Wind power plant current collection line fault positioning method and system
By setting a monitoring terminal on the wind farm collecting line, using wave head arrival time difference and multi-dimensional analysis technology, the fault points are quickly and accurately positioned, solving the problem of difficulty in positioning the wind farm collecting line faults, and improving fault maintenance efficiency and grid stability.
Patent Information
- Application Number
- CN202510450116.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-10
- Publication Date
- 2025-07-04
AI Technical Summary
It is difficult to locate faults in wind farm collector lines. The existing technology relies on manual inspections, which consumes time and effort, and cannot restore power supply in time.
Based on the branch structure of the collector line, multiple monitoring terminals are set up, and by collecting the wave head arrival time difference when the fault occurs, fault characteristic parameters are constructed, fault line segments are quickly screened, and multi-dimensional analysis is carried out to accurately locate the fault points.
The fault positioning efficiency and maintenance efficiency of wind farm collector lines have been improved, ensuring the safe production of the wind farm and the stable operation of the power grid.
Smart Images
Figure CN120254493A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of collector line fault location, and particularly to a method and system for fault location of collector lines in a wind farm. Background Art
[0002] Most of the collector lines in a wind farm are distributed in mountain ridges, lakes, etc. Affected by the geographical location and the layout position of the wind turbines, the overall line structure shows a multi-T connection and an overhead mixed connection of the lines. And in some wind farm collector lines, Z-type transformers are added at the busbars, resulting in the inability to use the fault ranging scheme in transmission lines to achieve fault determination.
[0003] Currently, limited by the terrain, landforms, ground objects of the line corridor, as well as the line facilities and equipment and vegetation conditions, often only the results of manual inspections can be obtained. Relying on the manual blind inspection mode to find the fault point is time-consuming and laborious. The time for finding the fault accounts for more than 2 / 3 of the fault handling time, which is not conducive to restoring power supply in a timely manner. Summary of the Invention
[0004] The purpose of the present application is: To solve the above technical problems, the present application provides a method and system for fault location of collector lines in a wind farm, aiming to improve the fault location accuracy of collector lines and ensure the safe production of collector lines in the wind farm and the safe and stable operation of the power grid.
[0005] In some embodiments of the present application, based on the branch structure of the collector line, a plurality of monitoring terminals are set, multiple line segments are constructed for the collector lines in the wind farm, and the fault characteristic parameters of each line segment are set based on the position parameters of each monitoring terminal. By collecting the time difference of the wavefront arrival times of each collector line when a fault occurs, the line segments where the fault may occur are quickly screened, improving the fault location efficiency of the collector lines.
[0006] In some embodiments of the present application, by collecting the feedback data packets of the risk line segments, multi-dimensional analysis is performed on each risk line segment to accurately locate each fault point, providing data support for the maintenance scheduling of management personnel, improving the fault repair efficiency of the collector lines, and ensuring the safe production of collector lines in the wind farm and the safe and stable operation of the power grid.
[0007] In some embodiments of the present application, a method for fault location of collector lines in a wind farm is provided, including: Setting a plurality of monitoring terminals according to the collector line parameters, and constructing a plurality of line segments according to all the monitoring terminals; Obtaining the wavefront time parameters of each device terminal, and generating a primary location result according to a preset fault location model and the wavefront time parameters; Obtaining a feedback data packet according to the primary location result, and generating a secondary location result according to the feedback data packet; When setting multiple monitoring terminals, it includes: Establish a sequence of monitoring terminals A, A = (a1, a2... a i … a n ), where a i is the i-th monitoring terminal; n is the number of monitoring terminals.
[0008] In some embodiments of the present application, when presetting a fault location model, it includes: Establish a sequence of line sections B, B = (b1, b2... b i … b m ), where b i is the i-th line section; m is the number of line sections; Set bi as the target line section in sequence according to the sequence of line sections B; Generate the correlation evaluation value between each monitoring terminal and the target line section; Construct a correlation terminal mapping table for the target line section according to all the correlation evaluation values; Generate multiple fault characteristics of the target line section based on the correlation terminal mapping table; Generate a diagnostic sub-model for the target line section according to all the fault characteristics; Construct diagnostic sub-models for each line section in sequence; Construct a fault location model according to all the diagnostic sub-models.
