CN112214860A - Power transmission line deicing jump fault prediction method, device, medium and electronic equipment - Google Patents
Power transmission line deicing jump fault prediction method, device, medium and electronic equipment Download PDFInfo
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Abstract
The disclosure relates to a method, a device, a medium and an electronic device for predicting ice shedding jump faults of a power transmission line, wherein the method comprises the following steps: acquiring a plurality of current different meteorological environment parameters at the power transmission line; determining the probability of the current ice shedding jump of the power transmission line based on the different meteorological environment parameters and a preset ice shedding jump probability model, wherein the ice shedding jump probability model comprises probability distribution of the ice shedding jump of the power transmission line under different historical meteorological environment parameters; and when the probability of the current ice shedding jump of the power transmission line is greater than a preset value, determining the probability of the current ice shedding jump of the power transmission line as a fault probability to output fault prompt information. According to the embodiment of the method and the device, the deicing jump fault prediction result is more accurate, and reliable and important decision basis can be provided for the deicing jump fault prevention and control of the power transmission line.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of power grid icing prediction, in particular to a power transmission line deicing jump fault prediction method, a power transmission line deicing jump fault prediction device, a computer readable storage medium and an electronic device for realizing the power transmission line deicing jump fault prediction method.
Background
The ice-shedding jump is one of common types of ice-coating disaster accidents, and can cause great damage to power grid transmission lines. The ice coating and deicing of the ice coated conductor can be generated under the action of the rising air temperature and/or natural wind force, so that the ice coating and deicing of the transmission conductor can be caused to jump.
When the ice coating and the ice removing jump, the sudden change of the instantaneous tension and the violent jump of the lead can cause the faults of electrical or mechanical accidents and the like which have great harm to the line. Therefore, the method has important practical significance for analyzing the ice shedding jump of the power transmission line and for the reliability and the economical efficiency of the safe operation of the power transmission line. However, the accuracy of the existing power transmission line ice-shedding jump fault prediction still needs to be improved.
Disclosure of Invention
In order to solve the technical problem described above or at least partially solve the technical problem, the present disclosure provides a power transmission line deicing jump fault prediction method, a power transmission line deicing jump fault prediction apparatus, a computer-readable storage medium and an electronic device implementing the power transmission line deicing jump fault prediction method.
In a first aspect, an embodiment of the present disclosure provides a power transmission line deicing jump fault prediction method, including:
acquiring a plurality of current different meteorological environment parameters at the power transmission line;
determining the probability of the current ice shedding jump of the power transmission line based on the different meteorological environment parameters and a preset ice shedding jump probability model, wherein the ice shedding jump probability model comprises probability distribution of the ice shedding jump of the power transmission line under different historical meteorological environment parameters;
and when the probability of the current ice shedding jump of the power transmission line is greater than a first preset value, determining the probability of the current ice shedding jump of the power transmission line as a fault probability to output fault prompt information.
In some embodiments of the present disclosure, the meteorological parameters include at least a wind speed and an air temperature, and the ice shedding jump probability model is determined by:
determining edge probability distribution corresponding to the air temperature based on the proportional distribution of the ice shedding jump of the power transmission line under different historical air temperatures;
determining marginal probability distribution corresponding to wind speed based on proportional distribution of ice shedding jumps of the power transmission line under different historical wind speeds;
and determining an ice shedding jump probability model based on the marginal probability distribution corresponding to the air temperature and the marginal probability distribution corresponding to the wind speed, wherein the ice shedding jump probability model comprises joint probability distribution of ice shedding jumps of the power transmission line under different historical air temperatures and wind speeds.
In some embodiments of the present disclosure, the determining an ice shedding jump probability model based on the marginal probability distribution corresponding to the air temperature and the marginal probability distribution corresponding to the wind speed includes:
constructing joint probability distribution of the power transmission line under different air temperatures and wind speeds based on the marginal probability distribution of the air temperatures, the marginal probability distribution of the wind speeds and a Copula function;
and using the constructed combined probability distribution as an ice-shedding jump probability model.
In some embodiments of the present disclosure, the determining, based on the multiple different meteorological environment parameters and a preset ice shedding jump probability model, a probability that the ice shedding jump of the power transmission line currently occurs includes:
and determining the joint probability of the ice shedding jump of the power transmission line at the current wind speed and the current air temperature based on the acquired current wind speed and the acquired air temperature of the power transmission line and the ice shedding jump probability model.
In some embodiments of the present disclosure, further comprising:
when the probability of the current ice shedding jump of the power transmission line is larger than a first preset value, determining the maximum amplitude of the current ice shedding jump of the power transmission line based on the current wind speed and temperature and a preset ice shedding jump amplitude model;
and when the maximum amplitude is greater than a preset safety distance, determining the probability of the current ice shedding jump of the power transmission line as a fault probability to output fault prompt information.
In some embodiments of the present disclosure, further comprising:
and when the maximum amplitude is not greater than the preset safety distance, determining that the probability of the current ice shedding jump of the power transmission line is zero.
