CN112785109A - Power grid equipment fault analysis method and system based on regulation cloud - Google Patents
Power grid equipment fault analysis method and system based on regulation cloud Download PDFInfo
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- CN112785109A CN112785109A CN201911092202.2A CN201911092202A CN112785109A CN 112785109 A CN112785109 A CN 112785109A CN 201911092202 A CN201911092202 A CN 201911092202A CN 112785109 A CN112785109 A CN 112785109A
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Abstract
The invention discloses a power grid equipment fault analysis method and system based on a regulation cloud, which comprises equipment fault association characteristic analysis and equipment fault association relation evaluation analysis, wherein the equipment fault association characteristic analysis is used for acquiring a frequent item set of equipment faults and an equipment fault association example; and the equipment fault association relation evaluation analysis is used for evaluating and analyzing the association degree from a plurality of indexes according to the equipment fault occurrence frequency item set and the equipment fault association example. The method is based on a power grid model and operation data provided by a regulation and control cloud system, through mining and analysis of fault information of equipment of the power grid regulation and control system, correlation fault relations among different equipment are found, and other equipment which may have faults is found through analysis of the influence range, the influence degree and the like of the equipment, so that an operation and maintenance prevention decision mechanism is established.
Description
Technical Field
The invention belongs to the technical field of electric power automation, relates to a method for applying a computer algorithm in the field of electric power automation, and particularly relates to a power grid equipment fault analysis method and system based on a regulation cloud.
Background
With the continuous development of large-scale operation and large capacity of a power grid system, the influence of safe and stable operation on the national civilians is larger and larger. If sudden power failure occurs in the power equipment which is an important component of the power grid system, huge economic loss and adverse social influence can be caused. The power grid dispatching control system undertakes the monitoring and control tasks of power grid electric equipment, carries out monitoring analysis and fault diagnosis on the power grid equipment, finds out the existing bad conditions in time, carries out corresponding overhaul and operation maintenance on the power grid equipment according to a scientific operation and inspection strategy, can greatly reduce the occurrence probability of sudden faults of the power grid equipment, and has very important significance on the safe and stable operation of the whole power grid system.
The monitoring analysis and evaluation of the power grid regulation and control system equipment are realized by measuring the state quantity of the power equipment in operation, analyzing and deducing the reason of the occurrence of bad state and judging the serious state of the fault. Along with the long-time operation of the power equipment, the safe operation state of the equipment is in a descending trend, and the probability of the fault of the power equipment is further increased. In order to ensure that the electric power regulation and control system operates at a safe and healthy level, the state monitoring and fault diagnosis of the electric power equipment are required, various faults of the equipment can be diagnosed as soon as possible, the equipment can be maintained or updated in time, and more serious faults are avoided.
Disclosure of Invention
Based on the purpose, the invention provides a power grid equipment fault analysis method and system based on a regulation cloud.
In order to realize the purpose of the invention, the invention provides a power grid equipment fault analysis method based on a regulation cloud, which comprises equipment fault association characteristic analysis and equipment fault association relation evaluation analysis,
the equipment fault correlation characteristic analysis is used for acquiring a frequent item set of equipment faults and an equipment fault correlation example;
and the equipment fault association relation evaluation analysis is used for evaluating and analyzing the association degree from a plurality of indexes according to the equipment fault occurrence frequency item set and the equipment fault association example.
Wherein the content of the first and second substances,
according to different data types, the associated characteristics are selected from the following aspects:
(1) device characteristics; (2) a timing characteristic; (3) concomitant features.
Wherein the content of the first and second substances,
the evaluation analysis of the degree of association is performed from several indexes:
(1) the support degree of the incidence relation;
(2) the confidence of the incidence relation;
(3) and (5) increasing the incidence relation.
Wherein the content of the first and second substances,
and respectively carrying out Z standardization or 0-1 standardization according to the support degree, confidence degree and promotion degree evaluation indexes of the incidence relation, replacing the value before calculation with the value after standardization, and carrying out summation calculation on the support degree, confidence degree and promotion degree of the incidence relation after standardization to obtain a new derivative comprehensive score.
Correspondingly, the application also provides a power grid equipment fault analysis system based on the regulation cloud, which comprises an equipment fault association characteristic analysis unit and an equipment fault association relation evaluation analysis unit,
the equipment fault correlation characteristic analysis unit is used for acquiring a frequent item set of equipment faults and an equipment fault correlation example;
and the equipment fault association relation evaluation and analysis unit is used for evaluating and analyzing the association degree from a plurality of indexes according to the equipment fault occurrence frequent item set and the equipment fault association example.
