Object-oriented online monitoring method for high-voltage electrical equipment of hydropower station

Abstract

The invention relates to the technical field of online monitoring of high-voltage electrical equipment of a hydropower station, in particular to an object-oriented online monitoring method for the high-voltage electrical equipment of the hydropower station. Comprising the following steps: high-voltage electrical equipment object modeling: carrying out abstract mapping on high-voltage electrical equipment in a hydropower station, and carrying out modeling on the high-voltage electrical equipment according to a mechanical structure and an electrical function to form a high-voltage electrical equipment module; through an object-oriented technical means, unified monitoring and management of the high-voltage electrical equipment are realized, and the defect that an existing monitoring system takes a sensor signal as independent data for analysis is overcome. And the diagnosis algorithm model is dynamically configured, so that the monitoring system can adapt to monitoring requirements of different devices and under different operation conditions, and the accuracy of device state evaluation is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of on-line monitoring of high-voltage electrical equipment in hydropower stations, and specifically to an object-oriented on-line monitoring method for high-voltage electrical equipment in hydropower stations. Background Art

[0002] As an important energy supply facility, the operating status of the high-voltage electrical equipment in a hydropower station is directly related to the safe operation and power generation efficiency of the power station. Traditional on-line monitoring systems for high-voltage electrical equipment in hydropower stations often suffer from problems such as system dispersion, data islands, and insufficient accuracy of evaluation algorithms, making it difficult for operation and maintenance personnel to comprehensively and accurately grasp the operating status of the equipment, increasing the operation and maintenance difficulty and cost. Summary of the Invention

[0003] In view of this, the present invention provides an object-oriented on-line monitoring method for high-voltage electrical equipment in hydropower stations.

[0004] An object-oriented on-line monitoring method for high-voltage electrical equipment in hydropower stations includes the following steps: High-voltage electrical equipment object modeling: Abstractly map the high-voltage electrical equipment in the hydropower station, and model it according to its mechanical structure and electrical function to form a high-voltage electrical equipment module; Dynamically configure the diagnostic algorithm model: Based on the high-voltage electrical equipment module, configure the monitoring index module, associate the data acquisition module to generate monitoring points, and create a monitoring task instance through the choreography module. Combine the algorithm module to evaluate the monitoring data and feedback the evaluation result to the high-voltage electrical equipment module; Data cleaning and data model: Clean the collected sensor data to make it consistent in the time domain, frequency domain, and value domain dimensions, forming a data model to provide effective data support for the state evaluation of high-voltage electrical equipment.

[0005] Furthermore, the high-voltage electrical equipment module further includes sub-objects of the generator outlet switch, sub-objects of the transformer, and sectional sub-objects of the cable. Each sub-object is modeled according to its actual mechanical structure and electrical function.

[0006] Furthermore, the dynamically configured diagnostic algorithm model further includes the following steps: Obtain the characteristics of the monitoring index; Obtain sensor data through the data acquisition module and establish an association with the monitoring index; Quantify the data of multiple sensors under the same index and use the algorithm module for evaluation.

[0007] Furthermore, the data cleaning step includes removing noise data, filling in missing data, and data normalization to ensure the consistency of the data in the time domain, frequency domain, and value domain dimensions.

[0008] Furthermore, it also includes a modeling method for monitoring high-voltage electrical equipment in a hydropower station, which comprises the following steps: Abstractly map the high-voltage electrical equipment in the hydropower station to form a high-voltage electrical equipment module; Configure a monitoring index module and associate it with a data acquisition module to generate monitoring points; According to the monitoring requirements, dynamically adjust the configurations of the high-voltage electrical equipment module, the monitoring index module, and the data acquisition module to achieve the flexibility and scalability of the monitoring system.

[0009] Furthermore, it also includes a method for extracting data for analyzing the high-voltage electrical equipment model, which comprises the following steps: Clean and preprocess the collected sensor data; Extract data related to the state assessment of high-voltage electrical equipment according to the data model; Use the extracted data for state assessment to provide decision-making support for maintenance personnel.

