CN113533906A - Intelligent overhead transmission line fault type diagnosis method and system - Google Patents
Intelligent overhead transmission line fault type diagnosis method and system Download PDFInfo
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Abstract
The invention relates to the technical field of power transmission line fault diagnosis, in particular to a method and a system for diagnosing fault types of an intelligent overhead power transmission line. The invention can firstly judge whether the system is a lightning stroke fault by utilizing the traveling wave characteristics of the installed system of the distributed fault monitoring and diagnosing device, and if the system is the lightning stroke fault, the lightning fault probability is corrected by utilizing the lightning positioning system, and a lightning stroke fault result is given. And if the fault is a non-lightning fault, further calculating the power frequency characteristic of the fault, correcting the probability of matching the fault reasons through monitoring information of power transmission line corridors such as forest fire, ice coating, weather and the like, and outputting a diagnosis result. And finally, for a system without the distributed fault monitoring and diagnosing device, directly utilizing information such as fault time, reclosing state, fault phase and the like provided by the scheduling system, calculating the probability corresponding to various fault reason types, correcting by combining monitoring information of the power transmission line corridor, and giving a diagnosis result. The invention can accurately position the transmission line fault.
Description
Technical Field
The invention relates to the technical field of power transmission line fault diagnosis, in particular to a method and a system for diagnosing fault types of an intelligent overhead power transmission line.
Background
At present, when the transmission line fault is diagnosed, the types of the transmission line faults and the positions of the fault points are analyzed one by adopting an elimination method based on manual experience, a comprehensive analysis and diagnosis technology of a system is lacked, and particularly, under the condition of multi-source information, clear fault reasons cannot be given, so that the analysis and the searching efficiency of the transmission line faults are low, and the safe and stable operation of the transmission line is seriously influenced.
The transmission line faults mainly comprise faults caused by lightning stroke, windage yaw, bird flashover, pollution flashover, tree flashover, mountain fire faults and the like. The identification of the cause of a fault requires mining and analyzing the characteristics of a specific fault on the basis of understanding various fault principles and processes, so as to form the basis for identifying the cause, and therefore, fault mechanism analysis needs to be performed on various fault types. Meanwhile, the occurrence of faults is related to the operating environment of the power transmission line, and the characteristics of different types of faults are represented differently on the wave recording data, so that external factors such as weather, time, seasons and the like at the line occurrence moment and internal factors represented by the wave recording data such as reclosing conditions, non-periodic component characteristics of fault phase current and transition resistance are mined on the basis of principle analysis, and characteristic rules are searched, so that a data source is provided for the establishment of a subsequent classification model.
Disclosure of Invention
In order to solve the problems, the invention provides an intelligent overhead transmission line fault type diagnosis method and system, and the specific technical scheme is as follows:
an intelligent overhead transmission line fault type diagnosis method comprises the following steps:
s1: judging whether a system with faults is provided with a distributed fault monitoring device and a power transmission line corridor environment monitoring device or not; if the distributed fault monitoring device and the power transmission line corridor environment monitoring device are installed, performing step S2, otherwise, performing step S4;
s2: searching the associated transient traveling wave characteristics to form a characteristic vector combination, inputting the characteristic vector combination into a first support vector machine, and carrying out lightning stroke and non-lightning stroke fault type diagnosis;
s21: if the lightning stroke fault is the lightning stroke fault, inquiring a lightning positioning system, combining fault positioning to obtain lightning current data closest to the fault tripping moment, and determining the lightning stroke fault probability PlightningCorrecting with lightning stroke fault probability correction function of f1；
S22: if the fault is a non-lightning fault, the system further queries an external online monitoring service platform comprising a forest fire monitoring system, an icing system and a meteorological system, calculates and extracts the power frequency waveform at the fault moment to obtain a characteristic combination, and inputs the characteristic combination into a second support vector machine to obtain the fault reason and the probability thereof;
correcting the fault result by combining mountain fire, weather and ice coating data in the multi-source information;
s4: the system with faults is not provided with a distributed fault monitoring device and a power transmission line corridor environment monitoring device, and a characteristic combination capable of being input into the Bayesian network is obtained by combining meteorological information according to the fault time, reclosing state and fault phase provided by the scheduling system information; and directly obtaining corresponding fault external feature prior probability distribution for the collected evidence information.
