Temperature monitoring method, system and equipment for electric control system of wind turbine generator and medium

Abstract

The invention discloses a temperature monitoring method, system and device for an electric control system of a wind turbine generator and a medium. The method comprises the steps of identifying a plurality of key electrical components in a target wind turbine generator based on acquired operation data; taking each key electrical component as a basic unit, performing temperature monitoring according to a preset first monitoring parameter to obtain a corresponding temperature state index, and determining temperature risk grade data for each key electrical component according to a preset temperature risk grade standard; calculating corresponding temperature anomaly risk data according to the corresponding temperature state indexes; by taking each key electrical component as a basic unit, calculating equipment health degree data according to a preset second monitoring parameter; based on the temperature anomaly risk data, the equipment health degree data and the temperature risk level data corresponding to each key electrical component, obtaining an overall health condition assessment result of the equipment; the early warning accuracy of temperature monitoring is improved, and the reliability of the electric control system of the wind turbine generator is enhanced.

Description

Technical Field

[0001] The present application relates to the field of wind power control technology, and in particular to a temperature monitoring method, system, equipment and medium for an electric control system of a wind turbine generator set. Background Art

[0002] As the global demand for clean energy grows, wind power generation has attracted widespread attention as an important green energy source. However, the heat accumulation generated by the long-term operation of the wind turbine electrical control system may cause electrical failures and affect the stability and safety of wind power equipment. Therefore, existing technologies have begun to explore various cooling technologies and thermal management systems for electrical control systems to ensure the normal operation of the equipment.

[0003] Currently, common temperature monitoring methods for electrical control systems mostly rely on a fixed threshold alarm mechanism, that is, once the temperature is detected to exceed the preset value, an alarm is triggered. This single threshold setting lacks flexibility and cannot adapt to the temperature change requirements under different working conditions. It is easy to produce false alarms or missed alarms, thereby reducing the reliability of the wind turbine electrical control system. In addition, there is a defect that the early warning accuracy of temperature monitoring is poor, and there is room for improvement. Summary of the invention

[0004] In order to improve the warning accuracy of temperature monitoring and enhance the reliability of wind turbine electrical control systems, the present application provides a temperature monitoring method, system, device and medium for wind turbine electrical control systems.

[0005] In the first aspect, the invention objective of the present application is achieved by adopting the following technical solutions: A temperature monitoring method for an electric control system of a wind turbine generator system, comprising: Acquiring operating data of a target wind turbine group, and identifying a plurality of key electrical components in the target wind turbine group based on the operating data; Taking each of the key electrical components as a basic unit, temperature monitoring is performed according to a preset first monitoring parameter to obtain a corresponding temperature state index, and temperature risk level data is determined for each key electrical component according to a preset temperature risk level standard; corresponding temperature abnormality risk data is calculated according to the corresponding temperature state index; Taking each of the key electrical components as a basic unit, the equipment health data is calculated according to the preset second monitoring parameters; based on the temperature anomaly risk data, equipment health data and temperature risk level data corresponding to each of the key electrical components, the overall health status assessment result of the equipment is obtained.

[0006] By adopting the above technical solution, multiple key electrical components are identified based on the operating data, ensuring the accuracy and pertinence of the monitored objects; at the same time, in order to provide a more comprehensive and detailed temperature status evaluation, the present application uses each key electrical component as a basic unit for temperature monitoring, not only considering the current temperature level, but also combining the preset first monitoring parameters (such as ambient temperature, working current, etc.) to calculate the temperature status index, and dynamically leveling the temperature risk of each key electrical component, combining the temperature status index and the preset temperature risk level standard (such as environmental conditions, operating mode, etc.) to determine the temperature risk level data, so that the wind turbine electrical control system can adjust according to the real-time situation. The warning threshold is adjusted to avoid false alarms or missed alarms caused by static threshold settings, thereby improving the accuracy and timeliness of the warning; further, the present application also introduces a second monitoring parameter to calculate the equipment health data, so as to achieve a comprehensive assessment of the overall health status of multiple key electrical components, and provide a scientific basis for the preventive maintenance of the wind turbine electrical control system, thereby reducing unplanned downtime. By continuously monitoring and evaluating the health status of the equipment, the maintenance strategy can be optimized, the service life of key electrical components can be extended, and the reliability and safety of the entire wind turbine can be improved. Therefore, the present application achieves the purpose of improving the warning accuracy of temperature monitoring and enhancing the reliability of the wind turbine electrical control system.

[0007] In a preferred example of the present application, the step of obtaining the operating data of the target wind turbine group and identifying a plurality of key electrical components in the target wind turbine group based on the operating data specifically includes: Performing data preprocessing on the operating data of the target wind turbine generator set to obtain preprocessed operating data; Performing heat flow analysis based on the preprocessed operating data to generate a heat flow distribution map; Calculate heat flux density data according to the heat flux distribution diagram, and generate a heat affected zone diagram based on the heat flux density data; Initial key electrical components are identified based on the heat affected zone map, and the initial key electrical components are modified according to the operating data to form final key electrical components.