[0009] In some embodiments of the present application, when generating a primary diagnostic result, it includes: Establish a sequence of wavefront times T; T = (t1, t2... t i … t n ), where t i is the time when the i-th monitoring terminal collects the wavefront; n is the number of monitoring terminals; Set b i as the line section to be diagnosed in sequence according to the sequence of line sections B; Generate the fault probability value f of the line section to be diagnosed according to the fault location model and the sequence of wavefront times T; Generate the fault probability values of each line section in sequence; Establish a sequence of fault probability values F, F = (f1, f2... f i … f m ), where f i is the fault probability value of the i-th line section; m is the number of line sections; Generate a primary diagnostic result according to the sequence of fault probability values F.
[0010] In some embodiments of the present application, when generating the fault probability value f of the line section to be diagnosed, it includes: Set the diagnostic sub - model of the line section to be diagnosed as the target diagnostic sub - model; f = U * β i *c i )]; Where, U is the conversion coefficient; θ1 is the number of fault characteristics in the target diagnostic sub - model; β i is the influence factor of the i - th fault characteristic in the target diagnostic sub - model; c i is the fitting value of the wavefront time series T and the i - th fault characteristic in the target diagnostic sub - model.
[0011] In some embodiments of the present application, when generating the first - level diagnosis result according to the fault probability value series F, it includes: Preset the fault probability value threshold F1; If f i > F1, set the i - th line section as the risk line section; Obtain all risk line sections; Establish a risk line section series A1, A1=(a 11 , a 12 … a 1i … a 1n1 ), where, a 1i is the i - th risk line section; n1 is the number of risk line sections, and n > n1.
[0012] In some embodiments of the present application, when generating the second - level positioning result according to the feedback data packet, it includes: Set a 1i as the target risk line section in turn according to the risk line section series A1; Set the monitoring strategy for the target risk line section; Establish a time interval series H according to the monitoring strategy, H=(h1, h2… h i … h r ), where, h i is the i - th time interval established based on the monitoring strategy; r is the number of time intervals established based on the monitoring strategy; Obtain the feedback data packet of the target risk line section according to the time interval series H; Generate the abnormal risk value d of the target risk line section according to the feedback data packet; Generate the abnormal risk values of each risk line section in turn; Establish an abnormal risk value series D, D=(d1, d2… d i … d n1 ), where, d i is the abnormal risk value of the i - th risk line section; Preset abnormal risk value threshold D1; If d i > D1, set the i-th risk line section as the fault line section; Generate fault points within each fault line section according to the fault location model; Generate a secondary positioning result based on all the fault points.
[0013] In some embodiments of the present application, when generating the abnormal risk value d of the target risk line section, it includes: d = e1 * Q1 * (g i - g') 2 + e2 * Q2 * (g i - Δg) 2 ; Wherein, e1 is a preset first weight coefficient; e2 is a preset second weight coefficient; Q1 is a preset first fixed coefficient; Q2 is a preset second fixed coefficient; g i is the temperature value collected within the i-th time interval h i ; g' is a preset standard temperature value; Δg is the average value of all the collected temperature values.
[0014] In some embodiments of the present application, a fault location system for a wind farm collector line is provided, including: A central control unit, configured to set a plurality of monitoring terminals according to the collector line parameters, and construct a plurality of line sections based on all the monitoring terminals; A monitoring unit, configured to collect feedback data packets; The central control unit includes: A first processing module, configured to obtain the wavefront time parameters of each device terminal, and generate a primary positioning result according to a preset fault location model and the wavefront time parameters; A second processing module, configured to obtain a feedback data packet according to the primary positioning result, and generate a secondary positioning result according to the feedback data packet; A third processing module, configured to establish a monitoring terminal sequence A, A = (a1, a2... a i … a n ), where a i is the i-th monitoring terminal; n is the number of monitoring terminals.