In some embodiments of the present disclosure, the preset ice shedding jump amplitude model is determined by:
establishing a finite element model of the ice shedding skip multi-span of the power transmission line;
based on the finite element model, determining the response time of the power transmission line for ice shedding jump under different wind speeds and air temperatures and the corresponding amplitude value for ice shedding jump;
and determining a preset deicing jump amplitude model based on the determined response time of the power transmission line when the deicing jump occurs and the corresponding amplitude of the deicing jump.
In a second aspect, an embodiment of the present disclosure provides a power transmission line deicing jump fault prediction apparatus, including:
the meteorological information acquisition module is used for acquiring a plurality of current different meteorological environment parameters at the power transmission line;
the deicing jump probability determination module is used for determining the probability of the current deicing jump of the power transmission line based on the different meteorological environment parameters and a preset deicing jump probability model, and the deicing jump probability model comprises probability distribution of the current deicing jump of the power transmission line under different meteorological environment parameters in history;
and the fault prediction prompting module is used for determining the probability of the current ice shedding jump of the power transmission line as the fault probability to output fault prompting information when the probability of the current ice shedding jump of the power transmission line is greater than a first preset value.
In a third aspect, the present disclosure provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the power transmission line ice shedding jump fault prediction method according to any one of the foregoing embodiments.
In a fourth aspect, an embodiment of the present disclosure provides an electronic device, including:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to execute the steps of the power transmission line deicing jump fault prediction method according to any one of the above embodiments via executing the executable instructions.
Compared with the prior art, the technical scheme provided by the embodiment of the disclosure has the following advantages:
in the embodiment of the disclosure, a plurality of current different meteorological environment parameters of the power transmission line can be obtained first, the probability of the power transmission line to generate the ice shedding jump at present is determined based on the obtained plurality of different meteorological environment parameters and a preset ice shedding jump probability model, the ice shedding jump probability model comprises probability distribution of the power transmission line to generate the ice shedding jump under different historical meteorological environment parameters, and when the probability of the power transmission line to generate the ice shedding jump at present is determined to be greater than a first preset value, the probability of the power transmission line to generate the ice shedding jump at present is determined to be a fault probability so as to output fault prompt information. Therefore, the deicing jump probability model in the scheme of the embodiment comprises probability distribution of deicing jump of the power transmission line under different historical meteorological environment parameters, namely a multi-factor coupling probability model considering key meteorological environment factors and probability distribution of the factors influencing the deicing jump of the power transmission line, and can directly calculate fault probability by considering a plurality of factors influencing the meteorological environment, thereby effectively overcoming errors caused by multi-factor conversion in the existing methods such as fuzzy membership and the like, enabling the deicing jump fault prediction result to be more accurate, and further providing a more reliable and important decision basis for prevention and control of the deicing jump fault of the power transmission line.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present disclosure, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a flowchart of a method for predicting ice shedding jump failure of a power transmission line according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of a marginal probability distribution of air temperatures in an example embodiment of the present disclosure;
FIG. 3 is a schematic diagram of a marginal probability distribution of wind speed in an example embodiment of the disclosure;
FIG. 4 is a graphical illustration of a joint probability distribution of air temperature and air speed in an example embodiment of the disclosure;
FIG. 5 is a flowchart of a method for predicting ice shedding jump failure of a power transmission line according to another embodiment of the present disclosure;
FIG. 6 is a diagram illustrating response times and corresponding amplitudes of power transmission lines in which ice shedding jumps occur according to an embodiment of the present disclosure;
fig. 7 is a schematic diagram of an ice shedding jump fault prediction apparatus for a power transmission line according to an embodiment of the present disclosure;
fig. 8 is a schematic diagram of an electronic device for implementing the method for predicting the ice shedding jump fault of the power transmission line according to the embodiment of the present disclosure.
Detailed Description
In order that the above objects, features and advantages of the present disclosure may be more clearly understood, aspects of the present disclosure will be further described below. It should be noted that the embodiments and features of the embodiments of the present disclosure may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure, but the present disclosure may be practiced in other ways than those described herein; it is to be understood that the embodiments disclosed in the specification are only a few embodiments of the present disclosure, and not all embodiments.
It is to be understood that, hereinafter, "at least one" means one or more, "a plurality" means two or more. "and/or" is used to describe the association relationship of the associated objects, meaning that there may be three relationships, for example, "a and/or B" may mean: only A, only B and both A and B are present, wherein A and B may be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of single item(s) or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.
At present, the research on the probability of the ice shedding jump fault of the power transmission line is not carried out at home and abroad. The existing ice shedding jump calculation method only calculates the dynamic response process of ice shedding jump and does not consider the failure probability of ice shedding jump. According to the scheme of the embodiment of the invention, factors such as air temperature, wind speed and the like which are key influences on the meteorological environment factors and probability distribution of the factors are analyzed, and the probability of the ice-shedding jump fault can be directly calculated based on a plurality of meteorological environment factors based on the ice-shedding jump probability model of the power transmission line, so that the prediction result of the ice-shedding jump fault is more accurate.
