Fan blade deicing system based on unmanned aerial vehicle and control method thereof

Abstract

The invention relates to the technical field of fan blade deicing, in particular to a fan blade deicing system based on an unmanned aerial vehicle and a control method thereof. Comprising the following steps: establishing a monitoring map according to position parameters of all fan blades; generating an inspection evaluation value of each fan blade according to a preset monitoring time node, and setting a primary inspection path of the inspection unmanned aerial vehicle according to all the inspection evaluation values; and obtaining monitoring data of each fan blade on the primary inspection path, and generating a deicing strategy according to all the monitoring data. The operation parameters and the environmental parameters of each fan blade are monitored, the icing probability of each fan blade is judged, and the corresponding inspection evaluation value is generated, so that the fan blades are screened, and part of the fan blades are selected for inspection monitoring, the inspection efficiency is improved, the inspection cost is reduced, and the overall deicing efficiency is improved. The deicing cost is reduced, operation faults caused by freezing of the fan blades are avoided, and stable operation of the fan is guaranteed.

Description

Technical Field

[0001] The present application relates to the technical field of wind turbine blade deicing, and in particular to a wind turbine blade deicing system based on an unmanned aerial vehicle and a control method thereof. Background Art

[0002] Ice is easily formed on the surface of wind turbine blades in high humidity and low temperature environments, and different high humidity and low temperature environments will form different types of ice. Heavy snow in winter will also cause snow and ice to form on wind turbine blades. The above situation will reduce the speed of the wind turbine rotor and reduce the efficiency of wind energy capture. At the same time, it will increase the operating load of the blades and increase the difficulty of control.

[0003] In extreme cases, the wind turbine may be overloaded and collapse, blades may break, and other accidents may occur. Unbalanced blade rotation caused by unbalanced ice coverage will reduce the service life of the wind turbine. Most wind turbines are forced to stop running when the blades are covered with ice or snow, resulting in a serious waste of wind resources. In order to continuously utilize wind energy for a long time in low temperature weather in winter, corresponding anti-icing and de-icing measures need to be taken. Summary of the invention

[0004] The purpose of this application is: to solve the above-mentioned technical problems, this application provides a fan blade deicing system based on a drone and a control method thereof, aiming to achieve efficient deicing of fan blades and improve the operating efficiency of the fan.

[0005] In some embodiments of the present application, the operating parameters and environmental parameters of each fan blade are monitored to determine the icing probability of each fan blade and generate a corresponding inspection evaluation value, so as to screen each fan blade and select some fan blades for inspection and monitoring, thereby improving inspection efficiency, reducing inspection costs, and achieving accurate monitoring of the icing status of the fan blades.

[0006] In some embodiments of the present application, the icing status of each first-level fan blade is judged based on the monitoring parameters collected by the inspection drone, and a corresponding de-icing strategy is generated based on the judgment result, so as to accurately de-ice each first-level fan blade, thereby improving the overall de-icing efficiency, reducing the de-icing cost, avoiding operational failures caused by icing of the fan blades, and ensuring the stable operation of the fan.

[0007] In some embodiments of the present application, a method for controlling a fan blade deicing system based on a drone is provided, comprising:

[0008] Establish a monitoring map based on the position parameters of all wind turbine blades;

[0009] Generate inspection evaluation values ​​for each wind turbine blade based on preset monitoring time nodes, and set the primary inspection path of the inspection drone based on all inspection evaluation values;

[0010] Obtain monitoring data of each wind turbine blade on the primary inspection path and generate a deicing strategy based on all monitoring data.

[0011] In some embodiments of the present application, when setting the primary inspection path of the inspection drone, it includes:

[0012] Establish a fan blade sequence A, A = (a1, a2...ai...an), where ai is the i-th fan blade; n is the number of fan blades;

[0013] Obtain environmental and operating data of each wind turbine blade;

[0014] Generate inspection evaluation values ​​of each wind turbine blade at the current monitoring time node in sequence;

[0015] Establish an inspection evaluation value sequence B, B = (b1, b2...bi...bn), where bi is the inspection evaluation value of the i-th wind turbine blade;

[0016] Preset the first inspection evaluation value threshold B1;

[0017] If bi>B1, set the i-th fan blade as a first-level fan blade;

[0018] All the first-level fan blades are obtained, and a first-level inspection path is generated according to the position parameters of each first-level fan blade.

