CN111178725A - Protective equipment state early warning method based on analytic hierarchy process - Google Patents

Protective equipment state early warning method based on analytic hierarchy process Download PDF

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CN111178725A
CN111178725A CN201911342002.8A CN201911342002A CN111178725A CN 111178725 A CN111178725 A CN 111178725A CN 201911342002 A CN201911342002 A CN 201911342002A CN 111178725 A CN111178725 A CN 111178725A
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early warning
consistency
judgment matrix
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邬小坤
白加林
牛静
赵武智
赵凌
齐雪雯
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Guizhou Power Grid Co Ltd
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
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    • G06—COMPUTING; CALCULATING OR COUNTING
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Abstract

The invention discloses a protective equipment state early warning method based on an analytic hierarchy process, which comprises the following steps: establishing a hierarchical analysis structure model; step two: constructing a judgment matrix; step three: determining the rank ordering, and determining a feature vector by using a root method; step four: checking the consistency of the sorting, and checking the consistency of the sorting through calculation; step five: and determining the relative importance of each element so as to select an optimal scheme to provide scientific decision basis. The method utilizes the Fuzzy Analytic Hierarchy Process (FAHP) theory to carry out state abnormity early warning on the relay protection equipment, can well solve the problems of fuzziness and difficulty in quantification, and is suitable for solving various non-deterministic problems. By the method, the relay protection equipment and elements thereof can be monitored and analyzed in real time, and the potential hazards of the protection device can be monitored and early warned.

Description

Protective equipment state early warning method based on analytic hierarchy process
Technical Field
The disclosure relates to the field of relay protection of power systems, in particular to a method for early warning the state of relay protection equipment by applying a fuzzy AHP theory.
Background
With the continuous development of the intelligent power grid, the digital and intelligent levels of the secondary equipment monitoring are greatly improved, and a foundation is laid for the online monitoring and state early warning application of the secondary equipment. At present, national power grids and southern power grids are researched on the aspects of equipment on-line monitoring, state maintenance and the like, and a complete, accurate and real-time information acquisition system is built in a transformer substation, so that comprehensive acquisition and real-time sharing of power grid operation data are realized, and the state early warning capability of various intelligent equipment is built and perfected. However, the intelligent degree of the equipment is limited, most researches stay in the theoretical model research stage, and standard specifications of online state abnormity early warning and remote operation and maintenance of secondary equipment of a conventional transformer substation and a digital transformer substation are not formed.
At present, few researches are carried out on the application aspects of monitoring and early warning of element-level state of relay protection equipment, remote operation and maintenance, comprehensive fault analysis and the like, and related researches are carried out on partial regional power grids, but the researches are not deep enough, and the capabilities of modeling, data acquisition and early warning of element-level state indexes of the relay protection equipment are not provided.
Disclosure of Invention
The invention aims to solve the problem that the modeling and early warning capabilities of element-level state indexes of relay protection equipment are not available at present.
In order to achieve the above object, a basic aspect of the present invention provides a method for early warning a state of a protection device based on an analytic hierarchy process, comprising the steps of,
the method comprises the following steps: establishing a hierarchical analysis structure model, collecting the obtained comprehensive relay protection fault information of the transformer substation, analyzing the protection states of relay protection equipment and elements thereof, combing all factors influencing the relay protection equipment and the elements thereof in the system, determining the cause-effect relationship among the factors, dividing risk early warning levels of all the factors influencing equipment abnormity, and establishing a multi-level hierarchical structure model;
step two: constructing a judgment matrix, comparing every two of the elements of the same layer with the elements of the previous layer as a criterion according to the multi-level hierarchical structure model established in the step one, determining the relative importance degree of the elements according to the evaluation scale, and finally establishing a fuzzy judgment matrix according to the relative importance degree;
step three: determining the rank ordering, and determining a feature vector by using a root method;
step four: checking the consistency of the sorting, and checking the consistency of the sorting through calculation;
step five: and determining the relative importance of each element, and performing priority sequencing on all the alternative schemes by integrating the calculation of the importance, so as to select the optimal scheme and provide scientific decision basis.
