CN106771370B - A kind of blower anemometer detection method and device - Google Patents
A kind of blower anemometer detection method and device Download PDFInfo
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- CN106771370B CN106771370B CN201611193030.4A CN201611193030A CN106771370B CN 106771370 B CN106771370 B CN 106771370B CN 201611193030 A CN201611193030 A CN 201611193030A CN 106771370 B CN106771370 B CN 106771370B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
- G01P21/00—Testing or calibrating of apparatus or devices covered by the preceding groups
- G01P21/02—Testing or calibrating of apparatus or devices covered by the preceding groups of speedometers
- G01P21/025—Testing or calibrating of apparatus or devices covered by the preceding groups of speedometers for measuring speed of fluids; for measuring speed of bodies relative to fluids
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Abstract
The present invention discloses a kind of blower anemometer detection method and device, the first wind speed numerical value that the method is exported by obtaining anemometer to be detected, and obtain the numerical value of corresponding each predetermined affecting parameters when exporting the first wind speed numerical value, such as, the numerical value of each predetermined affecting parameters can be when anemometer to be detected in the case where there is strong association blower in wind field, the numerical value of second wind speed numerical value and wind direction numerical value and specific environment parameter of its strong association blower output obtained；Numerical value later based on each predetermined affecting parameters, calculation of wind speed are the conditional probability of the first wind speed numerical value；The detection being based ultimately upon needed for the conditional probability carries out anemometer, for example, detection anemometer whether failure etc..As it can be seen that can realize whether failure is effectively detected to anemometer using the method for the present invention, and then it can realize and targetedly the failure anemometer detected is overhauled, reduce resource consumption when trouble hunting, improve overhaul efficiency.
Description
Technical Field
The invention belongs to the technical field of wind power research and detection, and particularly relates to a method and a device for detecting a wind meter of a fan.
Background
Wind energy is a source of all power of wind power generation, and in the wind power industry, a measured wind speed value is often used as basic data of wind power conversion to perform some related calculations, such as calculation of power generation amount by using the measured wind speed value, so as to provide a basis for making related strategies in the industry, and therefore, measurement of related wind parameters, particularly measurement of wind speed, is particularly important in the industry.
One of the main sources of wind speed measurement data is the wind meter of the wind turbine (i.e., wind turbine, which is referred to herein as wind turbine). The wind meter of the fan is influenced by factors such as working age, various natural environment factors (such as air humidity, rainfall, air density, air pressure and the like) and wake flow generated by blades of other fans in a wind field, so that certain errors exist in the measurement precision, and the corresponding deviation exists between the generated energy calculated by the system by using the measured wind speed value and the actual generated energy. In the prior art, the fault of each fan anemoscope in the wind field is overhauled manually on time to reduce the error of the anemoscope when the wind speed is measured, however, the fault of each fan anemoscope is overhauled manually on time, which consumes more human resources and time resources, and has low overhauling efficiency.
Disclosure of Invention
In view of the above, the present invention provides a method and an apparatus for detecting a wind meter of a wind turbine, so as to at least solve the above problems in the prior art, reduce resource consumption during troubleshooting of the wind meter, and improve troubleshooting efficiency.
Therefore, the invention discloses the following technical scheme:
a detection method for a wind meter of a wind turbine comprises the following steps:
obtaining a first wind speed value output by a wind meter of a fan to be detected, and obtaining the wind direction of a wind field, the distance between fans in the wind field and the value of a preset environmental parameter corresponding to the first wind speed value output by the wind meter of the fan; the wind field is the wind field where the wind meter of the fan to be detected is located;
determining a strongly-associated fan corresponding to the fan anemometer to be detected from the wind field according to the wind direction of the wind field and the distance between fans in the wind field; the strong correlation fan is a fan which is determined from a wind field according to a preset mode and has a large influence on the wind measuring condition of the fan wind meter;
if the strong correlation fan corresponding to the fan anemoscope to be detected is determined, obtaining a second wind speed value and a wind direction value which are correspondingly output by the strong correlation fan at the output moment of the first wind speed value; calculating the conditional probability of the wind speed being the first wind speed value on the premise of the second wind speed value, the wind direction value and the value of the preset environmental parameter;
if the strongly-associated fan corresponding to the fan anemometer to be detected is not determined, a third wind speed value correspondingly output by the anemometer tower at the output moment of the first wind speed value is obtained; calculating the conditional probability of the wind speed being the first wind speed value on the premise of the third wind speed value and the value of the preset environmental parameter;
and based on the conditional probability, carrying out corresponding detection processing on the fan anemoscope.
Preferably, the determining, according to the wind direction of the wind field and the distance between the fans in the wind field, the strongly correlated fan corresponding to the fan anemometer to be detected from the wind field includes:
determining a frontmost boundary fan in a wind field according to the wind direction of the wind field, wherein the frontmost boundary fan is a fan which is not influenced by wake flows of other fans in the wind field;
taking the frontmost row boundary fan as a root node fan, and generating a directed graph according to the distance between the fans; the connection relation among fan nodes in the directed graph and the direction of the connection edge reflect the wake influence relation and the wake influence degree among fans in the wind field;
and determining the strongly-associated fan corresponding to the fan anemoscope to be detected based on the directed graph.
In the above method, preferably, the generating a directed graph by using the frontmost boundary fan as a root node fan according to the distance between the fans includes:
the node where the frontmost boundary fan is located is made to be the first layer L of the directed graph1And said first layer L1As a current layer to be processed;
starting from each node in the current layer, connecting fan nodes with the distance smaller than a threshold value d in the wind field nodes;
taking a node layer newly generated in the directed graph as the current layer, and skipping to execute the step in a loop mode: starting from each node in the current layer, connecting fan nodes with the distance smaller than a threshold value d in the wind field nodes;
when L < th > appearsiAll nodes in the layer arrive at lthi+1When the distances of all nodes in the layer are larger than the threshold d, the threshold d is adjusted to d + delta d, and the size of delta d can determine Li+1One node in the layer, then Li+2And the nodes of the layer continue to be determined by adopting the threshold value d until all the fan nodes in the wind field are connected to the directed graph.