[0015] In some embodiments of the present application, the first processing module is further configured to: Establish a line section sequence B, B = (b1, b2... b i … b m ), where b i is the i-th line section; m is the number of line sections; Set bi as the target line section in sequence according to the line section sequence B; Generate the association evaluation value between each monitoring terminal and the target line section; Construct the association terminal mapping table of the target line section according to all association evaluation values; Generate multiple fault features of the target line section based on the association terminal mapping table; Generate the diagnostic sub-model of the target line section according to all fault features; Construct the diagnostic sub-models of each line section in sequence; Construct the fault location model according to all diagnostic sub-models; Establish the wavefront time sequence T; T = (t1, t2…t i …t n ), where t i is the time when the i-th monitoring terminal collects the wavefront; n is the number of monitoring terminals; Set b i as the line section to be diagnosed in sequence according to the line section sequence B; Generate the fault probability value f of the line section to be diagnosed according to the fault location model and the wavefront time sequence T; Generate the fault probability values of each line section in sequence; Establish the fault probability value sequence F, F = (f1, f2…f i …f m ), where f i is the fault probability value of the i-th line section; m is the number of line sections; Preset the fault probability value threshold F1; If f i > F1, set the i-th line section as the risk line section; Obtain all risk line sections; Establish the risk line section sequence A1, A1 = (a 11 , a 12 …a 1i …a 1n1 ), where a 1i is the i-th risk line section; n1 is the number of risk line sections, and n > n1.
[0016] In some embodiments of the present application, the second processing module is further configured to: Set a 1i as the target risk line section in sequence according to the risk line section sequence A1; Set the monitoring strategy for the target risk line section; Establish the time interval sequence H according to the monitoring strategy, H = (h1, h2…h i…h r ), where h i is the i-th time interval established based on the monitoring strategy; r is the number of time intervals established based on the monitoring strategy; Obtain the feedback data packet of the target risk line section according to the time interval sequence H; Generate the abnormal risk value d of the target risk line section according to the feedback data packet; Generate the abnormal risk values of each risk line section in sequence; Establish an abnormal risk value sequence D, D = (d1, d2…d i …d n1 ), where d i is the abnormal risk value of the i-th risk line section; Preset the abnormal risk value threshold D1; If d i > D1, set the i-th risk line section as the faulty line section; Generate the fault points in each faulty line section according to the fault location model; Generate the secondary positioning result according to all the fault points.
[0017] Compared with the prior art, the beneficial effects of the method and system for fault location of a wind farm collector line in an embodiment of the present application are as follows: Based on the branch structure of the collector line, a plurality of monitoring terminals are set, the collector line of the wind farm is partitioned to construct a plurality of line segments, and the fault characteristic parameters of each line section are set based on the position parameters of each monitoring terminal. By collecting the arrival time difference of the wave heads of each collector line when a fault occurs, the line sections where the fault may occur are quickly screened, and the fault location efficiency of the collector line is improved.
[0018] By collecting the feedback data packets of the risk line sections, multi-dimensional analysis is performed on each risk line section, and each fault point is accurately located, providing data support for the maintenance scheduling of management personnel, improving the fault repair efficiency of the collector line, and ensuring the safe production of the wind farm collector line and the safe and stable operation of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS
[0019] Figure 1 is a schematic flow chart of a method for fault location of a wind farm collector line in a preferred embodiment of an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0020] The following further describes in detail the specific embodiments of the present application in conjunction with the drawings and embodiments. The following embodiments are used to illustrate the present application, but are not used to limit the scope of the present application.
[0021] In the description of the present application, it should be understood that the orientation or positional relationship indicated by terms such as "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present application.
[0022] The terms "first" and "second" are only used for descriptive purposes and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.
[0023] In the description of the present application, it should be noted that unless otherwise clearly specified and limited, the terms "installation", "connection", and "coupling" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.
