Power generation networking management method and device based on FTTR-B, terminal and medium

Abstract

The invention discloses a power generation networking management method and device based on FTTR-B, a terminal and a medium, and relates to the technical field of information processing. The method comprises the following steps: acquiring wind energy capture monitoring data corresponding to each wind turbine generator, mechanical conversion monitoring data corresponding to each wind turbine generator and electric energy conversion monitoring data corresponding to each wind turbine generator; screening out a first wind turbine generator from the wind turbine generators; screening out a second wind turbine generator from the first wind turbine generator; screening out a third wind turbine generator from the second wind turbine generator; and through a preset wind turbine generator operation scheme generation model, obtaining a target operation scheme, and based on the target operation scheme, managing each wind turbine generator. The invention aims to reduce the overall fault frequency of the wind power generation network and further optimize the overall operation efficiency of the wind power generation network.

Description

Technical Field

[0001] This invention relates to the field of information processing technology, and in particular to a power generation grid management method, device, terminal, and medium based on FTTR-B. Background Technology

[0002] Wind power grid connection refers to the use of communication technologies, control strategies, and energy management systems to connect multiple wind turbine generators (WTGs) and related equipment into a collaborative system. Its core objectives are to optimize wind energy capture efficiency, improve grid stability, achieve resource sharing and collaborative control, and support efficient operation and maintenance of large-scale wind farms.

[0003] However, existing wind power grids have the following shortcomings: traditional wired / 4G communication has latency (typically >100ms), making it difficult to cope with dynamic scenarios such as rapidly changing wind speed and grid load; the massive amount of operational data in wind power grids has not been fully explored, and the prediction, judgment, and resolution of faults rely on the monitoring and analysis of staff. It is easy to overlook some key data due to human error or other factors, which leads to the failure rate not being effectively reduced and affects the overall operating efficiency of wind turbine generators. Summary of the Invention

[0004] The main objective of this invention is to provide a power generation network management method, device, terminal, and medium based on FTTR-B, which aims to reduce the overall failure frequency of the wind power generation network and thereby optimize the overall operating efficiency of the wind power generation network.

[0005] To achieve the above objectives, the present invention provides a power generation network management method based on FTTR-B, which is applied to the FTTR-B wind power network management system. The FTTR-B wind power network management system is used to manage each wind turbine. The FTTR-B wind power network management system includes a master gateway, slave gateways, sensors, and a server terminal. Each slave gateway corresponds to one of the wind turbines. The method includes: Acquire wind energy capture monitoring data, mechanical conversion monitoring data, and electrical energy conversion monitoring data from multiple wind turbine units; Based on the wind energy capture monitoring data and the preset wind energy capture stage data standards, the first wind turbine unit with normal operation of relevant components during the wind energy capture stage is selected from the multiple wind turbine units. Based on the mechanical conversion monitoring data and the preset mechanical conversion stage data standards, a second wind turbine unit with normal operation of relevant components during the mechanical conversion stage is selected from the first wind turbine unit. Based on the power conversion monitoring data and the preset power conversion stage data standards, a third wind turbine unit with normal operation of relevant components in the power conversion stage is selected from the second wind turbine unit. Based on the wind energy capture monitoring data, mechanical conversion monitoring data, and electrical energy conversion monitoring data corresponding to the third wind turbine, a target operation plan is obtained, and the multiple wind turbines are managed based on the target operation plan.

[0006] Specifically, the wind energy capture monitoring data includes actual wind speed, actual blade angle and theoretical optimal angle, and the preset wind energy capture stage data standard includes the safe wind speed range of wind turbine and the blade angle deviation threshold of wind turbine. Based on the wind energy capture monitoring data and the preset wind energy capture stage data standards, the first wind turbine unit whose relevant components are operating normally during the wind energy capture stage is selected from the plurality of wind turbine units, including: Based on the actual wind speed and the safe wind speed range of the wind turbine, a first preliminary wind turbine is selected from the various wind turbines, wherein the first preliminary wind turbine is a wind turbine whose actual wind speed is within the safe wind speed range of the wind turbine. Based on the actual blade angle, the theoretical optimal angle, and the wind turbine blade angle deviation threshold, a first wind turbine is selected from the first preliminary selection of wind turbines, wherein the first wind turbine is the first preliminary selection of wind turbines whose deviation between the actual blade angle and the theoretical optimal angle is less than the wind turbine blade angle deviation threshold.

[0007] Specifically, the wind energy capture monitoring data includes actual wind speed, actual blade angle and theoretical optimal angle, and the preset wind energy capture stage data standard includes the safe wind speed range of wind turbine and the blade angle deviation threshold of wind turbine. Based on the wind energy capture monitoring data and the preset wind energy capture stage data standards, the first wind turbine unit whose relevant components are operating normally during the wind energy capture stage is selected from the plurality of wind turbine units, including: Based on the actual wind speed and the safe wind speed range of the wind turbine, a first condition weight value is determined. If the actual wind speed is within the safe wind speed range of the wind turbine, the first condition weight value is determined to be a preset first condition weight value; if the actual wind speed is not within the safe wind speed range of the wind turbine, the first condition weight value is determined to be 0. Based on the actual blade angle, the theoretical optimal angle, and the wind turbine blade angle deviation threshold, a second condition weight value is determined. If the deviation between the actual blade angle and the theoretical optimal angle is less than the wind turbine blade angle deviation threshold, the second condition weight value is determined to be a preset second condition weight value; if the deviation between the actual blade angle and the theoretical optimal angle is not less than the wind turbine blade angle deviation threshold, the second condition weight value is determined to be 0. The sum of the first condition weight value and the second condition weight value is calculated to obtain the wind energy capture stage evaluation results for each wind turbine. Based on the comparison between the wind energy capture stage evaluation results and the preset wind energy capture stage evaluation threshold, the first wind turbine is selected from the various wind turbine units.

[0008] Specifically, the mechanical conversion monitoring data includes gearbox status data, generator status data, and main shaft status data, and the preset mechanical conversion stage data standards include the safe operating range of the wind turbine gearbox, the safe operating data threshold of the wind turbine generator, and the safe threshold of the wind turbine main shaft. The process of selecting second wind turbine units from the first wind turbine units based on the mechanical conversion monitoring data and preset mechanical conversion stage data standards, where relevant components are operating normally during the mechanical conversion stage, includes: Based on the gearbox status data and the safe operating range of the wind turbine gearbox, a second preliminary wind turbine is selected from the first wind turbine. The second preliminary wind turbine is a wind turbine whose gearbox status data is within the safe operating range of the wind turbine gearbox. Based on the generator status data and the wind turbine generator safe operation data threshold, a third preliminary wind turbine is selected from the second preliminary wind turbine, wherein the third preliminary wind turbine is a wind turbine whose generator status data is less than the wind turbine generator safe operation data threshold in the second preliminary wind turbine. Based on the spindle status data and the wind turbine spindle safety threshold, the second wind turbine is selected from the third preliminary selection of wind turbines, wherein the second wind turbine is a wind turbine whose spindle status data is less than the wind turbine spindle safety threshold in the third preliminary selection of wind turbines.

[0009] Specifically, the generator status data includes the deviation between the actual torque of the generator and the theoretical torque of the generator; Before selecting a third preliminary wind turbine from the second preliminary wind turbine based on the generator status data and the wind turbine generator safe operation data threshold, the method further includes: Obtain the actual generator torque and actual generator speed of each wind turbine; Based on the actual torque and actual speed of the generators of each wind turbine, a generator curve model is established for each wind turbine. The generator curve model is used to characterize the relationship between the engine speed and torque of each wind turbine. The theoretical engine torque corresponding to the actual generator speed is calculated using the engine curve model. The deviation value is calculated according to the following formula:

[0010] in, This indicates the deviation value. This represents the theoretical torque of the engine. This indicates the actual torque of the engine.

[0011] Specifically, the power conversion monitoring data includes DC-side voltage data and AC-side frequency deviation, and the preset power conversion stage data standard includes the DC-side voltage range of the wind turbine and the AC-side frequency deviation threshold of the wind turbine. The process of selecting third wind turbine units from the second wind turbine units based on the power conversion monitoring data and preset power conversion stage data standards, where relevant components are operating normally during the power conversion stage, includes: Based on the DC-side voltage data and the DC-side voltage range of the wind turbine, a fourth preliminary wind turbine is selected from the second wind turbine, wherein the fourth preliminary wind turbine is a wind turbine whose DC-side voltage data is within the DC-side voltage range of the wind turbine. Based on the AC side frequency deviation and the AC side frequency deviation threshold of the wind turbine, a third wind turbine is selected from the fourth preliminary wind turbine. The third wind turbine is a wind turbine in the fourth preliminary wind turbine whose AC side frequency deviation is not greater than the AC side frequency deviation threshold of the wind turbine.

