Method and system for evaluating reliability of offshore wind power monitoring system
By employing multi-sensor redundancy and adaptive fault monitoring in the offshore wind power monitoring system, and calculating deviation for fault judgment, the problems of slow evaluation rate and high complexity in existing technologies are solved, and fast and accurate reliability assessment is achieved.
Patent Information
- Application Number
- CN202410625996.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-05-20
- Publication Date
- 2025-11-21
AI Technical Summary
Existing reliability assessment methods for offshore wind power monitoring systems are highly complex, resulting in slow assessment rates and susceptibility to human factors.
By employing multiple redundant sensors of the same type, and calculating the deviation of sensor data to set thresholds for fault diagnosis, automatic detection and isolation are achieved, thereby improving the system's fault tolerance and reliability.
This improved the assessment speed and accuracy of offshore wind power monitoring systems, reduced human interference, and enhanced the objectivity and efficiency of assessments.
Smart Images
Figure CN120990815A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The application belongs to the technical field of offshore wind turbine monitoring system reliability evaluation, and relates to an offshore wind power monitoring system reliability evaluation method and system. BACKGROUND
[0002] With the transformation of energy structure and the enhancement of environmental awareness, wind energy as a clean and renewable energy has received more and more attention. Offshore wind power as an important form of wind energy utilization, its installed capacity and power generation capacity are increasing year by year, which plays a crucial role in the stable operation of power system and the optimization of energy structure. However, offshore wind farms are usually located in the sea far from land, with harsh environment and difficult equipment maintenance, which makes the reliability of wind power monitoring system a problem to be solved.
[0003] The main function of offshore wind power monitoring system is to monitor the running state of wind farm in real time, including wind speed, wind direction, generator state, grid connection state and other aspects. By collecting and analyzing these data, the monitoring system can timely find and handle potential safety hazards to ensure the stable operation of wind farm. However, due to the complexity and uncertainty of offshore environment, the monitoring system may be disturbed in the running process, leading to data anomaly or loss, and thus affecting its reliability.
[0004] In order to improve the reliability of offshore wind power monitoring system, it needs to be evaluated and maintained regularly. The traditional evaluation method mainly depends on manual inspection and experience judgment, which is not only low in efficiency, but also easily affected by human factors. With the rapid development of big data, artificial intelligence and other technologies, new ideas are provided for the reliability evaluation of offshore wind power monitoring system. By collecting a large amount of monitoring data, machine learning algorithm is used to process and analyze the data, but the algorithm complexity is high, which leads to slow evaluation rate. SUMMARY
[0005] The purpose of the present application is to solve the problem of high algorithm complexity in the prior art, which leads to slow evaluation rate, and to provide a kind of offshore wind power monitoring system reliability evaluation method and system.
[0006] To achieve the above purpose, the following technical solutions are adopted:
[0007] The offshore wind power monitoring system reliability evaluation method proposed by the present application comprises the following steps:
[0008] Obtaining data of assembled thermocouple, armored thermocouple, pressure sensor, gas sensor and acceleration sensor;
[0009] According to the assembled thermocouple, armored thermocouple, pressure sensor, gas sensor and acceleration sensor data, the deviation degree of the sensor data is calculated;
[0010] According to the set deviation threshold and the deviation degree, the fault of the wind power monitoring system is judged, and the reliability evaluation of the offshore wind power monitoring system is realized.
[0011] Preferably, the deviation degree of the sensor data is represented as follows:
[0012]
[0013] Wherein, x i is the monitoring value of the i th sensor, is the average value of the monitoring values of all sensors at the same time, is the average value of the historical monitoring values of the same sensor, s is the operating state parameter in the unit SCADA system, is the average value of the operating state parameters in the same time period, δ1 is the weight reflecting the difference between the same type of sensors, and δ2 is the difference reflecting the change with time of the same sensor.
[0014] Preferably, the calculation method of the average value of the monitoring values of all sensors at the same time is:
[0015]
[0016] Wherein, n is the number of sensors.