[0019] In some embodiments of the present application, when the inspection evaluation value of each wind turbine blade is generated in sequence, it includes:

[0020] According to the fan blade sequence A, the i-th fan blade is sequentially set as the target fan blade;

[0021] Obtain environmental data and operating data of target wind turbine blades;

[0022] Generate an inspection evaluation value b of the target wind turbine blade;

[0023]

[0024] Among them, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; θ1 is the number of environmental evaluation indicators; ηi is the influencing factor of the i-th environmental evaluation indicator; ki is the reference value of the i-th environmental evaluation indicator in the target wind turbine blade; θ2 is the number of operation evaluation indicators; βi is the influencing factor of the i-th operation evaluation indicator; ti is the reference value of the i-th operation evaluation indicator in the target wind turbine blade.

[0025] In some embodiments of the present application, when a deicing strategy is generated based on all monitoring data, it includes:

[0026] Establish a first-level fan blade sequence C, C = (c1, c2...ci...cm), where ci is the i-th first-level fan blade, and m is the number of first-level fan blades;

[0027] Generate icing evaluation values ​​of each first-level fan blade in sequence according to all monitoring data;

[0028] Establishing an icing evaluation value sequence D, D = (d1, d2...di...dm), where di is the icing evaluation value of the i-th primary fan blade;

[0029] Preset a first icing evaluation value threshold D1;

[0030] If di<D1, set the i-th level 1 fan blade as the level 1 freezing point;

[0031] If di>D1, set the i-th level fan blade as the level 2 freezing point

[0032] De-icing strategy is set based on all ice points.

[0033] In some embodiments of the present application, when the icing evaluation value of each primary wind turbine blade is generated in sequence, it includes:

[0034] Select the i-th primary fan blade as the target primary fan blade;

[0035] Acquire monitoring data of target first-stage fan blades, and generate reference values ​​of various characteristic monitoring indicators of target first-stage fan blades according to preprocessing results;

[0036] Generate an icing evaluation value d of a target first-stage wind turbine blade;

[0037]

[0038] Among them, r is the number of characteristic monitoring indicators; αi is the influencing factor of the i-th characteristic monitoring indicator; fi is the reference value of the i-th characteristic monitoring indicator in the target first-stage wind turbine blade.

[0039] In some embodiments of the present application, when setting a deicing strategy according to all freezing points, it includes:

[0040] Generate a first-level deicing plan based on all first-level icing points;

[0041] Establish a secondary freezing point sequence H, H = (h1, h2…h i …h m1 ), where h i is the i-th secondary freezing point; m1 is the number of secondary freezing points; and m1≤m;

[0042] According to the equipment parameters of the deicing drone, the deicing drone sequence G at the current monitoring time node is established, G = (g1, g2…gi …g m2 ), where gi is the i-th deicing UAV; m2 is the number of deicing UAVs;

[0043] Generate a secondary deicing plan based on the secondary icing point sequence H and the deicing drone sequence G;

[0044] Generate a deicing strategy for the current monitoring time node based on the primary deicing plan and the secondary deicing plan.

[0045] In some embodiments of the present application, a wind turbine blade deicing system based on a drone is provided, comprising:

[0046] The central control unit is used to establish a monitoring map based on the position parameters of all wind turbine blades;

[0047] A monitoring unit, comprising a plurality of monitoring submodules, wherein the monitoring submodules are used to collect environmental data and operating data of the fan blades;

[0048] UAV units, including multiple inspection drones and multiple de-icing drones;

[0049] The inspection drone is used to collect monitoring data of each wind turbine blade.

[0050] The deicing drone is used to assist in deicing;

[0051] The central control unit comprises:

[0052] The first processing module generates an inspection evaluation value of each wind turbine blade according to a preset monitoring time node;

[0053] The second processing module is used to set the primary inspection path of the inspection drone according to all inspection evaluation values;

[0054] The third processing module is used to obtain the monitoring data of each wind turbine blade on the first-level inspection path and generate a deicing strategy based on all the monitoring data.