Preferably, the determining the strongly correlated wind turbine corresponding to the wind meter of the wind turbine to be detected based on the directed graph includes:
finding out a father node of a node where the fan anemoscope to be detected is located from the directed graph; and taking the fan corresponding to the father node as a strong association fan of the fan anemoscope to be detected.
Preferably, in the method, the performing, based on the conditional probability, corresponding detection processing on the wind meter of the wind turbine includes:
judging whether the conditional probability is lower than a preset threshold value or not;
and if the conditional probability is lower than the preset threshold, judging that the fan anemoscope fails, and performing fault early warning.
A fan anemometer detection device comprising:
the system comprises an acquisition unit, a detection unit and a control unit, wherein the acquisition unit is used for acquiring a first wind speed value output by a wind meter of a fan to be detected, and acquiring the wind direction of a wind field, the distance between fans in the wind field and the value of a preset environmental parameter corresponding to the first wind speed value output by the wind meter of the fan; the wind field is the wind field where the wind meter of the fan to be detected is located;
the determining unit is used for determining a strongly-associated fan corresponding to the fan anemoscope to be detected from the wind field according to the wind direction of the wind field and the distance between fans in the wind field; the strong correlation fan is a fan which is determined from the wind field according to a preset mode and has a large influence on the wind measuring condition of the fan wind meter;
the first calculation unit is used for obtaining a second wind speed numerical value and a wind direction numerical value which are correspondingly output by the strong correlation fan at the output moment of the first wind speed numerical value when the strong correlation fan corresponding to the wind meter to be detected is determined; calculating the conditional probability of the wind speed being the first wind speed value on the premise of the second wind speed value, the wind direction value and the value of the preset environmental parameter;
the second calculation unit is used for obtaining a third wind speed value correspondingly output by the wind measuring tower at the output moment of the first wind speed value when the strong correlation fan corresponding to the wind meter to be detected is not determined; calculating the conditional probability of the wind speed being the first wind speed value on the premise of the third wind speed value and the value of the preset environmental parameter;
and the detection processing unit is used for carrying out corresponding detection processing on the fan anemoscope based on the conditional probability.
The above apparatus, preferably, the determining unit is further configured to:
determining a frontmost boundary fan in a wind field according to the wind direction of the wind field, wherein the frontmost boundary fan is a fan which is not influenced by wake flows of other fans in the wind field; taking the frontmost row boundary fan as a root node fan, and generating a directed graph according to the distance between the fans; the connection relation among fan nodes in the directed graph and the direction of the connection edge reflect the wake influence relation and the wake influence degree among fans in the wind field; and determining the strongly-associated fan corresponding to the fan anemoscope to be detected based on the directed graph.
In the above apparatus, preferably, the determining unit generates a directed graph by using the frontmost boundary fan as a root node fan according to a distance between the fans, and further includes:
the node where the frontmost boundary fan is located is made to be the first layer L of the directed graph1And said first layer L1As a current layer to be processed; starting from each node in the current layer, connecting fan nodes with the distance smaller than a threshold value d in the wind field nodes; taking a node layer newly generated in the directed graph as the current layer, and skipping to execute the step in a loop mode: starting from each node in the current layer, connecting fan nodes with the distance smaller than a threshold value d in the wind field nodes; when L < th > appearsiAll nodes in the layer arrive at lthi+1When the distances of all nodes in the layer are larger than the threshold d, the threshold d is adjusted to d + delta d, and the size of delta d can determine Li+1One node in the layer, then Li+2And the nodes of the layer continue to be determined by adopting the threshold value d until all the fan nodes in the wind field are connected to the directed graph.
Preferably, in the above apparatus, the determining unit determines the strongly correlated wind turbine of the wind meter to be detected based on the directed graph, and further includes:
finding out a father node of a node where the fan anemoscope to be detected is located from the directed graph; and taking the fan corresponding to the father node as a strong association fan of the fan anemoscope to be detected.
The above apparatus, preferably, the detection processing unit is further configured to:
judging whether the conditional probability is lower than a preset threshold value or not; and if the conditional probability is lower than the preset threshold, judging that the fan anemoscope fails, and performing fault early warning.
According to the above scheme, the method and the device for detecting the wind meter of the fan disclosed by the invention are characterized in that a first wind speed value output by the wind meter to be detected is obtained, and values of each corresponding preset influence parameter when the wind meter outputs the first wind speed value are obtained, wherein the values of each preset influence parameter can be a second wind speed value and a wind direction value output by a strongly-associated fan and a value of a preset environment parameter obtained under the condition that the strongly-associated fan exists in a wind field of the wind meter to be detected, or can be values of the preset environment parameter and a wind tower wind speed obtained under the condition that the strongly-associated fan does not exist; then, based on the numerical values of the preset influence parameters, calculating the conditional probability of the wind speed being the first wind speed numerical value output by the anemometer; finally, the wind meter is detected based on the conditional probability, for example, whether the wind meter is in failure or not is detected. Therefore, whether the fan anemoscope is in fault or not can be effectively detected by adopting the method, the detected fault anemoscope can be maintained in a targeted manner, the resource consumption during fault maintenance is reduced, and the maintenance efficiency is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a flowchart of a detection method for a wind meter of a wind turbine according to an embodiment of the present invention;
fig. 2 is a flowchart of a detection method for a wind meter of a wind turbine according to a second embodiment of the present invention;
fig. 3 is a flowchart of a detection method for a wind meter of a wind turbine according to a third embodiment of the present invention;
fig. 4 is a schematic view of each fan node correspondingly included in a wind farm provided by the third embodiment of the present invention;
fig. 5(a) and 5(b) are directed graphs corresponding to wind fields in different wind directions, respectively, according to a second embodiment of the present invention;
FIG. 6 is a schematic structural diagram of a V-shaped node according to a third embodiment of the present invention
Fig. 7 is a schematic structural diagram of a detection device of a wind meter of a wind turbine provided in the fourth embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention provides a method and a device for detecting a wind meter of a fan, and the scheme of the invention is explained through a plurality of embodiments.