Fig. 1 is a flowchart of a method for predicting an ice shedding jump fault of a power transmission line according to an embodiment of the present disclosure, where the method for predicting an ice shedding jump fault of a power transmission line may include the following steps:
step S101: obtaining a plurality of current different meteorological environment parameters at the power transmission line.
For example, a plurality of different current meteorological parameters, such as wind speed and air temperature, at the power transmission line can be obtained through the meteorological forecast center platform. Of course, the specific manner of obtaining the meteorological parameters is not limited thereto, and this embodiment is not limited thereto.
Step S102: and determining the probability of the current ice shedding jump of the power transmission line based on the different meteorological environment parameters and a preset ice shedding jump probability model, wherein the ice shedding jump probability model comprises the probability distribution of the ice shedding jump of the power transmission line under the different meteorological environment parameters.
For example, the ice shedding jump probability model may be pre-established, which may include historical probability distributions of ice shedding jumps occurring on the transmission line at different meteorological parameters, such as different wind speeds and different air temperatures. After acquiring the current wind speed and air temperature at the power transmission line, for example, the corresponding probability of occurrence of ice shedding jump can be searched in the ice shedding jump probability model based on the current wind speed and air temperature.
Step S103: and when the probability of the current ice shedding jump of the power transmission line is greater than a first preset value, determining the probability of the current ice shedding jump of the power transmission line as a fault probability to output fault prompt information.
For example, in this embodiment, after the probability of the current occurrence of the ice shedding jump of the power transmission line is found and determined, it may be determined whether the probability is greater than a first preset value, and if so, it may be determined that the probability of the current occurrence of the ice shedding jump of the power transmission line is a failure probability, and at this time, failure prompt information, such as text, sound, picture, and the like, may be output. The first preset value may be set according to specific needs, which is not limited in this embodiment.
In the method of the embodiment, the deicing jump probability model comprises probability distribution of the power transmission line having the deicing jump under different historical meteorological environment parameters, namely, a multi-factor coupling probability model considering key meteorological environment factors and probability distribution thereof affected by the deicing jump of the power transmission line, and the fault probability can be directly calculated by considering a plurality of factors affecting the meteorological environment, so that errors caused by multi-factor conversion in the existing methods such as fuzzy membership and the like are effectively overcome, the deicing jump fault prediction result is more accurate, and more reliable and important decision basis can be provided for the control of the deicing jump fault of the power transmission line.
Optionally, in some embodiments of the present disclosure, the meteorological parameters may include, but are not limited to, wind speed and air temperature, or any other factors that affect the meteorological parameters and cause the power transmission line to generate ice shedding jump, which is only an example. Accordingly, by way of example, the ice shedding jump probability model may be determined by, but is not limited to, the following, including the steps of:
step 1): and determining edge probability distribution corresponding to the air temperature based on the proportion distribution of the power transmission line in which the ice shedding jump occurs at different historical air temperatures.
Illustratively, air temperature is one of the key contributing factors to de-icing, and it has been statistically found that melting de-icing occurs primarily above-5 ℃ and mechanical de-icing occurs primarily below-5 ℃. In the embodiment, the marginal probability distribution of the air temperature under the ice shedding condition can be calculated according to the air temperature observation data of the transmission line which has the ice shedding jump historically.
In one example, the marginal probability distribution of air temperature is calculated as follows:
F(t)＝P(T＜t)
wherein T is the random variable air temperature, F (T) is the marginal probability distribution function of the air temperature, and T is the value of the random variable air temperature. In one specific example, but not limited to, the calculated marginal probability distribution of air temperature under ice shedding conditions is shown in FIG. 2 as a dashed line of break points.
Step 2): and determining marginal probability distribution corresponding to the wind speed based on the proportional distribution of the ice shedding jump of the power transmission line under different historical wind speeds.
Illustratively, wind is also one of the key influencing factors causing the ice-shedding, and it is statistically found that the mechanical ice-breaking is more likely to be caused by high wind such as wind with the wind speed of more than 10m/s, while the ice-shedding melting is most likely to occur under the wind speed condition with the wind speed of less than 10 m/s. In the embodiment, the marginal probability distribution of the wind speed under the ice shedding condition can be calculated according to the wind speed observation data of ice shedding jump which occurs in the history of the power transmission line.
In one example, the marginal probability distribution of wind speed is calculated as follows:
F(w)＝P(W＜w)
wherein, W is the random variable wind speed, F (W) is the marginal probability distribution function of the wind speed, and W is the value of the random variable wind speed; in one particular example, but without limitation, the calculated marginal probability distribution of wind speed under ice shedding conditions is shown in FIG. 3 by the dashed line of break points.