[0024] As Figure 1 shown, a method for fault location of a wind farm collector line according to a preferred embodiment of an embodiment of the present application includes: S101: Set a plurality of monitoring terminals according to the collector line parameters, and construct a plurality of line sections based on all the monitoring terminals; S102: Obtain the wavefront time parameters of each device terminal, and generate a primary location result according to a preset fault location model and the wavefront time parameters; S103: Obtain a feedback data packet according to the primary location result, and generate a secondary location result according to the feedback data packet; Among them, when setting a plurality of monitoring terminals, it includes: Establish a monitoring terminal sequence A, A = (a1, a2... a i …a n ), where a i is the i-th monitoring terminal; n is the number of monitoring terminals.
[0025] Specifically, set a plurality of monitoring points according to the branch structure parameters of the collector line, and add monitoring terminals at each monitoring point, and the monitoring terminals are used to collect the traveling wave parameters when a fault occurs.
[0026] Specifically, according to the branch structure parameters of the collector line, the collector line is divided into multiple minimum T-connection unit collector networks, and a single minimum T-connection unit collector network is set as a single line section.
[0027] Specifically, when presetting the fault location model, it includes: Establish a line section sequence B, B = (b1, b2…b i …b m ), where b i is the i-th line section; m is the number of line sections; Set bi as the target line section in turn according to the line section sequence B; Generate the correlation evaluation value between each monitoring terminal and the target line section; Construct the correlation terminal mapping table of the target line section according to all the correlation evaluation values; Generate multiple fault characteristics of the target line section based on the correlation terminal mapping table; Generate the diagnostic sub-model of the target line section according to all the fault characteristics; Construct the diagnostic sub-models of each line section in turn; Construct the fault location model according to all the diagnostic sub-models.
[0028] Specifically, generate the corresponding correlation evaluation value according to whether each monitoring terminal can receive the fault traveling wave signal of the target line section. The more significant the received signal is, the greater the corresponding correlation evaluation value is.
[0029] Specifically, preset the correlation evaluation value threshold. If the real-time correlation evaluation value is greater than the correlation evaluation value threshold, it means that the current monitoring terminal can receive the fault traveling wave of the target line section, and the current monitoring terminal is the first-level correlation terminal of the target line section. Set the correlation terminal mapping table of the target line section according to all the first-level correlation terminals.
[0030] Specifically, generate multiple fault characteristics according to the time difference of the fault traveling wave of the target line section arriving at each first-level correlation terminal. For example, when a fault occurs in the target line section, the monitoring terminal that can receive the fault traveling wave signal; when a fault occurs in the target line section, the sequence of each first-level correlation terminal receiving the fault traveling wave signal, the time difference interval and the standard time difference between any two first-level correlation terminals receiving the fault traveling wave signal, etc.
[0031] Specifically, construct the corresponding diagnostic sub-model according to all the fault characteristics of the target line section.
[0032] It can be understood that in the above embodiments, based on the branch structure of the collector line, multiple monitoring terminals are set up to partition the collector line of the wind farm to construct multiple line segments, and the fault characteristic parameters of each line segment are set based on the position parameters of each monitoring terminal. By collecting the time difference of the wavefront arrival times of each collector line when a fault occurs, the line segment where the fault may occur is quickly screened, improving the fault location efficiency of the collector line.
[0033] In a preferred embodiment of the present application, when generating the first-level diagnosis result, it includes: Establish a wavefront time sequence T; T = (t1, t2…t i …t n ), where t i is the time when the wavefront is collected by the i-th monitoring terminal; n is the number of monitoring terminals; According to the line segment sequence B, set b i as the line segment to be diagnosed; Generate the fault probability value f of the line segment to be diagnosed according to the fault location model and the wavefront time sequence T; Generate the fault probability values of each line segment in turn; Establish a fault probability value sequence F, F = (f1, f2…f i …f m ), where f i is the fault probability value of the i-th line segment; m is the number of line segments; Generate the first-level diagnosis result according to the fault probability value sequence F.
[0034] Specifically. The larger the fault probability value, the greater the possibility of a running fault in the current line segment to be diagnosed.
[0035] Specifically, when generating the fault probability value f of the line segment to be diagnosed, it includes: Set the diagnostic sub-model of the line segment to be diagnosed as the target diagnostic sub-model; f = U * β i * c i )]; Where U is the conversion coefficient; θ1 is the number of fault characteristics in the target diagnostic sub-model; β i is the influence factor of the i-th fault characteristic in the target diagnostic sub-model; c i is the fit value between the wavefront time sequence T and the i-th fault characteristic in the target diagnostic sub-model.