[0017] Preferably, the calculation method of the average value of the historical monitoring values of the same sensor is:
[0018]
[0019] Wherein, Δt is a time period for sensor measurement, t c is the termination measurement time.
[0020] Preferably, the calculation method of the average value of the operating state parameters in the same time period is as follows:
[0021]
[0022] Wherein, Δt is a time period for sensor measurement, t c is the termination measurement time.
[0023] Preferably, the method for judging the fault of the wind power monitoring system according to the set deviation threshold and the deviation degree is as follows:
[0024] If the deviation degree of the sensor data exceeds the set threshold, the corresponding sensor is judged as a fault, and is isolated, that is, the data measured by the sensor is no longer used.
[0025] If the number of any one of the assembled thermocouple, armored thermocouple, pressure sensor, gas sensor and acceleration sensor is less than or equal to 1, it is determined that the entire monitoring system fails, and the fault of the wind power monitoring system is judged.
[0026] Preferably, the method for obtaining the set threshold value is as follows:
[0027] The average value of the sensor data deviation of the assembled thermocouple, armored thermocouple, pressure sensor, gas sensor and acceleration sensor is calculated, and the average value of the sensor data deviation is taken as the set threshold value.
[0028] The offshore wind power monitoring system reliability evaluation system provided by the application comprises:
[0029] A data acquisition module is arranged, and the data acquisition module is used to acquire the data of the assembled thermocouple, armored thermocouple, pressure sensor, gas sensor and acceleration sensor.
[0030] A deviation acquisition module is arranged, and the deviation acquisition module is used to calculate the deviation of the sensor data according to the data of the assembled thermocouple, armored thermocouple, pressure sensor, gas sensor and acceleration sensor.
[0031] A fault judgment module is arranged, and the fault judgment module is used to judge the fault of the wind power monitoring system according to the set deviation threshold value and deviation, so as to realize the reliability evaluation of the offshore wind power monitoring system.
[0032] An electronic device comprises a memory and a processor, the memory stores a computer program, and the processor realizes the steps of the offshore wind power monitoring system reliability evaluation method when executing the computer program.
[0033] A computer readable storage medium stores a computer program, and the computer program realizes the steps of the offshore wind power monitoring system reliability evaluation method when executed by a processor.
[0034] Compared with the prior art, the application has the following beneficial effects:
[0035] The offshore wind power monitoring system reliability evaluation method provided by the present application can improve the reliability of the offshore wind power monitoring system, and the reliability of the sensor and the monitoring system itself is improved, a plurality of same type sensors are arranged, the deviation of various sensor data is calculated in combination with other sensor data, the wind power monitoring system is judged according to the set deviation threshold and deviation, and the reliability of the offshore wind power monitoring system is evaluated. The method has low complexity, fast evaluation rate, high evaluation accuracy and efficiency, reduces the interference of human factors, and improves the objectivity of the evaluation.
[0036] The offshore wind power monitoring system reliability evaluation system provided by the present application can realize the reliability evaluation of the offshore wind power monitoring system by dividing the system into a data acquisition module, a deviation acquisition module and a fault judgment module. The modular design makes the modules independent of each other, facilitating unified management of the modules. BRIEF DESCRIPTION OF DRAWINGS
[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiments will be briefly introduced as follows, and it should be understood that the following drawings only show some embodiments of the present application, and should not be regarded as a limitation on the scope. Other related drawings can also be obtained by those skilled in the art without creative labor on the premise of not paying creative labor.
[0038] Figure 1 The offshore wind power monitoring system reliability evaluation method flowchart of the present application.
[0039] Figure 2 The monitoring system adaptive fault isolation flowchart of the present application.
[0040] Figure 3 The offshore wind power monitoring system reliability evaluation system diagram of the present application.