[0010] Furthermore, the object-oriented online monitoring method for high-voltage electrical equipment in a hydropower station provides a unified system entry, enabling maintenance personnel to conveniently manage and view the operating status of high-voltage electrical equipment in the hydropower station.

[0011] Advantages of the present invention: Through object-oriented technical means, the present application realizes the unified monitoring and management of high-voltage electrical equipment, and solves the defect that the existing monitoring system analyzes sensor signals as independent data. Dynamically configuring the diagnostic algorithm model enables the monitoring system to adapt to the monitoring requirements under different equipment and different operating conditions, and improves the accuracy of equipment state assessment.

[0012] The present application provides a unified system entry, enabling maintenance personnel to conveniently manage and view the operating status of high-voltage electrical equipment in the hydropower station, and reducing the work cost of maintenance personnel.

[0013] Through dynamic configuration and modular design, the present application enables the monitoring system to flexibly adapt to different monitoring requirements, and has scalability, facilitating subsequent function upgrades and optimizations. Description of the Drawings

[0014] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0015] Figure 1 It is a schematic diagram of an embodiment of the present invention; Figure 2 Schematic diagram of the modeling method for monitoring high-voltage electrical equipment in a hydropower station according to an embodiment of the present invention; Figure 3 Schematic diagram of the method for extracting model analysis data of high-voltage electrical equipment according to an embodiment of the present invention. Detailed implementation manners

[0016] To make the objectives, 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 with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention. Embodiment 1

[0017] Combined with the attached Figures 1 to 3 , according to an embodiment of the present invention, an object-oriented on-line monitoring method for high-voltage electrical equipment in a hydropower station includes the following steps: Modeling of high-voltage electrical equipment objects: Abstractly map the high-voltage electrical equipment in the hydropower station, and model it according to its mechanical structure and electrical function to form a high-voltage electrical equipment module; Among them, by abstracting and mapping the high-voltage electrical equipment in the hydropower station, it is first classified according to the mechanical structure and electrical function of the equipment. For example, high-voltage electrical equipment such as transformers, switchgear, and circuit breakers in the hydropower station are divided into multiple modules, and each module represents a specific electrical equipment. The equipment module includes the basic attributes of the equipment (such as equipment type, model, rated voltage, etc.) and the related electrical functions (such as monitoring indicators such as current, voltage, and temperature).

[0018] The function of each high-voltage electrical equipment module not only includes the electrical parameters of the equipment itself, but also includes the monitoring of the working state and abnormal state of the equipment, forming a complete equipment model.

[0019] Dynamically configure the diagnostic algorithm model: Based on the high-voltage electrical equipment module, configure the monitoring index module, associate the data acquisition module to generate monitoring points, and create a monitoring task instance through the choreography module. Combine the algorithm module to evaluate the monitoring data, and feedback the evaluation result to the high-voltage electrical equipment module; Among them, based on the established high-voltage electrical equipment module, the system will configure the monitoring index module related to the operation of the equipment. For example, the current monitoring module, temperature monitoring module, vibration monitoring module, etc. Each monitoring module corresponds to different sensor data acquisition points, and the sensor data is obtained through the data acquisition module.

[0020] Through the scheduling module, each monitoring index module is combined with a monitoring task instance. Each monitoring task instance represents a specific monitoring task. Combined with the diagnostic algorithm module, the algorithm analyzes and evaluates the collected real-time data to determine whether the device is in a normal working state.

[0021] Monitoring task instances are generated in real time according to the actual operating conditions of the device, and the real-time data is evaluated through intelligent algorithms. The evaluation results will be fed back to the corresponding high-voltage electrical equipment module to form a device status evaluation report.

[0022] Data cleaning and data model: Clean the collected sensor data to make it consistent in the time domain, frequency domain, and value domain dimensions, form a data model, and provide effective data support for the status evaluation of high-voltage electrical equipment.

[0023] Among them, during the data collection process, the data collected by sensors may be affected by factors such as noise and interference, resulting in unstable data quality. Therefore, the system needs to clean the collected sensor data, remove invalid data, and correct abnormal data.

