Intelligent temperature control type fan blade electric heating deicing control system and method

Abstract

The invention discloses an intelligent temperature control type fan blade electric heating deicing control system and method, and relates to the technical field of fan blade deicing. According to the intelligent temperature control type fan blade electric heating deicing control system and method, the icing thickness, temperature, humidity and environmental conditions of the surface of a blade are collected in real time through an icing monitoring unit, a demand degree matrix is generated, and high-demand, medium-demand and low-demand areas of the blade are divided according to the demand degree matrix; only activating modules in a high-demand area under the guidance of the demand degree matrix, and keeping the non-activated modules in a standby state to save energy consumption; meanwhile, the multi-level power adjusting unit dynamically distributes power according to the heating demand quantity of the activated area, matches the icing thicknesses and actual demands of different areas, continuously collects feedback information in each heating period, and generates a feedback report and optimizes a deicing strategy, wherein the feedback information comprises the temperature change of the blade surface, the energy consumption efficiency, the deicing progress and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of deicing of fan blades, in particular to an intelligent temperature-controlled electric deicing control system and method for fan blades. Background Art

[0002] At present, the de-icing method for fan blades mainly relies on electric heating elements at the blade position. The attached ice layer is melted by raising the temperature. This method is intuitive and effective, but in actual application, the de-icing requirements at different positions are different. The heating system mostly uses the entire blade as the heating unit, which will cause a waste of resources.

[0003] Some traditional methods use time-sharing heating or regional heating to optimize energy consumption. However, due to the rough regional division and control methods, it is still difficult to identify and respond to the degree of icing in different areas. Even if some systems are equipped with temperature sensors to monitor some areas, their heating power and regulation are still insufficient to cope with rapidly changing environmental conditions. In addition, most of these solutions are single-level power supply designs, which limit the flexibility of heating power regulation, resulting in insufficient heating of some icy areas due to insufficient power, while other areas may waste heat energy due to excessive heating. Therefore, an intelligent temperature-controlled fan blade electric deicing control system and method are urgently needed to solve such problems. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] The present invention provides an intelligent temperature-controlled fan blade electric heating deicing control system and method to solve the problem.

[0006] The traditional electric blade deicing solution lacks control logic and intelligence of the heating unit, resulting in energy waste and increased maintenance costs, and serious problems with the operational stability of the wind turbine in extreme weather.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, an embodiment of the present invention provides an intelligent temperature-controlled electric deicing control system for fan blades, which comprises:

[0009] Icing monitoring unit, composed of multiple sensors, including temperature, humidity and ice thickness sensors, is used to monitor the ice condition on the surface of wind turbine blades in real time, including ice thickness, distribution and environmental conditions, and define the regional priority of different blades;

[0010] The modular heating unit includes multiple heating modules, which are used to heat the surface of the fan blades in different areas independently. Each heating module corresponds to an independent blade area and has an independent circuit and control system.

[0011] The multi-level regional power adjustment unit works with the intelligent algorithm optimization module to dynamically adjust the power output of the activated heating module and allocate heating intensity to different areas according to ice thickness and feedback information through multi-level control technology;

[0012] Signal transmission unit, used to connect all modules and units;

[0013] The data processing module is used to receive the raw data from the icing monitoring unit, perform preprocessing and generate a demand matrix; the demand matrix is ​​calculated based on ice thickness, distribution, environmental conditions and regional priority, and serves as the control basis for the modular heating unit; at the same time, this module provides real-time feedback information for the intelligent algorithm optimization module.

[0014] As a preferred solution of the intelligent temperature-controlled fan blade electric deicing control system of the present invention, it also includes: an intelligent algorithm optimization module for evaluating the effect of the current heating strategy and adjusting the periodic control law parameters;

[0015] Feedback collection and analysis module, used to continuously collect blade feedback information and environmental parameters in each heating cycle, and transmit them to the data processing module and intelligent algorithm optimization module through the signal transmission unit for the next round of heating strategy evaluation and adjustment;

[0016] The main control and coordination module is responsible for managing the activation and deactivation of the modular heating units, power adjustment, and execution of the cycle control law.

[0017] As a preferred solution of the intelligent temperature-controlled fan blade electric deicing control system described in the present invention, the modular heating unit activates specific heating areas according to the demand matrix, and the unactivated modules remain in standby mode; at the same time, the unit adjusts the heating allocation power through a multi-level regional power adjustment unit.

[0018] As a preferred solution of the intelligent temperature-controlled fan blade electric deicing control system described in the present invention, the signal transmission unit transmits the data collected by the icing monitoring unit to the data processing module, and sends the calculation results of the data processing module to the modular heating unit and the multi-level regional power adjustment unit; in addition, the unit is responsible for returning feedback information to the intelligent algorithm optimization module.