[0012] Specifically, the method further includes: Obtain grid-connected transmission monitoring data corresponding to the third wind turbine, wherein the grid-connected transmission monitoring data includes step-up transformer data and transmission line data corresponding to the third wind turbine. The wind energy capture monitoring data, mechanical conversion monitoring data, electrical energy conversion monitoring data, and grid connection transmission monitoring data corresponding to the third wind turbine are used as input data and input into the preset wind turbine operation scheme generation model to obtain the updated target operation scheme. Based on the updated target operation scheme, the multiple wind turbines are managed.

[0013] To achieve the above objectives, the present invention also provides a power generation grid management device based on FTTR-B, which is applied to the FTTR-B wind power grid management system. The FTTR-B wind power grid management system is used to manage each wind turbine. The FTTR-B wind power grid management system includes a master gateway, a slave gateway, sensors, and a server terminal. Each slave gateway corresponds to one of the wind turbines. The device includes: The first unit is used to acquire wind energy capture monitoring data, mechanical conversion monitoring data, and electrical energy conversion monitoring data from multiple wind turbine units. The second unit is used to select the first wind turbine unit whose relevant components are operating normally during the wind energy capture stage from the multiple wind turbine units based on the wind energy capture monitoring data and the preset wind energy capture stage data standard. The third unit is used to select, based on the mechanical conversion monitoring data and the preset mechanical conversion stage data standards, a second wind turbine unit from the first wind turbine unit whose relevant components are operating normally during the mechanical conversion stage. The fourth unit is used to select, based on the power conversion monitoring data and the preset power conversion stage data standards, a third wind turbine unit from the second wind turbine unit whose relevant components are operating normally during the power conversion stage. The fifth unit is used to obtain a target operation plan based on the wind energy capture monitoring data, mechanical conversion monitoring data and electrical energy conversion monitoring data corresponding to the third wind turbine, and to manage the multiple wind turbines based on the target operation plan.

[0014] To achieve the above objectives, the present invention also provides a terminal, including a memory storing a plurality of instructions; the processor loads the instructions from the memory to execute the steps in any of the methods provided by the present invention.

[0015] To achieve the above objectives, the present invention also provides a medium storing a plurality of instructions adapted for loading by a processor to execute the steps in any of the methods provided by the present invention.

[0016] This invention provides a power generation grid management method, device, terminal, and medium based on FTTR-B. It first acquires wind energy capture monitoring data, mechanical conversion monitoring data, and electrical energy conversion monitoring data corresponding to each wind turbine. Then, based on the wind energy capture monitoring data and a preset wind energy capture stage data standard, a first wind turbine is selected from the wind turbines. This first wind turbine characterizes the wind turbines whose relevant components are operating normally during the wind energy capture stage. Next, based on the mechanical conversion monitoring data and the preset mechanical conversion stage data standard, a second wind turbine is selected from the first wind turbine. The second wind turbine is used to characterize the wind turbine in the first wind turbine where the relevant components of the mechanical conversion phase are operating normally. Then, based on the power conversion monitoring data and preset power conversion phase data standards, a third wind turbine is selected from the second wind turbines. This third wind turbine is used to characterize the wind turbine in the second wind turbine where the relevant components of the power conversion phase are operating normally. Finally, through a preset wind turbine operation scheme generation model, based on the wind energy capture monitoring data, mechanical conversion monitoring data, and power conversion monitoring data corresponding to the third wind turbine, a target operation scheme is obtained. Based on this target operation scheme, each wind turbine is managed. Thus, based on FTTR-B technology, through ultra-high-speed data communication and in-depth data analysis, the overall failure frequency of the wind power grid is reduced, thereby optimizing the overall operating efficiency of the wind power grid. Attached Figure Description

[0017] Figure 1 A flowchart illustrating the method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the device provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the terminal structure provided in an embodiment of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] The existing wind power grid has the following shortcomings: traditional wired / 4G communication has latency (typically >100ms), making it difficult to cope with dynamic scenarios such as rapidly changing wind speed and grid load; the massive amount of operational data in the wind power grid has not been fully explored, and the prediction, judgment and resolution of faults rely on the monitoring and analysis of staff. It is easy to overlook some key data due to human error or other factors, which leads to the failure rate not being effectively reduced and affects the overall operating efficiency of wind turbine generators.

[0020] Therefore, embodiments of the present invention provide a power generation grid management method, device, terminal, and medium based on FTTR-B to solve practical technical problems.

[0021] In some embodiments, the device may be integrated into an electronic device, such as a terminal or server.

[0022] In some embodiments, the server may also be implemented as a terminal.

[0023] The server can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0024] The terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. The terminal and the server can be connected directly or indirectly through wired or wireless communication, which is not limited herein.

[0025] This invention provides a power generation network management method based on FTTR-B, which can reduce the overall failure frequency of the wind power generation network and thus optimize the overall operating efficiency of the wind power generation network.

[0026] A power generation network management method based on FTTR-B is applied to an FTTR-B wind power network management system. The FTTR-B wind power network management system is used to manage wind power networks, which include at least one wind turbine. The FTTR-B wind power network management system includes a master gateway, slave gateways, sensors, and a server terminal, with each slave gateway corresponding to one of the wind turbines.

[0027] In some embodiments, the FTTR-B wind power grid management system (Fiber to the Room for Business) is an all-optical networking solution specifically designed for wind power grid management scenarios. It constructs a low-latency, highly reliable communication network by directly connecting core equipment via optical fiber. In wind power grid management scenarios, the FTTR-B wind power grid management system may consist of the following components: Main Gateway: As the central control unit of the entire system, it is responsible for connecting the slave gateways, sensor network and server terminal; real-time aggregation of multi-dimensional monitoring data (data from wind energy capture / mechanical conversion / electrical energy conversion stages) uploaded by each slave gateway; unified conversion and parsing of different communication protocols (such as Modbus, OPC UA); execution of basic data preprocessing (outlier removal, data normalization, etc.); and forwarding optimization instructions generated by the server terminal to the corresponding slave gateway.

[0028] From the gateway: It corresponds one-to-one with each wind turbine and is deployed at the bottom of the tower or inside the nacelle; it connects to the turbine's sensors (such as vibration sensors, temperature sensors, etc.) via RS485 / CAN bus; it has a built-in lightweight AI model to monitor the turbine's status in real time (such as early warning of abnormal gearbox vibration); it supports dual-channel communication with 4G / 5G cellular networks and fiber optics to ensure data transmission reliability; it receives commands from the main gateway and performs basic control operations such as pitch / yaw.

[0029] The sensors include: Wind energy capture layer: anemometer (ultrasonic / mechanical), wind vane, blade angle sensor; Mechanical conversion layer: bearing temperature sensor, gearbox vibration sensor, spindle torque sensor; Power conversion layer: generator winding temperature sensor, converter IGBT temperature sensor, power quality analyzer; Environmental monitoring: weather station (temperature, humidity / air pressure), ultraviolet sensor (corrosion monitoring).

[0030] Server terminal: Uses time-series databases (such as InfluxDB) to store all historical data; deploys digital twin models (based on ANSYS Twin Builder) and machine learning algorithms (LSTM / CNN); runs scheme generation models (such as MPPT strategies optimized by genetic algorithms); a 3D wind farm GIS system that supports real-time data dashboards and fault prediction alarms; and implements containerized deployment through Kubernetes to support elastic scaling and cross-regional cluster management.

[0031] like Figure 1 As shown, the specific process of the method can be as follows: S110: Acquire wind energy capture monitoring data, mechanical conversion monitoring data, and electrical energy conversion monitoring data from multiple wind turbine units.