Step 3): and determining an ice shedding jump probability model based on the marginal probability distribution corresponding to the air temperature and the marginal probability distribution corresponding to the wind speed, wherein the ice shedding jump probability model comprises joint probability distribution of ice shedding jumps of the power transmission line under different historical air temperatures and wind speeds.
Illustratively, after calculating a plurality of key influence factors of ice coating and ice shedding jump, such as marginal probability distribution of wind speed and air temperature, a joint probability distribution of the ice shedding jump at a plurality of factors, such as wind speed and air temperature, can be obtained based on the above.
For example, in some embodiments of the present disclosure, the determining, in step 3), an ice shedding jump probability model based on the marginal probability distribution corresponding to the air temperature and the marginal probability distribution corresponding to the wind speed may specifically include: constructing joint probability distribution of the power transmission line under different air temperatures and wind speeds based on the marginal probability distribution of the air temperatures, the marginal probability distribution of the wind speeds and a Copula function; and using the constructed combined probability distribution as an ice-shedding jump probability model.
Specifically, since there may be a positive correlation and a negative correlation between the key influence factors of ice coating and ice shedding jump, in order to make the prediction result of ice shedding jump failure more accurate, in this embodiment, a Copula function capable of describing positive correlation and negative correlation random variables may be selected to construct a joint probability distribution. Exemplary, commonly used joint probability distribution functions are: AMH Copula function, Frank Copula function, elliptical Copula, etc.
As an example, the present embodiment may select Frank Copula function to construct the joint probability distribution. An exemplary calculation formula is as follows:
wherein w is the wind speed, t is the air temperature, and theta is the Copula parameter. In one specific example, the joint probability distribution of the computed configuration is as shown in FIG. 4, but is not so limited.
Optionally, in some embodiments of the present disclosure, in step S102, determining the probability of the current occurrence of ice shedding jump of the power transmission line based on the multiple different meteorological environment parameters and a preset ice shedding jump probability model may specifically include: and determining the joint probability of the ice shedding jump of the power transmission line at the current wind speed and the current air temperature based on the acquired current wind speed and the acquired air temperature of the power transmission line and the ice shedding jump probability model.
For example, based on the acquired current wind speed and air temperature at the power transmission line, the corresponding joint probability of the current wind speed and air temperature is searched in an ice-shedding jump probability model containing the joint probability distribution of ice-shedding jumps at the wind speed and the air temperature. When the joint probability is larger than a first preset value, the probability that the ice shedding jump of the power transmission line currently occurs can be determined as the fault probability so as to output fault prompt information.
In the embodiment, the deicing jump probability model of the joint probability distribution can be determined, which is a Copula multi-factor coupling probability model considering key meteorological environment influencing factors such as air temperature and air speed of the power transmission line deicing jump and the probability distribution thereof, can directly calculate the fault probability by considering a plurality of meteorological environment influencing factors such as air temperature and air speed at the same time, does not need to convert the multi-factor into a single factor or assume that each factor obeys the designated probability distribution, can directly calculate the joint probability of the icing and deicing jump faults of the power transmission line, effectively overcomes the error caused by multi-factor conversion in the existing methods such as fuzzy membership and the like, and further enables the deicing jump fault prediction result to be more accurate.
Optionally, on the basis of any one of the above embodiments, in some embodiments of the present disclosure, with reference to fig. 5, the method may further include the following steps:
step S501: and when the probability of the current ice shedding jump of the power transmission line is greater than a first preset value, determining the maximum amplitude of the current ice shedding jump of the power transmission line based on the current wind speed and temperature and a preset ice shedding jump amplitude model.
For example, the preset ice jumping amplitude model may be pre-established and include a corresponding relationship between different wind speeds and air temperatures and the amplitude of ice jumping occurring under the different wind speeds and air temperatures. Step S501 may be performed after step S102. The inventor finds that the ice shedding jump of the power transmission line can cause line damage or can not cause line damage. Therefore, in order to make the prediction result of the ice shedding jump fault more accurate, the maximum amplitude of the line jump caused by the ice shedding jump of the power transmission line currently occurs can be calculated again in the embodiment.
Step S502: and when the maximum amplitude is greater than a preset safety distance, determining the probability of the current ice shedding jump of the power transmission line as a fault probability to output fault prompt information.
Illustratively, after the maximum amplitude of the power transmission line at the current occurrence of the ice shedding jump is obtained through calculation, whether the maximum amplitude is larger than a preset safety distance or not is judged, and if yes, the probability of the power transmission line at the current occurrence of the ice shedding jump can be determined as a fault probability so as to output fault prompt information. The preset safety distance may be set according to specific situations, and is not limited in this regard. In this embodiment, when the maximum amplitude of the line jump caused by the ice-shedding jump is greater than the preset safety distance, the line hazard is certainly caused, and at this time, accurate prediction is required. Therefore, in the embodiment, while the fault probability is directly calculated by considering a plurality of factors affecting meteorological environment such as air temperature and air speed to predict the ice-shedding jump fault, the judgment and prediction are further carried out on the basis of the maximum amplitude of the line jump caused by the ice-shedding jump, so that the prediction result of the ice-shedding jump fault is more accurate.