Example one
The embodiment of the invention provides a fan anemoscope detection method which can be realized in various types of computers or upper computers and other equipment, and the method can be particularly applied to various detections such as fault detection, wind measurement accuracy detection and the like on the fan anemoscope to be detected, and further can realize targeted maintenance on the detected fault anemoscope on the basis of the fault detection, wherein the fan anemoscope to be detected is positioned in a wind field, a plurality of fans are distributed in the wind field, and each fan in the wind field comprises a corresponding anemoscope.
The wind speed of a wind meter in a wind field is generally influenced by a plurality of factors such as the wind meter self factor (such as faults occur in a long working life), a natural environment factor, wake flow generated by blades of other fans in the wind field (the situation that individual fans are not influenced by the wake flow also exists), and the like. Next, a method for detecting a wind meter of a wind turbine according to an embodiment of the present invention will be described with reference to fig. 1.
Referring to fig. 1, fig. 1 shows a flow chart of a method for detecting a wind meter of a wind turbine according to the present invention, which may include the following steps:
step 101, obtaining a first wind speed value output by a wind meter of a fan to be detected, and obtaining a wind direction of a wind field, a distance between fans in the wind field and a value of a preset environmental parameter corresponding to the first wind speed value output by the wind meter of the fan; and the wind field is the wind field where the wind meter of the fan to be detected is located.
In this embodiment, other factors (i.e., non-self influence factors) except the self factor of the wind meter of the wind turbine to be detected, such as environmental factors and wake influence factors corresponding to wakes generated by blades of other wind turbines in the wind field, are considered, and each influence parameter influencing the wind measuring condition of the wind meter to be detected is determined. Generally, the wind measuring instrument of the wind turbine is affected by environmental factors, and the affecting parameters of the wind measuring instrument of the wind turbine include environmental parameters determined according to the environmental factors, such as air humidity, air density, rainfall, air pressure, and the like.
Besides, the wind meters of the wind field may be affected by the wake flow of other wind meters in the wind field (there is a case that individual wind meters are not affected by the wake flow), which may cause the influence on the wind measuring situation, whether the wind meters of the wind field are affected by the wake flow of other wind meters in the wind field at a certain time, and the source wind meters which are affected by the wake flow, are related to the wind direction of the wind field and the distance between the wind meters in the wind field at the time, based on which, in step 101, the first wind speed value output by the wind meters of the wind field to be detected is obtained, and the corresponding wind direction of the wind field and the distance between the wind meters in the wind field when the first wind speed value is output are obtained, and simultaneously, the wind direction of the wind field and the distance between the wind meters in the wind field are obtained for determining whether the wind meters to be detected are affected by the wake flow in the wind field subsequently, and further determining the basis of a source fan and the like which generate the wake flow influence under the condition of being influenced by the wake flow.
102, determining a strongly-associated fan corresponding to the fan anemometer to be detected from the wind field according to the wind direction of the wind field and the distance between fans in the wind field; the strong-correlation fan is a fan which is determined from the wind field and has a large influence on the wind measuring condition of the fan anemometer according to a preset mode.
When a certain fan is influenced by wake flow in a wind field, the wake flow influence of the certain fan is usually from a plurality of other fans in the wind field, and the wake flow influence generated by the plurality of other fans can be divided into strong and weak points according to the distance between the influenced fan and the relative positions of the influenced fan and the influenced fan, in this embodiment, when the wind meter to be detected is detected, such as whether the wind meter to be detected is in failure or not, the partial fan(s) which have a larger influence on the wake flow generated in the wind field are considered in an important way, and accordingly, the fan which has a smaller influence on the wake flow or does not have the influence on the wake flow is omitted, and the fan which can generate a larger influence on the wake flow of the fan is considered as a strong associated fan corresponding to the wind meter in the wind field, and the existence of the strong associated fan in the wind field of the wind meter to be detected indicates that the wind meter to be detected is influenced by wake flow in the wind field, otherwise, if the strongly-associated fan does not exist, the wind meter to be detected is not influenced by wake flow in the wind field. Under the condition of being influenced by wake flow, relevant wind measuring parameter values (such as wind speed and wind direction) output by a strongly-associated fan of the wind measuring instrument to be detected can be subsequently used for carrying out relevant detection on the wind measuring instrument to be detected by taking the environmental parameter values as reference, such as detecting whether the wind measuring instrument is in fault or not.
For the wind meter of the fan to be detected, in general, the fan which is closer to the wind meter and located in the windward direction of the wind meter based on the current wind field wind direction will generate a larger wake effect on the wind meter of the fan to be detected, and based on this, the strongly associated fan corresponding to the wind meter of the fan to be detected is determined from the wind field specifically according to the wind direction of the wind field obtained in step 101 and the distance between the fans in the wind field. The implementation of this part will be explained in detail in the corresponding embodiments below.
103, if a strong correlation fan corresponding to the fan anemoscope to be detected is determined, obtaining a second wind speed value and a wind direction value which are correspondingly output by the strong correlation fan at the output moment of the first wind speed value; and taking the second wind speed value, the wind direction value and the corresponding environmental parameter value at that time as the occurred event, and calculating the conditional probability that the wind speed is the first wind speed value under the precondition.
If a strongly-associated fan corresponding to the wind meter to be detected can be determined from a wind field, namely the strongly-associated fan exists in the wind field, the strongly-associated fan indicates that the wind meter to be detected is influenced by wake flow in the wind field, under the condition, the step obtains a second wind speed value and a wind direction value which are correspondingly output by the strongly-associated fan when the first wind speed value is output by the wind meter to be detected, takes the second wind speed value and the wind direction value which are output by the strongly-associated fan and the current environmental parameter value as the occurred event, and calculates the conditional probability that the wind speed is the first wind speed value output by the wind meter to be detected on the premise that the second wind speed value, the wind direction value and the current environmental parameter value are taken as the occurred event.