[0024] The data cleaning process includes data consistency verification in dimensions such as the time domain, frequency domain, and value domain. For example, perform interpolation and smoothing processing on the data to ensure the continuity of the time-domain data; remove noise through frequency-domain filtering; convert the collected data to a unified dimension and numerical range to ensure the consistency of the data in the value domain.

[0025] The cleaned data will enter the data model module to form a structured data model. The data model not only retains the integrity of the original data but also provides high-quality data support through data processing for subsequent device status evaluation and diagnostic analysis.

[0026] Finally, after data cleaning and algorithm evaluation, the device status evaluation results will be fed back to the high-voltage electrical equipment module, providing real-time device health status, fault prediction, and maintenance suggestions. The monitoring system will provide detailed reports to the operation and maintenance personnel based on the status evaluation results to help them make maintenance decisions.

[0027] When the system detects that a certain device may malfunction, the feedback system will send a warning message in advance to prompt the operation and maintenance personnel to check or handle it, thereby reducing the probability of device failures and ensuring the stable operation of the hydropower station.

[0028] This application provides an effective device status evaluation system by modeling high-voltage electrical equipment in hydropower stations, combining dynamically configured diagnostic algorithms and real-time data monitoring. Through data cleaning and model construction, it ensures the accuracy and reliability of the real-time monitoring data of high-voltage electrical equipment, helps managers discover potential faults in advance, reduces maintenance costs, and improves the service life of the equipment and the operation efficiency of the hydropower station. Furthermore, the high-voltage electrical equipment module further includes sub-objects of generator outlet switches, sub-objects of transformers, and sectional sub-objects of cables. Each sub-object is modeled according to its actual mechanical structure and electrical function.

[0029] Furthermore, the dynamic configuration diagnosis algorithm model further includes the following steps: Obtain the characteristics of monitoring indicators; Obtain sensor data through the data acquisition module and establish an association with the monitoring indicators; Quantify the data of multiple sensors under the same indicator and evaluate them using the algorithm module.

[0030] Among them, the system obtains the characteristics of monitoring indicators related to the health status of equipment according to the functions and operating requirements of each high-voltage electrical equipment in the hydropower station. Different equipment modules have different monitoring requirements, and each monitoring indicator has corresponding characteristics, such as current, voltage, temperature, vibration, etc.

[0031] Obtaining the characteristics of monitoring indicators may include the following steps: Equipment type identification: Determine the indicators to be monitored according to the equipment type (such as transformers, circuit breakers, cables, etc.). The monitoring indicators corresponding to each equipment type may be different. For transformers, common monitoring indicators include oil temperature, current, voltage, load, etc.; for circuit breakers, monitoring indicators may include the start-stop status of the switch, contact current, insulation resistance value, etc.; for cables, monitoring indicators may include leakage current, temperature, etc.

[0032] Indicator characteristic definition: The characteristics of each monitoring indicator need to be defined in detail, including the normal operating range, alarm threshold, acquisition frequency, etc. of the indicator. The system will prepare for subsequent data acquisition and algorithm evaluation according to these characteristics.

[0033] Obtain sensor data through the data acquisition module and establish an association with the monitoring indicators. In this step, the system obtains sensor data related to high-voltage electrical equipment through the data acquisition module. Each monitoring indicator corresponds to a different sensor, and the data acquisition module is responsible for obtaining real-time data from these sensors and establishing an association between the data and the corresponding monitoring indicators.

[0034] Obtaining sensor data through the data acquisition module and establishing an association with the monitoring indicators may include the following steps: The data acquisition module is connected to sensors: The system is connected to various sensors (such as temperature sensors, current sensors, voltage sensors, vibration sensors, etc.) through preset acquisition points, and regularly or real-time obtains sensor data through the acquisition module. For example, the oil temperature sensor of the transformer collects the oil temperature data, the current sensor obtains the current data passing through the transformer, and the vibration sensor detects the vibration data of the transformer.

[0035] Associate data with monitoring indicators: The system associates the collected raw data with the corresponding monitoring indicators according to the monitoring requirements of the equipment. For example: The data of the temperature sensor will be associated with the "oil temperature" monitoring indicator of the transformer; the data of the current sensor will be associated with the "current" monitoring indicator of the transformer; the data of the vibration sensor will be associated with the "vibration" monitoring indicator.

