzed, and the data fluctuation patterns are classified according to the characteristics and distribution of the deviations, including periodic fluctuations, sudden fluctuations, and persistent deviations. Correlation analysis is performed between the abnormal data of the hydropower plant and the data fluctuation patterns. By calculating the correlation coefficient, the degree of association between the abnormal data and different fluctuation patterns is determined. Based on the degree of association and combined with the historical fault records of the hydropower plant equipment, the source of the abnormal data of the hydropower plant is inferred.

2. The intelligent operation and maintenance management method for hydropower plants based on machine learning according to claim 1, characterized in that, The collected multi-dimensional data from the hydropower plant, including equipment operation data, sensor monitoring data, and power grid dispatch data, undergoes data preprocessing and is stored in a data warehouse, including: According to the set acquisition frequency, the system collects equipment operation data, including hydro-generator sets, transformers, and switchgear, and transmits the data to the data acquisition server via fieldbus or wireless network; sensors monitor the equipment and environmental conditions in real time, and the collected sensor monitoring data is converted into digital signals for transmission; the sensors include environmental sensors, electrical sensors, and mechanical sensors; a data connection is established with the power grid dispatching system, and the latest power grid dispatching data, including dispatching instructions and power grid operation data, is obtained according to the update frequency of the power grid dispatching system. The data preprocessing includes data cleaning and data transformation. The data warehouse is built using a data lake, specifically Delta Lake.

3. The intelligent operation and maintenance management method for hydropower plants based on machine learning according to claim 1, characterized in that, The aforementioned process of defining unified metadata tags and establishing a data lineage tracking mechanism in the data warehouse includes: Based on the sources of multi-dimensional data from hydropower plants, metadata is generated; naming rules are established, a hierarchical structure of metadata tags is constructed, and metadata tags are entered; the collection path of each type of metadata from the data source to the data warehouse is obtained, and the collection timestamp is marked. Record the storage location of the multi-dimensional data of the hydropower plant in the data warehouse, specifically the file path in the data lake; record the access status of the multi-dimensional data of the hydropower plant, specifically which users or systems performed operations on which multi-dimensional data of the hydropower plant at what time; and draw a data flow diagram by analyzing the flow of the multi-dimensional data of the hydropower plant within the data warehouse and between it and external systems.

4. The intelligent operation and maintenance management method for hydropower plants based on machine learning according to claim 1, characterized in that, The process of determining the starting point for tracing the source, tracing back based on storage location, retracing data access history, and tracing back according to the data flow graph to integrate and obtain the data tracing chain includes: The user inputs the data characteristics to be traced, and based on the data characteristics to be traced, the data type and identification information of the data characteristics specified by the user are determined. Based on the determined data type and identification information, a search is performed in the file path records of the data lake. When the searched data is historical backup data of equipment operation data, the specific file storing the data is located according to the pre-recorded file naming rules and the structure of the file path in the data lake. In the log table that records the multi-dimensional data access of the hydropower plant, all access records of the data are queried using the storage location of the located data as an index. Based on the queried operation records, the impact of each operation on the data is analyzed. Based on the data flow diagram, starting with the located data, the path of data flowing from the data source into the data warehouse is determined; along the data flow path, the data is traced back to the data collection stage. All information obtained during the process of tracing back to the storage location, tracing back to the data access history, and tracing back based on the data flow diagram will be integrated to form a data traceability chain.

5. The intelligent operation and maintenance management method for hydropower plants based on machine learning according to claim 1, characterized in that, The analysis of the relationship between power grid dispatch data and equipment operation includes command impact analysis and chain reaction analysis, and adjustment of the correlation graph, including: The methods for analyzing the impact of dispatch instructions include: using regression analysis algorithms to establish a mathematical model between power grid dispatch instructions and changes in equipment operating parameters; and determining the critical paths and key parameters that affect equipment operation through model analysis and verification with actual data. The methods for chain reaction analysis include: obtaining the correlation between equipment based on the physical connection and operating logic of hydropower plant equipment; simulating the chain reaction process by combining the direct impact of power grid dispatch instructions on equipment operation; analyzing the direct impact of the power generation adjustment instruction on the generator when the power grid issues it, and gradually deriving how the resulting impact is transmitted to other equipment based on the correlation between equipment. Based on the results of the instruction impact analysis and chain reaction analysis, new relationship nodes and edges related to power grid dispatch are added to the existing correlation graph. For the indirect relationships between devices involved in the chain reaction, corresponding edges are added to the correlation graph to connect them and label the relationship attributes. The relationship attributes are information used to describe the characteristics and properties of the relationships between nodes in the correlation graph.