[0008] By adopting the above technical scheme, data preprocessing improves the data quality of operating data. The present invention generates a heat flow distribution map through thermal flow analysis, intuitively analyzes the heat flow inside the wind turbine, improves the scientific basis for identifying high-risk areas, accurately locates key electrical components that may be affected by high temperature, and makes temperature monitoring more targeted. Then, corrections are made through actual operating data to ensure that the key electrical components finally identified are in line with the actual situation, thereby improving the temperature monitoring efficiency of the wind turbine electrical control system.

[0009] In a preferred example of the present application: each of the key electrical components is used as a basic unit, temperature monitoring is performed according to a preset first monitoring parameter, a corresponding temperature state index is obtained, and temperature risk level data is determined for each key electrical component according to a preset temperature risk level standard, specifically including: The first monitoring parameters include ambient temperature, operating current, voltage level, power factor, equipment load rate, heat dissipation efficiency and historical fault records; Divide the operating interval of the target wind turbine group by average time, determine all time intervals involved in a single key electrical component, assign a value to each time interval in the single key electrical component according to a first monitoring parameter, and obtain a first time interval score; Extracting and calculating the arithmetic mean of the first time interval scores of all time intervals involved in a single key electrical component, calculating the first monitoring parameter discrimination score of the single key electrical component, and calculating the temperature state index using the first monitoring parameter discrimination score and the comprehensive weight method; According to the preset temperature risk level standard, combined with the temperature status index and set related factors, the temperature risk level data is determined for each key electrical component.

[0010] By adopting the above technical solution, the temperature of each key electrical component is monitored according to the first monitoring parameter. Multiple factors such as ambient temperature, working current, voltage level, etc. are considered through multi-dimensional monitoring parameters, making temperature monitoring more comprehensive and accurate, and can more truly reflect the working status of the equipment; the status evaluation of key electrical components at different time points helps to capture instantaneous change trends and timely warn of potential problems. The comprehensive weight method is applied to calculate the temperature status index. Different weights are assigned to each monitoring parameter considering its different importance, which makes risk assessment more scientific and reasonable. The temperature risk level standard combines the temperature status index and other setting-related factors, providing a quantitative way to divide risk levels.

[0011] In a preferred example of the present application, the corresponding temperature anomaly risk data is calculated according to the corresponding temperature state index, specifically including: Acquire historical operation data, and acquire historical temperature anomaly frequency data of key electrical components based on the historical operation data; and calculate temperature anomaly risk data using the temperature state index and the historical temperature anomaly frequency data.

[0012] By adopting the above technical solution, historical operating data and temperature status index are used to assess the risk of temperature anomaly. By combining the real-time monitored temperature status index with historical data, it not only reflects the current temperature conditions, but also takes into account the long-term temperature change trend, thereby more accurately predicting the possibility of failure of key electrical components.

[0013] In a preferred example of the present application, the device health data is calculated based on each of the key electrical components as a basic unit according to the preset second monitoring parameters, specifically including: The second monitoring parameters include equipment age, maintenance records, ambient humidity, vibration level, insulation resistance and cooling system efficiency; Divide the monitoring interval of the target wind turbine group by average time, determine all time intervals involved in a single key electrical component, assign a value to each time interval in the single key electrical component according to a second monitoring parameter for all time intervals designed for the single key electrical component, and obtain a second time interval score; The arithmetic mean of the second time interval scores of all time intervals involved in a single key electrical component is extracted to calculate the second monitoring parameter discrimination score of the single key electrical component, and the equipment health data is calculated using the second monitoring parameter discrimination score and the comprehensive weight method.

[0014] By adopting the above technical solution, a multi-dimensional health assessment method of second monitoring parameters such as equipment service life, maintenance records, ambient humidity, vibration level, insulation resistance and cooling system efficiency is introduced, which not only takes into account the current working conditions, but also comprehensively considers the influencing factors of long-term operation; the monitoring interval is divided into average time, and each time interval is assigned a value according to the second monitoring parameter, so as to obtain the second time interval score, which helps to capture the health change trend of the equipment in different time periods; the present application uses the arithmetic mean and comprehensive weight method to calculate the equipment health data, and can assign corresponding weights according to the importance of different parameters to ensure that the health data finally calculated can better reflect the actual situation.

[0015] In a preferred example of the present application, based on the temperature anomaly risk data, equipment health data and temperature risk level data corresponding to each of the key electrical components, obtaining an overall equipment health status assessment result specifically includes: Taking each of the key electrical components as a basic unit, the corresponding temperature anomaly risk data is used as row vector data, the corresponding equipment health data is used as column vector data, and the temperature risk level data is used as a weight factor, and inputted into a preset health status assessment matrix to obtain a corresponding overall health status score; According to the overall health status score corresponding to each of the key electrical components, an overall health status assessment result of the equipment of the wind turbine generator set electrical control system is obtained.