Preferably, the lightning stroke fault probability P is determined in the step S21lightningThe correction is specifically as follows:
Plightning-new＝f1(Plightning-old)＝a+(1-a)(Plightning-old)；
wherein, Plightning-newIndicating the corrected lightning stroke fault probability, Plightning-oldRepresenting the lightning stroke fault probability before correction, wherein a is a lightning stroke fault probability correction parameter;
the correction parameter a is comprehensively judged according to the amplitude A and the distance D of the lightning current nearest to the fault point, which are found in the lightning positioning system, and the calculation formula is as follows:
preferably, the method further comprises the step of correcting the fault probability of other reasons except the lightning stroke by adopting the corrected lightning stroke fault probability in the first vector machine, and the specific steps are as follows:
wherein, Pother-newIndicating the probability of failure due to other causes, P, after correctionother-oldIndicating the probability of failure due to other causes before correction.
Preferably, in step S22, the failure result is corrected by using a forest fire in the multi-source information, specifically as follows:
Pfire-new＝f2(Pfire-old)＝b+(1-b)(Pfire-old)；
wherein, Pfire-newIndicates the probability of mountain fire fault after correction, Pfire-oldIndicates the probability of mountain fire failure before correction, f2B is a mountain fire fault probability correction function, and b is a mountain fire fault probability correction parameter;
the calculation method of the mountain fire fault probability correction parameter b is as follows:
t is the fire point temperature.
Preferably, in step S22, the wind yaw in the multi-source information is used to correct the fault result, which is specifically as follows:
Pwind-new＝f3(Pwind-old)＝c+(1-c)(Pfwind-old)；
wherein, Pwind-newPresentation correctionRear windage yaw fault probability, Pwind-oldIndicating windage yaw fault probability before correction, f3C is a windage yaw fault probability correction function and a windage yaw fault probability correction parameter;
the windage yaw fault probability correction parameter c is calculated as follows:
c＝[min(W,12)+2]/14；
w is the line corridor maximum wind speed.
Preferably, in step S22, the fault result is corrected by using the ice coating in the multi-source information, specifically as follows:
Pice-new＝f4(Pice-old)＝d+(1-d)(Pfice-old)；
wherein, Pice-newIndicating the corrected icing fault probability, Pice-oldRepresenting the probability of icing failure before correction, f4D is an icing fault probability correction function and an icing fault probability correction parameter;
the icing fault probability correction parameter d is calculated as follows:
d＝0.5log(H+1)；
h is the maximum ice coating thickness monitored on-line.
Preferably, the probability distribution in step S5 includes:
setting failure weather as an event A; reclosing action is taken as an event B; the fault phase is event C; the failed month is event D; the failure time is event E; the fault wind power level is event F; assuming that the above events are independent of each other, the fault type is event Vi1,2,3,4,5,6, which respectively represent a lightning stroke fault, a windage yaw fault, a bird damage fault, a tree flash fault and a mountain fire fault; the failure probability is calculated as follows:
and after the fault probability is obtained, inquiring the multi-source information again, performing the same correction on the fault probability by using the correction function, and outputting the final result. A
An intelligent overhead transmission line fault type diagnosis system comprises a system judgment module, a data module and a correction module; the system judgment module, the data module and the correction module are sequentially connected;
the system judgment module is used for judging whether a system with faults is provided with a distributed fault monitoring device and a power transmission line corridor environment monitoring device or not;
the data module is used for processing data according to the judgment result of the system judgment module, and specifically comprises the following steps:
if the system judgment module judges that a system with faults is provided with the distributed fault monitoring device and the transmission line corridor environment monitoring device, the data module searches the associated transient traveling wave characteristics to form a characteristic vector combination, and inputs the characteristic vector combination into a first vector machine to carry out lightning stroke and non-lightning stroke fault type diagnosis;
if the lightning stroke fault is diagnosed, the data module inquires a lightning positioning system, lightning current data closest to the fault tripping moment is obtained by combining fault positioning, and the correction module corrects the lightning stroke probability through a correction function;
if the non-lightning fault is diagnosed, the data module further queries an external online monitoring service platform comprising a forest fire monitoring system, an icing system and a meteorological system; calculating and extracting the characteristics of the power frequency waveform at the fault moment to obtain a characteristic combination, inputting the characteristic combination into a second support vector machine to obtain the fault reason and the probability thereof; the correction module corrects the fault result by combining mountain fire, weather and icing data in the multi-source information;
if the system judgment module judges that the system with the fault is not provided with the distributed fault monitoring device and the power transmission line corridor environment monitoring device, the data module obtains a characteristic combination capable of being input into the Bayesian network according to the fault time, the reclosing state and the fault phase provided by the scheduling system information in combination with meteorological information; and directly obtaining corresponding fault external feature prior probability distribution for the collected evidence information.
