CN112578232A - Lightning early warning method and lightning early warning device of wind generating set - Google Patents
Lightning early warning method and lightning early warning device of wind generating set Download PDFInfo
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Abstract
The invention provides a lightning early warning method and lightning early warning equipment of a wind generating set. The lightning early warning method comprises the following steps: predicting the lightning risk of a plurality of wind generating sets based on machine position data of the wind generating sets in the wind power plant and real-time lightning data of the wind power plant; determining a lightning early warning level of each wind generating set in the plurality of wind generating sets based on the lightning risk size of the plurality of wind generating sets; and generating and outputting at least one piece of thunder early warning information according to the thunder early warning level of each wind generating set. The lightning early warning method can accurately and timely carry out lightning risk early warning.
Description
Technical Field
The invention relates to the field of wind power generation, in particular to a lightning early warning method and lightning early warning equipment of a wind generating set.
Background
Wind power generation is one of the most important clean power generation energy sources in recent years, and safe and stable operation of a wind power generator set is particularly important for stabilizing a power grid and ensuring power generation amount. With the rapid layout of wind generating sets in recent years, the adaptability problem of the wind generating sets to the environment is gradually outstanding, the whole industrial chain of wind power generation is emphasized by the problems of high turbulence, large shear, thunder and the like, and the wind generating sets can effectively resist the severe natural conditions from suppliers, complete manufacturers to power generation enterprises only by level-to-level control.
Lightning protection is the most important electrical design link of a wind generating set, and the design of each link such as a blade, a generator, an electrical cabinet and a tower frame needs to meet the international lightning protection standard, but the current wind generating set is only limited to lightning protection, and a master control system of the wind generating set only monitors whether each lightning protection system normally operates or not, so that timely warning can not be formed on the condition after the lightning occurs.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a lightning early warning method and lightning early warning equipment for a wind generating set, which are used for early warning lightning stroke damage of the wind generating set, reminding operation and maintenance personnel to pay attention to protection, enabling the operation and maintenance personnel to timely find potential blade damage caused by the lightning stroke, and avoiding serious accidents such as serious blade fracture, falling and the like caused by the lightning phenomenon.
One aspect of the present invention is to provide a lightning early warning method for a wind turbine generator system, the lightning early warning method including: predicting the lightning risk of a plurality of wind generating sets based on machine position data of the wind generating sets in the wind power plant and real-time lightning data of the wind power plant; determining a lightning early warning level of each wind generating set in the plurality of wind generating sets based on the lightning risk size of the plurality of wind generating sets; and generating and outputting at least one piece of thunder early warning information according to the thunder early warning level of each wind generating set.
The lightning early warning method further comprises the following steps: establishing a thunder risk prediction model according to the machine location data of the wind generating sets, the historical thunder data of the wind power plant and the historical thunder risk data of the wind generating sets, wherein the historical thunder risk data indicates the known thunder risk of the wind generating sets, and the historical thunder data is associated with the historical thunder risk data. The step of predicting the lightning risk size of the plurality of wind turbine generators comprises the following steps: and predicting the lightning risk of the wind generating sets by utilizing the lightning risk prediction model based on the machine location data of the wind generating sets and the real-time lightning data of the wind power plant.
The lightning early warning method further comprises the following steps: grouping the plurality of wind turbine generator sets based on the machine site data of the plurality of wind turbine generator sets to generate at least one wind turbine generator group; predicting a lightning risk size of the at least one wind turbine group based on the lightning risk sizes of the plurality of wind turbine groups.
The step of determining a lightning early warning level for each of the plurality of wind power generation units comprises: determining a lightning early warning level for each of the plurality of wind turbine generator sets based on the lightning risk size of the plurality of wind turbine generator sets and the lightning risk size of the at least one wind turbine generator set group.
The lightning early warning method further comprises the following steps: selecting a wind generating set within a specific range from the plurality of wind generating sets based on the machine site data of the plurality of wind generating sets and the real-time lightning data of the wind power plant; monitoring the operation data of the wind generating sets in the specific range within a preset time period to determine the operation condition of the blades of the wind generating sets in the specific range within the preset time period, wherein the operation condition indicates whether the blades of the wind generating sets in the specific range are normally operated within the preset time period, and the preset time period is determined according to the real-time lightning data of the wind power plant. The step of determining a lightning early warning level for each of the plurality of wind power generation units comprises: determining a lightning early warning level for each of the plurality of wind turbine generator sets based on the operational condition of the blades of the wind turbine generator sets within the particular range and based on the lightning risk size of the plurality of wind turbine generator sets.
The lightning early warning method further comprises the following steps: selecting a wind generating set within a specific range from the plurality of wind generating sets based on the machine site data of the plurality of wind generating sets and the real-time lightning data of the wind power plant; monitoring the operation data of the wind generating sets in the specific range within a preset time period to determine the operation condition of the blades of the wind generating sets in the specific range within the preset time period, wherein the operation condition indicates whether the blades of the wind generating sets in the specific range are normally operated within the preset time period, and the preset time period is determined according to the real-time lightning data of the wind power plant. The step of determining a lightning early warning level for each of the plurality of wind power generation units comprises: determining a lightning early warning level for each of the plurality of wind turbine generator sets according to the operating conditions of the blades of the wind turbine generator sets within the specific range and based on the lightning risk size of the plurality of wind turbine generator sets and/or the lightning risk size of the at least one wind turbine generator set group.
The step of determining the operating condition of the blades of the wind turbine generator set within the specific range comprises: calculating the characteristic frequency and/or the characteristic amplitude of the nacelle acceleration of the wind generating sets in the specific range in the preset time period based on the operation data of the wind generating sets in the specific range in the preset time period; and determining whether the blades of the wind generating set in the specific range normally operate in the preset time period according to the characteristic frequency and/or the characteristic amplitude.