104, if the strongly associated fan corresponding to the fan anemometer to be detected is not determined, obtaining a third wind speed value correspondingly output by the anemometer tower at the output moment of the first wind speed value; and calculating the conditional probability that the wind speed is the first wind speed value on the premise of the third wind speed value and the value of the preset environmental parameter.
If the strongly-associated wind turbine corresponding to the wind meter to be detected cannot be determined from the wind field, that is, the wind meter to be detected does not have a strongly-associated wind turbine in the wind field, it indicates that the wind meter to be detected is not influenced by wake flow in the wind field, under such a condition, a third wind speed value correspondingly output by the wind measuring tower at the output moment of the first wind speed value is obtained, the third wind speed value and the current environmental parameter value are taken as the occurred events, and the conditional probability that the wind speed is the first wind speed value output by the wind meter is calculated by taking the third wind speed value and the current environmental parameter value as the precondition.
And 105, based on the conditional probability, carrying out corresponding detection processing on the wind meter of the fan.
The greater the value of the conditional probability is, the greater the possibility that the actual wind speed is the wind speed value measured by the anemometer to be detected (i.e. the first wind speed value) is, so that the higher the measurement accuracy of the anemometer to be detected is, the smaller the possibility that the anemometer fails is; otherwise, the smaller the value of the conditional probability is, the smaller the probability that the actual wind speed is the wind speed value measured by the anemometer to be detected is, so that the lower the measurement accuracy of the anemometer to be detected is, the higher the probability of the fault is.
On the basis, on the premise of calculating the conditional probability, the step can specifically perform corresponding detection on the fan anemometer to be detected according to the magnitude of the conditional probability, such as detecting the wind measuring accuracy or detecting whether the fan anemometer is in fault or the like.
According to the scheme, the method for detecting the wind meter of the fan comprises the steps of obtaining a first wind speed value output by the wind meter to be detected and obtaining values of various preset influence parameters corresponding to the first wind speed value output by the wind meter, wherein the values of the various preset influence parameters can be a second wind speed value and a wind direction value output by a strongly-associated fan and values of preset environment parameters when the strongly-associated fan exists in a wind field of the wind meter to be detected, or can be values of the preset environment parameters and wind tower wind speed when the strongly-associated fan does not exist; then, based on the numerical values of the preset influence parameters, calculating the conditional probability of the wind speed being the first wind speed numerical value output by the anemometer; finally, the wind meter is detected based on the conditional probability, for example, whether the wind meter is in failure or not is detected. Therefore, whether the fan anemoscope is in fault or not can be effectively detected by adopting the method, the detected fault anemoscope can be maintained in a targeted manner, the resource consumption during fault maintenance is reduced, and the maintenance efficiency is improved.
Example two
This embodiment provides a possible implementation manner of performing fault detection on a to-be-detected wind meter of a wind turbine, and referring to fig. 2, step 105 may perform fault detection on the wind turbine by the following steps:
step 1051, judging whether the conditional probability is lower than a predetermined threshold;
step 1052, if the conditional probability is lower than the preset threshold, determining that the fan anemoscope has a fault, and performing fault early warning.
The higher the conditional probability is, the higher the possibility that the actual wind speed is the wind speed value measured by the anemometer to be detected is, so that the measurement accuracy of the anemometer to be detected is higher, otherwise, the lower the value of the conditional probability is, the lower the measurement accuracy of the anemometer to be detected is, and when the value of the conditional probability is low to a certain degree, so that the measurement accuracy of the anemometer is not in the expected range, the occurrence of a fault of the anemometer is indicated.
Based on this, a determination threshold for measuring whether the anemometer has a fault is preset in this embodiment, the threshold may be set based on a fault condition in an actual anemometer process, when the calculated conditional probability value is lower than the predetermined threshold, it indicates that the anemometer has a fault, and at this time, a warning may be given to relevant personnel such as wind farm maintenance personnel through fault warning.
EXAMPLE III
In this embodiment, a detailed description is given to an implementation process of the detection method for a wind meter of a wind turbine according to the present invention, and with reference to fig. 3, the method may be implemented by the following steps:
301, obtaining a first wind speed value output by a wind meter of a fan to be detected, and obtaining a wind direction of a wind field, a distance between fans in the wind field and a value of a preset environmental parameter corresponding to the first wind speed value output by the wind meter of the fan; and the wind field is the wind field where the wind meter of the fan to be detected is located.
The method comprises the steps of obtaining a first wind speed value output by a wind meter of a fan to be detected, and obtaining the wind direction of a wind field, the distance between fans in the wind field and the value of a preset environmental parameter corresponding to the first wind speed value output by the wind meter of the fan, so as to provide data support for subsequent processing.
And 302, determining the frontmost boundary fan in the wind field according to the wind direction of the wind field, wherein the frontmost boundary fan is a fan which is not influenced by wake flows of other fans in the wind field.
303, generating a directed graph by taking the frontmost boundary fan as a root node fan according to the distance between the fans; the connection relation among fan nodes and the direction of the connection edge in the directed graph reflect the wake influence relation and the wake influence degree among fans in the wind field.
And 304, determining a strong correlation fan of the fan anemometer to be detected based on the directed graph.
In the embodiment of the invention, all fans in the whole wind field are regarded as fan nodes one by one, and the fans correspond to the fan nodes one by one, and referring to fig. 4, fig. 4 shows a schematic diagram of each fan node correspondingly included in the wind field. The nodes #1- #4 and the nodes #7- #9 in the figure correspond to boundary fans of the whole wind field, wherein the boundary fans can be set by related personnel, and the rest nodes, namely the nodes #5 and #6, correspond to non-boundary fans of the wind field.
Aiming at each fan node correspondingly contained in the wind field, maintaining a matrix D for recording the distance between fans:
wherein n represents the number of fans in the wind field, namely the number of fan nodes; dijAnd the distance between the ith fan and the jth fan in the wind field is represented.