[0036] In this way, the data acquisition module can ensure that the data obtained from different sensors corresponds one by one to the specific monitoring indicators of the equipment.

[0037] Quantify the data of multiple sensors under the same indicator and use the algorithm module for evaluation. In this step, the system quantifies the data from different sensors under the same monitoring indicator and evaluates these data through the algorithm module. This process can integrate the information of multiple data sources and provide more accurate and reliable evaluation results.

[0038] Quantifying the data of multiple sensors under the same indicator and using the algorithm module for evaluation may include the following steps: Quantify the data of multiple sensors: For the same monitoring indicator, multiple sensors may provide data. For example, the "current" monitoring indicator of the transformer may be provided with data by multiple current sensors. The system will perform quantization processing on these data, including: Data fusion: Weighted average or summation of the data from different sensors to generate a comprehensive value. For example, if there are multiple current sensors, the system will perform weighted calculation on their measurement values to obtain a representative current value.

[0039] Outlier detection: Detect and correct outliers by comparing the data of different sensors. For example, if the current data of a sensor is significantly higher than the readings of other sensors, the system will automatically exclude this abnormal data.

[0040] Use the algorithm module for evaluation: After quantization processing, the system inputs the data into the algorithm module. The algorithm module evaluates the quantized data to determine whether the equipment is in a normal state. The evaluation process includes: Data analysis: The system analyzes the real-time collected data according to the normal working range of the equipment. For example, whether the current of the transformer exceeds the predetermined working range, whether the oil temperature is abnormal, etc.

[0041] Fault diagnosis: If a monitored indicator exceeds a preset threshold, the algorithm module will trigger a fault diagnosis and output an alarm message. For example, if the oil temperature of a transformer exceeds the preset upper limit, the system will detect this anomaly and report the possible cause of the fault.

[0042] Feedback of evaluation results: The evaluation results will be fed back to the equipment module as a reference for the health status of the equipment. The system will generate an equipment status report and provide decision-making support for the operation and maintenance personnel.

[0043] Specifically, for a monitoring system of a transformer, the specific steps are as follows: Obtain the characteristics of monitored indicators: The system defines the indicators that need to be monitored for the transformer, such as oil temperature, current, load power, etc.

[0044] The data acquisition module acquires data: The system acquires data such as the oil temperature and current of the transformer through sensors and associates this data with the monitored indicators of the transformer.

[0045] Data quantization and evaluation: The system quantizes the data from multiple temperature sensors and current sensors to generate a comprehensive oil temperature and current value, and then evaluates it through the algorithm module to determine whether the transformer is in a normal operating state.

[0046] This embodiment details the implementation process of the dynamic configuration diagnosis algorithm model, including the steps of obtaining the characteristics of monitored indicators, acquiring sensor data and establishing associations, quantizing multiple sensor data, and performing algorithm evaluation. This process helps the operation and maintenance personnel to grasp the operating status of high-voltage electrical equipment in real time, discover potential faults in a timely manner, and take corresponding maintenance measures by integrating multi-party data and performing intelligent evaluation, thereby improving the reliability of the equipment and the operating efficiency of the hydropower station.

[0047] Furthermore, the data cleaning step includes removing noise data, filling in missing data, and data normalization to ensure the consistency of data in the time domain, frequency domain, and value domain dimensions.

[0048] Among them, during the process of collecting sensor data, it may be affected by factors such as environmental noise, sensor failures, and communication interference, resulting in abnormal fluctuations in the data. In order to ensure the accuracy of the data, it is necessary to remove this noise data.

[0049] The steps for removing noise data may include the following: Noise detection: Use noise detection algorithms (such as statistical analysis methods, filtering algorithms, etc.) to detect outliers in the collected data. For example, if the readings of the temperature sensor suddenly fluctuate violently, or the values of the current sensor are significantly outside the normal range, this data may be noise data.