Another aspect of the present invention is to provide a lightning early warning device of a wind turbine generator system, the lightning early warning device including: a lightning risk prediction module configured to: predicting the lightning risk of a plurality of wind generating sets based on machine position data of the wind generating sets in the wind power plant and real-time lightning data of the wind power plant; and a lightning early warning module configured to: determining a lightning early warning level of each wind generating set in the plurality of wind generating sets based on the lightning risk size of the plurality of wind generating sets; and generating and outputting at least one piece of thunder early warning information according to the thunder early warning level of each wind generating set.
The lightning risk prediction module is configured to: establishing a thunder risk prediction model according to the machine location data of the wind generating sets, the historical thunder data of the wind power plant and the historical thunder risk data of the wind generating sets, wherein the historical thunder risk data indicates the known thunder risk of the wind generating sets, and is associated with the historical thunder risk data; and predicting the lightning risk of the wind generating sets by utilizing the lightning risk prediction model based on the machine location data of the wind generating sets and the real-time lightning data of the wind power plant.
The lightning risk prediction module is configured to: grouping the plurality of wind turbine generator sets based on the machine site data of the plurality of wind turbine generator sets to generate at least one wind turbine generator group; predicting a lightning risk size of the at least one wind turbine group based on the lightning risk sizes of the plurality of wind turbine groups.
The lightning early warning module is configured to: determining a lightning early warning level for each of the plurality of wind turbine generator sets based on the lightning risk size of the plurality of wind turbine generator sets and the lightning risk size of the at least one wind turbine generator set group.
The lightning early warning device further comprises an operational data monitoring module configured to: selecting a wind generating set within a specific range from the plurality of wind generating sets based on the machine site data of the plurality of wind generating sets and the real-time lightning data of the wind power plant; monitoring the operation data of the wind generating sets in the specific range within a preset time period to determine the operation condition of the blades of the wind generating sets in the specific range within the preset time period, wherein the operation condition indicates whether the blades of the wind generating sets in the specific range are normally operated within the preset time period, and the preset time period is determined according to the real-time lightning data of the wind power plant. The lightning early warning module is configured to: determining a lightning early warning level for each of the plurality of wind turbine generator sets based on the operational condition of the blades of the wind turbine generator sets within the particular range and based on the lightning risk size of the plurality of wind turbine generator sets.
The lightning early warning device further comprises an operational data monitoring module configured to: selecting a wind generating set within a specific range from the plurality of wind generating sets based on the machine site data of the plurality of wind generating sets and the real-time lightning data of the wind power plant; monitoring the operation data of the wind generating sets in the specific range within a preset time period to determine the operation condition of the blades of the wind generating sets in the specific range within the preset time period, wherein the operation condition indicates whether the blades of the wind generating sets in the specific range are normally operated within the preset time period, and the preset time period is determined according to the real-time lightning data of the wind power plant. The lightning early warning module is configured to: determining a lightning early warning level for each of the plurality of wind turbine generator sets according to the operating conditions of the blades of the wind turbine generator sets within the specific range and based on the lightning risk size of the plurality of wind turbine generator sets and/or the lightning risk size of the at least one wind turbine generator set group.
The operational data monitoring module is configured to: calculating the characteristic frequency and/or the characteristic amplitude of the nacelle acceleration of the wind generating sets in the specific range in the preset time period based on the operation data of the wind generating sets in the specific range in the preset time period; and determining whether the blades of the wind generating set in the specific range normally operate in the preset time period according to the characteristic frequency and/or the characteristic amplitude.
Another aspect of the present invention is to provide a computing device comprising a processor and a readable medium storing a computer program, wherein the computer program comprises instructions for the processor to perform the steps of the lightning alerting method as described above.
Through the lightning early warning method and the lightning early warning device of the wind generating set, the following technical effects can be at least realized: different from a passive lightning protection system, the lightning early warning method and the lightning early warning device of the wind generating set can actively predict the lightning risk of the wind generating set, identify the potential blade damage condition of the wind generating set in time, and correct the prediction error caused by the position error of the machine site of the wind generating set through a clustering algorithm, so that the lightning early warning accuracy is improved, and more accurate lightning risk early warning is carried out; meanwhile, the thunder and lightning early warning method and the thunder and lightning early warning device can also apply statistical analysis and advanced machine learning algorithm to establish and optimize the thunder and lightning early warning model, the thunder and lightning early warning model can be deployed on a Management platform (for example, an intelligent prediction and Health Management (SPHM) system) of the wind generating set, timing and automatic distributed calculation of the thunder and lightning early warning model at the cloud end is achieved, and compared with a traditional single platform deployment application mode, the calculation speed is improved, the iteration speed is higher, and the calculation amount is larger.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Drawings
The above and other objects and features of exemplary embodiments of the present invention will become more apparent from the following description taken in conjunction with the accompanying drawings which illustrate exemplary embodiments, wherein:
FIG. 1 is a flow chart of a lightning early warning method of a wind turbine generator set according to an exemplary embodiment of the invention;
FIG. 2 is a flow chart of predicting lightning risk magnitude of a wind park according to an exemplary embodiment of the invention;
FIG. 3 is a flow chart of grouping a plurality of wind turbine generator sets and predicting lightning risk magnitude according to an exemplary embodiment of the invention;
FIG. 4 is a flow chart of monitoring an operational condition of a blade of a wind park according to an exemplary embodiment of the present invention;
FIG. 5 is a flow chart for determining a lightning warning level of a wind turbine generator set, according to an exemplary embodiment of the present invention;
fig. 6 is a block diagram of a lightning early warning device according to an exemplary embodiment of the invention.
The present invention will hereinafter be described in detail with reference to the drawings, wherein like or similar elements are designated by like or similar reference numerals throughout.