[0055] In some embodiments of the present application, the first processing module is further used to:

[0056] Establish a fan blade sequence A, A = (a1, a2...ai...an), where ai is the i-th fan blade; n is the number of fan blades;

[0057] According to the fan blade sequence A, the i-th fan blade is sequentially set as the target fan blade;

[0058] Obtain environmental data and operating data of target wind turbine blades;

[0059] Generate an inspection evaluation value b of the target wind turbine blade;

[0060]

[0061] Among them, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; θ1 is the number of environmental evaluation indicators; ηi is the influencing factor of the i-th environmental evaluation indicator; ki is the reference value of the i-th environmental evaluation indicator in the target wind turbine blade; θ2 is the number of operation evaluation indicators; βi is the influencing factor of the i-th operation evaluation indicator; ti is the reference value of the i-th operation evaluation indicator in the target wind turbine blade;

[0062] Generate inspection evaluation values ​​of each wind turbine blade at the current monitoring time node in sequence;

[0063] Establish an inspection evaluation value sequence B, B = (b1, b2...bi...bn), where bi is the inspection evaluation value of the i-th wind turbine blade.

[0064] In some embodiments of the present application, the second processing module is further used to:

[0065] Preset the first inspection evaluation value threshold B1;

[0066] If bi>B1, set the i-th fan blade as a first-level fan blade;

[0067] All the first-level fan blades are obtained, and a first-level inspection path is generated according to the position parameters of each first-level fan blade.

[0068] In some embodiments of the present application, the third processing module is further used to:

[0069] Establish a first-level fan blade sequence C, C = (c1, c2...ci...cm), where ci is the i-th first-level fan blade, and m is the number of first-level fan blades;

[0070] Select the i-th primary fan blade as the target primary fan blade;

[0071] Acquire monitoring data of target first-stage fan blades, and generate reference values ​​of various characteristic monitoring indicators of target first-stage fan blades according to preprocessing results;

[0072] Generate an icing evaluation value d of a target first-stage wind turbine blade;

[0073]

[0074] Among them, r is the number of characteristic monitoring indicators; αi is the influencing factor of the i-th characteristic monitoring indicator; fi is the reference value of the i-th characteristic monitoring indicator in the target first-level wind turbine blade;

[0075] Establishing an icing evaluation value sequence D, D = (d1, d2...di...dm), where di is the icing evaluation value of the i-th primary fan blade;

[0076] Preset a first icing evaluation value threshold D1;

[0077] If di<D1, set the i-th level 1 fan blade as the level 1 freezing point;

[0078] If di>D1, set the i-th level fan blade as the level 2 freezing point

[0079] De-icing strategy is set based on all ice points.

[0080] Compared with the prior art, the fan blade deicing system and control method based on a drone in the embodiment of the present application have the following beneficial effects:

[0081] By monitoring the operating parameters and environmental parameters of each fan blade, the icing probability of each fan blade is determined, and a corresponding inspection evaluation value is generated, so that each fan blade can be screened and some fan blades can be selected for inspection and monitoring, thereby improving inspection efficiency, reducing inspection costs, and achieving accurate monitoring of the icing status of fan blades.

[0082] According to the monitoring parameters collected by the inspection drone, the icing status of each first-level fan blade is judged, and the corresponding deicing strategy is generated according to the judgment result, so as to accurately de-ice each first-level fan blade, thereby improving the overall deicing efficiency, reducing deicing costs, avoiding operational failures caused by icing of fan blades, and ensuring the stable operation of the fan. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 It is a flow chart of a method for controlling a wind turbine blade deicing system based on a drone in a preferred embodiment of the present application. DETAILED DESCRIPTION

[0084] The specific implementation methods of the present application are further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present application but are not intended to limit the scope of the present application.

[0085] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0086] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0087] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0088] like Figure 1 As shown, a fan blade deicing system control method based on a drone in a preferred embodiment of the present application includes:

[0089] S101: Establishing a monitoring map according to position parameters of all wind turbine blades;

[0090] S102: Generate an inspection evaluation value for each wind turbine blade according to a preset monitoring time node, and set a primary inspection path for the inspection drone according to all inspection evaluation values;

[0091] S103: Acquire monitoring data of each wind turbine blade on the primary inspection path, and generate a deicing strategy based on all monitoring data.