[0050] Noise filtering: According to the type of noise (such as high-frequency noise, mutation values, etc.), appropriate filtering methods (such as median filtering, Kalman filtering, low-pass filtering, etc.) are used for denoising. For example, for the temperature data measured by a sensor, the median filtering method can be used to remove extreme temperature fluctuations. Among them, Kalman filtering: Applied to the data of dynamic systems, the noise is reduced by estimating the difference between the estimated value and the actual value. Median filtering: Suitable for removing occasionally occurring mutation noise, taking the value in the middle of the data set as a representative to remove outliers.

[0051] Through these methods, the system can remove the noise data, retain the effective monitoring data, and ensure that the subsequent analysis is not interfered by noise.

[0052] In the actual data acquisition process, due to reasons such as sensor failures, communication interruptions, and environmental factors, missing data may occur. The missing data will affect the accuracy of equipment status assessment, so it must be filled.

[0053] Filling missing data can include the following steps: Missing data detection: The system automatically detects the missing parts in the data. For example, in sensor data, if the value at a certain moment is not collected, the system will mark it as missing data.

[0054] Data filling methods: 1. Interpolation method: Interpolate the missing data. Common interpolation methods include linear interpolation, spline interpolation, and polynomial interpolation. The missing value is calculated through the data at adjacent time points. For example, if the data of a temperature sensor is lost at a certain moment, the system can estimate the missing temperature value using the temperature values at the previous and subsequent moments by linear interpolation. 2. Mean filling: For the case of missing data in a continuous time period, the mean value of this section of data can be used to fill the missing value. Especially when there is a lot of missing data, mean filling is a simple and effective method. 3. Multiple imputation method: In some cases, the system will use the multiple imputation method, that is, fill the missing data by multiple interpolation calculations to ensure that the filling result is more robust.

[0055] Through these methods, the system can effectively fill the missing data and ensure the continuity and integrity of the data.

[0056] Since the dimensions of different sensors are different (such as temperature, current, voltage, vibration, etc.), it is difficult to directly analyze this data. Therefore, the data must be normalized to convert different types of data into a unified range to ensure the consistency of the data in the value range.

[0057] Data normalization can include the following steps: Normalization method selection: 1. Min-max normalization: Convert the data to the range [0, 1]. The specific method is to subtract the minimum value of the data from each data point, and then divide by the difference between the maximum value and the minimum value of the data. 2. Z-score standardization: By calculating the mean and standard deviation of the data, convert the data into a standard normal distribution (mean is 0, standard deviation is 1). It is applicable to situations where the data fluctuates greatly.

[0058] Application of normalization: Convert the collected data of all sensors into a unified scale through the above normalization methods. For example, convert the data of different sensors such as temperature, current, voltage, vibration, etc. into the range of [0, 1], so that subsequent state evaluation and algorithm models can process various data without being affected by dimensional differences.

[0059] Ensure data consistency: Consistency in the time domain, frequency domain, and value domain dimensions. The data will maintain consistency in the time domain, frequency domain, and value domain, thereby ensuring the accuracy and effectiveness of subsequent algorithms. Among them, time-domain consistency: Ensure the continuity of all sensor data in the time dimension, that is, the time intervals of data collection are the same. If some data is lost or the collection times are out of sync, supplement or adjust the time axis through methods such as interpolation to maintain data synchronization. Frequency-domain consistency: For data involving frequency analysis, such as vibration monitoring data, ensure the consistency of the signal in the frequency domain. Remove unnecessary high-frequency noise through frequency-domain filtering to ensure the stability of the signal spectrum. Value-domain consistency: Through means such as normalization, ensure that sensor data with different dimensions and units can be compared and analyzed within a unified scale, so that various data can equally participate in subsequent state evaluation.

[0060] The data cleaning process ensures the high quality of the collected data through steps such as noise removal, filling missing data, and normalization. These steps ensure data consistency, maintaining unity in the time domain, frequency domain, and value domain dimensions, providing reliable data support for equipment state evaluation.