Detailed Description
In actual production, after a wind generating set component is struck by lightning, failure and warning cannot be timely formed in a main control system, and field operation and maintenance personnel cannot be informed to timely find the problem of lightning damage, so that the wind generating set runs with diseases for a long time, particularly, the blade is damaged frequently, the phenomena of skin shedding, cracking, lightning receptor falling and the like usually occur after the blade is struck by lightning, if abnormal monitoring characteristics such as over-limit fan vibration and the like are not caused, the lightning damage is difficult to find by central control personnel in a short time, the blade is damaged and rapidly worsened, and finally larger production accidents are caused, such as blade breakage and falling, tower sweeping and the like.
How to realize that wind generating set will damage the condition and inform well accuse personnel, operation and maintenance personnel in the short time after taking place the thunderbolt is a big difficult problem of current research, and this paper combines to realize that traditional industrial system combines with cloud computing, big data analysis, thing networking are organic, excavates the thunder and lightning damage characteristic through the data to form prediction model, arrange in the high in the clouds, realize the accurate early warning of the fan blade damage after the thunderbolt.
When the wind generating set is designed, the lightning protection problem of each component is comprehensively considered, a comprehensive lightning protection system is formed, however, the wind generating set is frequently damaged by lightning due to the problems of overlarge lightning current, paint covering of a lightning receptor and the like of actual lightning, and the problem that the fan is damaged is further worsened due to the fact that the fan is not timely inspected after lightning stroke or the really damaged fan is not accurately inspected in many cases, so that major production accidents are formed. At present, the wind driven generator group mainly takes hardware electrical protection as main part for lightning, a master control system is used for monitoring as auxiliary part (mainly monitoring the working state of a lightning protection device), accurate early warning of failure parts after lightning stroke is difficult to realize, and great trouble is brought to operation and maintenance personnel of fans in multiple lightning areas.
The invention provides a lightning early warning method and lightning early warning equipment for a wind generating set, which are used for early warning lightning damage of the wind generating set, reminding operation and maintenance personnel of paying attention to protection, enabling the operation and maintenance personnel to find potential blade damage caused by the lightning stroke in time and avoiding serious accidents such as serious blade fracture, falling and the like caused by the occurrence of lightning phenomena.
The following description is provided with reference to the accompanying drawings to assist in a comprehensive understanding of exemplary embodiments of the invention as defined by the claims and their equivalents. The description includes various specific details to aid understanding, but these details are to be regarded as illustrative only. Thus, one of ordinary skill in the art will recognize that: various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present invention. Moreover, descriptions of well-known functions and constructions may be omitted for clarity and conciseness.
Fig. 1 is a flowchart of a lightning alerting method of a wind turbine generator set according to an exemplary embodiment of the present invention.
As shown in fig. 1, at step S101, machine location point data of a plurality of wind turbine generators in a wind farm is acquired. The machine location data for the plurality of wind turbine generator sets includes, but is not limited to, longitude and latitude data for the plurality of wind turbine generator sets, an identification (e.g., a set number) of the plurality of wind turbine generator sets. In step S102, real-time lightning data of the wind farm is obtained. Real-time lightning data includes, but is not limited to, the type of real-time lightning (including, but not limited to, ground lightning, cloud lightning), the location and time of occurrence, lightning current values, and the like. In addition, historical lightning data of the wind power plant and historical lightning risk data of the plurality of wind generating sets can be obtained, and the historical lightning risk data can indicate known lightning risk sizes of the plurality of wind generating sets. Historical lightning data may be associated with historical lightning risk data and include, but are not limited to, the type of historical lightning (including, but not limited to, ground lightning, cloud lightning), location and time of occurrence, lightning current values, and the like.
In step S103, lightning risk levels of the plurality of wind turbine generators are predicted based on the acquired machine site data and real-time lightning data of the wind farm (which will be described in detail below with reference to fig. 2). The lightning risk size may indicate whether the corresponding wind turbine generator system is at risk of lightning. Or alternatively, the lightning risk magnitude may also indicate the lightning risk level of the corresponding wind turbine generator set, with a higher level indicating a greater risk. The lightning risk prediction model can be deployed on a management platform (for example, an SPHM system) of the wind generating set, timing and automatic distributed calculation of the lightning risk prediction model in a cloud is achieved, and compared with a traditional single platform deployment application mode, the calculation speed is improved, the iteration speed is higher, and the calculation amount is larger.
According to the embodiment of the invention, a thunder risk prediction model can be established according to machine location data of a plurality of wind generating sets, historical thunder data of a wind power plant and historical thunder risk data of the plurality of wind generating sets. The historical lightning risk data may be indicative of known lightning risk magnitudes for the plurality of wind generating sets, the historical lightning data being associated with the historical lightning risk data. In this way, the lightning risk prediction model may be trained through machine learning. Then, the lightning risk prediction model can be used for predicting the lightning risk of the wind generating sets based on the machine position data of the wind generating sets and the real-time lightning data of the wind power plant.
In step S104, the lightning early warning level of each wind generating set in the plurality of wind generating sets is determined based on the lightning risk size of the plurality of wind generating sets. In the embodiments described below, the higher the lightning risk, the higher the corresponding lightning early warning level. However, the present invention is not limited to this, and the lower the risk of lightning is, the higher the lightning warning level is. In step S105, at least one lightning early warning message is generated according to the lightning early warning level of each wind generating set. The lightning warning information may include, but is not limited to, an identification of the wind generating set (e.g., without limitation, a set number), a lightning warning rating of the corresponding wind generating set, a lightning warning time of the corresponding wind generating set (e.g., without limitation, a time at which a lightning risk is predicted to occur), and/or a blade operating condition of the corresponding wind generating set.
Then, at step S106, at least one lightning early warning information is output. The lightning early warning information can be immediately output after the lightning early warning information is generated, so that operation and maintenance personnel of the wind power plant can be timely informed to conduct timely troubleshooting.
FIG. 2 is a flow chart of predicting lightning risk magnitude of a wind park according to an exemplary embodiment of the invention. In an embodiment of the present invention, the lightning risk size indicates whether the corresponding wind turbine generator set is at risk of lightning, but is not limited thereto.