[0019] As a preferred solution of the intelligent temperature-controlled fan blade electric deicing control system described in the present invention, the intelligent algorithm optimization module dynamically adjusts the modular heating and power allocation strategy through real-time feedback information, including surface temperature changes, energy efficiency and deicing progress; and adopts a fuzzy logic algorithm combined with feedback information to output the updated heating duration and frequency allocation plan.

[0020] In a second aspect, the present invention provides an intelligent temperature-controlled fan blade electric deicing control method, comprising:

[0021] Step S1, collecting raw data, including ice thickness, distribution, environmental conditions and regional priorities, generating a demand matrix based on the raw data, partitioning the blades according to the demand matrix, assigning different priorities to different partitions, and calculating the heating demand for each partition;

[0022] Step S2, according to the demand matrix threshold, only activate the modular heating units corresponding to the high demand area, and keep other heating units on standby;

[0023] After activation, it uses multi-level control technology to dynamically allocate power and adjust heating intensity according to regional demand;

[0024] Step S3: In each heating cycle, feedback information of the blades is continuously collected, including surface temperature changes, energy efficiency, and deicing progress, and the heating strategy effects of the modular heating unit and the multi-level regional power adjustment unit are evaluated. During the evaluation process, the temperature recovery rate and energy efficiency are used as evaluation indicators to generate a feedback report.

[0025] Step S4, based on the feedback report, calling the fuzzy logic algorithm to adjust the periodic control law parameters, and outputting the updated heating duration and frequency;

[0026] Step S5, directly applying the adjusted heating duration and frequency to the modular heating unit and the multi-level regional power adjustment unit to adjust the power and cycle of each heating module in real time;

[0027] In step S5, feedback data is further collected for use in the next control cycle to continuously optimize heating efficiency and energy consumption.

[0028] As a preferred solution of the intelligent temperature-controlled fan blade electric deicing control method of the present invention, the steps of partitioning the blades according to the demand matrix, assigning different priorities to different partitions, and calculating the heating demand for each partition are as follows:

[0029] Assume that the blade surface is divided into n×m grids, and each grid unit is represented by P ij ;

[0030] The collected raw data include: ij Represents the grid P ij Ice thickness at T ij Represents the grid P ij The ambient temperature, W ij Represents the grid P ij The ambient humidity at ij Represents the grid P ijThe regional priority is defined based on the original data. ij , the matrix formula is:

[0031] D ij =α1·H ij +α2·f(T ij )+α3·g(W ij )+α4·U ij ,

[0032] Among them, α1, α2, α3, α4 represent the weight factors of ice thickness, temperature, humidity and regional priority, respectively, satisfying α1+α2+α3+α4=1, where f(T ij ) is related to temperature T ij The related nonlinear function, Where K1 is the temperature adjustment coefficient, g(W ij ) is related to humidity W ij The relevant nonlinear function, g(W ij )=ln(1+W ij / K2), where K2 is the humidity adjustment coefficient;

[0033] The demand matrix D ij According to the threshold partition, define the area category R ij :

[0034] If D ij >τ1, then R ij =3,

[0035] If τ2≤D ij ≤τ1, then R ij =2,

[0036] If D ij <τ2, then R ij =1,

[0037] Among them, R ij is the regional category, and the higher the value, the higher the demand priority. τ1 and τ2 are the demarcation thresholds of high demand area and medium demand area, respectively;

[0038] Calculate the heating demand of each zone using the following formula:

[0039] Q ij =β·H ij ·D ij ,

[0040] Among them, Q ij Represents the grid P ij The heating demand is, where β is the heating efficiency adjustment factor;

[0041] Calculate the total heating demand for each zone using the formula:

[0042]

[0043] Among them, Q total,k represents the total heating demand of the kth zone, P k represents the set of all grids belonging to the kth region;

[0044] According to the total heating demand Q total,k Sort from large to small and determine the priority of each area P k :

[0045] P k =rank(Q total,k ), where P k Indicates the priority of the kth region. The smaller the value, the higher the priority.

[0046] As a preferred solution of the intelligent temperature-controlled fan blade electric deicing control method of the present invention, the step of activating only the modular heating units corresponding to the high-demand area and keeping other heating units on standby according to the demand matrix threshold is as follows:

[0047] According to the demand matrix D generated in step S1 ij and the partition priority P k , determine the conditions for the activation region:

[0048] If P k ≤P threshold , then activate,

[0049] If P k >P threshold , then standby, where P threshold Indicates the priority threshold. Only the priority not higher than P is activated. threshold area,

[0050] Each heating module M k Corresponding to a region P k , activation module state matrix A k for:

[0051] If P k ≤P threshold , then A k =1,

[0052] If P k >P threshold , then A k =0,

[0053] Among them, A k =1 means the module is activated, A k=0 means standby;