Preferably, the correction function is:
Plightning-new＝f1(Plightning-old)＝a+(1-a)(Plightning-old)；
wherein, Plightning-newIndicating the corrected lightning stroke fault probability, Plightning-oldAnd (4) representing the lightning stroke fault probability before correction, wherein a is a lightning stroke fault probability correction parameter.
The invention has the beneficial effects that:
the invention can intelligently identify whether a system with faults is provided with a distributed fault monitoring device and a power transmission line corridor environment monitoring device, and the collected power transmission line body data and the collected line corridor environment monitoring data, and corrects and judges the fault type. For the installed system of the distributed fault monitoring and diagnosing device, the traveling wave characteristics are firstly utilized to judge whether the system is a lightning stroke, if the system is the lightning stroke, the lightning fault probability is corrected by utilizing the lightning positioning system, and a lightning stroke fault result is given. And if the power frequency characteristic is not the lightning stroke, further calculating the power frequency characteristic of the fault, then correcting the probability of the fault reason matching through monitoring information of power transmission line corridors such as mountain fire, ice coating, weather and the like, and outputting a diagnosis result. And finally, for a system without the distributed fault monitoring and diagnosing device, directly utilizing information such as fault time, reclosing state, fault phase and the like provided by the scheduling system, calculating the probability corresponding to various fault reason types, correcting by combining monitoring information of the power transmission line corridor, and giving a diagnosis result. The invention accurately positions the transmission line fault, carries out the analysis of the line tripping fault reason, can greatly reduce the line inspection workload and can improve the power supply reliability.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a method of the present invention;
fig. 2 is a system configuration diagram of the present invention.
Detailed Description
For a better understanding of the present invention, reference is made to the following detailed description taken in conjunction with the accompanying drawings in which:
as shown in fig. 1, an intelligent overhead transmission line fault type diagnosis method includes the following steps:
s1: judging whether a system with faults is provided with a distributed fault monitoring device and a power transmission line corridor environment monitoring device or not; if the distributed fault monitoring device and the power transmission line corridor environment monitoring device are installed, performing step S2, otherwise, performing step S4;
s2: searching the associated transient traveling wave characteristics to form a characteristic vector combination, inputting the characteristic vector combination into a first support vector machine, and carrying out lightning stroke and non-lightning stroke fault type diagnosis;
s21: if the lightning stroke fault is the lightning stroke fault, inquiring a lightning positioning system, combining fault positioning to obtain lightning current data closest to the fault tripping moment, and determining the lightning stroke fault probability PlightningCorrecting with lightning stroke fault probability correction function of f1(ii) a For lightning stroke fault probability PlightningThe correction is specifically as follows:
Plightning-new＝f1(Plightning-old)＝a+(1-a)(Plightning-old)；
wherein, Plightning-newIndicating the corrected lightning stroke fault probability, Plightning-oldRepresenting the lightning stroke fault probability before correction, wherein a is a lightning stroke fault probability correction parameter;
the correction parameter a is comprehensively judged according to the amplitude A and the distance D of the lightning current nearest to the fault point, which are found in the lightning positioning system, and the calculation formula is as follows:
because the whole data sample has more lightning stroke faults, in order to achieve a relatively ideal classification effect and control the punishment coefficient of a lightning stroke fault label in the first support vector machine, the adjusted value is larger in the parameter adjusting process, so that more non-lightning stroke faults are easily judged as lightning stroke faults by mistake, and the probability of the lightning stroke faults needs to be corrected according to the amplitude and the distance of lightning current; after the lightning stroke probability is corrected, in order to make the sum of all the reason probabilities unique, other fault reasons are corrected in proportion, and the method specifically comprises the following steps:
wherein, Pother-newIndicating the probability of failure due to other causes, P, after correctionother-oldIndicating the probability of failure due to other causes before correction.