[0016] By adopting the above technical solution, the temperature anomaly risk data, equipment health data and temperature risk level data are integrated to obtain the overall equipment health status assessment results of the wind turbine electrical control system; a health status assessment matrix is ​​adopted, with key electrical components as basic units, and temperature anomaly risk data as row vectors, equipment health data as column vectors, and temperature risk level data as weight factor inputs, forming a structured assessment model to improve assessment efficiency and accuracy; the overall health status score obtained through the health status assessment matrix can quantitatively represent the health status of each key electrical component and even the entire wind turbine electrical control system, which is convenient for intuitively understanding and comparing the health differences between different components or different units.

[0017] In the second aspect, the invention objective of the present application is achieved by adopting the following technical solutions: A temperature monitoring system for an electric control system of a wind turbine generator set is applied to the temperature monitoring method for an electric control system of a wind turbine generator set as described above, and the system comprises: A data acquisition module, configured to acquire operating data of a target wind turbine group and identify a plurality of key electrical components in the target wind turbine group based on the operating data; A temperature monitoring module, taking each of the key electrical components as a basic unit, performs temperature monitoring according to a preset first monitoring parameter, obtains a corresponding temperature state index, and determines temperature risk level data for each key electrical component according to a preset temperature risk level standard; an abnormal risk calculation module, used to calculate the corresponding temperature abnormality risk data according to the corresponding temperature state index; a health evaluation module, using each of the key electrical components as a basic unit, and calculating the equipment health data according to the preset second monitoring parameters; The overall health status assessment module is used to obtain the overall health status assessment result of the equipment based on the temperature anomaly risk data, equipment health data and temperature risk level data corresponding to each of the key electrical components.

[0018] By adopting the above technical solution, the data acquisition module can accurately obtain the operating data of the target wind turbine set. By preprocessing the operating data, performing heat flow analysis, and generating a heat-affected zone map, the system can accurately locate key electrical components that are greatly affected by temperature, thereby improving the effectiveness and pertinence of monitoring. The temperature monitoring module not only considers the current temperature level, but also combines multiple first monitoring parameters such as ambient temperature, operating current, and voltage level to calculate the temperature status index of each key electrical component. The abnormal risk calculation module uses the temperature anomaly frequency information in the historical operating data and combines it with the real-time temperature status index to calculate the temperature anomaly risk data, which not only reflects the current temperature conditions, but also considers the long-term trend. The health assessment module introduces second monitoring parameters such as equipment service life, maintenance records, ambient humidity, vibration level, insulation resistance, and cooling system efficiency, and provides a comprehensive assessment of the health status of key electrical components. The multi-dimensional assessment method not only considers the current working conditions, but also comprehensively considers the influencing factors of long-term operation, making the health assessment more in-depth and accurate.

[0019] In a preferred example of the present application: the data acquisition module specifically includes: A data preprocessing submodule performs data preprocessing on the operating data of the target wind turbine group to obtain preprocessed operating data; A heat flow analysis submodule, performing heat flow analysis based on the preprocessed operation data to generate a heat flow distribution diagram; a heat affected zone identification submodule, which calculates heat flux density data according to the heat flux distribution map, generates a heat affected zone map based on the heat flux density data; and identifies initial key electrical components based on the heat affected zone map; The key component correction submodule corrects the initial key electrical components according to the operation data to form final key electrical components.

[0020] In the third aspect, the invention objective of this application is achieved by adopting the following technical solutions: A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the temperature monitoring method of the wind turbine electric control system when executing the computer program.

[0021] In a fourth aspect, the invention objective of the present application is achieved by adopting the following technical solutions: A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the temperature monitoring method for an electric control system of a wind turbine generator set.

[0022] In summary, the present application includes at least one of the following beneficial technical effects: 1. Taking each key electrical component as a basic unit for temperature monitoring, not only the current temperature level is considered, but also the temperature state index is calculated in combination with the preset first monitoring parameter (such as ambient temperature, working current, etc.). By dynamically leveling the temperature risk of each key electrical component, the temperature risk level data is determined in combination with the temperature state index and the preset temperature risk level standard (such as environmental conditions, operating mode, etc.), so that the wind turbine electrical control system can adjust the warning threshold according to the real-time situation, avoid the false alarm or missed alarm caused by the static threshold setting, and improve the accuracy and timeliness of the warning; further, the present application also introduces a second monitoring parameter to calculate the equipment health data, so as to realize the comprehensive evaluation of the overall health status of multiple key electrical components, provide a scientific basis for the preventive maintenance of the wind turbine electrical control system, thereby reducing unplanned downtime, and continuously monitoring and evaluating the equipment health status, so as to optimize the maintenance strategy, extend the service life of key electrical components, and thus improve the reliability and safety of the entire wind turbine; 2. Integrate temperature anomaly risk data, equipment health data and temperature risk level data to obtain the overall health status assessment results of the wind turbine electrical control system; adopt a health status assessment matrix, take key electrical components as basic units, input temperature anomaly risk data as row vectors, equipment health data as column vectors, and temperature risk level data as weight factors to form a structured assessment model to improve assessment efficiency and accuracy; the overall health status score obtained through the health status assessment matrix can quantitatively represent the health status of each key electrical component and even the entire wind turbine electrical control system, which is convenient for intuitive understanding and comparison of health differences between different components or different units. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is a flow chart of a temperature monitoring method of a wind turbine generator system in an embodiment of the present application; Figure 2 This is a flow chart of step S1 in a method for monitoring the temperature of an electric control system of a wind turbine generator set in one embodiment of the present application; Figure 3 It is a schematic diagram of a device in an embodiment of the present application. DETAILED DESCRIPTION