[0036] Specifically, when generating the first-level diagnosis result according to the fault probability value sequence F, it includes: Preset a fault probability value threshold F1; If f i > F1, set the i-th line section as a risk line section; Obtain all risk line sections; Establish a risk line section sequence A1, A1 = (a 11 , a 12 … a 1i … a 1n1 ), where a 1i is the i-th risk line section; n1 is the number of risk line sections, and n > n1.
[0037] Specifically, preprocess the wave head time sequence T according to the target diagnosis sub-model, so as to generate a fitting value for each fault feature in the target diagnosis sub-model. The larger the fitting value, the greater the similarity between each data in the wave head time sequence T and the corresponding fault feature.
[0038] Specifically, the fault probability value threshold can be set according to historical parameters. If the real-time fault probability value of the line section to be diagnosed is greater than the preset fault probability value threshold, it indicates that there is a potential fault risk in the current line section to be diagnosed.
[0039] Specifically, a risk line section refers to a section where there is a potential fault risk in the current line section. It is necessary to combine multi-dimensional data for further analysis to complete fault location in a timely manner to assist management personnel in making maintenance decisions.
[0040] It can be understood that in the above embodiments, by collecting the time difference of the wave head arrival times of each collector line when a fault occurs, the line sections where a fault may occur are quickly screened, improving the fault location efficiency for the collector lines.
[0041] In the preferred embodiment of the present application, when generating a secondary positioning result according to the feedback data packet, it includes: Set a 1i as the target risk line section in sequence according to the risk line section sequence A1; Set the monitoring strategy for the target risk line section; Establish a time interval sequence H according to the monitoring strategy, H = (h1, h2… h i … h r ), where h i is the i-th time interval established based on the monitoring strategy; r is the number of time intervals established based on the monitoring strategy; Obtain the feedback data packet of the target risk line section according to the time interval sequence H; Generate an abnormal risk value d for the target risk line section according to the feedback data packet; Generate the abnormal risk values of each risk line section in sequence; Establish an abnormal risk value sequence D, D = (d1, d2…d i …d n1 ), where d i is the abnormal risk value of the i-th risk line section; Preset the abnormal risk value threshold D1; If d i > D1, set the i-th risk line section as the faulty line section; Generate the fault points within each faulty line section according to the fault location model; Generate the secondary positioning result based on all the fault points.
[0042] Specifically, when generating the abnormal risk value d of the target risk line section, it includes: d = e1 * Q1 * (g i - g') 2 + e2 * Q2 * (g i - Δg) 2 ; Among them, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; g i is the temperature value collected within the i-th time interval h i ; g' is the preset standard temperature value; Δg is the average value of all the collected temperature values.
[0043] Specifically, by setting temperature sensing optical fibers on the collector line, the real-time temperature of each line section of the collector line is collected, and by analyzing the temperature data, the faulty line section is identified in a timely manner.
[0044] Specifically, the greater the abnormal risk value, the greater the possibility of a fault risk in the current risk line section. Set the abnormal risk value threshold according to historical parameters. If the abnormal risk value of the current risk line section is greater than the preset abnormal risk value threshold, it indicates that there is a fault in the current risk line section.
[0045] Specifically, by analyzing the fault traveling wave signals collected by all the primary associated terminals in the faulty line section, the fault points within the faulty line section are located.
[0046] It can be understood that in the above embodiments, by collecting the feedback data packets of the risk line sections, multi-dimensional analysis is performed on each risk line section, the fault points are accurately located, providing data support for the maintenance scheduling of the management personnel, improving the fault maintenance efficiency of the collector line of the wind farm, and ensuring the safe production of the collector line of the wind farm and the safe and stable operation of the power grid.
[0047] Specifically, all parameters in the model are normalized by presetting a first fixed coefficient and a second fixed coefficient, so that each parameter in the model is within the same value range.