[0054] For each activation region P k , the total heating demand of the zone Q total,k Converted into preliminary power demand, the conversion formula is:

[0055]

[0056] in, is the initial power demand, γ is the power demand adjustment factor,

[0057] Assume that the total system power budget is P max , then the actual power P is allocated to all active areas k The following constraints are met:

[0058] ∑ k∈激活区域 P k ≤P max ,

[0059] The normalization method is used for allocation, and the allocation formula is:

[0060]

[0061] For each activated zone, the heating module adjusts the power level L according to the ice thickness and demand. k , the adjustment formula is:

[0062] If D ij >τ1, then L k =L max ,

[0063] If τ2≤D ij ≤τ1, then L k =L mid ,

[0064] If D ij <τ2, then L k =L min ,

[0065] Among them, L max ,L mid ,L min Respectively represent high, medium and low power levels;

[0066] Based on level L k , adjust the modular heating unit M k The output power is adjusted as follows:

[0067]

[0068] in, is the actual output power,

[0069] Each heating module is based on the actual output power And feedback temperature adjusts the output, the adjustment formula is:

[0070] ΔP k =λ·(T target -T current,k ),

[0071] Among them, ΔP k is the power correction value, and λ is the adjustment factor.

[0072] As a preferred solution of the intelligent temperature-controlled fan blade electric deicing control method of the present invention, the step of evaluating the heating strategy effect of the modular heating unit and the multi-level regional power adjustment unit is as follows:

[0073] Define evaluation metrics, including:

[0074] Temperature recovery rate R k :Describe region R k The rate at which the temperature changes from the current value to the target value,

[0075] Energy efficiency η k : describes the change in ice thickness corresponding to unit energy consumption,

[0076] De-icing Progress C k : describes the actual de-icing amount in the area;

[0077] The formula is defined as:

[0078]

[0079] Among them, T current,k Indicates the current temperature, T start,k represents the starting temperature, Δt is the time interval,

[0080]

[0081] Where ΔH k is the reduction of ice thickness per unit time, P k is the power output,

[0082]

[0083] Among them, C k Indicates the cumulative de-icing progress;

[0084] Using the signal transmission unit, the following feedback data is obtained during each heating cycle:

[0085] Current surface temperature of the area T current,k, the current power allocation value P k and the current ice thickness change ΔH k ,

[0086] The above indicators are combined into a comprehensive heating effect score S k :

[0087] S k =ω1·R k +ω2·η k +ω3·C k ,

[0088] Among them, ω1, ω2, ω3 are weight coefficients, satisfying ω1+ω2+ω3=1, S k The larger the value, the better the heating strategy is;

[0089] For all activated regions R k , calculate the global comprehensive score S total , the calculation formula is:

[0090]

[0091] Evaluate the global strategy effect in a weighted average manner and generate a feedback report, including:

[0092] Temperature variation curve T of each area k (t), energy consumption and deicing progress diagram of each area and the global score S total and suggestions for adjusting the weights of each indicator;

[0093] Compare the actual effect total With the expected effect S target :

[0094] ΔS=S total -S target ,

[0095] If ΔS<0, it means that the strategy effect is lower than expected and the strategy needs to be adjusted.

[0096] Use feedback deviation ΔS to adjust the strategy focus direction for the next cycle:

[0097] If ΔS<0, increase the power allocation priority.

[0098] If ΔS ≥ 0, keep the current strategy.

[0099] As a preferred solution of the intelligent temperature-controlled fan blade electric deicing control method of the present invention, the step of calling the fuzzy logic algorithm to adjust the periodic control law parameters based on the feedback report and outputting the updated heating time and frequency is as follows:

[0100] Collect deviation information, temperature recovery rate and energy efficiency in feedback reports;

[0101] The deviation information, temperature recovery rate and energy efficiency are divided into low, medium and high fuzzy sets according to different ranges, and the membership degree of each range is expressed by triangular membership function.

[0102] Through the preset fuzzy rule table, the adjustment strategy is determined according to the input membership:

[0103] When the temperature recovery rate is slow and the energy efficiency is low, it is preferred to increase the heating time or power.

[0104] When the energy efficiency is high and de-icing progress is fast, the power allocation is reduced to reduce energy consumption;

[0105] The fuzzy logic output is defuzzified to obtain specific adjustment parameters, including new heating duration and heating frequency. According to the new duration and frequency, the current periodic control law parameters are adjusted.

[0106] The beneficial effects of the present invention are as follows: the present invention collects the ice thickness, temperature, humidity and environmental conditions of the blade surface in real time through the ice monitoring unit, generates a demand matrix, and divides the blade into high-demand, medium-demand and low-demand areas. Under the guidance of the demand matrix, only the modules in the high-demand area are activated, and the unactivated modules remain on standby to save energy. At the same time, the multi-level power adjustment unit dynamically allocates power according to the heating demand of the activated area, matches the ice thickness and actual demand of different areas, and continuously collects feedback information in each heating cycle, including temperature changes on the blade surface, energy efficiency and deicing progress, etc., generates a feedback report, and optimizes the deicing strategy.