Optionally, in some embodiments of the present disclosure, the following steps may be further included: and when the maximum amplitude is not greater than the preset safety distance, determining that the probability of the current ice shedding jump of the power transmission line is zero. That is, the line damage is not caused at this time, and the fault prompt information is not output at this time.
Optionally, in some embodiments of the present disclosure, the preset ice shedding jump amplitude model may be determined by, but is not limited to, the following method, which may specifically include the following steps:
step i): and establishing a finite element model of the ice shedding skip multi-span of the power transmission line. The ice-shedding skip multi-gear can be understood by referring to the prior art, and the details are not repeated.
Step ii): and based on the finite element model, simulating and determining the response time of the power transmission line for generating the ice shedding jump under different wind speeds and air temperatures and the corresponding amplitude value for generating the ice shedding jump.
For example, fig. 6 shows the response time of the power transmission line when the ice shedding jump occurs and the corresponding amplitude of the power transmission line when the ice shedding jump occurs under the condition of wind speed and air temperature. The response time is the time length of the ice shedding under the wind speed and air temperature conditions, and the amplitude of the ice shedding jump is the amplitude of the jump of the power transmission line caused by the ice shedding. In the embodiment, the response time of the power transmission line with the ice shedding jump and the corresponding amplitude of the ice shedding jump under different wind speeds and air temperatures can be determined in a simulation mode.
Step iii): and determining a preset deicing jump amplitude model based on the determined response time of the power transmission line when the deicing jump occurs and the corresponding amplitude of the deicing jump.
For example, after determining the response time of the transmission line for the occurrence of the ice shedding jump and the corresponding amplitude value of the occurrence of the ice shedding jump under a plurality of different wind speed and air temperature conditions through simulation, the preset ice shedding jump amplitude model may be determined, and the preset ice shedding jump amplitude model may include the corresponding relationship between different wind speeds and air temperatures and the amplitude values of the occurrence of the ice shedding jump under the different wind speed and air temperature conditions.
Aspects of embodiments of the disclosure are described below in conjunction with a specific embodiment. The specific embodiment takes a certain provincial power grid as an example, and discloses a method for calculating the probability of coupling faults of multiple meteorological environment factors during ice shedding jump of a power transmission line, which comprises the following steps:
Illustratively, a multi-span finite element model of the ice coating and ice shedding jump of the power transmission line is established, the response time of the ice coating and ice shedding jump of the power transmission line and the corresponding amplitude of the ice shedding jump under different meteorological environments such as wind speed, air temperature and the like are simulated based on the finite element model, and the ice shedding jump amplitude model is established based on the finite element model. For a specific calculation process, reference may be made to the corresponding detailed description in the foregoing embodiments, which is not repeated herein.
Wind is one of the key influencing factors causing the ice-shedding, and statistics shows that the mechanical ice-breaking is more easily caused by high wind speed, and the ice-shedding during melting is mostly carried out under the wind speed condition within 10 m/s. And calculating the marginal probability distribution of the wind speed under the ice-shedding condition according to the historical wind speed observation data under the ice-shedding jump condition of the power transmission line. For a specific calculation process, reference may be made to the corresponding detailed description in the foregoing embodiments, which is not repeated herein.
The air temperature is one of the key influence factors causing the deicing, and statistics shows that the melting deicing mainly occurs above minus 5 ℃, and the mechanical deicing mainly occurs below minus 5 ℃. And calculating the marginal probability distribution of air temperature under the ice-shedding condition according to the air temperature observation data under the historical ice-shedding jump condition of the power transmission line. For a specific calculation process, reference may be made to the corresponding detailed description in the foregoing embodiments, which is not repeated herein.
Step 4, calculating a joint probability model of ice coating and ice shedding jump of the power transmission line
And calculating the marginal probability distribution of the wind speed and the air temperature of the icing and deicing jump key factors, and constructing the multi-factor deicing and jumping joint probability distribution based on the Copula function, namely forming a deicing and jumping joint probability model. For a specific calculation process, reference may be made to the corresponding detailed description in the foregoing embodiments, which is not repeated herein.
Calculating the joint probability of the power transmission line under the conditions of the wind speed and the air temperature according to the obtained current wind speed and the air temperature of the power transmission line, when the joint probability is greater than a preset value, determining the maximum amplitude of the power transmission line under the conditions of the current wind speed and the air temperature by adopting the ice-shedding jump amplitude model determined in the step 1, and if the maximum amplitude exceeds a safe distance, finally determining that the joint probability of the current ice-shedding jump is the ice-shedding jump fault probability, and at the moment, outputting fault prompt information; otherwise, the probability of ice shedding jump failure is 0.