As shown in fig. 2, at step S201, machine location data of a plurality of wind turbine generators in a wind farm is obtained.
In step S202, real-time lightning data of the wind farm is obtained. In step S203, it is identified whether the lightning data is a cloud lightning or a ground lightning based on the acquired real-time lightning data to classify the real-time lightning data. If the cloud flash is detected, step S204 is executed. If the flash is ground flash, step S206 is executed.
At step S204, it is determined, for each of the plurality of wind generating sets, whether a cloud flash occurred within a first predetermined distance (e.g., without limitation, 1km) of the wind generating set based on real-time lightning data of the wind farm and the set point data of the plurality of wind generating sets. If not, executing step S208 to determine that the wind generating set corresponding to the cloud flash has no lightning risk. Otherwise, step S205 is executed.
In step S205, it is determined whether the lightning current value of the cloud flash is greater than a predetermined threshold value, the predetermined threshold value being used to indicate that the cloud flash may damage the wind turbine generator set, resulting in a lightning risk for the wind turbine generator set. If the lightning current value of the cloud flash is smaller than the predetermined threshold value, step S208 is executed to determine that the wind generating set corresponding to the cloud flash has no lightning risk. If the lightning current value of the cloud flash is greater than the predetermined threshold value, step S209 is executed to determine that the wind generating set corresponding to the cloud flash is at risk of lightning.
At step S206, real-time lightning data within a second predetermined distance (e.g., without limitation, 10km) of the wind park is counted for each of the plurality of wind park units based on the machine site data of the plurality of wind park units and the real-time lightning data of the wind park. The real-time lightning data includes, but is not limited to, a number of lightning N within a predetermined time period (e.g., 24 hours of the current time) within a second predetermined distance of the wind park, a maximum lightning current Imax within the predetermined time period within the second predetermined distance of the wind park, a distance Dimax between the lightning and the wind park corresponding to the maximum lightning current Imax within the second predetermined distance of the wind park, an average lightning current Imean within the predetermined time period within the second predetermined distance of the wind park, a distance Dmin between the lightning closest to the wind park and the wind park within the predetermined time period within the second predetermined distance of the wind park and a corresponding lightning current value Idmin thereof, an average distance Dmean between the lightning and the wind park within the predetermined time period within the second predetermined distance of the wind park, a lightning current value, The altitude Att of the wind generating set.
Optionally, derivative variables including, but not limited to, current surge rate Ir, distance surge rate Dr, and limit lightning strike indicator E may also be calculated from lightning strike data in real time for improving lightning risk prediction accuracy. Here, Ir may be represented by Imax/Dimax, and may be represented by KA/m. Dr is Idmin/Dmin, and can be expressed in KA/m. And E is Imax multiplied by Att/Dmin. The extreme lightning index E can be used for predicting the lightning strike conditions with extremely short distance, extremely strong current intensity and extremely high altitude of a machine position point, so that the lightning risk prediction accuracy is improved, and the wind generating set is prevented from being damaged by extreme lightning.
In step S207, for each wind park it is determined whether at least one of the lightning data and/or at least one of the derived variables in real time fulfils a lightning risk condition. According to an embodiment of the present invention, a threshold value corresponding to at least one of lightning data and derivative variables in real time may be set as an index for determining whether a lightning risk condition is satisfied. For example, if N > 5, Imax > 200KA, Imean > 100KA, Dmean < 8km, and Dmin < 6km, it is determined that the lightning risk condition is satisfied, otherwise it is not satisfied. Or alternatively, if N > 3, Imax > 100KA, Imean > 45KA, and Dmin < 550m, determining that the lightning risk condition is satisfied, otherwise, not satisfying the lightning risk condition. Or alternatively, if N > 10, Dr > 95KA/m, Imean > 30KA, and Dmean < 5km, determining that the lightning risk condition is met, otherwise, not meeting the lightning risk condition. If the lightning risk condition is determined to be met, executing step S209, and predicting that the wind generating set has lightning risk; otherwise, step S208 is executed, and the wind generating set is predicted to have no lightning risk.
Further, a lightning risk prediction model may be built according to the logic shown in FIG. 2. The lightning risk prediction model may also be built and optimized by a machine learning algorithm, such as, but not limited to, fusing lightning early warning expert experience into the lightning risk prediction model.
The lightning early warning method can comprise the step of determining the lightning early warning level of each wind generating set in the plurality of wind generating sets based on the lightning risk of the plurality of wind generating sets. Optionally, the lightning early warning method according to the invention may further comprise grouping the plurality of wind turbine generators based on the machine location data of the plurality of wind turbine generators to generate at least one wind turbine generator group; and predicting the lightning risk of at least one wind turbine generator group based on the lightning risk of the plurality of wind turbine generator groups. And then, determining the lightning early warning level of each wind generating set in the plurality of wind generating sets based on the lightning risk size of the plurality of wind generating sets and the lightning risk size of at least one wind generating set group.
FIG. 3 is a flow chart for grouping a plurality of wind turbine generator sets and predicting lightning risk magnitude according to an exemplary embodiment of the invention.
As shown in fig. 3, in step S301, machine location data of a plurality of wind generating sets in a wind farm is obtained, and then step S302 is executed.
In step S302, the plurality of wind turbine generators are grouped based on the machine site data of the plurality of wind turbine generators. For example, but not limiting of, a plurality of wind turbine generator sets are grouped using a clustering algorithm based on machine site data of the plurality of wind turbine generator sets to produce at least one wind turbine generator group. According to an embodiment, a plurality of wind generating sets in a wind farm may be grouped using an unsupervised k-means clustering algorithm. Therefore, the prediction error caused by the positioning error of the wind generating set and/or the positioning error of the real-time lightning data can be corrected, the false report or the missing report of the lightning early warning can be avoided, and the accuracy of the lightning early warning can be improved. However, the invention is not limited thereto, and a plurality of wind turbine generators in the wind farm can also be grouped by using a clustering algorithm based on random selection (CLARANS), a K-medoids clustering algorithm and the like. At least one wind turbine group is then generated in step S303, wherein each wind turbine group comprises at least one wind turbine set.