[0061] Furthermore, it also includes a modeling method for monitoring high-voltage electrical equipment in hydropower stations, including the following steps: Abstractly map the high-voltage electrical equipment in the hydropower station to form a high-voltage electrical equipment module; Configure the monitoring index module and associate the data acquisition module to generate monitoring points; According to the monitoring requirements, dynamically adjust the configurations of the high-voltage electrical equipment module, monitoring index module, and data acquisition module to achieve the flexibility and scalability of the monitoring system.

[0062] Furthermore, it also includes a method for extracting data for analyzing the high-voltage electrical equipment model, including the following steps: Clean and preprocess the collected sensor data; Extract data related to the status assessment of high-voltage electrical equipment according to the data model; Use the extracted data for status assessment to provide decision-making support for operation and maintenance personnel.

[0063] Furthermore, the object-oriented on-line monitoring method for high-voltage electrical equipment in hydropower stations provides a unified system entry, enabling operation and maintenance personnel to conveniently manage and view the operating status of high-voltage electrical equipment in hydropower stations. Embodiment 2

[0064] Combined with the attached Figures 1 to 3 In this embodiment, an object-oriented on-line monitoring method for high-voltage electrical equipment in hydropower stations is provided, aiming to achieve real-time monitoring and status assessment of high-voltage electrical equipment in hydropower stations through accurate modeling, dynamic configuration of diagnostic algorithms, and data cleaning and processing. This method can provide accurate feedback on the equipment operating status and decision-making support for operation and maintenance personnel, thereby improving the reliability of the equipment and the operating efficiency of the hydropower station.

[0065] 1. Object modeling of high-voltage electrical equipment In a hydropower station, all high-voltage electrical equipment such as transformers, generator outlet switches, cables, etc. are modeled in an object-oriented manner. First, the equipment is abstractly mapped according to its mechanical structure and electrical functions to form a high-voltage electrical equipment module. Each equipment module includes the basic attributes of the equipment (such as equipment model, rated voltage, operating status, etc.) and its specific electrical functions (such as monitoring indicators such as current, voltage, temperature, etc.).

[0066] Steps of equipment modeling: Generator outlet switch sub-object: This module is modeled according to the electrical characteristics of the generator outlet switch, including parameters such as the start-stop status, load current, temperature, etc. of the equipment.

[0067] Transformer sub-object: The modeling of the transformer involves multiple monitoring indicators such as oil temperature, current, voltage, load power, etc., and these parameters will be monitored in real time to ensure the stable operation of the transformer.

[0068] Cable section sub-object: The cable module is used to monitor data such as the insulation resistance, temperature, current, etc. of the cable to ensure the safety of the cable system.

[0069] Through this modeling method, each high-voltage electrical equipment in the hydropower station is disassembled into multiple functional modules, making the monitoring and management of the equipment more modular and flexible.

[0070] 2. Dynamically configure the diagnostic algorithm model This step includes dynamically configuring a diagnostic algorithm model based on the monitoring requirements of high-voltage electrical equipment modules. Each equipment module is configured with a monitoring index module, and the monitoring index module obtains sensor data related to the equipment status through a data acquisition module. Then, the monitoring data will create specific monitoring task instances through an orchestration module, evaluate the data in combination with a diagnostic algorithm module, and feedback the results to the high-voltage electrical equipment module.

[0071] Specific steps for dynamically configuring the diagnostic algorithm: Obtain monitoring index characteristics: According to the types and operating environments of the equipment in the hydropower station, the system first obtains the monitoring index characteristics required by the equipment. For example, a transformer needs to monitor oil temperature, current, load, etc.; the generator outlet switch needs to monitor switch status, current, temperature, etc.

[0072] Obtain sensor data through the data acquisition module: According to the monitoring indexes, the system uses the data acquisition module to obtain real-time data of the equipment from the sensors. For example, obtain the oil temperature data of the transformer from the temperature sensor and the generator outlet current data from the current sensor.

[0073] Data quantization and algorithm evaluation: The same monitoring index may have data provided by multiple sensors. The system quantizes this data and evaluates it through an algorithm module. The algorithm module will judge whether the equipment is in a normal working state based on the sensor data. If the system detects an abnormal situation, the algorithm module will generate a warning message.