[0092] Specifically, a corresponding monitoring map is constructed according to the equipment parameters and location parameters of each wind turbine.

[0093] Specifically, when setting the first-level inspection path of the inspection drone, it includes:

[0094] Establish a fan blade sequence A, A = (a1, a2...ai...an), where ai is the i-th fan blade; n is the number of fan blades;

[0095] Obtain environmental and operating data of each wind turbine blade;

[0096] Generate inspection evaluation values ​​of each wind turbine blade at the current monitoring time node in sequence;

[0097] Establish an inspection evaluation value sequence B, B = (b1, b2...bi...bn), where bi is the inspection evaluation value of the i-th wind turbine blade;

[0098] Preset the first inspection evaluation value threshold B1;

[0099] If bi>B1, set the i-th fan blade as a first-level fan blade;

[0100] All the first-level fan blades are obtained, and a first-level inspection path is generated according to the position parameters of each first-level fan blade.

[0101] Specifically, the larger the inspection evaluation value, the greater the probability that the current wind turbine blades are frozen. When the inspection evaluation value is greater than the preset first inspection evaluation value threshold, it means that there is a possibility that the current wind turbine blades are frozen, and it is necessary to collect image data of the wind turbine blades through an inspection drone for further judgment.

[0102] Specifically, when the inspection evaluation value of each wind turbine blade is generated in sequence, it includes:

[0103] According to the fan blade sequence A, the i-th fan blade is sequentially set as the target fan blade;

[0104] Obtain environmental data and operating data of target wind turbine blades;

[0105] Generate an inspection evaluation value b of the target wind turbine blade;

[0106]

[0107] Among them, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; θ1 is the number of environmental evaluation indicators; ηi is the influencing factor of the i-th environmental evaluation indicator; ki is the reference value of the i-th environmental evaluation indicator in the target wind turbine blade; θ2 is the number of operation evaluation indicators; βi is the influencing factor of the i-th operation evaluation indicator; ti is the reference value of the i-th operation evaluation indicator in the target wind turbine blade.

[0108] Specifically, each parameter in the model is normalized by presetting a first fixed coefficient and a second fixed coefficient, so that each parameter is in the same value range.

[0109] Specifically, its environmental evaluation indicators include parameters such as the average temperature, average humidity, light intensity, whether there is rain or snow between the current monitoring time node and the previous monitoring time node. Operational evaluation indicators include parameters such as the fan blade area, fan blade flatness, and the time interval between the last deicing time node of the fan blade and the current time node.

[0110] Specifically, the larger the inspection evaluation value, the greater the probability of icing on the target wind turbine blades. All inspection evaluation values ​​are used to screen each wind turbine blade, so that wind turbine blades with icing risks can be inspected without the need for a comprehensive inspection, thereby reducing inspection costs and improving inspection efficiency.

[0111] It can be understood that in the above embodiment, the operating parameters and environmental parameters of each fan blade are monitored, the icing probability of each fan blade is determined, and a corresponding inspection evaluation value is generated, so that each fan blade is screened, and some fan blades are selected for inspection and monitoring, thereby improving the inspection efficiency, reducing the inspection cost, and realizing accurate monitoring of the icing status of the fan blades.

[0112] In a preferred embodiment of the present application, when generating a deicing strategy based on all monitoring data, it includes:

[0113] Establish a first-level fan blade sequence C, C = (c1, c2...ci...cm), where ci is the i-th first-level fan blade, and m is the number of first-level fan blades;

[0114] Generate icing evaluation values ​​of each first-level fan blade in sequence according to all monitoring data;

[0115] Establishing an icing evaluation value sequence D, D = (d1, d2...di...dm), where di is the icing evaluation value of the i-th primary fan blade;

[0116] Preset a first icing evaluation value threshold D1;

[0117] If di<D1, set the i-th level 1 fan blade as the level 1 freezing point;

[0118] If di>D1, set the i-th level fan blade as the level 2 freezing point

[0119] De-icing strategy is set based on all ice points.