[0107] The present invention adjusts the periodic control law parameters through a fuzzy logic algorithm, comprehensively considers the temperature recovery rate, energy efficiency and feedback deviation information, and adjusts the heating duration and frequency; the fuzzy logic algorithm combines the multi-dimensional indicators in the feedback report, dynamically optimizes the control law parameters based on rule reasoning and membership calculation, thereby realizing adaptive heating strategy optimization; the adjusted parameters directly act on the modular heating unit and the multi-level power adjustment unit, adjust the heating intensity and period of each module in real time, and further collect feedback data for the next control cycle, so as to continuously improve the heating efficiency and energy consumption management level. BRIEF DESCRIPTION OF THE DRAWINGS

[0108] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0109] Figure 1 It is a structural schematic diagram of the intelligent temperature-controlled fan blade electric deicing control system of the present invention.

[0110] Figure 2 It is a schematic flow chart of the intelligent temperature-controlled fan blade electric deicing control method of the present invention. DETAILED DESCRIPTION

[0111] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0112] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0113] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0114] Example 1, reference Figure 1 and Figure 2 This embodiment provides an intelligent temperature-controlled fan blade electric deicing control system, including:

[0115] Icing monitoring unit, composed of multiple sensors, including temperature, humidity and ice thickness sensors, is used to monitor the ice condition on the surface of wind turbine blades in real time, including ice thickness, distribution and environmental conditions, and define the regional priority of different blades;

[0116] The modular heating unit includes multiple heating modules, which are used to heat the surface of the fan blades in different areas independently. Each heating module corresponds to an independent blade area and has an independent circuit and control system.

[0117] The modular heating unit activates specific heating zones according to the demand matrix, and the inactivated modules remain in standby mode; at the same time, the unit adjusts the heating allocation power through the multi-level regional power adjustment unit;

[0118] The multi-level regional power adjustment unit works with the intelligent algorithm optimization module to dynamically adjust the power output of the activated heating module and allocate heating intensity to different areas according to ice thickness and feedback information through multi-level control technology;

[0119] Signal transmission unit, used to connect all modules and units;

[0120] The signal transmission unit transmits the data collected by the icing monitoring unit to the data processing module, and sends the calculation results of the data processing module to the modular heating unit and the multi-level regional power adjustment unit; in addition, this unit is responsible for returning feedback information to the intelligent algorithm optimization module;

[0121] The data processing module is used to receive the raw data from the icing monitoring unit, perform preprocessing and generate a demand matrix; the demand matrix is ​​calculated based on ice thickness, distribution, environmental conditions and regional priority, and serves as the control basis for the modular heating unit; at the same time, this module provides real-time feedback information for the intelligent algorithm optimization module;

[0122] Intelligent algorithm optimization module, used to evaluate the effect of the current heating strategy and adjust the cycle control law parameters;

[0123] The intelligent algorithm optimization module dynamically adjusts the modular heating and power allocation strategies through real-time feedback information, including surface temperature changes, energy efficiency and de-icing progress; it uses fuzzy logic algorithms combined with feedback information to output updated heating duration and frequency allocation plans;

[0124] Feedback collection and analysis module, used to continuously collect blade feedback information and environmental parameters in each heating cycle, and transmit them to the data processing module and intelligent algorithm optimization module through the signal transmission unit for the next round of heating strategy evaluation and adjustment;

[0125] The main control and coordination module is responsible for managing the activation and deactivation of the modular heating units, power adjustment, and execution of the cycle control law.

[0126] This embodiment also provides an intelligent temperature-controlled fan blade electric deicing control method, including:

[0127] Step S1, collecting raw data, including ice thickness, distribution, environmental conditions and regional priorities, generating a demand matrix based on the raw data, partitioning the blades according to the demand matrix, assigning different priorities to different partitions, and calculating the heating demand for each partition;

[0128] The steps of partitioning the blades according to the demand matrix, assigning different priorities to different partitions, and calculating the heating demand of each partition are as follows:

[0129] Assume that the blade surface is divided into n×m grids, and each grid unit is represented by P ij ;

[0130] The collected raw data include: ij Represents the grid P ij Ice thickness at T ijRepresents the grid P ij The ambient temperature, W ij Represents the grid P ij The ambient humidity at ij Represents the grid P ij The regional priority is defined based on the original data. ij , the matrix formula is:

[0131] D ij =α1·H ij +α2·f(T ij )+α3·g(W ij )+α4·U ij ,

[0132] Among them, α1, α2, α3, α4 represent the weight factors of ice thickness, temperature, humidity and regional priority, respectively, satisfying α1+α2+α3+α4=1, where f(T ij ) is related to temperature T ij The related nonlinear function, Where K1 is the temperature adjustment coefficient, g(W ij ) is related to humidity W ij The relevant nonlinear function, g(W ij )=ln(1+W ij / K2), where K2 is the humidity adjustment coefficient;

[0133] The demand matrix D ij According to the threshold partition, define the area category R ij :

[0134] If D ij >τ1, then R ij =3,

[0135] If τ2≤D ij ≤τ1, then R ij =2,

[0136] If D ij <τ2, then R ij =1,

[0137] Among them, R ij is the regional category, and the higher the value, the higher the demand priority. τ1 and τ2 are the demarcation thresholds of high demand area and medium demand area, respectively;

[0138] Calculate the heating demand of each zone using the following formula:

[0139] Q ij =β·H ij ·D ij ,

[0140] Among them, Q ij Represents the grid P ij The heating demand is, where β is the heating efficiency adjustment factor;

[0141] Calculate the total heating demand for each zone using the formula:

[0142]

[0143] Among them, Q total,k represents the total heating demand of the kth zone, R k represents the set of all grids belonging to the kth region;

[0144] According to the total heating demand Q total,k Sort from large to small and determine the priority of each area P k :

[0145] P k =rank(Q total,k ), where P k Indicates the priority of the kth region. The smaller the value, the higher the priority.

[0146] Specifically, by collecting and comprehensively analyzing information such as ice thickness, temperature, humidity and priority on the surface of wind turbine blades, a demand matrix is ​​constructed, and the blade surface is partitioned and prioritized according to the matrix, combining the actual characteristics of ice formation with energy requirements.

[0147] Step S2, according to the demand matrix threshold, only activate the modular heating units corresponding to the high demand area, and keep other heating units on standby;

[0148] After activation, it uses multi-level control technology to dynamically allocate power and adjust heating intensity according to regional demand;

[0149] According to the demand matrix threshold, only the modular heating units corresponding to the high demand area are activated, and the steps of keeping other heating units on standby are as follows:

[0150] According to the demand matrix D generated in step S1 ij and the partition priority P k , determine the conditions for the activation region:

[0151] If P k ≤P threshold , then activate,

[0152] If P k >P threshold , then standby, where P threshold Indicates the priority threshold. Only the priority not higher than P is activated. threshold area,

[0153] Each heating module M k Corresponding to a region P k , activation module state matrix A k for:

[0154] If P k ≤P threshold , then A k =1,

[0155] If P k >P threshold , then A k =0,

[0156] Among them, A k =1 means the module is activated, A k =0 means standby;

[0157] For each activation region P k , the total heating demand of the zone Q total,k Converted into preliminary power demand, the conversion formula is:

[0158]

[0159] in, is the initial power demand, γ is the power demand adjustment factor,

[0160] Assume that the total system power budget is P max , then the actual power R is allocated to all active areas k The following constraints are met:

[0161] ∑ k∈激活区域 P k ≤P max ,

[0162] The normalization method is used for allocation, and the allocation formula is:

[0163]

[0164] For each activated zone, the heating module adjusts the power level L according to the ice thickness and demand. k , the adjustment formula is:

[0165] If D ij >τ1, then L k =L max ,

[0166] If τ2≤D ij ≤τ1, then L k =L mid ,

[0167] If D ij <τ2, then L k =L min ,

[0168] Among them, L max ,L mid ,L min Respectively represent high, medium and low power levels;

[0169] Based on level L k , adjust the modular heating unit M k The output power is adjusted as follows:

[0170]

[0171] in, is the actual output power,

[0172] Each heating module is based on the actual output power And feedback temperature adjusts the output, the adjustment formula is:

[0173] ΔP k =λ·(T target -T current,k ),

[0174] Among them, ΔP k is the power correction value, λ is the adjustment factor,

[0175] Specifically, the demand matrix threshold activation strategy is used to start the heating unit in the active and standby states, effectively reducing unnecessary energy consumption. After activation, the multi-level control technology is used to dynamically adjust the heating intensity. During the allocation process, the total power limit is met while matching the actual power with the heating demand.

[0176] Step S3: In each heating cycle, feedback information of the blades is continuously collected, including surface temperature changes, energy efficiency, and deicing progress, and the heating strategy effects of the modular heating unit and the multi-level regional power adjustment unit are evaluated. During the evaluation process, the temperature recovery rate and energy efficiency are used as evaluation indicators to generate a feedback report.