Wherein the content of the first and second substances,
according to different data types, the associated characteristics are selected from the following aspects:
(1) device characteristics; (2) a timing characteristic; (3) concomitant features.
Wherein the content of the first and second substances,
the evaluation analysis of the degree of association is performed from several indexes:
(1) the support degree of the incidence relation;
(2) the confidence of the incidence relation;
(3) and (5) increasing the incidence relation.
Wherein the content of the first and second substances,
and respectively carrying out Z standardization or 0-1 standardization according to the support degree, confidence degree and promotion degree evaluation indexes of the incidence relation, replacing the value before calculation with the value after standardization, and carrying out summation calculation on the support degree, confidence degree and promotion degree of the incidence relation after standardization to obtain a new derivative comprehensive score.
Compared with the prior art, the method has the advantages that based on the power grid model and the operation data provided by the regulation and control cloud system, the correlation fault relation among different devices is found through mining and analyzing the fault information of the power grid regulation and control system, and other devices which are likely to have faults are found through analyzing the influence range, the influence degree and the like of the devices, so that an operation and maintenance prevention decision mechanism is established.
Drawings
FIG. 1 is a diagram illustrating the correlation between weather, cause type and equipment category to fault type according to the present application;
FIG. 2 is a schematic diagram illustrating a device type associated fault according to the present application;
FIG. 3 is a schematic diagram illustrating the association of fault types according to the present application;
fig. 4 shows an interface of the device correlation failure analysis system according to the present application.
Detailed Description
The invention is described in further detail below with reference to the figures and specific examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when used in this specification the singular forms "a", "an" and/or "the" include "specify the presence of stated features, steps, operations, elements, or modules, components, and/or combinations thereof, unless the context clearly indicates otherwise.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
Analyzing relevant factors of equipment faults based on time intervals, areas, fault types and other influencing factors of equipment faults, analyzing relevant faults of the equipment based on the analysis results, and analyzing the association of the same fault attribute and the associated relation among different attributes, wherein the association analysis comprises area association fault analysis, equipment type association fault analysis, single equipment fault analysis, station association fault analysis, mixed association analysis of equipment, stations and areas, fault type association analysis, cross-station, weather, month and equipment type association fault analysis and the like.
Device fault association feature
According to different data types, the associated characteristics are selected from the following aspects:
(1) the equipment characteristics mainly come from power grid equipment accounts and equipment fault data and are used for analyzing the distribution rule of the heavy overload transformer area under different types of equipment, different fault types and different user composition proportions;
(2) and the time sequence characteristics are used for analyzing the reason proportion and the associated trend rule of the change of equipment failure time periods, important weather, festivals and holidays along with time.
(3) And the companion characteristics are used for analyzing companion factors related to equipment failure, including association relation with equipment categories, weather, reason types and the like and association degree of each dimension.
The purpose of the equipment fault correlation characteristic analysis is to acquire a frequent item set of equipment fault occurrence and an equipment fault correlation example according to a fault equipment object, fault occurrence time, fault ending time, fault occurrence type, fault reason type, line tripping condition, line reclosing condition and the like.
Evaluation and analysis of equipment fault association relation
Based on the frequent occurrence item set of the equipment faults and the equipment fault correlation example, the evaluation and analysis of the correlation degree are carried out from the following indexes:
(1) support degree of association relation
Support(X→Y)＝P(X,Y)/P(I)＝P(X∪Y)/P(I)＝num(XUY)/num(I)
Wherein I represents a total transaction set; num () represents the number of occurrences of a particular item set in the transaction set, num (i) represents the number of total transaction sets, and num (X @ Y) represents the number of transaction sets containing { X, Y } (the number is also called the number).
(2) Confidence of association
Confidence(X→Y)＝P(Y|X)＝P(X,Y)/P(X)＝P(XUY)/P(X)
(3) Degree of promotion of association
Lift(X→Y)＝P(Y|X)/P(Y)
(4) Derived composite score
The Z-normalization (or 0-1 normalization) may be performed according to the evaluation indexes such as the support degree, the confidence degree, and the promotion degree of the correlation, respectively, and the normalized value may be substituted for the value before the calculation. And summing the support degree, the confidence degree, the promotion degree and the like of the normalized incidence relation to obtain a new derivative comprehensive score.