[0032] In some embodiments, wind energy capture monitoring data for each wind turbine can be acquired through a wind energy capture layer, i.e., sensors such as anemometers (ultrasonic / mechanical), wind vanes, and blade angle sensors; mechanical conversion monitoring data for each wind turbine can be acquired through a mechanical conversion layer, i.e., bearing temperature sensors, gearbox vibration sensors, and main shaft torque sensors; and electrical conversion monitoring data for each wind turbine can be acquired through an electrical conversion layer, i.e., generator winding temperature sensors, converter IGBT temperature sensors, and power quality analyzers.

[0033] S120. Based on the wind energy capture monitoring data and the preset wind energy capture stage data standard, select the first wind turbine unit from the multiple wind turbine units whose relevant components are operating normally during the wind energy capture stage.

[0034] In some embodiments, the wind energy capture monitoring data includes actual wind speed, actual blade angle, and theoretical optimal angle, and the preset wind energy capture stage data standard includes the safe wind speed range of the wind turbine and the blade angle deviation threshold of the wind turbine.

[0035] Specifically, based on the wind energy capture monitoring data and the preset wind energy capture stage data standards, the first wind turbine unit whose relevant components are operating normally during the wind energy capture stage is selected from the multiple wind turbine units, including the steps A1 to A2 shown below: A1. Based on the actual wind speed and the safe wind speed range of the wind turbine, a first preliminary wind turbine is selected from the various wind turbines, wherein the first preliminary wind turbine is a wind turbine whose actual wind speed is within the safe wind speed range of the wind turbine.

[0036] A2. Based on the actual blade angle, the theoretical optimal angle, and the wind turbine blade angle deviation threshold, select a first wind turbine from the first preliminary selection of wind turbines, wherein the first wind turbine is the first preliminary selection of wind turbines whose deviation between the actual blade angle and the theoretical optimal angle is less than the wind turbine blade angle deviation threshold.

[0037] In other embodiments, the wind energy capture monitoring data includes the actual wind speed corresponding to each wind turbine, the actual blade angle corresponding to each wind turbine, the theoretical optimal angle corresponding to each wind turbine, the root mean square value of vibration corresponding to each wind turbine, the vibration kurtosis corresponding to each wind turbine, the ambient temperature corresponding to each wind turbine, and the ambient humidity corresponding to each wind turbine. The preset wind energy capture stage data standards include the safe wind speed range of the wind turbine, the blade angle deviation threshold of the wind turbine, the blade fault threshold of the wind turbine, the vibration kurtosis threshold of the wind turbine, the icing temperature threshold of the wind turbine, and the maximum humidity threshold of the wind turbine.

[0038] Specifically, the step of selecting the first wind turbine from the various wind turbines based on the wind energy capture monitoring data and the preset wind energy capture stage data standard includes the following steps S121a to S124a: S121a. Based on the actual wind speed and the safe wind speed range of the wind turbine, select a first preliminary wind turbine from the various wind turbines, wherein the first preliminary wind turbine is a wind turbine whose actual wind speed is within the safe wind speed range of the wind turbine.

[0039] Continuing with the above embodiments, the safe wind speed range for the wind turbine can be [3m / s, 25m / s]. If the actual wind speed is 5m / s, then the actual wind speed is within the safe wind speed range for the wind turbine; if the actual wind speed is 27m / s, then the actual wind speed is not within the safe wind speed range for the wind turbine.

[0040] S122a. Based on the actual blade angle, the theoretical optimal angle, and the wind turbine blade angle deviation threshold, a second preliminary wind turbine is selected from the first preliminary wind turbine, wherein the second preliminary wind turbine is a wind turbine in the first preliminary wind turbine where the deviation between the actual blade angle and the theoretical optimal angle is less than the wind turbine blade angle deviation threshold.

[0041] Continuing with the above embodiments, the blade angle deviation threshold of the wind turbine can be ±2°. Since the blade angle deviation threshold of the wind turbine is ±2°, the wind turbine meets the requirements when the deviation is less than or equal to 2°.

[0042] S123a. Based on the root mean square value of vibration, the kurtosis of vibration, the wind turbine blade fault threshold, and the wind turbine vibration kurtosis threshold, a third preliminary wind turbine is selected from the second preliminary wind turbine, wherein the third preliminary wind turbine is a wind turbine in the second preliminary wind turbine whose root mean square value of vibration is less than the wind turbine blade fault threshold and whose kurtosis of vibration is not greater than the wind turbine vibration kurtosis threshold.

[0043] Continuing with the above embodiments, the wind turbine blade fault threshold can be 1.5g (g is the acceleration due to gravity), and the wind turbine vibration kurtosis threshold can be 4. If the root mean square value of the wind turbine vibration is less than 1.5g and the vibration kurtosis is not greater than 4, then the wind turbine is one of the third preliminary wind turbines.

[0044] S124a. Based on the ambient temperature, the ambient humidity, the icing temperature threshold of the wind turbine, and the maximum humidity threshold of the wind turbine, select the first wind turbine from the third preliminary selection of wind turbines, wherein the first wind turbine is a wind turbine from the third preliminary selection whose ambient temperature is greater than the icing temperature threshold of the wind turbine and whose ambient humidity is less than the maximum humidity threshold of the wind turbine.

[0045] Continuing with the above embodiments, the icing temperature threshold of the wind turbine can be 2°C, and the maximum humidity threshold of the wind turbine can be 85%.

[0046] If the ambient temperature around the wind turbine in the third preliminary selection is 3℃, which is greater than the icing temperature threshold of 2℃ for wind turbines, and the ambient humidity is 80%, which is less than the maximum humidity threshold of 85% for wind turbines, then the wind turbine in the third preliminary selection meets the screening conditions, and the wind turbine in the third preliminary selection is one of the first wind turbines.

[0047] If the ambient temperature around the wind turbine in the third preliminary selection is 1℃, which is less than the icing temperature threshold of 2℃ for wind turbines, and the ambient humidity is 90%, which is greater than the maximum humidity threshold of 85% for wind turbines, then the wind turbine in the third preliminary selection does not meet the screening conditions, and therefore the wind turbine in the third preliminary selection is not one of the first wind turbines.

[0048] In some embodiments, the wind energy capture monitoring data includes actual wind speed, actual blade angle, and theoretical optimal angle, and the preset wind energy capture stage data standard includes the safe wind speed range of the wind turbine and the blade angle deviation threshold of the wind turbine.

[0049] The step of selecting the first wind turbine unit from the multiple wind turbine units based on the wind energy capture monitoring data and the preset wind energy capture stage data standard, including the steps B1 to B4 as shown below: B1. Based on the actual wind speed and the safe wind speed range of the wind turbine, determine a first condition weight value. If the actual wind speed is within the safe wind speed range of the wind turbine, then determine the first condition weight value as a preset first condition weight value; if the actual wind speed is not within the safe wind speed range of the wind turbine, then determine the first condition weight value as 0.

[0050] B2. Based on the actual blade angle, the theoretical optimal angle, and the wind turbine blade angle deviation threshold, determine a second condition weight value. If the deviation between the actual blade angle and the theoretical optimal angle is less than the wind turbine blade angle deviation threshold, then determine the second condition weight value as a preset second condition weight value; if the deviation between the actual blade angle and the theoretical optimal angle is not less than the wind turbine blade angle deviation threshold, then determine the second condition weight value as 0.

[0051] B3. Calculate the sum of the first condition weight value and the second condition weight value to obtain the wind energy capture stage evaluation results for each wind turbine.

[0052] B4. Based on the comparison between the wind energy capture stage evaluation results and the preset wind energy capture stage evaluation threshold, the first wind turbine is selected from the various wind turbine units.

[0053] In other embodiments, the wind energy capture monitoring data includes the actual wind speed corresponding to each wind turbine, the actual blade angle corresponding to each wind turbine, the theoretical optimal angle corresponding to each wind turbine, the root mean square value of vibration corresponding to each wind turbine, the vibration kurtosis corresponding to each wind turbine, the ambient temperature corresponding to each wind turbine, and the ambient humidity corresponding to each wind turbine. The preset wind energy capture stage data standards include the safe wind speed range of the wind turbine, the blade angle deviation threshold of the wind turbine, the blade fault threshold of the wind turbine, the vibration kurtosis threshold of the wind turbine, the icing temperature threshold of the wind turbine, and the maximum humidity threshold of the wind turbine.