According to the scheme of the embodiment of the invention, key influence factors such as wind speed and air temperature of the power transmission line deicing jump and probability distribution thereof are analyzed, and the fault probability can be directly calculated by considering a plurality of influence factors such as wind speed and air temperature based on the power transmission line deicing jump multi-factor coupling probability model, so that errors caused by multi-factor conversion in methods such as fuzzy membership and the like are effectively overcome, the deicing jump fault prediction result is more accurate, and an important decision basis is provided for prevention and control of the deicing jump fault of the power transmission line.
It should be noted that although the various steps of the methods of the present disclosure are depicted in the drawings in a particular order, this does not require or imply that these steps must be performed in this particular order, or that all of the depicted steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc. Additionally, it will also be readily appreciated that the steps may be performed synchronously or asynchronously, e.g., among multiple modules/processes/threads.
Based on the same concept, an embodiment of the present disclosure further provides a power transmission line ice shedding jump fault prediction apparatus, and as shown in fig. 7, the power transmission line ice shedding jump fault prediction apparatus 70 may include: the meteorological information obtaining module 701 is configured to obtain a plurality of current different meteorological environment parameters at the power transmission line. And the deicing jump probability determining module 702 is configured to determine the probability of the current deicing jump of the power transmission line based on the multiple different meteorological environment parameters and a preset deicing jump probability model, where the deicing jump probability model includes probability distribution of the current deicing jump of the power transmission line under different meteorological environment parameters. And the fault prediction prompting module 703 is configured to determine, when the probability that the power transmission line currently has the ice shedding jump is greater than a first preset value, that the probability that the power transmission line currently has the ice shedding jump is a fault probability, and output fault prompting information.
In the device for predicting the ice shedding jump fault of the power transmission line, the ice shedding jump probability model comprises probability distribution of ice shedding jump of the power transmission line under different historical meteorological environment parameters, namely a multi-factor coupling probability model considering key meteorological environment factors and probability distribution of the ice shedding jump of the power transmission line, and can directly calculate the fault probability by considering a plurality of meteorological environment factors simultaneously, thereby effectively overcoming errors caused by multi-factor conversion in the existing methods such as fuzzy membership and the like, ensuring that the ice shedding jump fault prediction result is more accurate, and further providing more reliable and important decision basis for prevention and control of the ice shedding jump fault of the power transmission line.
In some embodiments of the present disclosure, the meteorological parameters may include at least wind speed and air temperature, and the ice shedding jump probability model may be determined by, but is not limited to:
determining edge probability distribution corresponding to the air temperature based on the proportional distribution of the ice shedding jump of the power transmission line under different historical air temperatures;
determining marginal probability distribution corresponding to wind speed based on proportional distribution of ice shedding jumps of the power transmission line under different historical wind speeds;
and determining an ice shedding jump probability model based on the marginal probability distribution corresponding to the air temperature and the marginal probability distribution corresponding to the wind speed, wherein the ice shedding jump probability model comprises joint probability distribution of ice shedding jumps of the power transmission line under different historical air temperatures and wind speeds.
Optionally, in some embodiments of the present disclosure, the determining an ice shedding jump probability model based on the marginal probability distribution corresponding to the air temperature and the marginal probability distribution corresponding to the wind speed includes: constructing joint probability distribution of the power transmission line under different air temperatures and wind speeds based on the marginal probability distribution of the air temperatures, the marginal probability distribution of the wind speeds and a Copula function; and using the constructed combined probability distribution as an ice-shedding jump probability model.
In some embodiments of the present disclosure, the determining module 702 determines the probability of the ice shedding jump currently occurring on the power transmission line based on the multiple different meteorological environment parameters and a preset ice shedding jump probability model, including: and determining the joint probability of the ice shedding jump of the power transmission line at the current wind speed and the current air temperature based on the acquired current wind speed and the acquired air temperature of the power transmission line and the ice shedding jump probability model.
Optionally, in some embodiments of the present disclosure, the failure prediction prompting module 703 is further configured to: when the probability of the current ice shedding jump of the power transmission line is larger than a first preset value, determining the maximum amplitude of the current ice shedding jump of the power transmission line based on the current wind speed and temperature and a preset ice shedding jump amplitude model; and when the maximum amplitude is greater than a preset safety distance, determining the probability of the current ice shedding jump of the power transmission line as a fault probability to output fault prompt information.
In some embodiments of the present disclosure, the failure prediction prompting module 703 is further configured to: and when the maximum amplitude is not greater than the preset safety distance, determining that the probability of the current ice shedding jump of the power transmission line is zero.
In some embodiments of the present disclosure, the preset ice shedding jump magnitude model may be determined by, but is not limited to: establishing a finite element model of the ice shedding skip multi-span of the power transmission line; based on the finite element model, determining the response time of the power transmission line for ice shedding jump under different wind speeds and air temperatures and the corresponding amplitude value for ice shedding jump; and determining a preset deicing jump amplitude model based on the determined response time of the power transmission line when the deicing jump occurs and the corresponding amplitude of the deicing jump.