S22: if the fault is a non-lightning fault, the system further queries an external online monitoring service platform comprising a forest fire monitoring system, an icing system and a meteorological system, calculates and extracts the power frequency waveform at the fault moment to obtain a characteristic combination, and inputs the characteristic combination into a second support vector machine to obtain the fault reason and the probability thereof;
correcting the fault result by combining mountain fire, weather and ice coating data in the multi-source information;
the method for correcting the fault result by adopting the forest fire in the multi-source information comprises the following steps:
Pfire-new＝f2(Pfire-old)＝b+(1-b)(Pfire-old)；
wherein, Pfire-newIndicates the probability of mountain fire fault after correction, Pfire-oldIndicates the probability of mountain fire failure before correction, f2B is a mountain fire fault probability correction function, and b is a mountain fire fault probability correction parameter;
the calculation method of the mountain fire fault probability correction parameter b is as follows:
t is the fire point temperature.
And correcting the fault result by adopting windage yaw in the multi-source information, which specifically comprises the following steps:
Pwind-new＝f3(Pwind-old)＝c+(1-c)(Pfwind-old)；
wherein, Pwind-newIndicating corrected windage yaw fault probability, Pwind-oldIndicating windage yaw fault probability before correction, f3C is a windage yaw fault probability correction function and a windage yaw fault probability correction parameter;
the windage yaw fault probability correction parameter c is calculated as follows:
c＝[min(W,12)+2]/14；
w is the line corridor maximum wind speed.
And correcting the fault result by adopting the icing in the multi-source information, which specifically comprises the following steps:
Pice-new＝f4(Pice-old)＝d+(1-d)(Pfice-old)；
wherein, Pice-newIndicating the corrected icing fault probability, Pice-oldRepresenting the probability of icing failure before correction, f4D is an icing fault probability correction function and an icing fault probability correction parameter;
the icing fault probability correction parameter d is calculated as follows:
d＝0.5log(H+1)；
h is the maximum ice coating thickness monitored on-line.
For evidence information which is not collected, if the system fails to find weather information at the time of failure, the corresponding probability distribution is 1.
S4: the system with faults is not provided with a distributed fault monitoring device and a power transmission line corridor environment monitoring device, and a characteristic combination capable of being input into the Bayesian network is obtained by combining meteorological information according to the fault time, reclosing state and fault phase provided by the scheduling system information; and directly obtaining corresponding fault external feature prior probability distribution for the collected evidence information. The probability distribution includes:
setting failure weather as an event A; reclosing action is taken as an event B; the fault phase is event C; the failed month is event D; the failure time is event E; fault wind power class as eventF; assuming that the above events are independent of each other, the fault type is event Vi1,2,3,4,5,6, which respectively represent a lightning stroke fault, a windage yaw fault, a bird damage fault, a tree flash fault and a mountain fire fault; the failure probability is calculated as follows:
and after the fault probability is obtained, inquiring the multi-source information again, performing the same correction on the fault probability by using the correction function, and outputting the final result.