[0054] Specifically, the step of selecting the first wind turbine from the various wind turbines based on the wind energy capture monitoring data and the preset wind energy capture stage data standard includes the following steps S121b to S126b: S121b. Based on the actual wind speed and the safe wind speed range of the wind turbine, determine a first condition weight value. If the actual wind speed is within the safe wind speed range of the wind turbine, then determine the first condition weight value as a preset first condition weight value; if the actual wind speed is not within the safe wind speed range of the wind turbine, then determine the first condition weight value as 0.

[0055] S122b. Based on the actual blade angle, the theoretical optimal angle, and the wind turbine blade angle deviation threshold, determine a second condition weight value. If the deviation between the actual blade angle and the theoretical optimal angle is less than the wind turbine blade angle deviation threshold, then determine the second condition weight value as a preset second condition weight value; if the deviation between the actual blade angle and the theoretical optimal angle is not less than the wind turbine blade angle deviation threshold, then determine the second condition weight value as 0.

[0056] S123b. Based on the root mean square value of vibration, the kurtosis of vibration, the wind turbine blade fault threshold, and the wind turbine vibration kurtosis threshold, a third condition weight value is determined. If the root mean square value of vibration is less than the wind turbine blade fault threshold and the kurtosis of vibration is not greater than the wind turbine vibration kurtosis threshold, then the third condition weight value is determined to be a preset third condition weight value. If the root mean square value of vibration is not less than the wind turbine blade fault threshold or the kurtosis of vibration is greater than the wind turbine vibration kurtosis threshold, then the third condition weight value is determined to be 0.

[0057] S124b. Based on the ambient temperature, ambient humidity, wind turbine icing temperature threshold, and wind turbine maximum humidity threshold, a fourth condition weight value is determined. If the ambient temperature is greater than the wind turbine icing temperature threshold and the ambient humidity is less than the wind turbine maximum humidity threshold, the fourth condition weight value is determined to be a preset fourth condition weight value. If the ambient temperature is not greater than the wind turbine icing temperature threshold or the ambient humidity is not less than the wind turbine maximum humidity threshold, the fourth condition weight value is determined to be 0.

[0058] S125b: Calculate the sum of the first condition weight, the second condition weight, the third condition weight, and the fourth condition weight to obtain the wind energy capture stage evaluation results corresponding to each wind turbine.

[0059] S126b: Based on the comparison between the wind energy capture stage evaluation results and the preset wind energy capture stage evaluation threshold, select the first wind turbine from the various wind turbines.

[0060] In some embodiments, the following settings can be configured: Safe wind speed range for wind turbines: [3 m / s, 25 m / s]; Wind turbine blade angle deviation threshold: ±2°; Wind turbine blade failure threshold: 1.5 g (gravitational acceleration); Vibration kurtosis threshold for wind turbine units: 4; The icing temperature threshold for wind turbine units is 2℃. Maximum humidity threshold for wind turbine units: 85%; Preset weight value for the first condition: 0.3; The preset weight value for the second condition is 0.25. The preset weight value for the third condition is 0.2. The preset weight value for the fourth condition is 0.15. Preset evaluation threshold for wind energy capture stage: 0.7.

[0061] Assume that each wind turbine unit includes wind turbine unit WTG1, wind turbine unit WTG2, wind turbine unit WTG3, and wind turbine unit WTG4.

[0062] For wind turbine WTG1: First condition weight: The actual wind speed of 5 m / s falls within the range of [3 m / s, 25 m / s], so the first condition weight is 0.3.

[0063] Second condition weight: The deviation between the actual blade angle and the theoretical optimal angle is |15-13|=2°, which is less than the deviation threshold ±2°, so the second condition weight value is 0.25.

[0064] The third condition weight is 0.2 if the root mean square value of vibration (RMS) 1.2 g is less than the blade failure threshold (RMS) 1.5 g, the vibration kurtosis (Ku) 3.2 is not greater than the vibration Kurtosis threshold (RMS) 4.

[0065] The fourth condition weight is 0.15: the ambient temperature is 5℃ higher than the freezing temperature threshold of 2℃, and the ambient humidity is 70% lower than the maximum humidity threshold of 85%.

[0066] For wind turbine WTG2: First condition weight: If the actual wind speed of 2 m / s is not within the range of [3 m / s, 25 m / s], the first condition weight is 0.

[0067] Second condition weight: The deviation between the actual blade angle and the theoretical optimal angle is |16-14|=2°, which is less than the deviation threshold ±2°, so the second condition weight value is 0.25.

[0068] The third condition weight is 0.2, which means that the root mean square value of vibration (RMS) 1.3 g is less than the blade failure threshold (RMS) 1.5 g, the vibration kurtosis (RMS) 3.5 is not greater than the vibration kurtosis threshold (RMS) 4.

[0069] The fourth condition weight is 0.15: the ambient temperature is 3℃, which is greater than the freezing temperature threshold of 2℃, and the ambient humidity is 75%, which is less than the maximum humidity threshold of 85%.

[0070] For WTG3 wind turbine units: First condition weight: The actual wind speed of 8 m / s falls within the range of [3 m / s, 25 m / s], so the first condition weight is 0.3.

[0071] Second condition weight: The deviation between the actual blade angle and the theoretical optimal angle is |14-12|=2°, which is less than the deviation threshold ±2°, so the second condition weight value is 0.25.

[0072] Third condition weight: The root mean square value of vibration 1.6 g is not less than the blade fault threshold 1.5 g, and the third condition weight value is 0.

[0073] Fourth condition weight: The ambient temperature is 4℃, which is greater than the freezing temperature threshold of 2℃, and the ambient humidity is 80%, which is less than the maximum humidity threshold of 85%. The weight of the fourth condition is 0.15.

[0074] For WTG4 wind turbine units: First condition weight: The actual wind speed of 12 m / s falls within the range of [3 m / s, 25 m / s], so the first condition weight is 0.3.

[0075] Second condition weight: The deviation between the actual blade angle and the theoretical optimal angle is |17-15|=2°, which is less than the deviation threshold ±2°, so the second condition weight value is 0.25.

[0076] The third condition weight is 0.2 if the root mean square value of vibration (RMS) 1.1 g is less than the blade failure threshold (RMS) 1.5 g, the vibration kurtosis 3 is not greater than the vibration kurtosis threshold 4.

[0077] Fourth condition weight: The ambient temperature is 6℃, which is greater than the freezing temperature threshold of 2℃, and the ambient humidity is 72%, which is less than the maximum humidity threshold of 85%. The weight of the fourth condition is 0.15.

[0078] Continuing with the above embodiments, the evaluation results of the wind energy capture stage for each wind turbine are calculated: WTG1: 0.3 + 0.25 + 0.2 + 0.15 = 0.9 WTG2: 0 + 0.25 + 0.2 + 0.15 = 0.6 WTG3: 0.3 + 0.25 + 0 + 0.15 = 0.7 WTG4: 0.3 + 0.25 + 0.2 + 0.15 = 0.9 Therefore, the evaluation results of the wind energy capture stage for each wind turbine are compared with the preset wind energy capture stage evaluation threshold of 0.7: The wind turbine's WTG1 rating is 0.9, which is greater than 0.7, and it was selected as the first wind turbine.

[0079] The WTG2 rating of the wind turbine was 0.6, which is less than 0.7, and it was not selected as the first wind turbine.

[0080] The wind turbine unit's WTG3 evaluation result was 0.7, which equals 0.7, making it the first wind turbine unit selected.

[0081] The wind turbine's WTG4 rating is 0.9, which is greater than 0.7, and it was selected as the first wind turbine.

[0082] S130. Based on the mechanical conversion monitoring data and the preset mechanical conversion stage data standards, select the second wind turbine unit from the first wind turbine unit whose relevant components are operating normally during the mechanical conversion stage.

[0083] In some embodiments, the mechanical conversion monitoring data includes gearbox status data, generator status data, and main shaft status data, and the preset mechanical conversion stage data standards include the safe operating range of the wind turbine gearbox, the safe operating data threshold of the wind turbine generator, and the safe threshold of the wind turbine main shaft.

[0084] Specifically, the step of selecting a second wind turbine unit from the first wind turbine unit whose relevant components are operating normally during the mechanical conversion stage, based on the mechanical conversion monitoring data and the preset mechanical conversion stage data standard, includes the following steps C1 to C3: C1. Based on the gearbox status data and the safe operating range of the wind turbine gearbox, select a second preliminary wind turbine from the first wind turbine, wherein the second preliminary wind turbine is a wind turbine whose gearbox status data is within the safe operating range of the wind turbine gearbox.