In step S304, based on the predicted lightning risk size of the plurality of wind turbine generators, a lightning risk size of each wind turbine generator group may be predicted, the lightning risk size indicating whether the wind turbine generator group has a lightning risk. According to an embodiment of the invention, it is determined that a wind turbine group is at risk of lightning if one or more wind turbine groups in the wind turbine group are at risk of lightning, according to the prediction result of the prediction process shown with reference to fig. 2. And if no wind generating set in the wind generating set group has the risk of lightning, determining that the wind generating set group has no risk of lightning.
The lightning early warning method further comprises the following steps: selecting a wind generating set in a specific range from a plurality of wind generating sets based on machine position data of the wind generating sets and real-time lightning data of a wind farm; monitoring the operation data of the wind generating sets in the specific range within the preset time period to determine the operation condition of the blades of the wind generating sets in the specific range within the preset time period, wherein the operation condition indicates whether the blades of the wind generating sets in the specific range are normally operated within the preset time period, and the specific description is provided with reference to fig. 4. The step of determining a lightning early warning level for each of the plurality of wind power generation units comprises: and determining the lightning early warning level of each wind generating set in the plurality of wind generating sets according to the operation condition of the blades of the wind generating sets within a specific range in a preset time period and based on the lightning risk size of the plurality of wind generating sets.
FIG. 4 is a flow chart of monitoring an operational condition of a blade of a wind park according to an exemplary embodiment of the present invention.
In step S401, machine location data of a plurality of wind turbine generators in a wind farm is obtained. In step S402, real-time lightning data of a wind farm is obtained. In step S403, wind turbine generators within a specific range are selected from the plurality of wind turbine generators based on the machine site data of the plurality of wind turbine generators and the real-time lightning data of the wind farm. A wind park within a particular range may be a wind park for which lightning is predicted to occur within a particular time period (e.g., without limitation, within a particular day) and within a particular distance (e.g., without limitation, 10km) thereof. However, the present invention is not limited thereto, and the wind turbine generator within the specific range may be a wind turbine generator that satisfies other conditions according to actual needs.
In step S404, the operation data of the wind generating set in a specific range in a preset time period is monitored. The predetermined time period may be determined from real-time lightning data of the wind farm. For example, and without limitation, the predetermined period of time may be a period of time (e.g., without limitation, 24 hours) after lightning occurrence within a particular distance of the wind turbine generator set within a particular range is predicted, such that it may be determined whether the lightning caused the wind turbine generator set to operate abnormally by monitoring operational data after lightning occurrence within the particular distance of the wind turbine generator set. The operational data includes, but is not limited to, wind turbine generator set operating time, nacelle acceleration, impeller speed, active power, wind speed, and the like. The nacelle acceleration may be an acceleration in a driving direction of the nacelle and an acceleration perpendicular to the driving direction.
In step S405, an operating condition of the blades of the wind turbine generator set within the specific range may be determined within the predetermined time period based on the monitored operating data within the predetermined time period, and the operating condition may indicate whether the blades of the wind turbine generator set within the specific range are operating normally within the predetermined time period.
According to one embodiment of the invention, the monitored operational data may be fourier transformed to extract the characteristic frequency and/or characteristic amplitude of the operational data in the frequency domain. For example, but not limiting of, a Fourier transform of the nacelle acceleration is performed to extract a characteristic frequency and/or a characteristic amplitude of the nacelle acceleration over a predetermined time period. If the abnormal characteristic frequency and/or the abnormal characteristic amplitude are found, the fact that the operation of the blade of the wind generating set is abnormal within a preset time period is determined, the blade of the wind generating set is possibly damaged due to lightning, and operation and maintenance personnel need to be informed to conduct site investigation in time.
According to the invention, since the operation condition of the blades of the wind generating set in the predetermined time period, the lightning risk size of the plurality of wind generating sets and the lightning risk size of the at least one wind generating set group respectively indicate the potential influence of lightning on the wind generating sets from different angles, the lightning early warning level of each wind generating set in the plurality of wind generating sets can be determined through a decoupling method according to the operation condition of the blades of the wind generating sets in the predetermined time period in a specific range and/or based on the lightning risk size of the plurality of wind generating sets and/or the lightning risk size of the at least one wind generating set group.
For example, but not limiting of, a lightning advance warning level for each of the plurality of wind power generation units may be determined based on a lightning risk size of the plurality of wind power generation units. Optionally, the lightning early warning level of each wind generating set of the plurality of wind generating sets may be determined by a decoupling method based on the lightning risk size of the plurality of wind generating sets and the lightning risk size of the at least one wind generating set group. Optionally, the lightning early warning level of each of the plurality of wind power generation units may be determined by a decoupling method according to the operation condition of the blades of the wind power generation units within a specific range within a predetermined time period and based on the lightning risk size of the plurality of wind power generation units. Optionally, the lightning advance level of each of the plurality of wind power generator units may be determined by a decoupling method depending on the operation of the blades of the wind power generator units within a certain range for a predetermined period of time and based on the lightning risk size of at least one wind power generator group.
Optionally, the lightning early warning level of each wind turbine generator set in the plurality of wind turbine generator sets may be determined by decoupling the operation condition of the blades of the wind turbine generator sets within a specific range within a predetermined time period, the lightning risk level of the plurality of wind turbine generator sets, and the lightning risk level of at least one wind turbine generator set group, which will be described below with reference to fig. 5.