When wind blows to the wind field from different angles, a directed graph is dynamically generated according to the specific wind direction of the wind field and the distance between fans in the maintained wind field. And the connection relation among fan nodes and the direction of the connection edge in the directed graph can reflect the wake flow influence relation and influence degree among fans in the wind field.
Let the number of each boundary fan in the wind field be R ═ { R _1, …, R _ m }, and m represents the number of boundary fans. The dynamic generation process of the directed graph is as follows:
1) and obtaining the wind direction of a wind field, and determining the boundary fan at the foremost row in the wind field according to the wind direction of the wind field.
The frontmost boundary fan is a fan which cannot be influenced by wake flows of other fans in the wind field.
The wind field may specifically use wind direction data provided by a wind tower, wherein the wind tower is used for measuring wind parameters, and generally provides at least wind speed and wind direction data.
For the determination of the frontmost row of boundary fans, the corresponding relationship between the wind direction range and the frontmost row of boundary fans in the wind farm as shown in table 1 below may be defined in advance.
TABLE 1
| Range of wind direction | Front exhaust fan node |
| θ1～θ2 | R_1,R_2,..,R_i |
| … | … |
| θk～θn | R_j,R_k,..,R_m |
For example, for the wind field fan node shown in fig. 4, it can be specifically defined that when the wind direction is 0 to 30 degrees, the frontmost boundary fans are #1, #2, and #3 fans. When the wind direction is 40-50 degrees, the frontmost boundary fan is a #3 fan. Therefore, after the wind direction of the wind field is obtained, the frontmost row boundary fan in the wind field can be determined in a table look-up mode. The wind direction is referenced to a predetermined reference direction.
2) Dynamic probabilistic network generation
After the frontmost row boundary fan in the wind field is determined according to the wind direction, a directed graph is dynamically generated according to the frontmost row boundary fan and by combining the matrix D.
The generation rule of the directed graph is as follows:
a) the node where the frontmost boundary fan is located is made to be the first layer L of the directed graph1The node in (1) is the root node of the directed graph; and mixing L1As the current layer to be processed.
b) And starting from each node in the current layer, connecting nodes with the distance smaller than a threshold value d in the wind field nodes. And define nodes in the same level of the directed graph to be unreachable (i.e., not considered to affect each other) and mark the connected nodes as nodes next to the current level, e.g., if L1For the current layer, the connected nodes are marked as a second layer L2Of the node (b).
c) Taking a newly generated node layer in the directed graph as the current layer, and repeatedly executing step b) in a cyclic manner until each node in the wind field is converted into one directed graph, where reference may be specifically made to the directed graphs of the wind field in different wind directions shown in fig. 5(a) and fig. 5 (b).
Wherein, in the process of constructing the directed graph, when the L < th > occursiAll nodes in the layer arrive at lthi+1When the distances of all nodes in the layer are greater than the threshold d, adjusting the threshold d by delta d, namely adjusting the threshold d to d + delta d, wherein the size of delta d can determine Li+1One node in a layer, on the basis of which Li+2The nodes of the layer continue to be determined using the threshold d.
The established directed graph can be represented by the following elements:
and (3) hierarchy: l ═ L1，...，LPP represents the number of node layers in the directed graph;
nodes in each layer: l isi＝{Ri，...，Rj}；
Side: e ═ E1i，...，Eij，...}。
According to the method and the device, the directed graph is generated based on the wind direction and the distance and the relative position between the fans in the wind field, so that the blade wake influence relationship and the wake influence degree between the fans in the wind field can be determined through the connection relationship between the fan nodes and the direction of the connection edge of the generated directed graph. For example, for a fan of a certain node in the directed graph, wake effects generated by one or more parent node fans are larger than those of other non-parent node fans, so that the fan corresponding to the parent node of the node can be determined as a strong associated fan. For the root node fan without a father node, namely the front row boundary fan, no corresponding strong correlation fan exists in the wind field, and the influence of wake flow is avoided.
Based on this, in this step, one or more father node fans of the fan where the anemometer to be detected is located can be determined from the directed graph corresponding to the wind field where the anemometer to be detected is located, and the father node fan is used as the strong-correlation fan of the anemometer to be detected. For example, if the anemometer to be detected corresponds to the node #5 in fig. 5(a), based on the directed graph in fig. 5(a), it can be determined that the strongly associated fans of the anemometer are the fans corresponding to the node #2 and the node # 6. And aiming at the condition that the fan where the anemoscope to be detected is located does not have a father node (such as a root node), the fan does not have a corresponding strong correlation fan.
It should be noted that, in practical applications, it is not limited to connect all the fan nodes correspondingly included in the wind farm to the directed graph, and for example, an upper limit value d of a fan pitch may be setmaxWhen the determined threshold value d is adjusted to d in the process of constructing the directed graph corresponding to the wind fieldmaxIn this case, because the distance between the fan of the fan node and other fans is far, the wake flow influence of other fans in the wind field can be ignored, only environmental factors are considered, and the condition probability that the wind speed is the first wind speed value output by the wind meter to be detected is calculated by taking the value of each environmental parameter corresponding to the environmental factors as a given condition.
305, if a strong correlation fan corresponding to the fan anemoscope to be detected is determined, obtaining a second wind speed value and a wind direction value which are correspondingly output by the strong correlation fan at the output time of the first wind speed value; and calculating the conditional probability of the wind speed being the first wind speed value output by the anemoscope on the premise of the second wind speed value, the wind direction value and the value of the preset environmental parameter.
Under the condition that a strong correlation fan corresponding to the anemometer to be detected exists in a wind field, the step takes a second wind speed numerical value and a wind direction numerical value output by the strong correlation fan and a current environmental parameter numerical value as preconditions, and calculates the conditional probability that the wind speed is the first wind speed numerical value output by the anemometer to be detected.