[0085] C2. Based on the generator status data and the wind turbine generator safe operation data threshold, select a third preliminary wind turbine from the second preliminary wind turbine, wherein the third preliminary wind turbine is a wind turbine in the second preliminary wind turbine whose generator status data is less than the wind turbine generator safe operation data threshold.

[0086] C3. Based on the main shaft status data and the main shaft safety threshold of the wind turbine, select the second wind turbine from the third preliminary selection of wind turbines, wherein the second wind turbine is a wind turbine whose main shaft status data is less than the main shaft safety threshold of the wind turbine in the third preliminary selection of wind turbines.

[0087] In some embodiments, the generator status data includes the deviation between the actual torque of the generator and the theoretical torque of the generator.

[0088] Before selecting the third preliminary wind turbine from the second preliminary wind turbine based on the generator status data and the wind turbine generator safe operation data threshold, the method further includes the following steps D1 to D4: D1. Obtain the actual generator torque and actual generator speed of each wind turbine.

[0089] D2. Based on the actual torque of the generators of each wind turbine and the actual speed of the generators of each wind turbine, establish a generator curve model for each wind turbine, wherein the generator curve model is used to characterize the relationship between the speed and torque of the engine of each wind turbine.

[0090] D3. Using the engine curve model, calculate the theoretical engine torque corresponding to the actual generator speed.

[0091] D4. The deviation value is calculated according to the following formula:

[0092] in, This indicates the deviation value. This represents the theoretical torque of the engine. This indicates the actual torque of the engine.

[0093] In other embodiments, the mechanical conversion monitoring data includes gearbox status data, generator status data, and main shaft status data for each wind turbine. The preset mechanical conversion stage data standards include the safe operating range of the wind turbine gearbox, the safe operating data threshold of the wind turbine generator, and the safe threshold of the wind turbine main shaft.

[0094] Specifically, the step of selecting a second wind turbine from the first wind turbine based on the mechanical conversion monitoring data and the preset mechanical conversion stage data standard includes the following steps S131 to S133: S131. Based on the gearbox status data and the safe operating range of the wind turbine gearbox, a fourth preliminary wind turbine is selected from the first wind turbine, wherein the fourth preliminary wind turbine is a wind turbine whose gearbox status data is within the safe operating range of the wind turbine gearbox.

[0095] In some embodiments, the gearbox status data includes gearbox oil temperature and gearbox oil pressure. The safe operating range of the wind turbine gearbox may include the gearbox oil temperature range, i.e., [40℃, 80℃], and the gearbox oil pressure range, i.e., [0.8 MPa, 1.2 MPa].

[0096] If the gearbox oil temperature of the wind turbine in the first wind turbine is 50℃, which is within the range of [40℃, 80℃], and the gearbox oil pressure is 1.0 MPa, which is within the range of [0.8 MPa, 1.2 MPa], then the gearbox status data of the wind turbine is within the safe operating range of the wind turbine gearbox and meets the screening criteria.

[0097] S132. Based on the generator status data and the wind turbine generator safe operation data threshold, select a fifth preliminary wind turbine from the fourth preliminary wind turbine, wherein the fifth preliminary wind turbine is a wind turbine whose generator status data is less than the wind turbine generator safe operation data threshold among the fourth preliminary wind turbines.

[0098] In some embodiments, the generator status data includes the deviation between the actual generator torque of each wind turbine and the theoretical generator torque of each wind turbine.

[0099] Specifically, before selecting the fifth preliminary wind turbine from the fourth preliminary wind turbine based on the generator status data and the wind turbine generator safe operation data threshold, the method further includes the following steps R1 to R4: R1. Obtain the actual generator torque and actual generator speed of each wind turbine.

[0100] R2. Based on the actual torque of the generators of each wind turbine and the actual speed of the generators of each wind turbine, establish a generator curve model for each wind turbine, wherein the generator curve model is used to characterize the relationship between the speed and torque of the engine of each wind turbine.

[0101] R3. Using the engine curve model, the theoretical engine torque corresponding to the actual generator speed is calculated.

[0102] R4. The deviation value is calculated according to the following formula:

[0103] in, This indicates the deviation value. This represents the theoretical torque of the engine. This indicates the actual torque of the engine.

[0104] In some embodiments, the actual torque and speed data of each wind turbine generator can be obtained from sensors in the FTTR-B wind power grid management system. Assuming a linear relationship between generator speed and torque, generator curve models for each wind turbine generator can be obtained through analysis of historical data.

[0105] Assume the generator curve model can be represented by T = 0.3n + 50, where T Indicates torque, n Indicates the rotational speed; the actual rotational speed of the generator is known. n =1500 r / min Substituting the values ​​into the generator curve model, the theoretical engine torque is obtained as 500. N · m If the actual torque of the wind turbine is 480... N · m ,get:

[0106] Based on The comparison results between the wind turbine generator's safe operation data threshold and the wind turbine generator's data threshold determine whether the wind turbine should be selected as one of the fifth preliminary wind turbine units.

[0107] S133. Based on the main shaft status data and the main shaft safety threshold of the wind turbine, select the second wind turbine from the fifth preliminary wind turbine, wherein the second wind turbine is a wind turbine whose main shaft status data is less than the main shaft safety threshold of the wind turbine in the fifth preliminary wind turbine.

[0108] Continuing with the above embodiments, the safety threshold for the wind turbine main shaft can be a vibration amplitude threshold, i.e., 1.0 g (g is the acceleration due to gravity). The main shaft state data can be the main shaft vibration amplitude.

[0109] If the main shaft vibration amplitude is not greater than 1.0g, it can be selected as one of the second wind turbine units; if the main shaft vibration amplitude is greater than 1.0g, it cannot be selected as one of the second wind turbine units.

[0110] S140. Based on the power conversion monitoring data and the preset power conversion stage data standards, select the third wind turbine unit from the second wind turbine unit whose relevant components are operating normally during the power conversion stage.

[0111] In some embodiments, the power conversion monitoring data includes DC-side voltage data and AC-side frequency deviation, and the preset power conversion stage data standard includes the DC-side voltage range of the wind turbine and the AC-side frequency deviation threshold of the wind turbine.

[0112] Specifically, the step of selecting a third wind turbine unit from the second wind turbine unit whose relevant components are operating normally during the power conversion stage, based on the power conversion monitoring data and the preset power conversion stage data standard, includes the following steps E1 to E2: E1. Based on the DC-side voltage data and the DC-side voltage range of the wind turbine, a fourth preliminary wind turbine is selected from the second wind turbine, wherein the fourth preliminary wind turbine is a wind turbine whose DC-side voltage data is within the DC-side voltage range of the wind turbine.

[0113] E2. Based on the AC side frequency deviation and the AC side frequency deviation threshold of the wind turbine, select a third wind turbine from the fourth preliminary wind turbine, wherein the third wind turbine is a wind turbine from the fourth preliminary wind turbine whose AC side frequency deviation is not greater than the AC side frequency deviation threshold of the wind turbine.

[0114] In other embodiments, the power conversion monitoring data includes DC-side voltage data for each wind turbine, AC-side frequency deviation for each wind turbine, power factor for each wind turbine, and converter temperature data for each wind turbine. The preset power conversion stage data standards include the DC-side voltage range of the wind turbine, the AC-side frequency deviation threshold of the wind turbine, the minimum power factor threshold of the wind turbine, and the maximum temperature threshold of the wind turbine converter.

[0115] Specifically, the step of selecting a third wind turbine from the second wind turbine based on the power conversion monitoring data and the preset power conversion stage data standard includes the following steps S141 to S144: S141. Based on the DC-side voltage data and the DC-side voltage range of the wind turbine, select a sixth preliminary wind turbine from the second wind turbine, wherein the sixth preliminary wind turbine is a wind turbine whose DC-side voltage data is within the DC-side voltage range of the wind turbine.

[0116] In some embodiments, the DC-side voltage range of the wind turbine generator can be 500V ≤ V dc ≤ 1200V, the selection of the sixth preliminary wind turbine is determined based on whether the DC side voltage data is within the DC side voltage range of the wind turbine.

[0117] S142. Based on the AC side frequency deviation and the AC side frequency deviation threshold of the wind turbine, a seventh preliminary wind turbine is selected from the sixth preliminary wind turbine, wherein the seventh preliminary wind turbine is a wind turbine among the sixth preliminary wind turbines whose AC side frequency deviation is not greater than the AC side frequency deviation threshold of the wind turbine.