FIG. 5 is a flow chart of determining a lightning warning level of a wind turbine generator set, according to an exemplary embodiment of the invention. The operation condition of the blades of the wind turbine generator set within a specific range within a predetermined time period, the lightning risk size of the plurality of wind turbine generator sets and the lightning risk size of the at least one wind turbine generator set group indicate potential impacts of lightning on the wind turbine generator sets from different angles, respectively. Compared with the three, the operation condition of the blade of the wind generating set in a preset time period can directly reflect the influence of lightning on the operation of the wind generating set, and especially the abnormal operation of the blade of the wind generating set can directly reflect the potential damage of the lightning on the wind generating set, so that the influence degree of the operation condition of the blade of the wind generating set on lightning early warning is highest; the lightning risk of the wind generating set can indirectly reflect the potential influence of lightning on the individual wind generating set, so that the influence degree of the lightning risk of the wind generating set on lightning early warning is ranked in the second place; the lightning risk of the wind power generator group corresponding to the wind power generator set can indirectly reflect the potential influence of lightning on the wind power generator group corresponding to the wind power generator set, so that the lightning risk of the wind power generator group has the lowest influence degree on lightning early warning.
According to the embodiment of the invention, the lightning early warning levels comprising a plurality of levels can be set according to the operation condition of the blades of the wind generating set in a preset time period, the lightning risk of the wind generating set and the influence degree of the lightning risk of the corresponding wind generating set group on the lightning early warning, so that the emergency degree of troubleshooting of operation and maintenance personnel under different conditions can be accurately reflected. As shown in fig. 5, in this embodiment, the lightning early warning level of the wind turbine generator set includes, but is not limited to, a first level, a second level, a third level, a fourth level and a fifth level, wherein a higher level indicates that the lightning risk of the corresponding wind turbine generator set is higher, and the urgency level of the lightning early warning level needs to be checked by the operation and maintenance personnel is higher. The lightning early warning level and the identification of the wind turbine generator corresponding to the lightning early warning level may be included in the lightning early warning information. The first grade can represent no lightning risk, and operation and maintenance personnel are not required to check, so that corresponding lightning early warning information can not be generated, and corresponding lightning early warning information can be generated and output only for a wind generating set with lightning risk in a wind power plant.
Referring to the flowchart shown in fig. 5, the lightning risk of the wind turbine generator system, the lightning risk of the corresponding wind turbine generator group, and the operation condition of the blades of the fixed wind turbine generator system in a predetermined time period may be decoupled, so as to determine the lightning early warning level of the wind turbine generator system.
In step S501, whether the wind generating set has a lightning risk is determined based on the lightning risk of the plurality of wind generating sets. For example, but not limiting of, determining whether the wind power plant is at risk of lightning according to the process described with reference to FIG. 1. If the lightning risk exists, step S502 is executed, whether the blades of the wind generating set operate normally is determined, and otherwise step S505 is executed. For example, but not limiting of, determining whether a blade of a wind park is functioning properly according to the process described with reference to FIG. 4.
And if the blades are determined to normally operate in the step S502, executing the step S503, determining that the lightning early warning level of the wind generating set is a third level, otherwise executing the step S504, determining that the lightning early warning level of the wind generating set is a fifth level, indicating that the wind generating set is possibly seriously damaged by lightning and requiring operation and maintenance personnel to perform site investigation in time.
In step S505 it is determined whether a wind turbine group corresponding to the wind turbine group is at risk of lightning, if so, step S506 is performed, otherwise, step S508 is performed. For example, but not limited to, determining whether the corresponding group of wind turbines is at risk of lightning according to the process described with reference to FIG. 3.
It is determined whether the blades of the wind turbine generator set are operating normally at step S506. And if the blades are determined to be normally operated in the step S506, executing the step S507, and determining that the lightning early warning level of the wind generating set is a second level, otherwise executing the step S504, and determining that the lightning early warning level of the wind generating set is a fifth level.
It is determined whether the blades of the wind park are functioning properly at step S508. If the blades normally operate in the step S508, the step S509 is executed, the lightning early warning level of the wind generating set is determined to be the fourth level, otherwise, the step S510 is executed, the lightning early warning level of the wind generating set is determined to be the first level, and the lightning does not have adverse effects on the wind generating set at the moment, so that the wind generating set does not need to be checked. Fig. 6 is a block diagram of a lightning early warning device 600 according to an exemplary embodiment of the invention. Lightning early warning device 600 includes data processing module 601, lightning risk prediction module 602, operational data monitoring module 603, and lightning early warning module 604.
The data processing module 601 is configured to receive or obtain input machine location data of a plurality of wind generating sets in a wind farm, real-time lightning data, historical lightning data, and/or historical lightning risk data of the wind farm, and process the input data. The operation of obtaining machine location data of a plurality of wind generating sets in a wind farm, real-time lightning data of the wind farm, historical lightning data and/or historical lightning risk data through the data processing module 601 has been described above with reference to fig. 1 to 4, and therefore, details are not repeated here, and relevant details can refer to the corresponding description above with reference to fig. 1 to 4.
The lightning risk prediction module 602 may predict lightning risk of the plurality of wind turbine generators based on machine location data of the plurality of wind turbine generators in the wind farm and real-time lightning data of the wind farm. The operation of the lightning risk prediction module 602 for predicting the lightning risk of a plurality of wind turbine generators and/or the lightning risk of at least one wind turbine generator group has been described above with reference to fig. 1 to 3, and therefore, the description is omitted here, and the relevant details may refer to the corresponding description above with reference to fig. 1 to 3.
The operation data monitoring module 603 may select a wind turbine generator set within a specific range from the plurality of wind turbine generator sets based on machine site data of the plurality of wind turbine generator sets and real-time lightning data of the wind farm; monitoring the operation data of the wind generating sets in the specific range within the preset time period to determine the operation conditions of the blades of the wind generating sets in the specific range within the preset time period, wherein the operation conditions indicate whether the blades of the wind generating sets in the specific range are normally operated within the preset time period. The operation of the operation data monitoring module 603 for monitoring the operation data and determining the operation condition of the blade has been described above with reference to fig. 4, and therefore, the detailed description is omitted here, and the relevant details can refer to the corresponding description above with reference to fig. 4.