[0048] Based on another preferred embodiment of a fault location method for a wind farm collector line in any of the above preferred embodiments, a fault location system for a wind farm collector line is provided in this preferred embodiment, including: A central control unit, configured to set a plurality of monitoring terminals according to the collector line parameters, and construct a plurality of line sections according to all the monitoring terminals; A monitoring unit, configured to collect feedback data packets; Specifically, the monitoring unit is preferably a temperature sensing optical fiber, which is arranged on the collector line and is used to collect the real-time temperature of each line section of the collector line to generate corresponding feedback data packets.
[0049] The central control unit includes: A first processing module, configured to obtain the wavefront time parameters of each device terminal, and generate a first-level positioning result according to a preset fault location model and the wavefront time parameters; A second processing module, configured to obtain a feedback data packet according to the first-level positioning result, and generate a second-level positioning result according to the feedback data packet; A third processing module, configured to establish a monitoring terminal sequence A, A = (a1, a2... a i …a n ), where a i is the i-th monitoring terminal; n is the number of monitoring terminals.
[0050] In the preferred embodiment of the present application, the first processing module is further configured to: Establish a line section sequence B, B = (b1, b2... b i …b m ), where b i is the i-th line section; m is the number of line sections; Successively set bi as the target line section according to the line section sequence B; Generate the association evaluation value between each monitoring terminal and the target line section; Construct an association terminal mapping table of the target line section according to all the association evaluation values; Generate multiple fault features of the target line section based on the association terminal mapping table; Generate a diagnostic sub-model of the target line section according to all the fault features; Successively construct the diagnostic sub-models of each line section; Construct a fault location model according to all the diagnostic sub-models; Establish a wavefront time sequence T; T = (t1, t2... ti …t n ), where t i is the moment when the wavefront is collected by the i-th monitoring terminal; n is the number of monitoring terminals; Set b successively according to the line section sequence B i as the line section to be diagnosed; Generate the fault probability value f of the line section to be diagnosed according to the fault location model and the wavefront time sequence T; Generate the fault probability values of each line section successively; Establish a fault probability value sequence F, F = (f1, f2... f i ... f m ), where f i is the fault probability value of the i-th line section; m is the number of line sections; Preset the fault probability value threshold F1; If f i > F1, set the i-th line section as a risk line section; Obtain all risk line sections; Establish a risk line section sequence A1, A1 = (a 11 , a 12 ... a 1i ... a 1n1 ), where a 1i is the i-th risk line section; n1 is the number of risk line sections, and n > n1.
[0051] In the preferred embodiment of the present application, the second processing module is further configured to: Set a 1i as the target risk line section successively according to the risk line section sequence A1; Set the monitoring strategy for the target risk line section; Establish a time interval sequence H according to the monitoring strategy, H = (h1, h2... h i ... h r ), where h i is the i-th time interval established based on the monitoring strategy; r is the number of time intervals established based on the monitoring strategy; Obtain the feedback data packet of the target risk line section according to the time interval sequence H; Generate the abnormal risk value d of the target risk line section according to the feedback data packet; Generate the abnormal risk values of each risk line section successively; Establish an abnormal risk value sequence D, D = (d1, d2... d i ... d n1 ), where d i is the abnormal risk value of the i-th risk line section; Preset abnormal risk value threshold D1; If d i > D1, set the i-th risk line section as the faulty line section; Generate fault points within each faulty line section according to the fault location model; Generate secondary location results based on all fault points.
[0052] According to the first concept of the present application, a plurality of monitoring terminals are set based on the branch structure of the collector line, multiple line segments are constructed by partitioning the collector line of the wind farm, and the fault characteristic parameters of each line section are set based on the position parameters of each monitoring terminal. By collecting the time difference of the wavefront arrival times of each collector line when a fault occurs, the line sections where the fault may occur are quickly screened, improving the fault location efficiency of the collector line.
[0053] According to the second concept of the present application, by collecting the feedback data packets of the risk line sections, multi-dimensional analysis is performed on each risk line section to accurately locate each fault point, providing data support for the maintenance scheduling of management personnel, improving the fault repair efficiency of the collector line, and ensuring the safe production of the collector line of the wind farm and the safe and stable operation of the power grid.