[0118] Continuing with the above embodiments, the AC side frequency deviation threshold of the wind turbine can be ±0.5Hz (i.e., the actual frequency needs to be within the range of 49.5Hz to 50.5Hz). Whether the AC side frequency deviation is not greater than 0.5Hz is used to determine whether to select it as the seventh preliminary wind turbine.

[0119] S143. Based on the power factor and the minimum power factor threshold of the wind turbine, select an eighth preliminary wind turbine from the seventh preliminary wind turbine, wherein the eighth preliminary wind turbine is a wind turbine from the seventh preliminary wind turbine whose power factor is not less than the minimum power factor threshold of the wind turbine.

[0120] Continuing with the above embodiments, the minimum power factor threshold for the wind turbine can be 0.9. If it is lower than 0.9, it may be due to insufficient reactive power compensation, i.e., capacitor aging or control strategy failure. If the power factor is not less than the minimum power factor threshold for the wind turbine, it is selected as the eighth initially selected wind turbine.

[0121] S144. Based on the converter temperature data and the maximum temperature threshold of the wind turbine converter, select the third wind turbine from the eighth preliminary wind turbine, wherein the third wind turbine is a wind turbine in the eighth preliminary wind turbine whose converter temperature data is not greater than the maximum temperature threshold of the wind turbine converter.

[0122] Continuing with the above embodiments, the maximum temperature threshold of the wind turbine converter can be 75°C.

[0123] S150. Based on the wind energy capture monitoring data, mechanical conversion monitoring data and electrical energy conversion monitoring data corresponding to the third wind turbine, a target operation plan is obtained, and the multiple wind turbines are managed based on the target operation plan.

[0124] In some embodiments, a model can be generated by pre-setting a wind turbine operation plan, and the target operation plan can be obtained based on the wind energy capture monitoring data, mechanical conversion monitoring data, and electrical energy conversion monitoring data corresponding to the third wind turbine.

[0125] The preset wind turbine operation scheme generation model may include an input layer, a feature extraction layer, a temporal modeling layer, an attention layer, and a decision head. The wind energy capture and monitoring data corresponding to the third wind turbine may include 12 feature data such as actual wind speed (m / s), blade angle deviation (°), root mean square value of vibration (g), and ambient temperature (°C). The mechanical conversion monitoring data corresponding to the third wind turbine may include 8 feature data such as gearbox oil temperature (°C), generator speed (rpm), and main shaft vibration amplitude (g). The power conversion monitoring data corresponding to the third wind turbine may include 10 feature data such as DC side voltage (V), AC side frequency (Hz), and power factor (cosφ).

[0126] Assuming there are 15 wind turbine units in the third layer, the output dimension corresponding to the input layer is 15×30. The input layer is used for data standardization and data normalization processing.

[0127] The feature extraction layer uses a CNN network structure to extract local features such as vibration signals. The input dimension of the feature extraction layer is 15×30, and the output dimension of the feature extraction layer is 15×128.

[0128] The time series modeling layer uses an LSTM network structure to capture the time dependencies of historical data. The input dimension of the time series modeling layer is 15×128×10 (time step is set to 10), and its output dimension is 15×256.

[0129] The attention layer is used to determine the collaborative weights among computer groups, with an input dimension of 15×256 and an output dimension of 15×256.

[0130] The decision head layer is used to output the target operation plan, which includes 6 optimized operation parameters. The output of the decision head is 15×6, and the 6 optimized operation parameters may include: Blade angle (°): 12.5; Active power (MW): 2.3; Reactive power (MVar): 0.5; Gearbox oil pump status: "ON" (on); Cooling fan speed percentage (%): 80; Enter hot standby mode: false (no).

[0131] In a wind power grid, each wind turbine corresponds one-to-one with a target operating scheme.

[0132] In some embodiments, the method further includes steps F1 to F2 as shown below: F1. Obtain the grid-connected transmission monitoring data corresponding to the third wind turbine, wherein the grid-connected transmission monitoring data includes the step-up transformer data corresponding to the third wind turbine and the transmission line data corresponding to the third wind turbine.

[0133] F2. Input the wind energy capture monitoring data, mechanical conversion monitoring data, electrical energy conversion monitoring data, and grid connection transmission monitoring data corresponding to the third wind turbine as input data into the preset wind turbine operation scheme generation model to obtain the updated target operation scheme, and manage the multiple wind turbines based on the updated target operation scheme.

[0134] In other embodiments, the method further includes steps T1 to T2 as shown below: T1. Obtain the grid-connected transmission monitoring data corresponding to the third wind turbine, wherein the grid-connected transmission monitoring data includes the step-up transformer data corresponding to the third wind turbine and the transmission line data corresponding to the third wind turbine.

[0135] In some embodiments, the step-up transformer data corresponding to the third wind turbine may include: Oil temperature (°C): reflects the heat dissipation status of insulating oil; Winding temperature (°C): Monitors winding overload conditions; On-load tap changer position: controls the output voltage; Reactive power compensation capacity (MVar): Regulates grid voltage.

[0136] The transmission line data corresponding to the third wind turbine may include: Conductor temperature (°C): obtained via distributed optical fiber temperature measurement (DTS); Active power (MW): The actual power transmitted by the line; Line impedance (Ω / km): Dynamically calculated loss; Zero-sequence current (A): Detects single-phase ground faults.

[0137] T2. By generating a model through the preset wind turbine operation scheme, and based on the wind energy capture monitoring data, mechanical conversion monitoring data, electrical energy conversion monitoring data, step-up transformer data, and transmission line data corresponding to the third wind turbine, an updated target operation scheme is obtained, and each wind turbine is managed based on the updated target operation scheme.

[0138] Specifically, the updated target operating scheme may include six updated optimized operating parameters: blade angle (°): 12.5; Active power (MW): 2.3; Reactive power (MVar): 0.5; Enter hot standby: false (No); (New) The transformer tap position for this wind turbine is 15. (Added) The power flow ratio of this wind turbine to a specific line: 0.85; Each wind turbine in the wind power grid corresponds one-to-one with an updated target operation plan.

[0139] As can be seen from the above, the embodiments of the present invention can first acquire wind energy capture monitoring data, mechanical conversion monitoring data, and electrical energy conversion monitoring data corresponding to each wind turbine; then, based on the wind energy capture monitoring data and a preset wind energy capture stage data standard, a first wind turbine is selected from the wind turbines, wherein the first wind turbine is used to characterize the wind turbines whose relevant components are operating normally during the wind energy capture stage; then, based on the mechanical conversion monitoring data and the preset mechanical conversion stage data standard, a second wind turbine is selected from the first wind turbine, wherein the second wind turbine is used to characterize... The first wind turbine is selected based on the normal operation of its components during the mechanical conversion phase. Then, based on the power conversion monitoring data and preset power conversion phase data standards, a third wind turbine is selected from the second wind turbines. This third wind turbine represents the wind turbine in the second wind turbines whose components are operating normally during the power conversion phase. Finally, a target operation plan is generated using a preset wind turbine operation scheme generation model, based on the wind energy capture monitoring data, mechanical conversion monitoring data, and power conversion monitoring data corresponding to the third wind turbine. Based on this target operation plan, each wind turbine is managed. Thus, based on FTTR-B technology, through ultra-high-speed data communication and in-depth data analysis, the overall failure frequency of the wind power grid is reduced, thereby optimizing the overall operating efficiency of the wind power grid.

[0140] To better implement the above methods, this embodiment of the invention also provides a power generation network management device based on FTTR-B. This device can be integrated into an electronic device, such as a terminal or server. The terminal can be a mobile phone, tablet computer, smart Bluetooth device, laptop computer, or personal computer; the server can be a single server or a server cluster composed of multiple servers.

[0141] For example, in this embodiment, the method of the present invention will be described in detail by taking the FTTR-B-based power generation grid management device specifically integrated into the terminal as an example.