The specific manner in which the above-mentioned embodiments of the apparatus, and the corresponding technical effects brought about by the operations performed by the respective modules, have been described in detail in the embodiments related to the method, and will not be described in detail herein.
It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units. The components shown as modules or units may or may not be physical units, i.e. may be located in one place or may also be distributed over a plurality of network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the wood-disclosed scheme. One of ordinary skill in the art can understand and implement it without inventive effort.
The embodiment of the present disclosure further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the method for predicting the ice shedding jump fault of the power transmission line according to any one of the above embodiments.
By way of example, and not limitation, such readable storage media can be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
The embodiment of the disclosure also provides an electronic device, which includes a processor and a memory, wherein the memory is used for storing the executable instruction of the processor. Wherein the processor is configured to execute the steps of the power transmission line ice shedding jump fault prediction method in any one of the above embodiments via execution of the executable instructions.
An electronic device 600 according to this embodiment of the invention is described below with reference to fig. 8. The electronic device 600 shown in fig. 8 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 8, the electronic device 600 is embodied in the form of a general purpose computing device. The components of the electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 that connects the various system components (including the storage unit 620 and the processing unit 610), a display unit 640, and the like.
Wherein the storage unit stores program code that is executable by the processing unit 610 such that the processing unit 610 performs the steps according to various exemplary embodiments of the present invention described in the power transmission line ice shedding jump fault prediction method section above in this specification. For example, the processing unit 610 may perform the steps of the power transmission line ice shedding jump fault prediction method as shown in fig. 1.
The storage unit 620 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)6201 and/or a cache memory unit 6202, and may further include a read-only memory unit (ROM) 6203.
The memory unit 620 may also include a program/utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 600, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 600 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 650. Also, the electronic device 600 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via the bus 630. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, or a network device, etc.) to execute the method for predicting the ice shedding jump fault of the power transmission line according to the embodiments of the present disclosure.
It is noted that, in this document, relational terms such as "first" and "second," 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 are merely exemplary embodiments of the present disclosure, which enable those skilled in the art to understand or practice the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method for predicting ice shedding jump fault of a power transmission line is characterized by comprising the following steps:
acquiring a plurality of current different meteorological environment parameters at the power transmission line;
determining the probability of the current ice shedding jump of the power transmission line based on the different meteorological environment parameters and a preset ice shedding jump probability model, wherein the ice shedding jump probability model comprises probability distribution of the ice shedding jump of the power transmission line under different historical meteorological environment parameters;
and when the probability of the current ice shedding jump of the power transmission line is greater than a first preset value, determining the probability of the current ice shedding jump of the power transmission line as a fault probability to output fault prompt information.
2. The method for predicting the ice shedding jump fault of the power transmission line according to claim 1, wherein the meteorological environment parameters at least comprise wind speed and air temperature, and the ice shedding jump probability model is determined by the following method:
determining edge probability distribution corresponding to the air temperature based on the proportional distribution of the ice shedding jump of the power transmission line under different historical air temperatures;
determining marginal probability distribution corresponding to wind speed based on proportional distribution of ice shedding jumps of the power transmission line under different historical wind speeds;
and determining an ice shedding jump probability model based on the marginal probability distribution corresponding to the air temperature and the marginal probability distribution corresponding to the wind speed, wherein the ice shedding jump probability model comprises joint probability distribution of ice shedding jumps of the power transmission line under different historical air temperatures and wind speeds.
3. The method for predicting the ice shedding jump fault of the power transmission line according to claim 2, wherein the determining the ice shedding jump probability model based on the marginal probability distribution corresponding to the air temperature and the marginal probability distribution corresponding to the wind speed comprises:
constructing joint probability distribution of the power transmission line under different air temperatures and wind speeds based on the marginal probability distribution of the air temperatures, the marginal probability distribution of the wind speeds and a Copula function;
and using the constructed combined probability distribution as an ice-shedding jump probability model.
4. The method for predicting the ice shedding jump fault of the power transmission line according to claim 2 or 3, wherein the determining the probability of the ice shedding jump of the power transmission line based on the plurality of different meteorological environment parameters and a preset ice shedding jump probability model comprises:
and determining the joint probability of the ice shedding jump of the power transmission line at the current wind speed and the current air temperature based on the acquired current wind speed and the acquired air temperature of the power transmission line and the ice shedding jump probability model.
5. The method for predicting the ice shedding jump fault of the power transmission line according to claim 4, further comprising:
when the probability of the current ice shedding jump of the power transmission line is larger than a first preset value, determining the maximum amplitude of the current ice shedding jump of the power transmission line based on the current wind speed and temperature and a preset ice shedding jump amplitude model;
and when the maximum amplitude is greater than a preset safety distance, determining the probability of the current ice shedding jump of the power transmission line as a fault probability to output fault prompt information.
6. The method for predicting the ice shedding jump fault of the power transmission line according to claim 5, further comprising:
and when the maximum amplitude is not greater than the preset safety distance, determining that the probability of the current ice shedding jump of the power transmission line is zero.