[0054] The above are only the preferred embodiments of the present application. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present application, several improvements and substitutions can be made, and these improvements and substitutions should also be regarded as the protection scope of the present application.
Claims
1. A method for fault location of a wind farm collector line, characterized in that Including: Set multiple monitoring terminals according to the collector line parameters, and construct multiple line sections based on all the monitoring terminals; Obtain the wavefront time parameters of each device terminal, and generate a primary positioning result according to the preset fault location model and the wavefront time parameters; Obtain a feedback data packet according to the primary positioning result, and generate a secondary positioning result according to the feedback data packet; Among them, when setting multiple monitoring terminals, it includes: Establish a monitoring terminal sequence A, A = (a1, a2…a i …a n ), where ai i is the i-th monitoring terminal; n is the number of monitoring terminals.
2. The method for fault location of the collector line of a wind farm according to claim 1, wherein, When presetting the fault location model, it includes: Establish a sequence of line segments B, B = (b1, b2…b i …b m ), where b i is the i-th line segment; m is the number of line segments; Successively set bi as the target line section according to the line section sequence B; Generate the association evaluation value between each monitoring terminal and the target line section; Construct the association terminal mapping table of the target line section based on all the association evaluation values; Generate multiple fault characteristics of the target line section based on the association terminal mapping table; Generate the diagnostic sub-model of the target line section according to all the fault characteristics; Successively construct the diagnostic sub-models of each line section; Construct the fault location model according to all the diagnostic sub-models.
3. The method for fault location of the collector line of a wind farm according to claim 2, wherein, When generating the primary diagnosis result, it includes: Establish the wavefront time sequence T; T=(t1, t2…t i …t n ), where t i is the moment when the wavefront is collected by the i-th monitoring terminal; n is the number of monitoring terminals; Set b sequentially according to the line section sequence number column B i as the line section to be diagnosed; Generate the fault probability value f of the line section to be diagnosed according to the fault location model and the wavefront time sequence T; Successively generate the fault probability values of each line section; Establish a failure probability value sequence F, F = (f1, f2... f i … f m ), where, f i is the failure probability value of the i-th line section; m is the number of line sections; Generate the primary diagnosis result according to the fault probability value sequence F.
4. The method for fault location of the wind farm collector line according to claim 3, characterized in that, When generating the fault probability value f of the line section to be diagnosed, it includes: Set the diagnostic sub-model of the line section to be diagnosed as the target diagnostic sub-model; f = U * β i * c i )]; Among them, U is the conversion coefficient; θ1 is the number of fault features in the target diagnosis sub-model; β i is the influence factor of the i-th fault feature in the target diagnosis sub-model; c i is the fitting value of the wavefront time series T and the i-th fault feature in the target diagnosis sub-model.
5. The method for fault location of the wind farm collector line according to claim 3, wherein When generating the primary diagnosis result according to the fault probability value sequence F, it includes: Preset the fault probability value threshold F1; If f i > F1, set the i-th line section as a risk line section; Obtain all the risk line sections; Establish a sequence of risk line sections \(A_1\), \(A_1=(a 11 , a 12 … a 1i … a 1n1 ), where \(a 1i is the \(i\)-th risk line section; \(n_1\) is the number of risk line sections, and \(n > n_1\).
6. The method for fault location of the collector line of a wind farm according to claim 5, wherein When generating the secondary positioning result according to the feedback data packet, it includes: Set \(a\) in sequence according to the risk line section sequence \(A1\). 1i as the target risk line section; Set the monitoring strategy for the target risk line section; According to the monitoring strategy, establish a time interval sequence H, H = (h1, h2…h i …h r ), where h i is the i-th time interval established based on the monitoring strategy; r is the number of time intervals established based on the monitoring strategy; Obtain the feedback data packet of the target risk line section according to the time interval sequence H; Generate the abnormal risk value d of the target risk line section according to the feedback data packet; Successively generate the abnormal risk values of each risk line section; Establish an abnormal risk value sequence D, D = (d1, d2…d i …d n1 ), where d i is the abnormal risk value of the i-th risk line section; Preset the abnormal risk value threshold D1; If d i > D1, set the i-th risk line section as the faulty line section; Generate the fault points within each fault line section according to the fault location model; Generate the secondary positioning result according to all the fault points.