[0142] For example, such as Figure 2 As shown, the FTTR-B-based power generation network management device 200 may include a first unit 201, a second unit 202, a third unit 203, a fourth unit 204, and a fifth unit 205, and is applied to the FTTR-B wind power network management system. The FTTR-B wind power network management system is used to manage the wind power network, which includes at least one wind turbine. The FTTR-B wind power network management system includes a master gateway, a slave gateway, sensors, and a server terminal, with each slave gateway corresponding to one of the wind turbines. The device includes: Unit 201 is used to acquire wind energy capture monitoring data, mechanical conversion monitoring data and electrical energy conversion monitoring data corresponding to each wind turbine. The second unit 202 is used to select a first wind turbine from the various wind turbines based on the wind energy capture monitoring data and the preset wind energy capture stage data standard. The first wind turbine is used to characterize the wind turbines in which the relevant components of the wind energy capture stage are operating normally. The third unit 203 is used to select a second wind turbine from the first wind turbine based on the mechanical conversion monitoring data and the preset mechanical conversion stage data standard. The second wind turbine is used to characterize the wind turbine in the first wind turbine where the relevant components of the mechanical conversion stage are operating normally. The fourth unit 204 is used to select a third wind turbine from the second wind turbine based on the power conversion monitoring data and the preset power conversion stage data standard. The third wind turbine is used to characterize the wind turbine in the second wind turbine whose relevant components are operating normally during the power conversion stage. The fifth unit 205 is used to generate a model through a preset wind turbine operation plan, obtain a target operation plan based on the wind energy capture monitoring data, mechanical conversion monitoring data and electrical energy conversion monitoring data corresponding to the third wind turbine, and manage each wind turbine based on the target operation plan.

[0143] In practice, each of the above units can be implemented as an independent entity or can be arbitrarily combined to be implemented as the same or several entities. For the specific implementation of each of the above units, please refer to the previous method embodiments, which will not be repeated here.

[0144] As can be seen from the above, the embodiments of the present invention can reduce the overall failure frequency of wind power grids and optimize the overall operating efficiency of wind power grids by using FTTR-B technology, through ultra-high-speed data communication and in-depth data analysis.

[0145] This invention also provides an electronic device, which can be a terminal, a server, or other similar devices. The terminal can be a mobile phone, tablet computer, smart Bluetooth device, laptop computer, personal computer, etc.; the server can be a single server or a server cluster composed of multiple servers, etc.

[0146] In some embodiments, the product processing device may also be integrated into multiple electronic devices, such as multiple servers, with multiple servers implementing the FTTR-B-based power generation network management method of the present invention.

[0147] In this embodiment, the electronic device will be described in detail as a terminal, for example, such as... Figure 3 As shown, it illustrates a structural schematic diagram of the terminal 300 involved in an embodiment of the present invention. Specifically: The terminal 300 may include components such as a processor 301 with one or more processing cores, a memory 302 with one or more media, a power supply 303, an input module 304, and a communication module 305. Those skilled in the art will understand that... Figure 3 The terminal 300 structure shown does not constitute a limitation on the terminal 300, and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein: The processor 301 is the control center of the terminal 300. It connects various parts of the terminal 300 via various interfaces and lines, and performs various functions and processes data by running or executing software programs and / or modules stored in the memory 302, and by calling data stored in the memory 302, thereby providing overall monitoring of the terminal 300. In some embodiments, the processor 301 may include one or more processing cores; in some embodiments, the processor 301 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may also not be integrated into the processor 301.

[0148] The memory 302 can be used to store software programs and modules. The processor 301 executes various functional applications and data processing by running the software programs and modules stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the terminal 300, etc. In addition, the memory 302 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 302 may also include a memory controller to provide the processor 301 with access to the memory 302.

[0149] The terminal 300 also includes a power supply 303 that supplies power to the various components. In some embodiments, the power supply 303 can be logically connected to the processor 301 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The power supply 303 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0150] The terminal 300 may also include an input module 304, which can be used to receive input digital or character information, and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0151] The terminal 300 may also include a communication module 305. In some embodiments, the communication module 305 may include a wireless module. The terminal 300 can perform short-range wireless transmission through the wireless module of the communication module 305, thereby providing users with wireless broadband Internet access. For example, the communication module 305 can be used to help users send and receive emails, browse web pages, and access streaming media.

[0152] Although not shown, terminal 300 may also include a display unit, etc., which will not be described in detail here. Specifically, in this embodiment, processor 301 in terminal 300 loads the executable files corresponding to the processes of one or more applications into memory 302 according to the following instructions, and processor 301 runs the applications stored in memory 302 to realize various functions, as follows: Acquire wind energy capture monitoring data, mechanical conversion monitoring data, and electrical energy conversion monitoring data for each wind turbine. Based on the wind energy capture monitoring data and the preset wind energy capture stage data standard, a first wind turbine is selected from each wind turbine, wherein the first wind turbine is used to characterize the wind turbine in which the relevant components of the wind energy capture stage are operating normally. Based on the mechanical conversion monitoring data and the preset mechanical conversion stage data standard, a second wind turbine is selected from the first wind turbine. The second wind turbine is used to characterize the wind turbine in the first wind turbine where the relevant components are operating normally during the mechanical conversion stage. Based on the power conversion monitoring data and the preset power conversion stage data standard, a third wind turbine is selected from the second wind turbine. The third wind turbine is used to characterize the wind turbine in the second wind turbine where the relevant components of the power conversion stage are operating normally. A model is generated by pre-setting a wind turbine operation plan. Based on the wind energy capture monitoring data, mechanical conversion monitoring data, and electrical energy conversion monitoring data corresponding to the third wind turbine, a target operation plan is obtained. Based on the target operation plan, each wind turbine is managed.

[0153] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0154] As can be seen from the above, the embodiments of the present invention can reduce the overall failure frequency of the wind power generation network, thereby optimizing the overall operating efficiency of the wind power generation network.

[0155] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be accomplished by instructions, or by instructions controlling related hardware. These instructions can be stored in a medium and loaded and executed by a processor.

[0156] To this end, embodiments of the present invention provide a medium storing multiple instructions that can be loaded by a processor to execute steps in any of the FTTR-B-based power generation network management methods provided in the embodiments of the present invention. For example, the instructions can execute the following steps: Acquire wind energy capture monitoring data, mechanical conversion monitoring data, and electrical energy conversion monitoring data for each wind turbine. Based on the wind energy capture monitoring data and the preset wind energy capture stage data standard, a first wind turbine is selected from each wind turbine, wherein the first wind turbine is used to characterize the wind turbine in which the relevant components of the wind energy capture stage are operating normally. Based on the mechanical conversion monitoring data and the preset mechanical conversion stage data standard, a second wind turbine is selected from the first wind turbine. The second wind turbine is used to characterize the wind turbine in the first wind turbine where the relevant components are operating normally during the mechanical conversion stage. Based on the power conversion monitoring data and the preset power conversion stage data standard, a third wind turbine is selected from the second wind turbine. The third wind turbine is used to characterize the wind turbine in the second wind turbine where the relevant components of the power conversion stage are operating normally. A model is generated by pre-setting a wind turbine operation plan. Based on the wind energy capture monitoring data, mechanical conversion monitoring data, and electrical energy conversion monitoring data corresponding to the third wind turbine, a target operation plan is obtained. Based on the target operation plan, each wind turbine is managed.

[0157] The medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0158] According to one aspect of the present invention, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a medium. A processor of a computer device reads the computer instructions from the medium, and the processor executes the computer instructions, causing the computer device to perform the methods provided in the various optional implementations of the above embodiments.

[0159] Since the instructions stored in the medium can execute the steps in any of the FTTR-B-based power generation network management methods provided in the embodiments of the present invention, the beneficial effects that any of the FTTR-B-based power generation network management methods provided in the embodiments of the present invention can achieve can be realized, as detailed in the preceding embodiments, and will not be repeated here.

[0160] The foregoing has provided a detailed description of a power generation grid management method, device, terminal, and medium based on FTTR-B provided by the embodiments of the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A power generation grid management method based on FTTR-B, characterized in that, The system is applied to the FTTR-B wind power grid management system, which is used to manage each wind turbine. The FTTR-B wind power grid management system includes a master gateway, slave gateways, sensors, and a server terminal. Each slave gateway corresponds to one of the wind turbines. The method includes: Acquire wind energy capture monitoring data, mechanical conversion monitoring data, and electrical energy conversion monitoring data from multiple wind turbine units; Based on the wind energy capture monitoring data and the preset wind energy capture stage data standards, the first wind turbine unit with normal operation of relevant components during the wind energy capture stage is selected from the multiple wind turbine units. Based on the mechanical conversion monitoring data and the preset mechanical conversion stage data standards, a second wind turbine unit with normal operation of relevant components during the mechanical conversion stage is selected from the first wind turbine unit. Based on the power conversion monitoring data and the preset power conversion stage data standards, a third wind turbine unit with normal operation of relevant components in the power conversion stage is selected from the second wind turbine unit. Based on the wind energy capture monitoring data, mechanical conversion monitoring data, and electrical energy conversion monitoring data corresponding to the third wind turbine, a target operation plan is obtained, and the multiple wind turbines are managed based on the target operation plan.