First, as for the characters or variables involved in the respective formulas employed in calculating the conditional probabilities, the following table 2 is used for unified explanation:
TABLE 2
| Wind velocity of anemometer tower | ATWS |
| Anemometer wind speed | WS |
| Humidity of air | AT |
| Density of air | AD |
| Amount of rainfall | RF |
| Air pressure | BP |
| Wind speed of father node | PWS |
| Wind direction of father node | PWDA |
Based on the generated directed graph, under the condition that the wind meter to be detected has a strong-correlation wind turbine in the wind field, the wind meter to be detected specifically corresponds to a non-root node of the directed graph, and the process of calculating the conditional probability under the condition comprises the following steps:
for the case that the wind meter of the wind turbine to be detected is a non-root node, a conditional probability P (WS ═ WS | E, factor) needs to be calculated, where factor ═ PWS, PWDA represents the wind speed and wind direction angle output by the fan at the PARENT node of the current node. The formula for calculating P (WS | E, part) refers to the following formula (1):
assuming that the environmental factor E and the PARENT node output factor are mutually independent events, the following equations (2) and (3) hold:
P(E，PARENT)＝P(E)P(PARENT)
＝P(ATWS＝atws，AD＝ad，AT＝at，RF＝rf，BP＝bp)
＊P(PWSi＝pwsi，PWDAi＝pwdai，...，PWSj＝pwsj，PWDAj＝pwdaj) (2)
P(E，PARENT|WS＝ws)＝P(E|WS＝ws)P(PARENT|WS＝ws) (3)
wherein,
referring to the V-shaped structure shown in FIG. 6, it can be demonstrated that in any case of unknown #3, inputs #1 and #2 are independent of each other. The procedure was specifically demonstrated as follows (# denoted by x, i.e., #1, #2, #3 for x1, x2, x3, respectively):
where P represents the probability.
Therefore, P (parameter | WS ═ WS) in equation (3) can be calculated by the following equation:
P(PARENT|WS＝ws)＝
P(PWSi＝pwsi，PWDAi，...，PWSi＝pwsj，PWDAj＝pwdaj|WS＝ws)＝
P(PWSi＝pwsi，PWDAi|WS＝ws)*...*P(PWSi＝pwsj，PWDAj＝pwdaj|WS＝ws) (6)
by substituting the expressions (2), (3), (4), and (6) into the expression (1), the conditional probability that the wind speed is the wind speed value ws output by the non-root node fan can be calculated, when the acquired environmental parameter data, the parent node wind speed, and the wind direction data are used as conditions.
Step 306, if the strongly associated fan corresponding to the fan anemometer to be detected is not determined, obtaining a third wind speed value correspondingly output by the anemometer tower at the output moment of the first wind speed value; and calculating the conditional probability of the wind speed being the first wind speed value output by the anemoscope on the premise of the third wind speed value and the value of the preset environmental parameter.
Under the condition that no strong-correlation fan corresponding to the anemoscope to be detected exists in the wind field, the step takes a third wind speed value correspondingly output by the anemoscope tower when the anemoscope to be detected outputs the first wind speed value and the current environmental parameter value as the precondition, and calculates the conditional probability that the wind speed is the first wind speed value output by the anemoscope to be detected.
Based on the generated directed graph, under the condition that the wind meter to be detected does not have a strong-correlation wind turbine in the wind field, the wind meter to be detected specifically corresponds to the root node of the directed graph, and the process of calculating the conditional probability under the condition comprises the following steps:
because the root node fan is not influenced by wake factors of other fans in the wind field, only the environmental factor and the anemometer tower wind speed are considered to calculate the conditional probability P (WS ═ WS | E, ATWS), wherein E represents the environmental factor, and ATWS represents the anemometer tower wind speed.
The formula for calculating the conditional probability of the root node is specifically referred to the following formula (7):
the lower case characters in the formula are constants, and the conditional probability that the wind speed is the wind speed value ws output by the root node fan can be calculated according to the formula and the historical data under the condition that the acquired environmental parameter data and the wind speed data of the anemometer tower are taken as conditions.
Based on the calculation mode of the conditional probabilities respectively corresponding to the root node and the non-root node in the directed graph, in practical application, the node type of the node corresponding to the anemometer to be detected in the directed graph can be determined, and the conditional probability of the wind speed as the first wind speed value output by the anemometer to be detected is calculated by adopting the corresponding calculation mode according to the node type.
And 307, based on the conditional probability, performing corresponding detection processing on the fan anemoscope.
Finally, the required detection of the wind turbine to be detected may be performed based on the conditional probability, for example, based on a set threshold, whether the wind turbine fails or not may be determined by determining whether the conditional probability is lower than the threshold, and the like. Specifically, when the conditional probability is lower than the threshold, the failure of the anemometer to be detected can be detected, and related personnel such as wind field maintenance personnel can be timely reminded to overhaul the failed anemometer in a targeted manner by performing failure early warning.
In practical application, because all fans in the whole wind field need to be maintained, a directed graph corresponding to the whole wind field can be dynamically constructed according to the wind direction (such as the wind direction of a wind measuring tower), the output (wind speed and wind direction) of a father node in the directed graph is used as the input when the conditional probability is calculated by a child node, and the conditional probability corresponding to the child node, namely the probability that the wind speed value measured by the child node is possible to occur, is calculated by combining with the natural environment parameter data. The probability network of the whole wind field can be finally obtained by performing conditional probability calculation on each node (the root node specifically takes the wind tower wind speed and the environmental parameters as conditional input) in the directed graph, and according to the probability network of the wind field, the working condition of the anemoscope of each fan in the wind field can be evaluated, whether the anemoscope breaks down or not can be judged, and then the effect of performing fault early warning on each fault anemoscope in the wind field can be achieved.
Example four
The fan anemoscope detection device provided by the embodiment of the invention can be realized in various types of computers or upper computers and other equipment, and can be particularly applied to various detections such as fault detection and anemometry accuracy of the fan anemoscope to be detected, so that the detected fault anemoscope can be maintained in a targeted manner on the basis of fault detection.