7. The method for fault location of the wind farm collector line according to claim 6, wherein, When generating the abnormal risk value d of the target risk line section, it includes: d = e1 * Q1 * (g i - g') 2 + e2 * Q2 * (g i - Δg) 2 ; Among them, e1 is a preset first weight coefficient; e2 is a preset second weight coefficient; Q1 is a preset first fixed coefficient; Q2 is a preset second fixed coefficient; g i is the temperature value collected in the i-th time interval h i ; g' is a preset standard temperature value; Δg is the average value of all the collected temperature values.
8. A fault location system for a wind farm collector line, which adopts the fault location method for a wind farm collector line described in any one of the above claims 1-7, is characterized in that Including: The central control unit is used to set multiple monitoring terminals according to the collector line parameters, and construct multiple line sections based on all the monitoring terminals; The monitoring unit is used to collect the feedback data packet; The central control unit includes: The first processing module is used to obtain the wavefront time parameters of each device terminal, and generate a primary positioning result according to the preset fault location model and the wavefront time parameters; The second processing module is used to obtain the feedback data packet according to the primary positioning result, and generate a secondary positioning result according to the feedback data packet; The third processing module is used to establish a sequence A of monitoring terminals, A = (a1, a2... a i …a n ), where a i is the i-th monitoring terminal; n is the number of monitoring terminals.
9. The fault location system for the wind farm collector line according to claim 8, characterized in that, The first processing module is also used for: Establish a sequence of line segments B, B = (b1, b2…b i …b m ), where b i is the i-th line segment; m is the number of line segments; Successively set bi as the target line section according to the line section sequence B; Generate the association evaluation value between each monitoring terminal and the target line section; Construct the association terminal mapping table of the target line section based on all the association evaluation values; Generate multiple fault characteristics of the target line section based on the association terminal mapping table; Generate the diagnostic sub-model of the target line section according to all the fault characteristics; Successively construct the diagnostic sub-models of each line section; Construct the fault location model according to all the diagnostic sub-models; Establish the wavefront time sequence T; T = (t1, t2…t i …t n ), where ti i is the time when the wavefront is collected by the i-th monitoring terminal; n is the number of monitoring terminals; Set b sequentially according to the line section sequence number column B i as the line section to be diagnosed; Generate the fault probability value f of the line section to be diagnosed according to the fault location model and the wavefront time series T; Generate the fault probability values of each line section in sequence; Establish a failure probability value sequence F, F = (f1, f2…f i …f m ), where f i is the failure probability value of the i-th line section; m is the number of line sections; Preset the fault probability value threshold F1; If f i > F1, set the i-th line section as a risk line section; Obtain all the risk line sections; Establish a sequence of risk line sections \(A_1\), \(A_1=(a 11 , a 12 … a 1i … a 1n1 ), where \(a 1i is the \(i\)-th risk line section; \(n_1\) is the number of risk line sections, and \(n > n_1\).
10. The fault location system for the wind farm collector line according to claim 9, wherein, The second processing module is further configured to: Set \(a\) successively according to the risk line section sequence \(A1\). 1i as the target risk line section; Set the monitoring strategy for the target risk line section; Establish a time interval sequence H according to the monitoring strategy, H = (h1, h2…h i …h r ), where h i is the i-th time interval established based on the monitoring strategy; r is the number of time intervals established based on the monitoring strategy; Obtain the feedback data packet of the target risk line section according to the time interval series H; Generate the abnormal risk value d of the target risk line section according to the feedback data packet; Generate the abnormal risk values of each risk line section in sequence; Establish an abnormal risk value sequence D, D = (d1, d2…d i …d n1 ), where d i is the abnormal risk value of the i-th risk line section; Preset the abnormal risk value threshold D1; If d i > D1, set the i-th risk line section as the faulty line section; Generate the fault points within each fault line section according to the fault location model; Generate the secondary location result according to all the fault points.