[0120] Specifically, the first icing evaluation value threshold is set according to historical parameters. The first-level icing point means that there is no obvious icing phenomenon on the current wind turbine blades, but the possibility of icing phenomenon is high. The second-level icing point means that the current wind turbine blades have obvious icing phenomenon.

[0121] Specifically, when the icing evaluation value of each primary wind turbine blade is generated in sequence, it includes:

[0122] Select the i-th primary fan blade as the target primary fan blade;

[0123] Acquire monitoring data of target first-stage fan blades, and generate reference values ​​of various characteristic monitoring indicators of target first-stage fan blades according to preprocessing results;

[0124] Generate an icing evaluation value d of a target first-stage wind turbine blade;

[0125]

[0126] Among them, r is the number of characteristic monitoring indicators; αi is the influencing factor of the i-th characteristic monitoring indicator; fi is the reference value of the i-th characteristic monitoring indicator in the target first-stage wind turbine blade.

[0127] Specifically, characteristic monitoring indicators include but are not limited to ice thickness, ice area, wind turbine blade surface temperature and other parameters. The larger the icing evaluation value, the more serious the current icing degree on the wind turbine blade surface.

[0128] Specifically, when setting the de-icing strategy based on all icing points, it includes:

[0129] Generate a first-level deicing plan based on all first-level icing points;

[0130] Establish a secondary freezing point sequence H, H = (h1, h2…h i …h m1 ), where h i is the i-th secondary freezing point; m1 is the number of secondary freezing points; and m1≤m;

[0131] According to the equipment parameters of the deicing drone, the deicing drone sequence G at the current monitoring time node is established, G = (g1, g2…g i …g m2 ), where gi is the i-th deicing UAV; m2 is the number of deicing UAVs;

[0132] Generate a secondary deicing plan based on the secondary icing point sequence H and the deicing drone sequence G;

[0133] Generate a deicing strategy for the current monitoring time node based on the primary deicing plan and the secondary deicing plan.

[0134] Specifically, the first-level icing plan refers to starting the heating and de-icing devices in the fan blades corresponding to each first-level icing point and heating them for a period of time, thereby increasing the surface temperature of the fan blades, removing sporadic ice layers on the surface of the fan blades, and reducing the risk of icing on the fan blades.

[0135] Specifically, the secondary icing plan refers to the deicing strategy of starting the heating device and assisting the deicing drone for each secondary icing point to quickly remove the ice layer on the wind turbine blades corresponding to each secondary icing point. Among them, if the number of deicing drones is less than the number of secondary icing points, the deicing drones are preferentially dispatched to the secondary icing points with high icing evaluation values ​​according to the icing evaluation values. After completing the deicing task of a single secondary icing point, the deicing drone can fly to the secondary icing point that has not completed the deicing task and has no deicing drone to continue the deicing task.

[0136] Specifically, the de-icing drone is equipped with heating equipment and blowing equipment to assist the fan blades in de-icing, increase the temperature of the fan blades to melt the ice and blow away the melted water droplets, thereby improving the de-icing efficiency.

[0137] Specifically, the de-icing strategy also includes that after the first and second de-icing plans are executed, the inspection drone will conduct a patrol inspection and perform secondary de-icing on the wind turbine blades that have not achieved the expected de-icing results, thereby reducing the operational risk of wind turbine blades caused by icing.

[0138] It can be understood that in the above embodiment, the icing status of each first-level fan blade is judged according to the monitoring parameters collected by the inspection drone, and a corresponding de-icing strategy is generated according to the judgment result, so as to accurately de-ice each first-level fan blade, thereby improving the overall de-icing efficiency, reducing the de-icing cost, avoiding operational failures caused by icing of the fan blades, and ensuring the stable operation of the fan.