Equipment correlation fault analysis display
Evaluating and analyzing results according to the equipment fault association relation, and displaying the equipment association fault analysis results, wherein the method specifically comprises the following steps:
(1) correlation of weather, reason type and equipment category to fault type
The correlation results of weather, reason types and equipment types to fault types are shown in fig. 1, four correlation dimensions such as a class name, a fault type, on-site weather and a reason type are classified by using different colors in the graph, and points with denser lines and more concentrated line colors in the graph represent deeper correlation degrees. From the analysis results in the figures, it can be seen that two types of causes, namely lightning strike and burning hill, are strong correlation causes for the occurrence of line faults.
(2) Device type-dependent fault analysis
The device type correlation fault result is shown in fig. 2, and it can be seen that when a certain fault occurs, cascaded fault reactions occur in various types of devices (such as lines, buses, circuit breakers, transformers, and various generator sets).
(3) Fault type relevance analysis
The correlation of fault types is shown in fig. 3, and it can be seen that the probability that a line fault and a unit fault occur simultaneously is high, and the line fault is likely to occur after a bus fault occurs.
And analyzing the potential association relation of equipment faults commonly occurring among different types of events by using the 7-year power grid fault data of a certain power grid and combining the data of fault types, field weather, reason types and the like.
Fig. 4 shows an equipment-associated fault analysis system interface.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (8)
1. A power grid equipment fault analysis method based on a regulation cloud is characterized by comprising equipment fault association characteristic analysis and equipment fault association relation evaluation analysis,
the equipment fault correlation characteristic analysis is used for acquiring a frequent item set of equipment faults and an equipment fault correlation example;
and the equipment fault association relation evaluation analysis is used for evaluating and analyzing the association degree from a plurality of indexes according to the equipment fault occurrence frequency item set and the equipment fault association example.
2. The power grid equipment fault analysis method based on the regulation cloud as claimed in claim 1,
according to different data types, the associated characteristics are selected from the following aspects:
(1) device characteristics; (2) a timing characteristic; (3) concomitant features.
3. The power grid equipment fault analysis method based on the regulation cloud as claimed in claim 1,
the evaluation analysis of the degree of association is performed from several indexes:
(1) the support degree of the incidence relation;
(2) the confidence of the incidence relation;
(3) and (5) increasing the incidence relation.
4. The power grid equipment fault analysis method based on the regulation cloud as claimed in claim 3,
and respectively carrying out Z standardization or 0-1 standardization according to the support degree, confidence degree and promotion degree evaluation indexes of the incidence relation, replacing the value before calculation with the value after standardization, and carrying out summation calculation on the support degree, confidence degree and promotion degree of the incidence relation after standardization to obtain a new derivative comprehensive score.
5. A power grid equipment fault analysis system based on a regulation cloud is characterized by comprising an equipment fault association characteristic analysis unit and an equipment fault association relation evaluation analysis unit,
the equipment fault correlation characteristic analysis unit is used for acquiring a frequent item set of equipment faults and an equipment fault correlation example;
and the equipment fault association relation evaluation and analysis unit is used for evaluating and analyzing the association degree from a plurality of indexes according to the equipment fault occurrence frequent item set and the equipment fault association example.
6. The power grid equipment fault analysis system based on the regulatory cloud as claimed in claim 5,
according to different data types, the associated characteristics are selected from the following aspects:
(1) device characteristics; (2) a timing characteristic; (3) concomitant features.
7. The power grid equipment fault analysis system based on the regulatory cloud as claimed in claim 5,
the evaluation analysis of the degree of association is performed from several indexes:
(1) the support degree of the incidence relation;
(2) the confidence of the incidence relation;
(3) and (5) increasing the incidence relation.
8. The system of claim 7, wherein the system comprises a plurality of cloud-based grid equipment fault analysis modules,
and respectively carrying out Z standardization or 0-1 standardization according to the support degree, confidence degree and promotion degree evaluation indexes of the incidence relation, replacing the value before calculation with the value after standardization, and carrying out summation calculation on the support degree, confidence degree and promotion degree of the incidence relation after standardization to obtain a new derivative comprehensive score.
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Cited By (2)
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| CN113535811A (en) * | 2021-06-28 | 2021-10-22 | 北京智芯微电子科技有限公司 | Data detection method, device and equipment based on improved FP-growth algorithm and storage medium |
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