7. The method for predicting the ice shedding jump fault of the power transmission line according to claim 5, wherein the preset ice shedding jump amplitude model is determined in the following way:
establishing a finite element model of the ice shedding skip multi-span of the power transmission line;
based on the finite element model, determining the response time of the power transmission line for ice shedding jump under different wind speeds and air temperatures and the corresponding amplitude value for ice shedding jump;
and determining a preset deicing jump amplitude model based on the determined response time of the power transmission line when the deicing jump occurs and the corresponding amplitude of the deicing jump.
8. The utility model provides a transmission line deicing jump fault prediction device which characterized in that includes:
the meteorological information acquisition module is used for acquiring a plurality of current different meteorological environment parameters at the power transmission line;
the deicing jump probability determination module is used for determining the probability of the current deicing jump of the power transmission line based on the different meteorological environment parameters and a preset deicing jump probability model, and the deicing jump probability model comprises probability distribution of the current deicing jump of the power transmission line under different meteorological environment parameters in history;
and the fault prediction prompting module is used for determining the probability of the current ice shedding jump of the power transmission line as the fault probability to output fault prompting information when the probability of the current ice shedding jump of the power transmission line is greater than a first preset value.
9. A computer-readable storage medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the steps of the method for predicting an ice shedding jump fault of a power transmission line according to any one of claims 1 to 7.
10. An electronic device, comprising:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to execute the steps of the power transmission line ice shedding jump fault prediction method according to any one of claims 1 to 7 via execution of the executable instructions.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105785243A (en) * | 2016-04-08 | 2016-07-20 | 国家电网公司 | Evaluation method for insulator icing flashover risk of ultrahigh voltage alternating-current transmission line |
| CN107403189A (en) * | 2017-06-30 | 2017-11-28 | 南京理工大学 | A kind of windage yaw discharge method for early warning based on Naive Bayes Classifier |
| CN108182514A (en) * | 2017-12-13 | 2018-06-19 | 国网湖南省电力有限公司 | A kind of power grid icing waves Risk Forecast Method, system and storage medium |
| CN108917617A (en) * | 2018-09-19 | 2018-11-30 | 贵州电网有限责任公司 | A method of for ice-melt operation field transmission line of electricity ice-shedding vision measurement |
| CN109359882A (en) * | 2018-10-29 | 2019-02-19 | 武汉理工大学 | Transmission line of electricity trip risk appraisal procedure under a kind of typhoon disaster |
| CN110598932A (en) * | 2019-09-11 | 2019-12-20 | 电子科技大学 | Weather feature fusion-based power transmission corridor icing early warning method |
| CN111460389A (en) * | 2020-05-13 | 2020-07-28 | 国网安徽省电力有限公司 | Method for calculating galloping trip probability of ice-coated conductor |
-
2020
- 2020-10-30 CN CN202011195002.2A patent/CN112214860A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105785243A (en) * | 2016-04-08 | 2016-07-20 | 国家电网公司 | Evaluation method for insulator icing flashover risk of ultrahigh voltage alternating-current transmission line |
| CN107403189A (en) * | 2017-06-30 | 2017-11-28 | 南京理工大学 | A kind of windage yaw discharge method for early warning based on Naive Bayes Classifier |
| CN108182514A (en) * | 2017-12-13 | 2018-06-19 | 国网湖南省电力有限公司 | A kind of power grid icing waves Risk Forecast Method, system and storage medium |
| CN108917617A (en) * | 2018-09-19 | 2018-11-30 | 贵州电网有限责任公司 | A method of for ice-melt operation field transmission line of electricity ice-shedding vision measurement |
| CN109359882A (en) * | 2018-10-29 | 2019-02-19 | 武汉理工大学 | Transmission line of electricity trip risk appraisal procedure under a kind of typhoon disaster |
| CN110598932A (en) * | 2019-09-11 | 2019-12-20 | 电子科技大学 | Weather feature fusion-based power transmission corridor icing early warning method |
| CN111460389A (en) * | 2020-05-13 | 2020-07-28 | 国网安徽省电力有限公司 | Method for calculating galloping trip probability of ice-coated conductor |
Non-Patent Citations (5)
| Title |
|---|
| PENG JIN ET AL.: "Fault probability prediction of transmission lines under icing disaster", 《2017 29TH CHINESE CONTROL AND DECISION CONFERENCE》 * |
| 吴天宝等: "输电线路脱冰跳跃动态模型研究", 《应用力学学报》 * |
| 吴琼等: "基于RBF神经网络的输电线路覆冰短期预测研究", 《贵州电力技术》 * |
| 徐文军: "冰风暴灾害下输电线路故障概率预测及应急对策研究", 《中国优秀硕士学位论文全文数据库（电子期刊）基础科学辑》 * |
| 朱斌，等: "考虑融冰因素的输电线路覆冰故障概率计算", 《电力系统保护与控制》 * |
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