[0041] Figure 4 The structure diagram of the electronic device of the present application. DETAILED DESCRIPTION
[0042] The offshore wind power monitoring system reliability evaluation method provided by the present application, as described above, comprises the following steps: Figure 1
[0043] S1, acquiring the data of the assembled thermocouple, the armored thermocouple, the pressure sensor, the gas sensor and the acceleration sensor;
[0044] S2, calculating the deviation degree of sensor data according to the assembled thermocouple, armored thermocouple, pressure sensor, gas sensor and acceleration sensor data;
[0045] The deviation degree of the sensor data is represented as follows:
[0046]
[0047] Wherein, x i is the monitoring value of the i-th sensor, is the average value of the monitoring values of all sensors at the same time, is the average value of the historical monitoring values of the same sensor, s is the operating state parameter in the unit SCADA system, is the average value of the operating state parameters in the same time period, δ1 is the weight reflecting the difference between sensors of the same type, and δ2 is the weight reflecting the difference between the same sensor over time.
[0048] The calculation method of the average value of the monitoring values of all sensors at the same time is as follows:
[0049]
[0050] Wherein, n is the number of sensors.
[0051] The calculation method of the average value of the historical monitoring values of the same sensor is as follows:
[0052]
[0053] Wherein, Δt is a time period for sensor measurement, t c is the termination measurement time.
[0054] The calculation method of the average value of the operating state parameters in the same time period is as follows:
[0055]
[0056] Wherein, Δt is a time period for sensor measurement, t c is the termination measurement time.
[0057] S3, judging the fault of the wind power monitoring system according to the set deviation degree threshold and the deviation degree, and realizing the reliability evaluation of the offshore wind power monitoring system.
[0058] The method for judging the fault of the wind power monitoring system according to the set deviation degree threshold and the deviation degree is as follows:
[0059] If the deviation degree of the sensor data exceeds the set threshold, the corresponding sensor is determined as a fault, and is isolated, i.e. the data measured by the sensor is no longer used;
[0060] If the number of any one of the assembled thermocouple, armored thermocouple, pressure sensor, gas sensor and acceleration sensor is less than or equal to 1, it is determined that the entire monitoring system fails, and the fault judgment of the wind power monitoring system is realized.
[0061] The method for obtaining the set threshold value is as follows:
[0062] The average value of the sensor data deviation of each sensor in the assembled thermocouple, armored thermocouple, pressure sensor, gas sensor and acceleration sensor is calculated, and the average value of the sensor data deviation is taken as the set threshold value.
[0063] The following will be described in detail in combination with Figure 2 Further detailed description:
[0064] To improve the reliability of the monitoring system, on the one hand, it is necessary to start from the reliability of the sensor and the monitoring system itself, but due to the limitation of technical level and cost, it is difficult to achieve a leap in improvement; on the other hand, the reliability of the monitoring system can be monitored by arranging multiple sensors of the same type and combining other sensor data. This project attempts to use sensor redundancy configuration and fault adaptive monitoring to realize automatic detection, isolation and reconstruction of sensor failure, and improve the fault tolerance and reliability of the system.
[0065] For the same kind of monitoring parameter, set the number of sensors greater than 2 for monitoring, and the deviation of the sensor monitoring data ε can be defined as follows:
[0066] Among them,
[0067] The deviation is the difference or degree of change between the actual measured value of the sensor and the expected value (such as the theoretical value, the historical average value or the value under normal operating conditions). This difference may be caused by equipment aging, environmental factor changes, improper operation or actual failure, etc. In the formula, x i is the monitoring value of the i-th sensor, is the average value of the monitoring values of all sensors at the same time, The average value of the historical monitoring values of the same sensor is usually due to the degradation of the sensor, that is, the performance of the sensor gradually decreases over time. A period of time Δt is usually selected before the measurement time, and s is the relevant operating state parameter in the unit SCADA system, The mean value of the operating state parameters for the same time period. δ1 and δ2 are respectively the system, the sum of which is 1. δ1 reflects the weight of the difference between sensors of the same type, and δ2 reflects the difference between the same sensor over time. In order to more accurately judge the reliability of the sensor over time, the SCADA historical data of the external unit of the monitoring system is introduced to reflect the operating state of the monitored unit. Only when the sensor monitoring data deviates from the unit operating state reflected by SCADA, the deviation will be caused, which reduces the false alarm caused by the abnormal state of the unit monitored by the sensor. The higher the deviation degree ε, the higher the false alarm.