[0024] The present application is further described in detail below in conjunction with the accompanying drawings.

[0025] In one embodiment, if Figure 1 As shown, the present application discloses a temperature monitoring method for an electric control system of a wind turbine generator set, which specifically includes the following steps: S1: Acquire operating data of a target wind turbine generator set, and identify multiple key electrical components in the target wind turbine generator set based on the operating data.

[0026] In this embodiment, the operating data of the target wind turbine include power generation, power factor, ambient temperature, etc.; various sensors installed on the wind turbine (such as temperature sensors, current sensors, voltage sensors, etc.) are used to collect operating data in real time; key electrical components refer to electrical components of the wind turbine that are affected by temperature, such as converters, generators, transformers, etc.

[0027] In step S1, Figure 2 As shown, the operating data of the target wind turbine group is obtained, and multiple key electrical components are identified in the target wind turbine group based on the operating data, specifically including: S11: performing data preprocessing on the operating data of the target wind turbine generator set to obtain preprocessed operating data.

[0028] In this embodiment, data preprocessing refers to operations such as cleaning, denoising, smoothing, and repairing null values ​​on the collected original operating data to ensure the data quality for subsequent analysis.

[0029] S12: Perform heat flow analysis based on the preprocessed operating data to generate a heat flow distribution map.

[0030] In this embodiment, thermal flow analysis refers to the analysis of heat flow path and heat distribution using preprocessed operating data; the heat affected zone map is a graphical representation generated based on heat flux density data, which is used to identify key electrical components and key areas where they are located that may be affected by high temperatures.

[0031] Specifically, the preprocessed operating data is used to analyze the heat flow path inside the wind turbine, determine the main flow direction of heat, and identify heat concentration areas, especially those places where high-temperature hotspots are prone to occur. According to the sensitivity of different key electrical components to temperature changes, corresponding matching temperature change thresholds can be used to judge high-temperature hotspots. Based on the results of the heat flow path analysis, a heat flow distribution map is generated to intuitively display the distribution of heat inside the wind turbine, so as to obtain a more accurate heat concentration area.

[0032] S13: Calculate heat flux density data according to the heat flux distribution diagram, and generate a heat affected zone diagram based on the heat flux density data.

[0033] In this embodiment, the heat flux density data refers to the heat flux density of each area calculated through the heat flux distribution diagram, that is, the heat flux per unit area. The heat flux density calculation is used to determine which areas have a higher heat flux density and may have overheating risks; the heat affected zone diagram is a graphical representation generated based on the heat flux density data.

[0034] S14: Identify initial key electrical components based on the heat affected zone map, and modify the initial key electrical components according to the operating data to form final key electrical components.

[0035] In this embodiment, the initial key electrical component identification refers to preliminarily identifying electrical components that may be greatly affected by high temperature based on the heat affected zone map.

[0036] Specifically, after the initial key electrical components are identified, the initial identification results are corrected in combination with actual operating data (such as current, voltage, power factor, etc.), and finally the key electrical components that need to be monitored are determined.

[0037] S2: Taking each key electrical component as a basic unit, temperature monitoring is performed according to a preset first monitoring parameter to obtain a corresponding temperature state index, and temperature risk level data is determined for each key electrical component according to a preset temperature risk level standard.

[0038] In this embodiment, the first monitoring parameter includes but is not limited to ambient temperature, operating current, voltage level, power factor, equipment load rate, heat dissipation efficiency and historical fault records; the temperature status index is an index based on a comprehensive evaluation of the first monitoring parameter, which is used to reflect the current temperature conditions; the temperature risk level standard refers to dynamically determining the temperature risk level based on pre-set temperature risk thresholds of different levels (such as low, medium, and high), combined with the temperature status index and other relevant factors (such as environmental conditions, operating modes).