Referring to fig. 7, a schematic structural diagram of a fan anemometer detection device is shown, which includes:
the acquiring unit 71 is configured to acquire a first wind speed value output by a to-be-detected fan anemoscope, and acquire a wind direction of a wind field, a distance between fans in the wind field, and a value of a predetermined environmental parameter, which correspond to the first wind speed value output by the fan anemoscope; the wind field is the wind field where the wind meter of the fan to be detected is located; the determining unit 72 is configured to determine a strongly-associated fan of the fan anemometer to be detected from the wind field according to the wind direction of the wind field and the distance between the fans in the wind field; the strong correlation fan is a fan which is determined from the wind field according to a preset mode and has a large influence on the wind measuring condition of the fan wind meter; the first calculating unit 73 is configured to, when a strongly associated fan corresponding to the fan anemometer to be detected is determined, obtain a second wind speed value and a wind direction value that are output by the strongly associated fan at the output time of the first wind speed value; calculating the conditional probability of the wind speed being the first wind speed value on the premise of the second wind speed value, the wind direction value and the value of the preset environmental parameter; the second calculating unit 74 is configured to obtain a third wind speed value, which is output by the wind measuring tower correspondingly at the output time of the first wind speed value, when the strongly-associated wind turbine corresponding to the wind meter to be detected is not determined; calculating the conditional probability of the wind speed being the first wind speed value on the premise of the third wind speed value and the value of the preset environmental parameter; and a detection processing unit 75, configured to perform corresponding detection processing on the wind meter of the wind turbine based on the conditional probability.
In an implementation manner of the embodiment of the present invention, the determining unit is further configured to: determining a frontmost boundary fan in a wind field according to the wind direction of the wind field, wherein the frontmost boundary fan is a fan which is not influenced by wake flows of other fans in the wind field; taking the frontmost row boundary fan as a root node fan, and generating a directed graph according to the distance between the fans; the connection relation among fan nodes in the directed graph and the direction of the connection edge reflect the wake influence relation and the wake influence degree among fans in the wind field; and determining the strongly-associated fan of the fan anemometer to be detected based on the directed graph.
In an embodiment of the present invention, the determining unit generates a directed graph by using the frontmost boundary fan as a root node fan according to a distance between the fans, and further includes:
enabling the node where the frontmost row boundary fan is located to be a node in a first layer L1 of the directed graph, and enabling the first layer L1 to serve as a current layer to be processed; starting from each node in the current layer, connecting fan nodes with the distance smaller than a threshold value d in the wind field nodes; taking a node layer newly generated in the directed graph as the current layer, and skipping to execute the step in a loop mode: starting from each node in the current layer, connecting fan nodes with the distance smaller than a threshold value d in the wind field nodes; when L < th > appearsiAll nodes in the layer arrive at lthi+1When the distances of all nodes in the layer are larger than the threshold d, the threshold d is adjusted to d + delta d, and the size of delta d can determine Li+1One node in the layer, then Li+2The nodes of the layer continue to be determined by the threshold value d until all the fan nodes in the wind field are connected to the directed graph。
In an embodiment of the present invention, the determining unit determines, based on the directed graph, a strongly associated wind turbine of the wind meter to be detected, and further includes: finding out a father node of a node where the fan anemoscope to be detected is located from the directed graph; and taking the fan corresponding to the father node as a strong association fan of the fan anemoscope to be detected.
In an implementation manner of the embodiment of the present invention, the detection processing unit is further configured to: judging whether the conditional probability is lower than a preset threshold value or not; and if the conditional probability is lower than the preset threshold value, judging that the anemoscope has a fault, and performing fault early warning.
Here, it should be noted that the description of the wind turbine anemometer detection device related to the present embodiment is similar to the description of the method above, and the beneficial effects of the method are described, for the technical details of the wind turbine anemometer detection device of the present invention that are not disclosed in the present embodiment, please refer to the description of the method embodiment of the present invention, which is not repeated herein.
It should be noted that, in the present specification, the embodiments are all described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments may be referred to each other.
For convenience of description, the above system or apparatus is described as being divided into various modules or units by function, respectively. Of course, the functionality of the units may be implemented in one or more software and/or hardware when implementing the present application.
From the above description of the embodiments, it is clear to those skilled in the art that the present application can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present application may be essentially or partially implemented in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments of the present application.
Finally, it is further noted that, herein, relational terms such as first, second, third, fourth, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A detection method for a wind meter of a fan is characterized by comprising the following steps:
obtaining a first wind speed value output by a wind meter of a fan to be detected, and obtaining the wind direction of a wind field, the distance between fans in the wind field and the value of a preset environmental parameter corresponding to the first wind speed value output by the wind meter of the fan; the wind field is the wind field where the wind meter of the fan to be detected is located;
determining a strongly-associated fan corresponding to the fan anemometer to be detected from the wind field according to the wind direction of the wind field and the distance between fans in the wind field; the strong correlation fan is a fan which is determined from a wind field and has a large influence on the wind measuring condition of the fan wind meter according to a preset mode, so that the influence of the fan which has a small influence on the wind measuring condition of the fan wind meter on the fan wind meter is ignored;
if the strong correlation fan corresponding to the fan anemoscope to be detected is determined, obtaining a second wind speed value and a wind direction value which are correspondingly output by the strong correlation fan at the output moment of the first wind speed value; calculating the conditional probability of the wind speed being the first wind speed value on the premise of the second wind speed value, the wind direction value and the value of the preset environmental parameter;
if the strongly-associated fan corresponding to the fan anemometer to be detected is not determined, a third wind speed value correspondingly output by the anemometer tower at the output moment of the first wind speed value is obtained; calculating the conditional probability of the wind speed being the first wind speed value on the premise of the third wind speed value and the value of the preset environmental parameter;
and based on the conditional probability, carrying out corresponding detection processing on the fan anemoscope.