[0139] Based on another preferred embodiment of a fan blade deicing system control method based on a drone in any of the above preferred embodiments, this preferred embodiment provides a fan blade deicing system control method based on a drone, including:

[0140] The central control unit is used to establish a monitoring map based on the position parameters of all wind turbine blades;

[0141] A monitoring unit, including a plurality of monitoring submodules, wherein the monitoring submodules are used to collect environmental data and operating data of the fan blades;

[0142] UAV units, including multiple inspection drones and multiple de-icing drones;

[0143] Inspection drones are used to collect monitoring data on each wind turbine blade.

[0144] De-icing drones are used to assist in de-icing;

[0145] The central control unit includes:

[0146] The first processing module generates an inspection evaluation value of each wind turbine blade according to a preset monitoring time node;

[0147] The second processing module is used to set the primary inspection path of the inspection drone according to all inspection evaluation values;

[0148] The third processing module is used to obtain the monitoring data of each wind turbine blade on the first-level inspection path and generate a deicing strategy based on all the monitoring data.

[0149] Specifically, the inspection drone is equipped with monitoring equipment and a variety of sensors to collect image data, temperature data, infrared data and other monitoring data of the wind turbine blades.

[0150] Specifically, the de-icing drone is equipped with heating equipment and blowing equipment to assist the fan blades in de-icing, increase the temperature of the fan blades to melt the ice and blow away the melted water droplets, thereby improving the de-icing efficiency.

[0151] Specifically, the first processing module is also used for:

[0152] Establish a fan blade sequence A, A = (a1, a2...ai...an), where ai is the i-th fan blade; n is the number of fan blades;

[0153] According to the fan blade sequence A, the i-th fan blade is sequentially set as the target fan blade;

[0154] Obtain environmental data and operating data of target wind turbine blades;

[0155] Generate an inspection evaluation value b of the target wind turbine blade;

[0156]

[0157] Among them, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; θ1 is the number of environmental evaluation indicators; ηi is the influencing factor of the i-th environmental evaluation indicator; ki is the reference value of the i-th environmental evaluation indicator in the target wind turbine blade; θ2 is the number of operation evaluation indicators; βi is the influencing factor of the i-th operation evaluation indicator; ti is the reference value of the i-th operation evaluation indicator in the target wind turbine blade;

[0158] Generate inspection evaluation values ​​of each wind turbine blade at the current monitoring time node in sequence;

[0159] Establish an inspection evaluation value sequence B, B = (b1, b2...bi...bn), where bi is the inspection evaluation value of the i-th wind turbine blade.

[0160] Specifically, the second processing module is further used for:

[0161] Preset the first inspection evaluation value threshold B1;

[0162] If bi>B1, set the i-th fan blade as a first-level fan blade;

[0163] All the first-level fan blades are obtained, and a first-level inspection path is generated according to the position parameters of each first-level fan blade.

[0164] In a preferred embodiment of the present application, the third processing module is also used for:

[0165] Establish a first-level fan blade sequence C, C = (c1, c2...ci...cm), where ci is the i-th first-level fan blade, and m is the number of first-level fan blades;

[0166] Select the i-th primary fan blade as the target primary fan blade;

[0167] Acquire monitoring data of target first-stage fan blades, and generate reference values ​​of various characteristic monitoring indicators of target first-stage fan blades according to preprocessing results;

[0168] Generate an icing evaluation value d of a target first-stage wind turbine blade;

[0169]

[0170] Among them, r is the number of characteristic monitoring indicators; αi is the influencing factor of the i-th characteristic monitoring indicator; fi is the reference value of the i-th characteristic monitoring indicator in the target first-level wind turbine blade;

[0171] Establishing an icing evaluation value sequence D, D = (d1, d2...di...dm), where di is the icing evaluation value of the i-th primary fan blade;

[0172] Preset a first icing evaluation value threshold D1;

[0173] If di<D1, set the i-th level 1 fan blade as the level 1 freezing point;

[0174] If di>D1, set the i-th level fan blade as the level 2 freezing point

[0175] De-icing strategy is set based on all ice points.

[0176] According to the first concept of the present application, by monitoring the operating parameters and environmental parameters of each fan blade, the icing probability of each fan blade is determined, and a corresponding inspection evaluation value is generated, so that each fan blade is screened and some fan blades are selected for inspection and monitoring, thereby improving the inspection efficiency, reducing the inspection cost, and realizing accurate monitoring of the icing status of the fan blades.