[0039] In this embodiment, in step S2, each key electrical component is used as a basic unit, temperature monitoring is performed according to a preset first monitoring parameter, a corresponding temperature state index is obtained, and temperature risk level data is determined for each key electrical component according to a preset temperature risk level standard, specifically including: S21: Divide the operating range of the target wind turbine generator set by average time, determine all time intervals involved in a single key electrical component, assign a value to each time interval in the single key electrical component according to a first monitoring parameter, and obtain a first time interval score.

[0040] In this embodiment, the operating cycle of the target wind turbine is divided into several equal time intervals (for example, every hour, every day, etc.); for each key electrical component, all time intervals involved in the entire operating cycle are clarified, and within each time interval, the key electrical component is scored according to the first monitoring parameter (ambient temperature, operating current, voltage level, power factor, equipment load rate, heat dissipation efficiency and historical fault records) to obtain a first time interval score. The specific score of each parameter can be based on industry standards or empirical values.

[0041] S22: extract and calculate the arithmetic mean of the first time interval scores of all time intervals involved in a single key electrical component, calculate the first monitoring parameter discrimination score of the single key electrical component, and use the first monitoring parameter discrimination score and the comprehensive weight method to calculate the temperature state index.

[0042] In this embodiment, the temperature status index refers to a value obtained by comprehensively evaluating multiple monitoring parameters, which is used to quantitatively describe the current temperature status of key electrical components and reflect their thermal risk level.

[0043] Specifically, the arithmetic mean of the first time interval scores of all time intervals involved in a single key electrical component is calculated to calculate the first monitoring parameter discrimination score of the component; according to the importance and influence of each monitoring parameter, a weight value is assigned to each parameter (for example, ambient temperature weight 0.2, working current weight 0.25, etc.).

[0044] The temperature state index is calculated using the first monitoring parameter discrimination score and the comprehensive weight method. The specific formula is as follows: Where TSI represents the temperature state index, ω i is the weight of the i-th monitoring parameter; S i is the discriminant score of the ith monitoring parameter, and n represents the number of monitoring parameters.

[0045] S23: According to the preset temperature risk level standard, combined with the temperature status index and set related factors, determine the temperature risk level data for each key electrical component.

[0046] In this embodiment, the temperature risk level standard refers to a set of pre-set rules or thresholds used to convert the temperature status index into a specific temperature risk level (such as low risk, medium risk, high risk).

[0047] Specifically, a set of temperature risk level standards are set in advance, such as low risk (TSI<50), medium risk (50≤TSI<75), and high risk (TSI≥75). The temperature risk level data of each key electrical component is determined based on the temperature status index and combined with other relevant factors (such as equipment type, age, maintenance history, etc.).

[0048] S3: Calculate corresponding temperature anomaly risk data according to the corresponding temperature state index.

[0049] In this embodiment, the temperature anomaly risk data refers to the probability of temperature anomaly occurring for each key electrical component under current operating conditions calculated based on the temperature status index and historical data, reflecting its potential risk level.

[0050] Specifically, in step S3, the corresponding temperature anomaly risk data is calculated according to the corresponding temperature state index, which specifically includes: S31: Acquire historical operation data, and acquire historical temperature abnormality frequency data of key electrical components based on the historical operation data.

[0051] In this embodiment, the historical operation data includes the operation records of the wind turbine over a period of time in the past, such as ambient temperature, operating current, voltage level, power factor, equipment load rate, heat dissipation efficiency and historical fault records; the historical temperature abnormality frequency data refers to the number and frequency of temperature abnormalities that occurred in key electrical components in the past extracted from the historical operation data, which is used to predict possible abnormal situations in the future.

[0052] S32: Calculate temperature anomaly risk data using the temperature status index and historical temperature anomaly frequency data.

[0053] In this embodiment, a mathematical model is established to combine the temperature state index with the historical temperature anomaly frequency data to calculate the temperature anomaly risk data. The model can use regression analysis, machine learning algorithms or other statistical methods.

[0054] Specifically, the temperature anomaly risk data can be calculated using a weighted comprehensive assessment method, in which the weight between the temperature status index and the historical temperature anomaly frequency is adjusted according to different application scenarios and needs to make the risk assessment more in line with the actual situation.

[0055] S4: Taking each key electrical component as a basic unit, the equipment health data is calculated according to the preset second monitoring parameters.

[0056] In this embodiment, the second monitoring parameters include equipment age, maintenance records, ambient humidity, vibration level, insulation resistance, and cooling system efficiency.

[0057] In this embodiment, in step S4, each key electrical component is used as a basic unit, and the equipment health data is calculated according to the preset second monitoring parameters, specifically including: S41: Divide the monitoring interval of the target wind turbine by average time, determine all time intervals involved in a single key electrical component, design all time intervals for the single key electrical component, assign a value to each time interval in the single key electrical component according to a second monitoring parameter, and obtain a second time interval score.

[0058] Specifically, for each critical electrical component, identify all time intervals it is involved in the entire monitoring cycle.