As shown in fig. 2, the intelligent overhead transmission line fault type diagnosis system includes a system judgment module, a data module, and a correction module; the system judgment module, the data module and the correction module are sequentially connected;
the system judgment module is used for judging whether a system with faults is provided with a distributed fault monitoring device and a power transmission line corridor environment monitoring device or not;
the data module is used for processing data according to the judgment result of the system judgment module, and specifically comprises the following steps:
if the system judgment module judges that a system with faults is provided with the distributed fault monitoring device and the transmission line corridor environment monitoring device, the data module searches the associated transient traveling wave characteristics to form a characteristic vector combination, and inputs the characteristic vector combination into a first vector machine to carry out lightning stroke and non-lightning stroke fault type diagnosis;
if the lightning stroke fault is diagnosed, the data module inquires a lightning positioning system, lightning current data closest to the fault tripping moment is obtained by combining fault positioning, and the correction module corrects the lightning stroke probability through a correction function;
if the non-lightning fault is diagnosed, the data module further queries an external online monitoring service platform comprising a forest fire monitoring system, an icing system and a meteorological system; calculating and extracting the characteristics of the power frequency waveform at the fault moment to obtain a characteristic combination, inputting the characteristic combination into a second support vector machine to obtain the fault reason and the probability thereof; the correction module corrects the fault result by combining mountain fire, weather and icing data in the multi-source information;
if the system judgment module judges that the system with the fault is not provided with the distributed fault monitoring device and the power transmission line corridor environment monitoring device, the data module obtains a characteristic combination capable of being input into the Bayesian network according to the fault time, the reclosing state and the fault phase provided by the scheduling system information in combination with meteorological information; and directly obtaining corresponding fault external feature prior probability distribution for the collected evidence information.
The correction function of the correction module for the lightning fault probability is as follows:
Plightning-new＝f1(Plightning-old)＝a+(1-a)(Plightning-old)；
wherein, Plightning-newIndicating the corrected lightning stroke fault probability, Plightning-oldAnd (4) representing the lightning stroke fault probability before correction, wherein a is a lightning stroke fault probability correction parameter. The correction parameter a is comprehensively judged according to the amplitude A and the distance D of the lightning current nearest to the fault point, which are detected in a lightning positioning system, and the calculation formula is as follows:
the correction function of the correction module for the mountain fire fault probability is as follows:
Pfire-new＝f2(Pfire-old)＝b+(1-b)(Pfire-old)；
wherein, Pfire-newIndicates the probability of mountain fire fault after correction, Pfire-oldIndicates the probability of mountain fire failure before correction, f2B is a mountain fire fault probability correction function, and b is a mountain fire fault probability correction parameter;
the calculation method of the mountain fire fault probability correction parameter b is as follows:
t is the fire point temperature.
The correction function of the correction module for the windage yaw fault probability is as follows:
Pwind-new＝f3(Pwind-old)＝c+(1-c)(Pfwind-old)；
wherein, Pwind-newIndicating corrected windage yaw fault probability, Pwind-oldIndicating windage yaw fault probability before correction, f3C is a windage yaw fault probability correction function and a windage yaw fault probability correction parameter;
the windage yaw fault probability correction parameter c is calculated as follows:
c＝[min(W,12)+2]/14；
w is the line corridor maximum wind speed.
The correction function of the correction module for the icing fault probability is as follows:
Pice-new＝f4(Pice-old)＝d+(1-d)(Pfice-old)；
wherein, Pice-newIndicating the corrected icing fault probability, Pice-oldRepresenting the probability of icing failure before correction, f4D is an icing fault probability correction function and an icing fault probability correction parameter;
the icing fault probability correction parameter d is calculated as follows:
d＝0.5log(H+1)；
h is the maximum ice coating thickness monitored on-line.