Further, in the first step, the established hierarchical analysis structure model sequentially comprises a target layer, a criterion layer and a scheme layer from top to bottom; the target layer is used for giving a risk early warning level; the criterion layer is used for sequencing risk indexes affecting the health of the device and dividing the categories of the power grid stations according to actual operation conditions, and the sequencing of the risk indexes affecting the health of the device sequentially comprises the following steps: the method comprises the following steps that secondary equipment and a secondary circuit are abnormal, a channel is abnormal, a double-channel is abnormal, countermeasures are not executed, familial defects, overdue service and overdue undetected, and power grid stations are divided into key stations, secondary key stations and general stations according to the actual operation condition of a power grid; the scheme layer is used for dividing risk levels, the risk level alarms are divided into five levels, and the alarm is carried out from the first risk level to the fifth risk level.
Further, the fuzzy judgment matrix establishing process in the step two is as follows:
(1) in fuzzy hierarchical analysis, when two factors are compared and judged, the importance degree of one factor to the other factor is quantitatively expressed, and the obtained fuzzy judgment matrix A is (a)ij) n × n, if the following properties are true, the fuzzy judgment matrix is obtained.
3)aii＝0.5,i＝1,2,…,n；
4)aij+aji＝1,i,j＝1,2,…,n；
(2) And (3) deducing a fuzzy complementary judgment matrix weight formula:
Figure BDA0002332521590000021
(3) and (3) consistency check for comparing and judging whether the weight values obtained by the formula (2) are reasonable or not, wherein when the deviation consistency is overlarge, the result of the calculation of the weight vector is used as a decision basis and is unreliable, so that a method for checking the consistency principle by using the compatibility of the fuzzy judgment matrix is deduced.
Further, in the third step, the specific calculation steps of the feature vector are as follows:
(1) calculating the product of all elements of each row of the judgment matrix:
Figure BDA0002332521590000031
(2) b is obtainediRoot of cubic (n times)
Figure BDA0002332521590000032
(3) Normalized vector p ═ p (p)1,p2,...,pn)TLet us order
Figure BDA0002332521590000033
Then
w＝(w1,w2,...,wn)TI.e., a feature vector, where the n components of w are the relative importance corresponding to the n elements.
Further, in step four, the specific calculation process for checking the consistency of the ranking is as follows:
(1) calculating the maximum characteristic root of the judgment matrix:
Figure BDA0002332521590000034
wherein AW is the product of the judgment matrix A and the characteristic vector W, (AW)iIs the ith component of AW;
(2) finding a consistency index, where CI is ═ lambdam-n)/(n-1)；
(3) A random consistency index is obtained, where CR is CI/RI, and RI is an average random consistency ratio sequence.
Further, in the fifth step, the specific calculation process of the comprehensive importance degree is as follows:
(1) calculating the relative importance of each level of elements according to the third step
Figure BDA0002332521590000036
And carrying out consistency check in the fourth step;
(2) calculating the comprehensive importance wz，
Figure BDA0002332521590000035
The invention has the following advantages:
(1) systematicness
The invention uses the Fuzzy Analytic Hierarchy Process (FAHP) theory to carry out state abnormity early warning on the relay protection equipment, takes a research object as a system, and carries out decision-making according to the analytical elements, the comparative judgment relative importance and the sequencing preferred thinking mode of the comprehensive importance. The method can well solve the problems of fuzziness and difficult quantization, and is suitable for solving various non-determinacy problems. By the method, real-time monitoring and abnormity analysis of the relay protection equipment and elements thereof are realized, and abnormity monitoring, early warning and the like of hidden dangers of the protection device are realized.
(2) Practicality of use
The invention can carry out real-time monitoring, abnormity analysis and early warning of relay protection equipment and elements thereof, and timely and actively pushes out equipment abnormity early warning information when a certain index of a certain dimension reaches a degradation limit.
(3) Simplicity of operation
The result obtained by the method is simple and clear, the calculation process is very simple and convenient, and the method is easy to be known and mastered by decision makers.
Drawings
FIG. 1 is a diagram of a hierarchical analysis based on an analytic hierarchy process according to the present invention;
FIG. 2 is a flow chart of risk analysis of a protection device in the method for early warning of the state of a protection device based on an analytic hierarchy process according to the present invention;
fig. 3 is a schematic diagram of element labeling in the method for early warning of the state of the protection device based on the analytic hierarchy process.