[0059] S42: extract the arithmetic mean of the second time interval scores of all time intervals involved in a single key electrical component, calculate the second monitoring parameter discrimination score of the single key electrical component, and use the second monitoring parameter discrimination score and the comprehensive weight method to calculate the equipment health data.

[0060] Specifically, equipment age: The equipment is scored based on its actual usage time, with new equipment scoring high and old equipment scoring low.

[0061] Maintenance records: Maintenance frequency and quality are considered, with frequent and high-quality maintenance scoring high.

[0062] Ambient humidity: High humidity environments receive lower scores as they may affect electrical performance.

[0063] Vibration Level: High vibration levels may cause mechanical fatigue and score low.

[0064] Insulation resistance: High insulation resistance indicates good insulation and is scored high.

[0065] Cooling system efficiency: An efficient cooling system scores high, whereas an inefficient one scores low.

[0066] In this embodiment, specifically, the arithmetic mean of the second time interval scores of all time intervals involved in a single key electrical component is calculated to calculate the second monitoring parameter discrimination score of the component; then, according to the importance and influence of each monitoring parameter, a weight value is assigned to each parameter (for example, a weight of 0.2 for the equipment service life, a weight of 0.25 for the maintenance record, etc.); the equipment health data is calculated using the second monitoring parameter discrimination score and the comprehensive weight method, and the specific formula is as follows: In the formula, HDI represents the equipment health index, δ i represents the weight of the i-th monitoring parameter, F i represents the discriminant score of the ith monitoring parameter, and n represents the number of monitoring parameters.

[0067] This application integrates multi-dimensional monitoring parameters into a single equipment health index through a mathematical model, making the health status of different components comparable. It adopts a comprehensive weighting method to fully consider the differences in the importance of each monitoring parameter, thereby improving the scientificity and rationality of the evaluation.

[0068] S5: Based on the temperature anomaly risk data, equipment health data, and temperature risk level data corresponding to each key electrical component, obtain the overall health status assessment result of the equipment.

[0069] In this embodiment, step S5 specifically includes: S51: Taking each key electrical component as the basic unit, the corresponding temperature abnormality risk data is used as the row vector data, the corresponding equipment health data is used as the column vector data, and the temperature risk level data is used as the weight factor, and is input into the preset health status assessment matrix to obtain the corresponding overall health status score.

[0070] Specifically, first construct a health status assessment matrix: Define row and column vectors: Row vector data: The temperature anomaly risk data of each key electrical component is input into the health status assessment matrix as a row vector.

[0071] Column Vector Data: The equipment health data of each critical electrical component is input into the health status assessment matrix as a column vector.

[0072] Weight factor setting: Temperature risk level data: As a weight factor, it is used to adjust the influence of row vectors and column vectors. Different temperature risk levels can be assigned different weight values ​​to reflect their importance.

[0073] Constructing the Assessment Matrix: Construct an n×m matrix, where n represents the number of critical electrical components and m represents the number of time intervals or evaluation dimensions.

[0074] Each matrix element A ij It is the overall health status score obtained by weighted summing of temperature anomaly risk data and equipment health data.

[0075] A ij =β1×R i +β2×HD j Among them, R i represents the abnormal temperature risk data of the i-th key electrical component; HD j represents the equipment health data in the jth time interval; β1 and β2 are weight factors of temperature anomaly risk data and equipment health data, respectively, satisfying β1+β2=1.

[0076] This embodiment provides a structured evaluation model, which integrates multi-dimensional data in a matrix form, flexibly adjusts weight factors according to different application scenarios, and ensures the adaptability and flexibility of the evaluation model.

[0077] S52: Obtain an overall health status assessment result of the wind turbine generator system electrical control system according to the overall health status score corresponding to each key electrical component.

[0078] In this embodiment, for each key electrical component, its corresponding temperature anomaly risk data (row vector), equipment health data (column vector) and temperature risk level data (weight factor) are input into the health status assessment matrix to calculate the overall health status score of each key electrical component.

[0079] Specifically, the scoring formula is: Where, HSI irepresents the overall health status score of the i-th critical electrical component, R ij represents the abnormal temperature risk data of the i-th key electrical component in the j-th time interval, HD ij represents the equipment health data of the i-th key electrical component in the j-th time interval, W i represents the temperature risk level weight factor of the i-th critical electrical component, and m represents the number of time intervals.

[0080] Furthermore, the HSI is calculated based on the overall health status score. i , set different risk levels (such as low risk, medium risk, and high risk), and use the threshold method or quantile method to classify the scores. Summarize the overall health status scores of each key electrical component to form an overall health status assessment result of the wind turbine electrical control system, and intuitively display the assessment results through visualization tools (such as dashboards, heat maps, etc.).

[0081] It should be understood that the serial numbers of the steps in the above embodiments do not imply a sequence of execution. The execution sequence of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0082] In one embodiment, a temperature monitoring system for an electric control system of a wind turbine generator set is provided. The temperature monitoring system for an electric control system of a wind turbine generator set corresponds to the temperature monitoring method for an electric control system of a wind turbine generator set in the above embodiment.