[0177] According to the second concept of the present application, the icing status of each first-level fan blade is judged according to the monitoring parameters collected by the inspection drone, and a corresponding de-icing strategy is generated according to the judgment result, so as to accurately de-ice each first-level fan blade, thereby improving the overall de-icing efficiency, reducing the de-icing cost, avoiding operational failures caused by icing of fan blades, and ensuring the stable operation of the fan.

[0178] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and substitutions can be made without departing from the technical principles of the present application. These improvements and substitutions should also be regarded as the scope of protection of the present application.

Claims

1. A control method for a fan blade deicing system based on an unmanned aerial vehicle, characterized in that: include: Establish a monitoring map based on the position parameters of all wind turbine blades; Generate inspection evaluation values ​​for each wind turbine blade based on preset monitoring time nodes, and set the primary inspection path of the inspection drone based on all inspection evaluation values; Obtain monitoring data of each wind turbine blade on the primary inspection path and generate a deicing strategy based on all monitoring data.

2. The control method for a wind turbine blade deicing system based on a drone according to claim 1, characterized in that: When setting the primary inspection path of the inspection drone, include: Establish a fan blade sequence A, A = (a1, a2...ai...an), where ai is the i-th fan blade; n is the number of fan blades; Obtain environmental and operating data of each wind turbine blade; Generate inspection evaluation values ​​of each wind turbine blade at the current monitoring time node in sequence; Establish an inspection evaluation value sequence B, B = (b1, b2...bi...bn), where bi is the inspection evaluation value of the i-th wind turbine blade; Preset the first inspection evaluation value threshold B1; If bi>B1, set the i-th fan blade as a first-level fan blade; All the first-level fan blades are obtained, and a first-level inspection path is generated according to the position parameters of each first-level fan blade.

3. The control method for a wind turbine blade deicing system based on a drone as claimed in claim 2, characterized in that: When the inspection evaluation value of each wind turbine blade is generated in sequence, it includes: According to the fan blade sequence A, the i-th fan blade is sequentially set as the target fan blade; Obtain environmental data and operating data of target wind turbine blades; Generate an inspection evaluation value b of the target wind turbine blade; Among them, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; θ1 is the number of environmental evaluation indicators; ηi is the influencing factor of the i-th environmental evaluation indicator; ki is the reference value of the i-th environmental evaluation indicator in the target wind turbine blade; θ2 is the number of operation evaluation indicators; βi is the influencing factor of the i-th operation evaluation indicator; ti is the reference value of the i-th operation evaluation indicator in the target wind turbine blade.

4. The control method for a wind turbine blade deicing system based on a drone as claimed in claim 2, characterized in that: When generating a de-icing strategy based on all monitoring data, it includes: Establish a first-level fan blade sequence C, C = (c1, c2...ci...cm), where ci is the i-th first-level fan blade, and m is the number of first-level fan blades; Generate icing evaluation values ​​of each first-level fan blade in sequence according to all monitoring data; Establishing an icing evaluation value sequence D, D = (d1, d2...di...dm), where di is the icing evaluation value of the i-th primary fan blade; Preset a first icing evaluation value threshold D1; If di<D1, set the i-th level 1 fan blade as the level 1 freezing point; If di>D1, set the i-th level fan blade as the level 2 freezing point De-icing strategy is set based on all ice points.

5. The control method for a wind turbine blade deicing system based on a drone as claimed in claim 4, characterized in that: When the icing evaluation value of each primary fan blade is generated in sequence, it includes: Select the i-th primary fan blade as the target primary fan blade; Acquire monitoring data of target first-stage fan blades, and generate reference values ​​of various characteristic monitoring indicators of target first-stage fan blades according to preprocessing results; Generate an icing evaluation value d of a target first-stage wind turbine blade; Among them, r is the number of characteristic monitoring indicators; αi is the influencing factor of the i-th characteristic monitoring indicator; fi is the reference value of the i-th characteristic monitoring indicator in the target first-stage wind turbine blade.