2. The method as described in claim 1, characterized in that, The wind energy capture monitoring data includes actual wind speed, actual blade angle and theoretical optimal angle. The preset wind energy capture stage data standard includes the safe wind speed range of wind turbine and the blade angle deviation threshold of wind turbine. Based on the wind energy capture monitoring data and the preset wind energy capture stage data standards, the first wind turbine unit whose relevant components are operating normally during the wind energy capture stage is selected from the plurality of wind turbine units, including: Based on the actual wind speed and the safe wind speed range of the wind turbine, a first preliminary wind turbine is selected from the various wind turbines, wherein the first preliminary wind turbine is a wind turbine whose actual wind speed is within the safe wind speed range of the wind turbine. Based on the actual blade angle, the theoretical optimal angle, and the wind turbine blade angle deviation threshold, a first wind turbine is selected from the first preliminary selection of wind turbines, wherein the first wind turbine is the first preliminary selection of wind turbines whose deviation between the actual blade angle and the theoretical optimal angle is less than the wind turbine blade angle deviation threshold.

3. The method as described in claim 1, characterized in that, The wind energy capture monitoring data includes actual wind speed, actual blade angle and theoretical optimal angle. The preset wind energy capture stage data standard includes the safe wind speed range of wind turbine and the blade angle deviation threshold of wind turbine. The process of selecting the first wind turbine unit from the plurality of wind turbine units based on the wind energy capture monitoring data and the preset wind energy capture stage data standards, including: Based on the actual wind speed and the safe wind speed range of the wind turbine, a first condition weight value is determined. If the actual wind speed is within the safe wind speed range of the wind turbine, the first condition weight value is determined to be a preset first condition weight value; if the actual wind speed is not within the safe wind speed range of the wind turbine, the first condition weight value is determined to be 0. Based on the actual blade angle, the theoretical optimal angle, and the wind turbine blade angle deviation threshold, a second condition weight value is determined. If the deviation between the actual blade angle and the theoretical optimal angle is less than the wind turbine blade angle deviation threshold, the second condition weight value is determined to be a preset second condition weight value; if the deviation between the actual blade angle and the theoretical optimal angle is not less than the wind turbine blade angle deviation threshold, the second condition weight value is determined to be 0. The sum of the first condition weight value and the second condition weight value is calculated to obtain the wind energy capture stage evaluation results for each wind turbine. Based on the comparison between the wind energy capture stage evaluation results and the preset wind energy capture stage evaluation threshold, the first wind turbine is selected from the various wind turbine units.

4. The method as described in claim 1, characterized in that, The mechanical conversion monitoring data includes gearbox status data, generator status data, and main shaft status data. The preset mechanical conversion stage data standards include the safe operating range of the wind turbine gearbox, the safe operating data threshold of the wind turbine generator, and the safe threshold of the wind turbine main shaft. The process of selecting second wind turbine units from the first wind turbine units based on the mechanical conversion monitoring data and preset mechanical conversion stage data standards, where relevant components are operating normally during the mechanical conversion stage, includes: Based on the gearbox status data and the safe operating range of the wind turbine gearbox, a second preliminary wind turbine is selected from the first wind turbine. The second preliminary wind turbine is a wind turbine whose gearbox status data is within the safe operating range of the wind turbine gearbox. Based on the generator status data and the wind turbine generator safe operation data threshold, a third preliminary wind turbine is selected from the second preliminary wind turbine, wherein the third preliminary wind turbine is a wind turbine whose generator status data is less than the wind turbine generator safe operation data threshold in the second preliminary wind turbine. Based on the spindle status data and the wind turbine spindle safety threshold, the second wind turbine is selected from the third preliminary selection of wind turbines, wherein the second wind turbine is a wind turbine whose spindle status data is less than the wind turbine spindle safety threshold in the third preliminary selection of wind turbines.

5. The method as described in claim 4, characterized in that, The generator status data includes the deviation between the actual torque of the generator and the theoretical torque of the generator. Before selecting a third preliminary wind turbine from the second preliminary wind turbine based on the generator status data and the wind turbine generator safe operation data threshold, the method further includes: Obtain the actual generator torque and actual generator speed of each wind turbine; Based on the actual torque and actual speed of the generators of each wind turbine, a generator curve model is established for each wind turbine. The generator curve model is used to characterize the relationship between the engine speed and torque of each wind turbine. The theoretical engine torque corresponding to the actual generator speed is calculated using the engine curve model. The deviation value is calculated according to the following formula: in, This indicates the deviation value. This represents the theoretical torque of the engine. This indicates the actual torque of the engine.

6. The method as described in claim 1, characterized in that, The power conversion monitoring data includes DC-side voltage data and AC-side frequency deviation, and the preset power conversion stage data standard includes the DC-side voltage range of the wind turbine and the AC-side frequency deviation threshold of the wind turbine. The process of selecting third wind turbine units from the second wind turbine units based on the power conversion monitoring data and preset power conversion stage data standards, where relevant components are operating normally during the power conversion stage, includes: Based on the DC-side voltage data and the DC-side voltage range of the wind turbine, a fourth preliminary wind turbine is selected from the second wind turbine, wherein the fourth preliminary wind turbine is a wind turbine whose DC-side voltage data is within the DC-side voltage range of the wind turbine. Based on the AC side frequency deviation and the AC side frequency deviation threshold of the wind turbine, a third wind turbine is selected from the fourth preliminary wind turbine. The third wind turbine is a wind turbine in the fourth preliminary wind turbine whose AC side frequency deviation is not greater than the AC side frequency deviation threshold of the wind turbine.

7. The method as described in claim 1, characterized in that, The method further includes: Obtain grid-connected transmission monitoring data corresponding to the third wind turbine, wherein the grid-connected transmission monitoring data includes step-up transformer data and transmission line data corresponding to the third wind turbine. The wind energy capture monitoring data, mechanical conversion monitoring data, electrical energy conversion monitoring data, and grid connection transmission monitoring data corresponding to the third wind turbine are used as input data and input into the preset wind turbine operation scheme generation model to obtain the updated target operation scheme. Based on the updated target operation scheme, the multiple wind turbines are managed.

8. A power generation grid management device based on FTTR-B, characterized in that, The system is applied to the FTTR-B wind power grid management system, which is used to manage each wind turbine. The FTTR-B wind power grid management system includes a master gateway, slave gateways, sensors, and a server terminal. Each slave gateway corresponds to one of the wind turbines. The device includes: The first unit is used to acquire wind energy capture monitoring data, mechanical conversion monitoring data, and electrical energy conversion monitoring data from multiple wind turbine units. The second unit is used to select the first wind turbine unit whose relevant components are operating normally during the wind energy capture stage from the multiple wind turbine units based on the wind energy capture monitoring data and the preset wind energy capture stage data standard. The third unit is used to select, based on the mechanical conversion monitoring data and the preset mechanical conversion stage data standards, a second wind turbine unit from the first wind turbine unit whose relevant components are operating normally during the mechanical conversion stage. The fourth unit is used to select, based on the power conversion monitoring data and the preset power conversion stage data standards, a third wind turbine unit from the second wind turbine unit whose relevant components are operating normally during the power conversion stage. The fifth unit is used to obtain a target operation plan based on the wind energy capture monitoring data, mechanical conversion monitoring data and electrical energy conversion monitoring data corresponding to the third wind turbine, and to manage the multiple wind turbines based on the target operation plan.

9. A terminal, characterized in that, The method includes a processor and a memory, the memory storing multiple instructions; the processor loads instructions from the memory to perform the steps of the method as described in any one of claims 1 to 7.

10. A medium, characterized in that, The medium stores a plurality of instructions adapted for loading by a processor to execute the steps of the method according to any one of claims 1 to 7.

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