Detailed Description
The following is further detailed by the specific embodiments:
example (b):
a protective equipment state early warning method based on an analytic hierarchy process comprises the following steps,
the method comprises the following steps: establishing a hierarchical analysis structure model, collecting the obtained comprehensive relay protection fault information of the transformer substation, analyzing the protection states of the relay protection equipment and elements thereof, combing all factors influencing the relay protection equipment and the elements thereof in the system, determining the cause-effect relationship among all the factors, dividing the risk early warning level of all the factors influencing the equipment abnormity, and establishing a multi-level hierarchical structure model. As shown in FIG. 1, the hierarchical analysis structure model is typically divided into three large layers: the target layer, the criterion layer and the scheme layer are arranged from top to bottom in sequence.
Target layer: the invention aims to analyze the abnormal state of the protection state so as to give the risk early warning level of the device, so that the target layer is the risk early warning level.
As shown in FIG. 2, the criteria layer: the establishment of the target layer comprises two parts, namely the influence condition of each index of the health risk of the device and the station category divided according to the actual operation condition of the power grid. As will be explained in detail below.
The main health risk evaluation indexes of the device are sequenced according to the influence degree on the device, and the method can be known according to the protection principle: the secondary equipment and the secondary loop are abnormal to cause the locking of the protection device, and the single set of protection exits the operation; channel abnormity can cause that protection communication is unavailable, protection locking is caused, and operation is quitted; the operation reliability of protection is reduced when the two channels of the protection device are changed into a single channel; the method has the advantages that the method can overcome the defect that the protective device has larger possible statistical faults under the existing statistical data when the protective device is not executed and has familial defects; if the protection device is not inspected for the overdue period and is in service for the overdue period, the protection device does not meet the maintenance plan and equipment aging, and the protection device may not operate normally.
According to the influence condition of each index of the health risk of the device, the influence degrees are sequentially ordered as follows:
secondary equipment and secondary circuit abnormity, channel abnormity, double-channel abnormity, failure in counter measure, familial defect, overdue service and overdue undetected.
And meanwhile, according to the actual operation condition of the power grid, dividing the power grid station into a special station (a key station), a secondary key station and a general station.
Scheme layer: the scheme layer is divided into risk levels, the risk level alarms are divided into five levels, and the alarm is from the risk level one alarm to the risk level five alarm, as shown in the risk level in fig. 2.
Step two: and constructing a judgment matrix. The factors are compared pairwise, the structure of the judgment matrix is completed according to the description importance, and the function of the judgment matrix reflects the relative importance of the factors of the level to a certain related factor of the previous level.
The establishment flow is as follows:
establishing a fuzzy complementary judgment matrix, in the fuzzy hierarchical analysis, when making pairwise comparison judgment between factors, adopting quantitative representation of the importance degree of one factor to another factor, and obtaining the fuzzy judgment matrix A ═ aij) n × n, if true, has the following properties:
5)aii＝0.5,i＝1,2,…,n；
6)aij+aji＝1,i,j＝1,2,…,n；
such a decision matrix is called a fuzzy complementary decision matrix. In order to quantify the relative importance of any two protocols with respect to a certain criteria, a quantitative scale is typically given using a scale of 0.1-0.9 as shown in the following table.
Figure BDA0002332521590000061
aii0.5 indicates that the factor is as important as itself; if aijE [0.1,0.5 ]), represents the factor xjRatio xiImportance; if aij∈[0.5,0.9]Then, it represents the factor xiRatio xjIt is important.
According to the above numerical scale, factor a1，a2，...，anComparing the two matrixes to obtain the following fuzzy complementary judgment matrix:
Figure BDA0002332521590000062
(1) weight formula of fuzzy complementary judging matrix
A general formula for solving the weight of the fuzzy complementary judgment matrix is deduced, the formula fully contains the excellent characteristics of the fuzzy consistency judgment matrix and the judgment information thereof, the calculation amount is small, the computer programming is convenient to realize, and great convenience is brought to practical application. The formula for solving the weight of the fuzzy complementary judgment matrix is as follows:
Figure BDA0002332521590000063
(2) consistency checking method for fuzzy complementary judgment matrix
Whether the weight value obtained by equation (2) is reasonable or not should be checked for consistency by comparison and judgment. When the deviation consistency is too large, the calculation result of the weight vector is not reliable as a decision basis. And deducing a method for checking the consistency principle of the fuzzy judgment matrix by using the compatibility of the fuzzy judgment matrix.