[0083] The temperature monitoring system of the wind turbine electric control system includes a data acquisition module, a temperature monitoring module, an abnormal risk calculation module, a health assessment module and an overall health assessment module. The detailed description of each functional module is as follows: A data acquisition module, configured to acquire operating data of a target wind turbine group and identify a plurality of key electrical components in the target wind turbine group based on the operating data; A temperature monitoring module, taking each of the key electrical components as a basic unit, performs temperature monitoring according to a preset first monitoring parameter, obtains a corresponding temperature state index, and determines temperature risk level data for each key electrical component according to a preset temperature risk level standard; an abnormal risk calculation module, used to calculate the corresponding temperature abnormality risk data according to the corresponding temperature state index; a health evaluation module, using each of the key electrical components as a basic unit, and calculating the equipment health data according to the preset second monitoring parameters; The overall health status assessment module is used to obtain the overall health status assessment result of the equipment based on the temperature anomaly risk data, equipment health data and temperature risk level data corresponding to each of the key electrical components.

[0084] Optionally, the data acquisition module specifically includes: A data preprocessing submodule performs data preprocessing on the operating data of the target wind turbine group to obtain preprocessed operating data; A heat flow analysis submodule, performing heat flow analysis based on the preprocessed operation data to generate a heat flow distribution diagram; a heat affected zone identification submodule, which calculates heat flux density data according to the heat flux distribution map, generates a heat affected zone map based on the heat flux density data; and identifies initial key electrical components based on the heat affected zone map; The key component correction submodule corrects the initial key electrical components according to the operation data to form final key electrical components.

[0085] For the specific limitations on the temperature monitoring system of the wind turbine electrical control system, please refer to the limitations on the temperature monitoring method of the wind turbine electrical control system in the above text, which will not be repeated here; the various modules in the temperature monitoring system of the wind turbine electrical control system mentioned above can be implemented in whole or in part through software, hardware and a combination thereof; the above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of the above modules.

[0086] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store operating data, temperature risk level data and equipment overall health status assessment results. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a temperature monitoring method for a wind turbine electric control system is implemented.

[0087] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: S1: Acquire operating data of a target wind turbine group, and identify a plurality of key electrical components in the target wind turbine group based on the operating data; S2: Taking each of the key electrical components as a basic unit, performing temperature monitoring according to a preset first monitoring parameter to obtain a corresponding temperature state index, and determining temperature risk level data for each key electrical component according to a preset temperature risk level standard; S3: Calculating corresponding temperature anomaly risk data according to the corresponding temperature state index; S4: Taking each of the key electrical components as a basic unit, calculate the equipment health data according to the preset second monitoring parameters; S5: Based on the temperature anomaly risk data, equipment health data and temperature risk level data corresponding to each of the key electrical components, obtain the overall health status assessment result of the equipment.

[0088] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: S1: Acquire operating data of a target wind turbine group, and identify a plurality of key electrical components in the target wind turbine group based on the operating data; S2: Taking each of the key electrical components as a basic unit, performing temperature monitoring according to a preset first monitoring parameter to obtain a corresponding temperature state index, and determining temperature risk level data for each key electrical component according to a preset temperature risk level standard; S3: Calculating corresponding temperature anomaly risk data according to the corresponding temperature state index; S4: Taking each of the key electrical components as a basic unit, calculate the equipment health data according to the preset second monitoring parameters; S5: Based on the temperature anomaly risk data, equipment health data and temperature risk level data corresponding to each of the key electrical components, obtain the overall health status assessment result of the equipment.

[0089] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0090] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0091] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments may still be modified, or some of the features thereof may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A temperature monitoring method for an electric control system of a wind turbine generator set, characterized in that: include: Acquiring operating data of a target wind turbine group, and identifying a plurality of key electrical components in the target wind turbine group based on the operating data; Taking each of the key electrical components as a basic unit, temperature monitoring is performed according to a preset first monitoring parameter to obtain a corresponding temperature state index, and temperature risk level data is determined for each key electrical component according to a preset temperature risk level standard; Calculate corresponding temperature anomaly risk data according to the corresponding temperature state index; Taking each of the key electrical components as a basic unit, calculating the equipment health data according to the preset second monitoring parameters; Based on the temperature anomaly risk data, equipment health data and temperature risk level data corresponding to each of the key electrical components, an overall equipment health status assessment result is obtained.

2. The temperature monitoring method of the wind turbine electric control system according to claim 1, characterized in that: The obtaining of operating data of the target wind turbine group and identifying a plurality of key electrical components in the target wind turbine group based on the operating data specifically includes: Performing data preprocessing on the operating data of the target wind turbine generator set to obtain preprocessed operating data; Performing heat flow analysis based on the preprocessed operating data to generate a heat flow distribution map; Calculate heat flux density data according to the heat flux distribution diagram, and generate a heat affected zone diagram based on the heat flux density data; Initial key electrical components are identified based on the heat affected zone map, and the initial key electrical components are modified according to the operating data to form final key electrical components.