[0074] 3. Data cleaning and data model Since sensor data may be affected by problems such as noise, interference, or missing data during the acquisition process, it is necessary to clean the data to ensure the data quality. The process of data cleaning includes operations such as removing noise data, filling in missing data, and data normalization. Through these steps, ensure the consistency of the data in the time domain, frequency domain, and value domain dimensions, so as to provide high-quality data support for subsequent equipment status evaluation.

[0075] Specific steps for data cleaning: Remove noise data: Through methods such as filtering algorithms and outlier detection, remove the noise and invalid data in the sensor data. For example, use the median filtering method to process the fluctuating data of the temperature sensor and remove occasional abnormal temperature readings.

[0076] Fill in missing data: If the sensor data is lost or interrupted at certain moments, the system can fill in the missing data through interpolation algorithms (such as linear interpolation or polynomial interpolation) to maintain the continuity of the data.

[0077] Data normalization: To address the dimensional differences of different sensors, the system normalizes different types of data. For example, it converts data with different dimensions such as temperature, current, and voltage into a unified standard range to ensure the consistency of data in the value range.

[0078] The cleaned data is stored in the data model. The data model not only retains the structure of the original data but also, through cleaning and normalization processes, ensures that the data can provide reliable support for equipment status assessment.

[0079] 4. High-voltage electrical equipment status assessment and feedback After data cleaning and algorithm evaluation, the system can real-time assess the status of high-voltage electrical equipment. For example, by analyzing the oil temperature, current, and load status of a transformer, the system can determine whether the transformer is in normal operation. If the system detects abnormalities (such as too high oil temperature, too large current, etc.), it will automatically trigger an alarm and feedback the fault information to the operation and maintenance personnel.

[0080] Status assessment process: Assess the equipment status: Using the data model and algorithm module, the system will assess the health status of the equipment. If the equipment is normal, the system will feedback the normal working status; if the equipment is abnormal, the system will feedback the fault type and possible fault reasons.

[0081] Decision support: The assessment results will be fed back to the high-voltage electrical equipment module and a report will be generated. The operation and maintenance personnel can take corresponding measures in a timely manner according to the assessment results, such as adjusting the load, cleaning the equipment, replacing components, etc.

[0082] 5. Modeling method for monitoring high-voltage electrical equipment in hydropower stations To improve the flexibility and scalability of the monitoring system, this embodiment also includes a modeling method for monitoring high-voltage electrical equipment. This method adapts to different monitoring requirements by dynamically configuring the monitoring module, data acquisition module, and equipment module.

[0083] Monitoring modeling method: Equipment abstraction mapping: First, various high-voltage electrical equipment in the hydropower station are abstractly mapped into equipment modules, and corresponding monitoring indicators are configured for each equipment module.

[0084] Configuration and association of the monitoring module: According to the actual needs of the hydropower station, configure the monitoring index module and associate the data acquisition module to generate monitoring points.

[0085] Dynamic configuration adjustment: During the operation of the equipment, the system can dynamically adjust the configuration of the high-voltage electrical equipment module, monitoring index module, and data acquisition module according to the changes in monitoring requirements. For example, when new equipment is connected, the system can automatically generate monitoring points and adjust the monitoring tasks.

[0086] 6. Method for Extracting Model Analysis Data of High-Voltage Electrical Equipment This method also includes steps of cleaning, preprocessing and data extraction for the collected sensor data. The purpose of data extraction is to extract data related to equipment status assessment to provide support for subsequent assessment.

[0087] Steps of data extraction: Data cleaning and preprocessing: Clean and preprocess the collected raw data to ensure data accuracy.

[0088] Data extraction: According to the requirements of equipment status assessment, the system will extract key data related to the health status of the equipment from the data model.

[0089] Status assessment: Use the extracted data for status assessment to provide decision support for operation and maintenance personnel to help them take appropriate maintenance or operation measures.

[0090] 7. Unified System Entrance and User Management To enable operation and maintenance personnel to conveniently manage and view the operating status of high-voltage electrical equipment in hydropower stations, the system provides a unified system entrance. Operation and maintenance personnel can view the operating status, monitoring data, alarm information, etc. of the equipment in real time through this entrance.