6. The control method for a wind turbine blade deicing system based on a drone as claimed in claim 4, characterized in that: When setting the de-icing strategy based on all ice points, include: Generate a first-level deicing plan based on all first-level icing points; Establish a secondary freezing point sequence H, H = (h1, h2…h i …h m1 ), where h i is the i-th secondary freezing point; m1 is the number of secondary freezing points; and m1≤m; According to the equipment parameters of the deicing drone, the deicing drone sequence G at the current monitoring time node is established, G = (g1, g2…g i …g m2 ), where gi is the i-th deicing UAV; m2 is the number of deicing UAVs; Generate a secondary deicing plan based on the secondary icing point sequence H and the deicing drone sequence G; Generate a deicing strategy for the current monitoring time node based on the primary deicing plan and the secondary deicing plan.

7. A UAV-based wind turbine blade deicing system, using the UAV-based wind turbine blade deicing system control method according to any one of claims 1 to 6, characterized in that: include: The central control unit is used to establish a monitoring map based on the position parameters of all wind turbine blades; A monitoring unit, comprising a plurality of monitoring submodules, wherein the monitoring submodules are used to collect environmental data and operating data of the fan blades; UAV units, including multiple inspection drones and multiple de-icing drones; The inspection drone is used to collect monitoring data of each wind turbine blade. The deicing drone is used to assist in deicing; The central control unit comprises: The first processing module generates an inspection evaluation value of each wind turbine blade according to a preset monitoring time node; The second processing module is used to set the primary inspection path of the inspection drone according to all inspection evaluation values; The third processing module is used to obtain the monitoring data of each wind turbine blade on the first-level inspection path and generate a deicing strategy based on all the monitoring data.

8. The wind turbine blade deicing system based on a drone as claimed in claim 7, characterized in that: The first processing module is also used for: Establish a fan blade sequence A, A = (a1, a2...ai...an), where ai is the i-th fan blade; n is the number of fan blades; According to the fan blade sequence A, the i-th fan blade is sequentially set as the target fan blade; Obtain environmental data and operating data of target wind turbine blades; Generate an inspection evaluation value b of the target wind turbine blade; Among them, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; θ1 is the number of environmental evaluation indicators; ηi is the influencing factor of the i-th environmental evaluation indicator; ki is the reference value of the i-th environmental evaluation indicator in the target wind turbine blade; θ2 is the number of operation evaluation indicators; βi is the influencing factor of the i-th operation evaluation indicator; ti is the reference value of the i-th operation evaluation indicator in the target wind turbine blade; Generate inspection evaluation values ​​of each wind turbine blade at the current monitoring time node in sequence; Establish an inspection evaluation value sequence B, B = (b1, b2...bi...bn), where bi is the inspection evaluation value of the i-th wind turbine blade.

9. The wind turbine blade deicing system based on a drone as claimed in claim 8, characterized in that: The second processing module is also used for: Preset the first inspection evaluation value threshold B1; If bi>B1, set the i-th fan blade as a first-level fan blade; All the first-level fan blades are obtained, and a first-level inspection path is generated according to the position parameters of each first-level fan blade.

10. The wind turbine blade deicing system based on a drone as claimed in claim 9, characterized in that: The third processing module is also used for: Establish a first-level fan blade sequence C, C = (c1, c2...ci...cm), where ci is the i-th first-level fan blade, and m is the number of first-level fan blades; Select the i-th primary fan blade as the target primary fan blade; Acquire monitoring data of target first-stage fan blades, and generate reference values ​​of various characteristic monitoring indicators of target first-stage fan blades according to preprocessing results; Generate an icing evaluation value d of a target first-stage wind turbine blade; Among them, r is the number of characteristic monitoring indicators; αi is the influencing factor of the i-th characteristic monitoring indicator; fi is the reference value of the i-th characteristic monitoring indicator in the target first-level wind turbine blade; Establishing an icing evaluation value sequence D, D = (d1, d2...di...dm), where di is the icing evaluation value of the i-th primary fan blade; Preset a first icing evaluation value threshold D1; If di<D1, set the i-th level 1 fan blade as the level 1 freezing point; If di>D1, set the i-th level fan blade as the level 2 freezing point De-icing strategy is set based on all ice points.

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