3. The temperature monitoring method of the wind turbine electric control system according to claim 1, characterized in that: The method of taking each of the key electrical components as a basic unit, performing temperature monitoring according to a preset first monitoring parameter, obtaining a corresponding temperature state index, and determining temperature risk level data for each key electrical component according to a preset temperature risk level standard specifically includes: The first monitoring parameters include ambient temperature, operating current, voltage level, power factor, equipment load rate, heat dissipation efficiency and historical fault records; Divide the operating interval of the target wind turbine group by average time, determine all time intervals involved in a single key electrical component, assign a value to each time interval in the single key electrical component according to a first monitoring parameter, and obtain a first time interval score; Extracting and calculating the arithmetic mean of the first time interval scores of all time intervals involved in a single key electrical component, calculating the first monitoring parameter discrimination score of the single key electrical component, and calculating the temperature state index using the first monitoring parameter discrimination score and the comprehensive weight method; According to the preset temperature risk level standard, combined with the temperature status index and set related factors, the temperature risk level data is determined for each key electrical component.

4. The temperature monitoring method of the wind turbine electric control system according to claim 1, characterized in that: The step of calculating the corresponding temperature anomaly risk data according to the corresponding temperature state index specifically includes: Acquire historical operation data, and acquire historical temperature abnormality frequency data of key electrical components based on the historical operation data; The temperature anomaly risk data is calculated using the temperature state index and historical temperature anomaly frequency data.

5. The temperature monitoring method of the wind turbine electric control system according to claim 1, characterized in that: The method of taking each of the key electrical components as a basic unit and calculating the equipment health data according to the preset second monitoring parameters specifically includes: The second monitoring parameters include equipment age, maintenance records, ambient humidity, vibration level, insulation resistance and cooling system efficiency; Divide the monitoring interval of the target wind turbine group by average time, determine all time intervals involved in a single key electrical component, assign a value to each time interval in the single key electrical component according to a second monitoring parameter for all time intervals designed for the single key electrical component, and obtain a second time interval score; The arithmetic mean of the second time interval scores of all time intervals involved in a single key electrical component is extracted to calculate the second monitoring parameter discrimination score of the single key electrical component, and the equipment health data is calculated using the second monitoring parameter discrimination score and the comprehensive weight method.

6. The temperature monitoring method of the wind turbine electric control system according to claim 1, characterized in that: The obtaining of the overall health status assessment result of the equipment based on the temperature anomaly risk data, equipment health data and temperature risk level data corresponding to each of the key electrical components specifically includes: Taking each of the key electrical components as a basic unit, the corresponding temperature anomaly risk data is used as row vector data, the corresponding equipment health data is used as column vector data, and the temperature risk level data is used as a weight factor, and inputted into a preset health status assessment matrix to obtain a corresponding overall health status score; According to the overall health status score corresponding to each of the key electrical components, an overall health status assessment result of the equipment of the wind turbine generator set electrical control system is obtained.

7. The temperature monitoring system of the wind turbine electric control system is characterized by: A temperature monitoring method for a wind turbine electric control system according to any one of claims 1 to 6, the system comprising: A data acquisition module, configured to acquire operating data of a target wind turbine group and identify a plurality of key electrical components in the target wind turbine group based on the operating data; A temperature monitoring module, taking each of the key electrical components as a basic unit, performs temperature monitoring according to a preset first monitoring parameter, obtains a corresponding temperature state index, and determines temperature risk level data for each key electrical component according to a preset temperature risk level standard; An abnormal risk calculation module, used to calculate corresponding temperature abnormal risk data according to the corresponding temperature state index; A health evaluation module, taking each of the key electrical components as a basic unit, and calculating the equipment health data according to a preset second monitoring parameter; The overall health status assessment module is used to obtain the overall health status assessment result of the equipment based on the temperature anomaly risk data, equipment health data and temperature risk level data corresponding to each of the key electrical components.

8. The temperature monitoring system of the wind turbine electric control system according to claim 7, characterized in that: The data acquisition module specifically includes: A data preprocessing submodule performs data preprocessing on the operating data of the target wind turbine group to obtain preprocessed operating data; A heat flow analysis submodule, performing heat flow analysis based on the preprocessed operation data to generate a heat flow distribution diagram; a heat affected zone identification submodule, which calculates heat flux density data according to the heat flux distribution map, generates a heat affected zone map based on the heat flux density data; and identifies initial key electrical components based on the heat affected zone map; The key component correction submodule corrects the initial key electrical components according to the operation data to form final key electrical components.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the temperature monitoring method of the wind turbine electric control system according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the temperature monitoring method of the wind turbine electric control system according to any one of claims 1 to 6 are implemented.

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