6. The intelligent operation and maintenance management method for hydropower plants based on machine learning according to claim 1, characterized in that, The method, based on equipment operation data and sensor monitoring data, employs isolated forest detection to identify explicit anomalies. Then, it uses an autoencoder to reconstruct the data distribution and identify implicit anomalies, including: The scikit-learn library in Python was selected as the Isolation Forest algorithm library to initialize the Isolation Forest model. Multi-dimensional data from the hydropower plant was input into the Isolation Forest model for training. Multiple isolation trees were constructed by randomly selecting subsets of data, with each tree dividing the data points until every data point was isolated. After training, the operating data of the equipment to be detected and the sensor monitoring data were input into the Isolation Forest model. The Isolation Forest model calculated an anomaly score for each data point. Based on a pre-set anomaly score threshold, it was determined whether a data point was an anomaly. When the anomaly score was higher than the threshold, the data point was marked as an explicit anomaly, and relevant information was recorded. The model architecture of the autoencoder is designed, including an encoder and a decoder. A multilayer perceptron is used as the basic structure of the encoder and decoder, and the performance is optimized by adjusting the number of hidden layers and neurons. The autoencoder is trained using multi-dimensional data from a hydropower plant. During training, the parameters are adjusted by minimizing the reconstruction error so that the data reconstructed by the decoder is close to the original input data. After training, the operating data of the equipment to be detected and the sensor monitoring data are input into the autoencoder to reconstruct the data and calculate the reconstruction error. A reconstruction error threshold is set. When the reconstruction error of the data exceeds the threshold, the data is determined to have a latent anomaly.

7. A machine learning-based intelligent operation and maintenance management system for hydropower plants, using the machine learning-based intelligent operation and maintenance management method for hydropower plants according to any one of claims 1-6, characterized in that, include: The data acquisition and data backtracking module includes a data acquisition unit, a data lineage tracing unit, and a data backtracking unit. The data acquisition unit collects multi-dimensional data from the hydropower plant, including equipment operation data, sensor monitoring data, and power grid dispatching data, performs data preprocessing, and stores the data in a data warehouse. The data lineage tracing unit defines unified metadata tags and establishes a data lineage tracing mechanism within the data warehouse. The data backtracking unit determines the starting point for tracing, backtracks based on storage location, traces data access history, and integrates the data traceability chain according to the data flow diagram. The association graph generation module includes an association graph construction unit and an association graph adjustment unit. The association graph construction unit performs entity identification and relationship extraction based on the data warehouse and data traceability chain, and uses Neo4j to build the association graph. The association graph adjustment unit analyzes the relationship between power grid dispatch data and equipment operation, including command impact analysis and chain reaction analysis, and adjusts the association graph. The hydropower plant anomaly detection module includes: a visible anomaly analysis unit, a hidden anomaly analysis unit, and a hydropower plant anomaly data generation unit. The visible anomaly analysis unit uses isolated forest detection based on equipment operation data and sensor monitoring data to identify visible anomalies. The hidden anomaly analysis unit reconstructs the data distribution using an autoencoder to identify hidden anomalies. The hydropower plant anomaly data generation unit fuses visible and hidden anomalies based on a correlation graph to obtain hydropower plant anomaly data. The hydropower plant abnormal data source analysis module includes a deviation value location unit and a hydropower plant abnormal data source analysis unit. The deviation value location unit predicts the trend of equipment operation data and sensor monitoring data through the Prophet model and locates the deviation value by combining residual analysis. The hydropower plant abnormal data source analysis unit obtains the data fluctuation pattern based on the deviation value, analyzes the correlation between the hydropower plant abnormal data and the data fluctuation pattern, and obtains the source of the hydropower plant abnormal data.

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