The lightning early warning module 604 may determine a lightning early warning level of each of the plurality of wind turbine generators based on the lightning risk of the plurality of wind turbine generators; and generating and outputting lightning early warning information aiming at least one of the wind generating sets according to the lightning early warning grades of the wind generating sets. The operations of the lightning early warning module 604 for determining the lightning early warning level and generating and outputting the lightning early warning information have been described above with reference to fig. 5, and therefore, the details are not repeated here, and the relevant details can refer to the corresponding description above with reference to fig. 5.
Each module in the lightning early warning device 600 performs the operations corresponding to each module and described with reference to fig. 1 to 5, which are not described herein again for brevity. It should be understood that: the system and its devices shown in fig. 6 may be respectively configured as software, hardware, firmware or any combination thereof to perform specific functions. For example, the systems or devices may correspond to application specific integrated circuits, to pure software code, or to modules combining software and hardware. Further, one or more functions implemented by these systems or apparatuses may also be performed collectively by components in a physical entity device (e.g., a processor, a client, or a server, etc.).
The lightning early warning device 600 can be deployed on a management platform (for example, an SPHM system) of a wind turbine generator system, and realizes the timed and automatic distributed calculation of lightning risk prediction processing and/or lightning early warning processing at a cloud.
Further, the present invention also provides a computer-readable storage medium storing a computer program, which may include instructions for performing various operations in the control method of the above-described double winding converter. In particular, the computer program may comprise instructions for carrying out the individual steps described with reference to fig. 1 to 5.
Furthermore, the present invention also provides a computing device comprising a processor and a readable storage medium storing a computer program comprising instructions for performing various operations in the control method of the above-described double winding converter. In particular, the program may comprise instructions for carrying out the various steps described with reference to fig. 1 to 5.
Through the lightning early warning method and the lightning early warning device of the wind generating set, the following technical effects can be at least realized: different from a passive lightning protection system, the lightning early warning method and the lightning early warning device of the wind generating set can actively predict the lightning risk of the wind generating set, identify the potential blade damage condition of the wind generating set in time, and correct the prediction error caused by the position error of the machine position of the wind generating set by grouping the wind generating set based on the machine position data (such as but not limited to grouping by using a clustering algorithm), so that the lightning early warning accuracy is improved, and more accurate lightning risk early warning is performed; meanwhile, the thunder and lightning early warning method and the thunder and lightning early warning device can also apply statistical analysis and advanced machine learning algorithm to establish and optimize the thunder and lightning early warning model, the thunder and lightning early warning model can be deployed on a management platform (for example, an SPHM system) of the wind generating set, timing and automatic distributed calculation of the thunder and lightning early warning model at the cloud end is achieved, and compared with a traditional single platform deployment application mode, the thunder and lightning early warning method and the thunder and lightning early warning device improve the calculation speed, achieve higher iteration speed and larger calculation amount. The invention has great reference significance for unattended and less-attended wind generating set intelligent monitoring.
Control logic or functions performed by a controller or program modules or the like may be represented by flow charts or the like in one or more of the figures. These figures provide representative control strategies and/or logic that may be implemented using one or more processing strategies (e.g., event-driven, interrupt-driven, multi-tasking, multi-threading, and so forth). As such, various steps or functions illustrated may be performed in the sequence illustrated, in parallel, or in some cases omitted. Although not always explicitly illustrated, one of ordinary skill in the art will recognize that one or more of the illustrated steps or functions may be repeatedly performed depending on the particular processing strategy being used.
The embodiments in the above embodiments can be further combined or replaced, and the embodiments are only used for describing the preferred embodiments of the present invention, and do not limit the concept and scope of the present invention, and various changes and modifications made to the technical solution of the present invention by those skilled in the art without departing from the design idea of the present invention belong to the protection scope of the present invention.
Claims (15)
1. A lightning early warning method of a wind generating set comprises the following steps:
predicting the lightning risk of a plurality of wind generating sets based on machine position data of the wind generating sets in the wind power plant and real-time lightning data of the wind power plant;
determining a lightning early warning level of each wind generating set in the plurality of wind generating sets based on the lightning risk size of the plurality of wind generating sets;
and generating and outputting at least one piece of thunder early warning information according to the thunder early warning level of each wind generating set.
2. The lightning alerting method of claim 1, wherein the lightning alerting method further comprises:
establishing a lightning risk prediction model according to the machine location data of the plurality of wind generating sets, the historical lightning data of the wind farm, and the historical lightning risk data of the plurality of wind generating sets, wherein the historical lightning risk data indicates the known lightning risk of the plurality of wind generating sets, and is associated with the historical lightning risk data,
the step of predicting the lightning risk size of the plurality of wind turbine generators comprises the following steps: and predicting the lightning risk of the wind generating sets by utilizing the lightning risk prediction model based on the machine location data of the wind generating sets and the real-time lightning data of the wind power plant.
3. The lightning alerting method of claim 1, wherein the lightning alerting method further comprises:
grouping the plurality of wind turbine generator sets based on the machine site data of the plurality of wind turbine generator sets to generate at least one wind turbine generator group;
predicting a lightning risk size of the at least one wind turbine group based on the lightning risk sizes of the plurality of wind turbine groups.
4. The lightning early warning method of claim 3, wherein the step of determining the lightning early warning level for each of the plurality of wind power generator units comprises:
determining a lightning early warning level for each of the plurality of wind turbine generator sets based on the lightning risk size of the plurality of wind turbine generator sets and the lightning risk size of the at least one wind turbine generator set group.