[0091] Unified entrance: Through this system entrance, operation and maintenance personnel can conveniently view the health status, monitoring results, historical data, etc. of different equipment, and monitor the operation of high-voltage electrical equipment in hydropower stations in real time.

[0092] User management and permission control: The system provides a user management function, and assigns corresponding permissions to operation and maintenance personnel according to different roles to ensure information security and management efficiency.

[0093] The object-oriented online monitoring method for high-voltage electrical equipment in hydropower stations provided in this embodiment constructs an efficient monitoring and diagnosis system through object modeling, dynamic configuration algorithm model, data cleaning and evaluation. The system can evaluate the health status of the equipment in real time, provide decision support for operation and maintenance personnel, help them timely discover and handle equipment failures, thereby improving the stability and operation efficiency of hydropower station equipment.

[0094] Although the embodiments of the present invention are described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. An object-oriented online monitoring method for high-voltage electrical equipment in a hydropower station, characterized in that: The following steps are involved: High-voltage electrical equipment object modeling: abstractly map the high-voltage electrical equipment in the hydropower station, model it according to its mechanical structure and electrical function, and form a high-voltage electrical equipment module; Dynamic configuration of diagnostic algorithm model: Based on the high-voltage electrical equipment module, configure the monitoring indicator module, associate the data acquisition module to generate monitoring points, and create monitoring task instances through the orchestration module. Combined with the algorithm module, the monitoring data is evaluated and the evaluation results are fed back to the high-voltage electrical equipment module. Data cleaning and data model: Clean the collected sensor data to make it consistent in the time domain, frequency domain, and value domain dimensions, form a data model, and provide effective data support for the status assessment of high-voltage electrical equipment.

2. The object-oriented online monitoring method for high-voltage electrical equipment in a hydropower station according to claim 1 is characterized in that: The high-voltage electrical equipment module also includes a sub-object of a generator output switch, a sub-object of a transformer, and a segmented sub-object of a cable, each of which is modeled according to its actual mechanical structure and electrical function.

3. The object-oriented online monitoring method for high-voltage electrical equipment in a hydropower station according to claim 2 is characterized in that: The dynamic configuration diagnosis algorithm model also includes the following steps: Obtain monitoring indicator characteristics; Acquire sensor data through the data acquisition module and associate it with monitoring indicators; The data of multiple sensors under the same indicator are quantified and evaluated using algorithm modules.

4. The object-oriented online monitoring method for high-voltage electrical equipment in a hydropower station according to claim 3 is characterized in that: The data cleaning step includes removing noise data, filling missing data and normalizing data to ensure the consistency of data in time domain, frequency domain and value domain dimensions.

5. The object-oriented online monitoring method for high-voltage electrical equipment in a hydropower station according to claim 4 is characterized in that: Also included is a modeling method for monitoring high voltage electrical equipment in a hydropower station, comprising the following steps: Abstractly map the high-voltage electrical equipment in the hydropower station to form a high-voltage electrical equipment module; Configure the monitoring indicator module and associate the data collection module to generate monitoring points; According to monitoring requirements, the configuration of high-voltage electrical equipment modules, monitoring indicator modules and data acquisition modules are dynamically adjusted to achieve flexibility and scalability of the monitoring system.

6. The object-oriented online monitoring method for high-voltage electrical equipment in a hydropower station according to claim 5 is characterized in that: Also included is a method for extracting high-voltage electrical equipment model analysis data, comprising the following steps: Clean and preprocess the collected sensor data; Extract data related to the status assessment of high-voltage electrical equipment according to the data model; The extracted data is used to conduct status assessment and provide decision support for operation and maintenance personnel.

7. The object-oriented online monitoring method for high-voltage electrical equipment in a hydropower station according to any one of claims 1 to 6, characterized in that: The object-oriented online monitoring method for high-voltage electrical equipment in a hydropower station provides a unified system entry, so that operation and maintenance personnel can conveniently manage and view the operating status of the high-voltage electrical equipment in the hydropower station.

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