[0177] The steps for evaluating the heating strategy effect of modular heating units and multi-level regional power adjustment units are as follows:

[0178] Define evaluation metrics, including:

[0179] Temperature recovery rate R k :Describe region R k The rate at which the temperature changes from the current value to the target value,

[0180] Energy efficiency η k: describes the change in ice thickness corresponding to unit energy consumption,

[0181] De-icing Progress C k : describes the actual de-icing amount in the area;

[0182] The formula is defined as:

[0183]

[0184] Among them, T current,k Indicates the current temperature, T start,k represents the starting temperature, Δt is the time interval,

[0185]

[0186] Where ΔH k is the reduction of ice thickness per unit time, P k is the power output,

[0187]

[0188] Among them, C k Indicates the cumulative de-icing progress;

[0189] Using the signal transmission unit, the following feedback data is obtained during each heating cycle:

[0190] Current surface temperature of the area T current,k , the current power allocation value P k and the current ice thickness change ΔH k ,

[0191] The above indicators are combined into a comprehensive heating effect score S k :

[0192] S k =ω1·R k +ω2·η k +ω3·C k ,

[0193] Among them, ω1, ω2, ω3 are weight coefficients, satisfying ω1+ω2+ω3=1, S k The larger the value, the better the heating strategy is;

[0194] For all activated regions R k , calculate the global comprehensive score S total , the calculation formula is:

[0195]

[0196] Evaluate the global strategy effect in a weighted average manner and generate a feedback report, including:

[0197] Temperature variation curve T of each area k (t), energy consumption and deicing progress diagram of each area and the global score S total and suggestions for adjusting the weights of each indicator;

[0198] Compare the actual effect total With the expected effect S target :

[0199] ΔS=S total -S target ,

[0200] If ΔS<0, it means that the strategy effect is lower than expected and the strategy needs to be adjusted.

[0201] Use feedback deviation ΔS to adjust the strategy focus direction for the next cycle:

[0202] If ΔS<0, increase the power allocation priority.

[0203] If ΔS ≥ 0, keep the current strategy.

[0204] Specifically, in this step, indicators are defined, a comprehensive scoring model is constructed, a multi-dimensional evaluation of the actual effect of the heating strategy is performed, and a feedback report is generated, which intuitively shows the current operating status of the system and the need for strategy adjustment.

[0205] Step S4, based on the feedback report, calling the fuzzy logic algorithm to adjust the periodic control law parameters, and outputting the updated heating duration and frequency;

[0206] Based on the feedback report, the fuzzy logic algorithm is called to adjust the periodic control law parameters, and the steps to output the updated heating duration and frequency are as follows:

[0207] Collect deviation information, temperature recovery rate and energy efficiency in feedback reports;

[0208] The deviation information, temperature recovery rate and energy efficiency are divided into low, medium and high fuzzy sets according to different ranges, and the membership degree of each range is expressed by triangular membership function.

[0209] Through the preset fuzzy rule table, the adjustment strategy is determined according to the input membership:

[0210] When the temperature recovery rate is slow and the energy efficiency is low, it is preferred to increase the heating time or power.

[0211] When the energy efficiency is high and de-icing progress is fast, the power allocation is reduced to reduce energy consumption;

[0212] Defuzzification is performed on the fuzzy logic output to obtain specific adjustment parameters, including new heating duration and heating frequency, and the current periodic control law parameters are adjusted according to the new duration and frequency;

[0213] Specifically, the multi-dimensional indicators in the comprehensive feedback report of the fuzzy logic algorithm, with rule reasoning and fuzzy membership calculation as the core, realize nonlinear dynamic adjustment; it can adaptively optimize the control law parameters, effectively improve the heating efficiency and save energy. Compared with fixed strategies, this method significantly enhances the adaptability to complex environments and improves the stability and economy of de-icing.

[0214] Step S5, directly applying the adjusted heating duration and frequency to the modular heating unit and the multi-level regional power adjustment unit to adjust the power and cycle of each heating module in real time;

[0215] In step S5, feedback data is further collected for use in the next control cycle to continuously optimize heating efficiency and energy consumption.

[0216] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. Intelligent temperature-controlled fan blade electric heating deicing control system, characterized by: include, Icing monitoring unit, composed of multiple sensors, including temperature, humidity and ice thickness sensors, is used to monitor the ice condition on the surface of wind turbine blades in real time, including ice thickness, distribution and environmental conditions, and define the regional priority of different blades; The modular heating unit includes multiple heating modules, which are used to heat the surface of the fan blades in different areas independently. Each heating module corresponds to an independent blade area and has an independent circuit and control system. The multi-level regional power adjustment unit, in collaboration with the intelligent algorithm optimization module, is used to dynamically adjust the power output of the activated heating modules, and allocates heating intensity to different areas according to ice thickness and feedback information through multi-level control technology; Signal transmission unit, used to connect all modules and units; The data processing module is used to receive the raw data from the icing monitoring unit, perform preprocessing and generate a demand matrix; the demand matrix is ​​calculated based on ice thickness, distribution, environmental conditions and regional priority, and serves as the control basis for the modular heating unit; at the same time, this module provides real-time feedback information for the intelligent algorithm optimization module.