[0068] The adaptive fault isolation process of the monitoring system is as shown in Figure 2 First, the deviation degree ε is calculated. When the deviation degree exceeds the threshold value, the probability of failure of two or more sensors at the same time is extremely low, which can be ignored. Therefore, one sensor whose deviation degree exceeds the set threshold value is found, which is determined as a fault and is isolated, that is, the data measured by the sensor is no longer used. When the number of sensors is less than or equal to 1, it is determined that the entire monitoring system fails and needs to be repaired immediately.
[0069] The purpose of setting the threshold value is to determine a reasonable limit to distinguish between normal data fluctuations and possible fault states. When the deviation degree of the sensor data exceeds this threshold value, the monitoring system should issue an alarm to prompt the operator to check and maintain the wind power equipment. It should be noted that the threshold value is not fixed, but needs to be checked and adjusted regularly according to the actual situation. At the same time, the characteristics of different sensors and data types, as well as the specific operating environment and requirements of the wind farm, need to be considered to ensure that the threshold value set can truly reflect the operating state and reliability of the wind power monitoring system.
[0070] Embodiment 2
[0071] The offshore wind power monitoring system reliability evaluation system proposed by the application is as shown in Figure 3 , which comprises a data acquisition module, a deviation degree acquisition module and a fault judgment module.
[0072] The data acquisition module is used to acquire the data of the assembled thermocouple, the armored thermocouple, the pressure sensor, the gas sensor and the acceleration sensor.
[0073] The deviation degree acquisition module is used to calculate the deviation degree of the sensor data according to the data of the assembled thermocouple, the armored thermocouple, the pressure sensor, the gas sensor and the acceleration sensor.
[0074] The fault judgment module is used to judge the fault of the wind power monitoring system according to the set deviation degree threshold value and the deviation degree, and to realize the reliability evaluation of the offshore wind power monitoring system.
[0075] Embodiment 3
[0076] Referring to Figure 4 The electronic device 100 comprises a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.
[0077] The memory 101 can be used to store the computer program 103, and the processor 102 can realize the steps of the offshore wind power monitoring system reliability evaluation method by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application program required by a function (such as a sound playing function, an image playing function, etc.), and the like; and the data storage area can store data (such as audio data) created according to the use of the electronic device 100. In addition, the memory 101 can include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device.
[0078] The at least one processor 102 can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The processor 102 can be a microprocessor or any conventional processor, etc. The processor 102 is the control center of the electronic device 100, and connects all parts of the electronic device 100 through various interfaces and lines.
[0079] The memory 101 in the electronic device 100 stores a plurality of instructions to realize an offshore wind power monitoring system reliability evaluation method, and the processor 102 can execute the plurality of instructions to realize:
[0080] Obtaining assembled thermocouple, armored thermocouple, pressure sensor, gas sensor and acceleration sensor data;
[0081] Calculating the deviation degree of sensor data according to the assembled thermocouple, armored thermocouple, pressure sensor, gas sensor and acceleration sensor data;
[0082] According to the set deviation threshold and the deviation, the wind power monitoring system is judged for failure, and the reliability evaluation of the offshore wind power monitoring system is realized.
[0083] Embodiment 4
[0084] The modules / units integrated in the electronic device 100 are stored in a computer readable storage medium if they are realized in the form of software function units and sold or used as independent products. Based on such understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned various method embodiments can be implemented. The computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate forms, etc. The computer readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory and read-only memory (ROM).