The invention provides a fault type diagnosis method and a fault type diagnosis system for an intelligent overhead transmission line. And if the power frequency characteristic is not the lightning stroke, further calculating the power frequency characteristic of the fault, then correcting the probability of the fault reason matching through monitoring information of power transmission line corridors such as mountain fire, ice coating, weather and the like, and outputting a diagnosis result. And finally, for a system without the distributed fault monitoring and diagnosing device, directly utilizing information such as fault time, reclosing state, fault phase and the like provided by the scheduling system, calculating the probability corresponding to various fault reason types, correcting by combining monitoring information of the power transmission line corridor, and giving a diagnosis result. The invention accurately positions the transmission line fault, carries out the analysis of the line tripping fault reason, can greatly reduce the line inspection workload and can improve the power supply reliability.
The present invention is not limited to the above-described embodiments, which are merely preferred embodiments of the present invention, and the present invention is not limited thereto, and any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. An intelligent overhead transmission line fault type diagnosis method is characterized in that: the method comprises the following steps:
s1: judging whether a system with faults is provided with a distributed fault monitoring device and a power transmission line corridor environment monitoring device or not; if the distributed fault monitoring device and the power transmission line corridor environment monitoring device are installed, performing step S2, otherwise, performing step S4;
s2: searching the associated transient traveling wave characteristics to form a characteristic vector combination, inputting the characteristic vector combination into a first support vector machine, and carrying out lightning stroke and non-lightning stroke fault type diagnosis;
s21: if the lightning stroke fault is the lightning stroke fault, inquiring a lightning positioning system, combining fault positioning to obtain lightning current data closest to the fault tripping moment, and determining the lightning stroke fault probability PlightningCorrecting with lightning stroke fault probability correction function of f1；
S22: if the fault is a non-lightning fault, the system further queries an external online monitoring service platform comprising a forest fire monitoring system, an icing system and a meteorological system, calculates and extracts the power frequency waveform at the fault moment to obtain a characteristic combination, and inputs the characteristic combination into a second support vector machine to obtain the fault reason and the probability thereof;
correcting the fault result by combining mountain fire, weather and ice coating data in the multi-source information;
s4: the system with faults is not provided with a distributed fault monitoring device and a power transmission line corridor environment monitoring device, and a characteristic combination capable of being input into the Bayesian network is obtained by combining meteorological information according to the fault time, reclosing state and fault phase provided by the scheduling system information; and directly obtaining corresponding fault external feature prior probability distribution for the collected evidence information.
2. The intelligent overhead transmission line fault type diagnosis method according to claim 1, characterized in that: the lightning stroke fault probability P in the step S21lightningThe correction is specifically as follows:
Plightning-new＝f1(Plightning-old)＝a+(1-a)(Plightning-old)；
wherein, Plightning-newIndicating the corrected lightning stroke fault probability, Plightning-oldRepresenting the lightning stroke fault probability before correction, wherein a is a lightning stroke fault probability correction parameter;
the correction parameter a is comprehensively judged according to the amplitude A and the distance D of the lightning current nearest to the fault point, which are found in the lightning positioning system, and the calculation formula is as follows:
3. the intelligent overhead transmission line fault type diagnosis method according to claim 2, characterized in that: the method further comprises the step of correcting the fault probability of other reasons except lightning stroke by adopting the corrected lightning stroke fault probability in the first vector machine, and the method specifically comprises the following steps:
wherein, Pother-newIndicating the probability of failure due to other causes, P, after correctionother-oldIndicating other cause failure before correctionProbability.
4. The intelligent overhead transmission line fault type diagnosis method according to claim 1, characterized in that: in step S22, the forest fire in the multi-source information is used to correct the fault result, which is specifically as follows:
Pfire-new＝f2(Pfire-old)＝b+(1-b)(Pfire-old)；
wherein, Pfire-newIndicates the probability of mountain fire fault after correction, Pfire-oldIndicates the probability of mountain fire failure before correction, f2B is a mountain fire fault probability correction function, and b is a mountain fire fault probability correction parameter;
the calculation method of the mountain fire fault probability correction parameter b is as follows:
t is the fire point temperature.