2. The intelligent temperature-controlled electric deicing control system for fan blades according to claim 1, characterized in that: Also includes: Intelligent algorithm optimization module, used to evaluate the effect of the current heating strategy and adjust the cycle control law parameters; Feedback collection and analysis module, used to continuously collect blade feedback information and environmental parameters in each heating cycle, and transmit them to the data processing module and intelligent algorithm optimization module through the signal transmission unit for the next round of heating strategy evaluation and adjustment; The main control and coordination module is responsible for managing the activation and deactivation of the modular heating units, power adjustment, and execution of the cycle control law.

3. The intelligent temperature-controlled electric deicing control system for fan blades according to claim 2, characterized in that: The modular heating unit activates specific heating areas according to the demand matrix, and the unactivated modules remain in a standby state; at the same time, the unit adjusts the heating distribution power through a multi-level regional power adjustment unit.

4. The intelligent temperature-controlled electric deicing control system for fan blades according to claim 3, characterized in that: The signal transmission unit transmits the data collected by the icing monitoring unit to the data processing module, and sends the calculation results of the data processing module to the modular heating unit and the multi-level regional power adjustment unit; in addition, the unit is responsible for returning feedback information to the intelligent algorithm optimization module.

5. The intelligent temperature-controlled electric deicing control system for fan blades according to claim 4, characterized in that: The intelligent algorithm optimization module dynamically adjusts the modular heating and power allocation strategies through real-time feedback information, including surface temperature changes, energy efficiency and de-icing progress; and uses a fuzzy logic algorithm combined with feedback information to output updated heating duration and frequency allocation plans.

6. An intelligent temperature-controlled electric deicing control method for fan blades, based on the intelligent temperature-controlled electric deicing control system for fan blades according to any one of claims 1 to 5, characterized in that: include: Step S1, collecting raw data, including ice thickness, distribution, environmental conditions and regional priority, generating a demand matrix based on the raw data, partitioning the blades according to the demand matrix, assigning different priorities to different partitions, and calculating the heating demand for each partition; Step S2, according to the demand matrix threshold, only activate the modular heating units corresponding to the high demand area, and keep other heating units on standby; After activation, it uses multi-level control technology to dynamically allocate power and adjust heating intensity according to regional demand; Step S3: In each heating cycle, feedback information of the blades is continuously collected, including surface temperature changes, energy efficiency, and deicing progress, and the heating strategy effects of the modular heating unit and the multi-level regional power adjustment unit are evaluated. During the evaluation process, the temperature recovery rate and energy efficiency are used as evaluation indicators to generate a feedback report. Step S4, based on the feedback report, calling the fuzzy logic algorithm to adjust the periodic control law parameters, and outputting the updated heating duration and frequency; Step S5, directly applying the adjusted heating duration and frequency to the modular heating unit and the multi-level regional power adjustment unit to adjust the power and cycle of each heating module in real time; In step S5, feedback data is further collected for use in the next control cycle to continuously optimize heating efficiency and energy consumption.

7. The intelligent temperature-controlled fan blade electric deicing control method according to claim 6, characterized in that: The steps of partitioning the blades according to the demand matrix, assigning different priorities to different partitions, and calculating the heating demand of each partition are as follows: Assume that the blade surface is divided into n×m grids, and each grid unit is represented by Pi j ; The collected raw data include: ij Represents the grid P ij Ice thickness at T ij Represents the grid P ij The ambient temperature, W ij Represents the grid P ij The ambient humidity at ij Represents the grid P ij The regional priority is defined based on the original data. 小 The matrix formula is: D ij =α1·H ij +α2·f(T ij )+α3·g(W ij )+α4·U ij , Among them, α1, α2, α3, α4 represent the weight factors of ice thickness, temperature, humidity and regional priority respectively, satisfying α1+α2+α3+α4=1, where f(T ij ) is related to temperature T ij The related nonlinear function, Where K1 is the temperature adjustment coefficient, g(W ij ) is related to humidity W ij The relevant nonlinear function, g(W ij )=ln(1+W ij / K2), where K2 is the humidity adjustment coefficient; The demand matrix D ij According to the threshold partition, define the area category R ij : If D ij >τ1, then R ij =3, If τ2≤D ij ≤τ1, then R ij =2, If D ij <τ2, then R ij =1, Among them, R ij is the regional category, and the higher the value, the higher the demand priority. τ1 and τ2 are the demarcation thresholds of high demand area and medium demand area, respectively; Calculate the heating demand of each zone using the following formula: Q ij =β·H ij ·D ij , Among them, Q ij Represents the grid P ij The heating demand, where β is the heating efficiency adjustment factor; Calculate the total heating demand for each zone using the formula: Among them, Q total，k represents the total heating demand of the kth zone, R k represents the set of all grids belonging to the kth region; According to the total heating demand Q total，k Sort from large to small and determine the priority of each area P k : P k =rank(Q total，k ), where P k Indicates the priority of the kth region. The smaller the value, the higher the priority.