2. The method according to claim 1, wherein the determining the strongly correlated wind turbine corresponding to the wind meter of the wind turbine to be detected from the wind field according to the wind direction of the wind field and the distance between the wind turbines in the wind field comprises:
determining a frontmost boundary fan in a wind field according to the wind direction of the wind field, wherein the frontmost boundary fan is a fan which is not influenced by wake flows of other fans in the wind field;
taking the frontmost row boundary fan as a root node fan, and generating a directed graph according to the distance between the fans; the connection relation among fan nodes in the directed graph and the direction of the connection edge reflect the wake influence relation and the wake influence degree among fans in the wind field;
and determining the strongly-associated fan corresponding to the fan anemoscope to be detected based on the directed graph.
3. The method of claim 2, wherein the generating a directed graph according to the distances between the fans with the frontmost row boundary fan as a root node fan comprises:
the node where the frontmost boundary fan is located is made to be the first layer L of the directed graph1And said first layer L1As a current layer to be processed;
starting from each node in the current layer, connecting fan nodes with the distance smaller than a threshold value d in the wind field nodes;
and taking a node layer newly generated in the directed graph as the current layer, and jumping to the execution step in a loop mode: starting from each node in the current layer, connecting fan nodes with the distance smaller than a threshold value d in the wind field nodes;
when L < th > appearsiAll nodes in the layer arrive at lthi+1When the distances of all nodes in the layer are larger than the threshold d, the threshold d is adjusted to d + delta d, and the size of delta d can determine Li+1One node in the layer, then Li+2And the nodes of the layer continue to be determined by adopting the threshold value d until all the fan nodes in the wind field are connected to the directed graph.
4. The method according to claim 3, wherein the determining the strongly correlated wind turbine corresponding to the wind meter of the wind turbine to be detected based on the directed graph comprises:
finding out a father node of a node where the fan anemoscope to be detected is located from the directed graph; and taking the fan corresponding to the father node as a strong association fan of the fan anemoscope to be detected.
5. The method according to any one of claims 1 to 4, wherein the performing the corresponding detection processing on the wind turbine anemometer based on the conditional probability comprises:
judging whether the conditional probability is lower than a preset threshold value or not;
and if the conditional probability is lower than the preset threshold, judging that the fan anemoscope fails, and performing fault early warning.
6. The utility model provides a fan anemometer detection device which characterized in that includes:
the system comprises an acquisition unit, a detection unit and a control unit, wherein the acquisition unit is used for acquiring a first wind speed value output by a wind meter of a fan to be detected, and acquiring the wind direction of a wind field, the distance between fans in the wind field and the value of a preset environmental parameter corresponding to the first wind speed value output by the wind meter of the fan; the wind field is the wind field where the wind meter of the fan to be detected is located;
the determining unit is used for determining a strongly-associated fan corresponding to the fan anemoscope to be detected from the wind field according to the wind direction of the wind field and the distance between fans in the wind field; the strong correlation fan is a fan which is determined from the wind field and has a large influence on the wind measuring condition of the fan wind meter according to a preset mode, so that the influence of the fan which has a small influence on the wind measuring condition of the fan wind meter on the fan wind meter is ignored;
the first calculation unit is used for obtaining a second wind speed numerical value and a wind direction numerical value which are correspondingly output by the strong correlation fan at the output moment of the first wind speed numerical value when the strong correlation fan corresponding to the wind meter to be detected is determined; calculating the conditional probability of the wind speed being the first wind speed value on the premise of the second wind speed value, the wind direction value and the value of the preset environmental parameter;
the second calculation unit is used for obtaining a third wind speed value correspondingly output by the wind measuring tower at the output moment of the first wind speed value when the strong correlation fan corresponding to the wind meter to be detected is not determined; calculating the conditional probability of the wind speed being the first wind speed value on the premise of the third wind speed value and the value of the preset environmental parameter;
and the detection processing unit is used for carrying out corresponding detection processing on the fan anemoscope based on the conditional probability.
7. The apparatus of claim 6, wherein the determining unit is further configured to:
determining a frontmost boundary fan in a wind field according to the wind direction of the wind field, wherein the frontmost boundary fan is a fan which is not influenced by wake flows of other fans in the wind field; taking the frontmost row boundary fan as a root node fan, and generating a directed graph according to the distance between the fans; the connection relation among fan nodes in the directed graph and the direction of the connection edge reflect the wake influence relation and the wake influence degree among fans in the wind field; and determining the strongly-associated fan corresponding to the fan anemoscope to be detected based on the directed graph.
8. The apparatus of claim 7, wherein the determining unit generates a directed graph according to the distances between the fans by using the frontmost boundary fan as a root node fan, and further comprises:
the node where the frontmost boundary fan is located is made to be the first layer L of the directed graph1And said first layer L1As a current layer to be processed; starting from each node in the current layer, connecting fan nodes with the distance smaller than a threshold value d in the wind field nodes; and taking a node layer newly generated in the directed graph as the current layer, and jumping to the execution step in a loop mode: starting from each node in the current layer, connecting fan nodes with the distance smaller than a threshold value d in the wind field nodes; when L < th > appearsiAll nodes in the layer arrive at lthi+1When the distances of all nodes in the layer are larger than the threshold d, the threshold d is adjusted to d + delta d, and the size of delta d can determine Li+1One node in the layer, then Li+2And the nodes of the layer continue to be determined by adopting the threshold value d until all the fan nodes in the wind field are connected to the directed graph.
9. The apparatus according to claim 8, wherein the determining unit determines the strongly associated wind turbine of the wind turbine anemometer to be detected based on the directed graph, further comprising:
finding out a father node of a node where the fan anemoscope to be detected is located from the directed graph; and taking the fan corresponding to the father node as a strong association fan of the fan anemoscope to be detected.
10. The apparatus according to any one of claims 6-9, wherein the detection processing unit is further configured to:
judging whether the conditional probability is lower than a preset threshold value or not; and if the conditional probability is lower than the preset threshold, judging that the fan anemoscope fails, and performing fault early warning.
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