5. The method for diagnosing the fault type of the intelligent overhead transmission line according to claim 1, characterized in that: in step S22, the windage yaw in the multi-source information is used to correct the fault result, which is specifically as follows:
Pwind-new＝f3(Pwind-old)＝c+(1-c)(Pfwind-old)；
wherein, Pwind-newIndicating corrected windage yaw fault probability, Pwind-oldIndicating windage yaw fault probability before correction, f3C is a windage yaw fault probability correction function and a windage yaw fault probability correction parameter;
the windage yaw fault probability correction parameter c is calculated as follows:
c＝[min(W,12)+2]/14；
w is the line corridor maximum wind speed.
6. The intelligent overhead transmission line fault type diagnosis method according to claim 1, characterized in that: in step S22, the icing in the multi-source information is used to correct the fault result, which is specifically as follows:
Pice-new＝f4(Pice-old)＝d+(1-d)(Pfice-old)；
wherein, Pice-newIndicating the corrected icing fault probability, Pice-oldRepresenting the probability of icing failure before correction, f4D is an icing fault probability correction function and an icing fault probability correction parameter;
the icing fault probability correction parameter d is calculated as follows:
d＝0.5log(H+1)；
h is the maximum ice coating thickness monitored on-line.
7. The intelligent overhead transmission line fault type diagnosis method according to claim 1, characterized in that: the probability distribution in step S4 includes:
setting failure weather as an event A; reclosing action is taken as an event B; the fault phase is event C; the failed month is event D; the failure time is event E; the fault wind power level is event F; assuming that the above events are independent of each other, the fault type is event Vi1,2,3,4,5,6, which respectively represent a lightning stroke fault, a windage yaw fault, a bird damage fault, a tree flash fault and a mountain fire fault; the failure probability is calculated as follows:
and after the fault probability is obtained, inquiring the multi-source information again, performing the same correction on the fault probability by using the correction function, and outputting the final result.
8. The utility model provides an intelligence overhead transmission line fault type diagnostic system which characterized in that: the system comprises a system judgment module, a data module and a correction module; the system judgment module, the data module and the correction module are sequentially connected;
the system judgment module is used for judging whether a system with faults is provided with a distributed fault monitoring device and a power transmission line corridor environment monitoring device or not;
the data module is used for processing data according to the judgment result of the system judgment module, and specifically comprises the following steps:
if the system judgment module judges that a system with faults is provided with the distributed fault monitoring device and the transmission line corridor environment monitoring device, the data module searches the associated transient traveling wave characteristics to form a characteristic vector combination, and inputs the characteristic vector combination into a first vector machine to carry out lightning stroke and non-lightning stroke fault type diagnosis;
if the lightning stroke fault is diagnosed, the data module inquires a lightning positioning system, lightning current data closest to the fault tripping moment is obtained by combining fault positioning, and the correction module corrects the lightning stroke probability through a correction function;
if the non-lightning fault is diagnosed, the data module further queries an external online monitoring service platform comprising a forest fire monitoring system, an icing system and a meteorological system; calculating and extracting the characteristics of the power frequency waveform at the fault moment to obtain a characteristic combination, inputting the characteristic combination into a second support vector machine to obtain the fault reason and the probability thereof; the correction module corrects the fault result by combining mountain fire, weather and icing data in the multi-source information;
if the system judgment module judges that the system with the fault is not provided with the distributed fault monitoring device and the power transmission line corridor environment monitoring device, the data module obtains a characteristic combination capable of being input into the Bayesian network according to the fault time, the reclosing state and the fault phase provided by the scheduling system information in combination with meteorological information; and directly obtaining corresponding fault external feature prior probability distribution for the collected evidence information.
9. The intelligent overhead transmission line fault type diagnostic system of claim 8, characterized in that: the correction function is:
Plightning-new＝f1(Plightning-old)＝a+(1-a)(Plightning-old)；
wherein, Plightning-newIndicating the corrected lightning stroke fault probability, Plightning-oldAnd (4) representing the lightning stroke fault probability before correction, wherein a is a lightning stroke fault probability correction parameter.
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