8. The intelligent temperature-controlled fan blade electric deicing control method according to claim 7, characterized in that: The step of activating only the modular heating units corresponding to the high demand areas according to the demand matrix threshold and keeping other heating units on standby is: According to the demand matrix D generated in step S1 ij and the partition priority P k , determine the conditions for the activation region: If P k ≤P threshold , then activate, If P k >P threshold , then standby, where P threshold Indicates the priority threshold. Only the priority not higher than P is activated. threshold area, Each heating module M k Corresponding to a region R k , activation module state matrix A k for: If P k ≤P threshold , then A k =1, If P k >P threshold , then A k =0, Among them, A k =1 means the module is activated, A k =0 means standby; For each activation region R k , the total heating demand of the zone Q total,k Converted into preliminary power demand, the conversion formula is: in, is the initial power demand, γ is the power demand adjustment factor, Assume that the total system power budget is P max , then the actual power P is allocated to all active areas k The following constraints are met: ∑ k∈激活区域 P k ≤P max , The normalization method is used for allocation, and the allocation formula is: For each activated zone, the heating module adjusts the power level L according to the ice thickness and demand. k , the adjustment formula is: If D ij >τ1, then L k =L max , If τ2≤D ij ≤τ1, then L k =L mid , If D ij <τ2, then L k =L min , Among them, L max , L mid , L min Respectively represent high, medium and low power levels; Based on level L k , adjust the modular heating unit M k The output power is adjusted as follows: in, is the actual output power, Each heating module is based on the actual output power And feedback temperature adjusts the output, the adjustment formula is: ΔP k =λ·(T target -T current，k ), Where ΔP k is the power correction value, and λ is the adjustment factor.

9. The intelligent temperature-controlled fan blade electric deicing control method according to claim 8, characterized in that: The step of evaluating the heating strategy effect of the modular heating unit and the multi-level regional power adjustment unit is as follows: Define evaluation metrics, including: Temperature recovery rate R k :Describe region R k The rate at which the temperature changes from the current value to the target value, Energy efficiency η k : describes the change in ice thickness corresponding to unit energy consumption, De-icing Progress C k : describes the actual de-icing amount in the area; The formula is defined as: Among them, T current，k Indicates the current temperature, T start，k represents the starting temperature, Δt is the time interval, Where ΔH k is the reduction of ice thickness per unit time, P k is the power output, Among them, C k Indicates the cumulative de-icing progress; Using the signal transmission unit, the following feedback data is obtained during each heating cycle: Current surface temperature of the area T current，k , the current power allocation value P k and the current ice thickness change ΔH k , The above indicators are combined into a comprehensive heating effect score S k : S k =ω1·R k +ω2·η k +ω3·C k , Among them, ω1, ω2, ω3 are weight coefficients, satisfying ω1+ω2+ω3=1, S k The larger the value, the better the heating strategy is; For all activated regions R k , calculate the global comprehensive score S total , the calculation formula is: Evaluate the global strategy effect in a weighted average manner and generate a feedback report, including: Temperature variation curve T of each area k (t), energy consumption and deicing progress diagram of each area and the global score S total and suggestions for adjusting the weights of each indicator; Compare the actual effect total With the expected effect S target : ΔS=S total -S target , If ΔS < 0, it means that the strategy effect is lower than expected and the strategy needs to be adjusted. Use feedback deviation ΔS to adjust the strategy focus direction for the next cycle: If ΔS<0, increase the power allocation priority. If ΔS ≥ 0, keep the current strategy.

10. The intelligent temperature-controlled fan blade electric deicing control method according to claim 9, characterized in that: The steps of calling the fuzzy logic algorithm to adjust the periodic control law parameters based on the feedback report and outputting the updated heating duration and frequency are as follows: Collect deviation information, temperature recovery rate and energy efficiency in feedback reports; The deviation information, temperature recovery rate and energy efficiency are divided into low, medium and high fuzzy sets according to different ranges, and the membership degree of each range is expressed by triangular membership function. Through the preset fuzzy rule table, the adjustment strategy is determined according to the input membership: When the temperature recovery rate is slow and the energy efficiency is low, it is preferred to increase the heating time or power. When the energy efficiency is high and de-icing progress is fast, the power allocation is reduced to reduce energy consumption; The fuzzy logic output is defuzzified to obtain specific adjustment parameters, including new heating duration and heating frequency. According to the new duration and frequency, the current periodic control law parameters are adjusted.

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