[0085] Those skilled in the art will appreciate that embodiments of the application can be provided as methods, systems, or computer program products. Therefore, the application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the application can take the form of a computer program product implemented on one or more computer usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.
[0086] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a machine that implements the functions described in the flowcharts and / or block diagrams.Figure 1 apparatuses that implement the functions specified in the flowchart Figure 1
[0087] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the flowchart Figure 1 apparatuses that implement the functions specified in the flowchart Figure 1
[0088] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions that are executed on the computer or other programmable apparatus provide steps for implementing the flowchart Figure 1 apparatuses that implement the functions specified in the flowchart Figure 1
[0089] The above merely provides the preferred embodiment of the present application, but not for limiting the present application. For the skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims
1. A reliability assessment method for an offshore wind power monitoring system, characterized in that, Includes the following steps: Acquire data from assembled thermocouples, armored thermocouples, pressure sensors, gas sensors, and acceleration sensors; The deviation of sensor data is calculated based on the data from assembled thermocouples, armored thermocouples, pressure sensors, gas sensors, and acceleration sensors. Based on the set deviation threshold and deviation, the wind power monitoring system is used to determine faults and achieve a reliability assessment of the offshore wind power monitoring system.
2. The reliability assessment method for an offshore wind power monitoring system according to claim 2, characterized in that, The deviation of the sensor data is expressed as follows: Where, x i Let i be the monitored value of the i-th sensor. This is the average value of the readings from all sensors at the same time. The average value of historical monitoring values from the same sensor, where s is the operating status parameter in the unit's SCADA system. δ1 represents the average value of the operating status parameters over the same time period, δ2 represents the weight reflecting the differences between sensors of the same type, and δ3 represents the differences between the same sensor over time.
3. The reliability assessment method for an offshore wind power monitoring system according to claim 2, characterized in that, The method for calculating the average value of all sensor readings at the same time: Where n is the number of sensors.
4. The reliability assessment method for an offshore wind power monitoring system according to claim 2, characterized in that, The method for calculating the average value of historical monitoring values from the same sensor is as follows: Where Δt is a time interval measured by the sensor, t c The time to terminate the measurement.
5. The reliability assessment method for an offshore wind power monitoring system according to claim 2, characterized in that, The method for calculating the average value of the operating status parameters over the same time period is as follows: Where Δt is a time interval measured by the sensor, t c The time to terminate the measurement.
6. The reliability assessment method for an offshore wind power monitoring system according to claim 1, characterized in that, The method for fault diagnosis of wind power monitoring system based on the set deviation threshold and deviation is as follows: If the deviation of the sensor data exceeds the set threshold, the corresponding sensor will be identified as faulty and isolated, meaning that the data measured by that sensor will no longer be used. If the number of any one of the following sensors—assembled thermocouples, armored thermocouples, pressure sensors, gas sensors, and acceleration sensors—is less than or equal to one, the entire monitoring system is deemed to have failed, thus enabling fault diagnosis of the wind power monitoring system.
7. The reliability assessment method for an offshore wind power monitoring system according to claim 1, characterized in that, The method for obtaining the set threshold is as follows: Calculate the average deviation of data from each of the assembled thermocouples, armored thermocouples, pressure sensors, gas sensors, and acceleration sensors, and use the average deviation of sensor data as a set threshold.
8. A reliability assessment system for an offshore wind power monitoring system, characterized in that, include: The data acquisition module is used to acquire data from assembled thermocouples, armored thermocouples, pressure sensors, gas sensors, and acceleration sensors. A deviation acquisition module is used to calculate the deviation of sensor data based on data from assembled thermocouples, armored thermocouples, pressure sensors, gas sensors, and acceleration sensors. The fault judgment module is used to judge the fault of the wind power monitoring system according to the set deviation threshold and deviation, so as to realize the reliability assessment of the offshore wind power monitoring system.
9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the offshore wind power monitoring system reliability assessment method as described in any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the offshore wind power monitoring system reliability assessment method as described in any one of claims 1 to 7.