5. The lightning alerting method of claim 1, wherein the lightning alerting method further comprises:
selecting a wind generating set within a specific range from the plurality of wind generating sets based on the machine site data of the plurality of wind generating sets and the real-time lightning data of the wind power plant;
monitoring operational data of the wind power plants within the specific range over a predetermined period of time to determine an operational condition of the blades of the wind power plants within the specific range over the predetermined period of time, the operational condition indicating whether the blades of the wind power plants within the specific range are operating normally within the predetermined period of time, the predetermined period of time being determined from real-time lightning data of the wind farm,
wherein the step of determining a lightning early warning level for each of the plurality of wind power generation units comprises: determining a lightning early warning level for each of the plurality of wind turbine generator sets based on the operational condition of the blades of the wind turbine generator sets within the particular range and based on the lightning risk size of the plurality of wind turbine generator sets.
6. The lightning alerting method of claim 3, wherein the lightning alerting method further comprises:
selecting a wind generating set within a specific range from the plurality of wind generating sets based on the machine site data of the plurality of wind generating sets and the real-time lightning data of the wind power plant;
monitoring operational data of the wind power plants within the specific range over a predetermined period of time to determine an operational condition of the blades of the wind power plants within the specific range over the predetermined period of time, the operational condition indicating whether the blades of the wind power plants within the specific range are operating normally within the predetermined period of time, the predetermined period of time being determined from real-time lightning data of the wind farm,
wherein the step of determining a lightning early warning level for each of the plurality of wind power generation units comprises: determining a lightning early warning level for each of the plurality of wind turbine generator sets according to the operating conditions of the blades of the wind turbine generator sets within the specific range and based on the lightning risk size of the plurality of wind turbine generator sets and/or the lightning risk size of the at least one wind turbine generator set group.
7. The lightning alerting method of claim 5 or 6, wherein the step of determining the operational condition of the blades of the wind turbine generator set within the specific range comprises:
calculating the characteristic frequency and/or the characteristic amplitude of the nacelle acceleration of the wind generating sets in the specific range in the preset time period based on the operation data of the wind generating sets in the specific range in the preset time period;
and determining whether the blades of the wind generating set in the specific range normally operate in the preset time period according to the characteristic frequency and/or the characteristic amplitude.
8. A lightning early warning device of a wind power plant, wherein the lightning early warning device comprises:
a lightning risk prediction module configured to: predicting the lightning risk of a plurality of wind generating sets based on machine position data of the wind generating sets in the wind power plant and real-time lightning data of the wind power plant; and
a lightning early warning module configured to: determining a lightning early warning level of each wind generating set in the plurality of wind generating sets based on the lightning risk size of the plurality of wind generating sets; and generating and outputting at least one piece of thunder early warning information according to the thunder early warning level of each wind generating set.
9. The lightning early warning device of claim 8, wherein the lightning risk prediction module is configured to: establishing a thunder risk prediction model according to the machine location data of the wind generating sets, the historical thunder data of the wind power plant and the historical thunder risk data of the wind generating sets, wherein the historical thunder risk data indicates the known thunder risk of the wind generating sets, and is associated with the historical thunder risk data;
and predicting the lightning risk of the wind generating sets by utilizing the lightning risk prediction model based on the machine location data of the wind generating sets and the real-time lightning data of the wind power plant.
10. The lightning early warning device of claim 8, wherein the lightning risk prediction module is configured to: grouping the plurality of wind turbine generator sets based on the machine site data of the plurality of wind turbine generator sets to generate at least one wind turbine generator group;
predicting a lightning risk size of the at least one wind turbine group based on the lightning risk sizes of the plurality of wind turbine groups.
11. The lightning early warning device of claim 10, wherein the lightning early warning module is configured to: determining a lightning early warning level for each of the plurality of wind turbine generator sets based on the lightning risk size of the plurality of wind turbine generator sets and the lightning risk size of the at least one wind turbine generator set group.
12. The lightning early warning device of claim 8, further comprising an operational data monitoring module configured to: selecting a wind generating set within a specific range from the plurality of wind generating sets based on the machine site data of the plurality of wind generating sets and the real-time lightning data of the wind power plant;
monitoring operational data of the wind power plants within the specific range over a predetermined period of time to determine an operational condition of the blades of the wind power plants within the specific range over the predetermined period of time, the operational condition indicating whether the blades of the wind power plants within the specific range are operating normally within the predetermined period of time, the predetermined period of time being determined from real-time lightning data of the wind farm,
the lightning early warning module is configured to: determining a lightning early warning level for each of the plurality of wind turbine generator sets based on the operational condition of the blades of the wind turbine generator sets within the particular range and based on the lightning risk size of the plurality of wind turbine generator sets.
13. The lightning early warning device of claim 10, further comprising an operational data monitoring module configured to: selecting a wind generating set within a specific range from the plurality of wind generating sets based on the machine site data of the plurality of wind generating sets and the real-time lightning data of the wind power plant;
monitoring operational data of the wind power plants within the specific range over a predetermined period of time to determine an operational condition of the blades of the wind power plants within the specific range over the predetermined period of time, the operational condition indicating whether the blades of the wind power plants within the specific range are operating normally within the predetermined period of time, the predetermined period of time being determined from real-time lightning data of the wind farm,
the lightning early warning module is configured to: determining a lightning early warning level for each of the plurality of wind turbine generator sets according to the operating conditions of the blades of the wind turbine generator sets within the specific range and based on the lightning risk size of the plurality of wind turbine generator sets and/or the lightning risk size of the at least one wind turbine generator set group.
14. The lightning early warning device of claim 12 or 13, wherein the operational data monitoring module is configured to: calculating the characteristic frequency and/or the characteristic amplitude of the nacelle acceleration of the wind generating sets in the specific range in the preset time period based on the operation data of the wind generating sets in the specific range in the preset time period;
and determining whether the blades of the wind generating set in the specific range normally operate in the preset time period according to the characteristic frequency and/or the characteristic amplitude.
15. A computing device comprising a processor and a readable medium having stored thereon a computer program, wherein the computer program comprises instructions for the processor to perform the steps of the lightning alerting method of any one of claims 1-7.
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