Definition 1: let matrix A ═ aij)n×nAnd B ═ Bij)n×nAre all fuzzy judgment matrixes, scales
Figure BDA0002332521590000071
Is an index of compatibility between A and B.
Definition 2: let W ═ W1,W2,...,Wn)TIs a feature vector of the fuzzy decision matrix A, wherein
Figure BDA0002332521590000072
Let Wij＝WiWi+Wj，( P i1,2,3,., n), then the matrix is called an n-order matrix: w*＝(Wij)n×nIs a feature matrix of the judgment matrix A. And regarding the attitude A of the decision maker, when the compatibility index I (A, W) is less than or equal to A, the judgment matrix is considered to be satisfactory and consistent. A smaller A indicates that the consistency requirement of a decision maker on the fuzzy judgment matrix is higher, and generally, A is equal to 0.1. For practical problems, a plurality of experts (let k be 1, 2.. multidot.m) generally give two-by-two comparison judgment matrixes on the same factor X: a. thek＝(aij (k))n×n(k ═ 1, 2.. times, m), which are fuzzy complementary decision matrices, then a set of weight sets can be obtained separately:
Figure BDA0002332521590000073
then, the consistency check of the fuzzy complementary judgment matrix is carried out, and the following two works are required:
1) and (3) checking the satisfactory consistency of the m judgment matrixes Ak: i (Ak, W)(k))≤A,k＝1,2,...,m
And (3) checking and judging satisfactory compatibility among the matrixes: i (A)k,Al) A is less than or equal to A, k is not equal to l; k, l 1,2, m may be proved in the fuzzy complementary judging matrix akIn the case where (k ═ 1, 2.., m) is consistently acceptable, their comprehensive judgment matrix is also consistently acceptable. The feature vector expression: w ═ W1,W2,...,Wn) In the above formula:
Figure BDA0002332521590000074
i.e. as long as both 1) and 2) are satisfied, it is reasonable and reliable that the mean of the m weight values is the weight distribution vector for the factor set X.
2) The determination of the median in the decision matrix is detailed by the following steps, as shown in fig. 3, two ends of the scale line are elements compared pairwise, and the strong element at which end is marked at which end. When filling in the matrix, if the value compared by the two elements is on the left side of 1, directly filling in the value; otherwise, the inverse of the value is filled, as the dot marked by the following figure is on the right side of 1, and the matrix point corresponding to the two elements is filled by 1/3 in the process of filling the matrix.
Establishing an analytic hierarchy process judgment matrix with the form shown in the following table according to the indexes formed in the step one and the rule formed by the judgment matrix:
Cs p1 p2 ... pn
p1 b11 b12 ... b1n
p2 b21 b22 ... b2n
... ... ... ... ...
pn bn1 bn2 ... bnn
scale bijThe meaning of (a): p is a radical ofiRatio pjThe degree of importance of.
Step three: the ordering of the hierarchy is determined. The feature vector is determined using a root method, the steps of which are as follows:
1) calculating the product of all elements of each row of the judgment matrix:
Figure BDA0002332521590000081
2) b is obtainediRoot of cubic (n times)
Figure BDA0002332521590000082
3) Normalized vector p ═ p (p)1,p2,...,pn)TLet us order
Figure BDA0002332521590000083
Then w is ═ w1,w2,...,wn)TI.e., a feature vector, where the n components of w are the relative importance corresponding to the n elements.
Step four: the ordering is checked for consistency.
1) Calculating the maximum characteristic root of the judgment matrix
Figure BDA0002332521590000084
Wherein AW is the product of the decision matrix A and the eigenvector W, (AW)iIs the ith component of AW.
2) Finding a consistency index, where CI is ═ lambdam-n)/(n-1)。
3) And (3) obtaining a random consistency index, wherein CR is CI/RI, RI is an average random consistency ratio column, and a reference table of RI values is shown as follows:
Figure BDA0002332521590000085
Figure BDA0002332521590000091
4) judging whether consistency is met, and when CR is less than 0.1, judging that the matrix has acceptable consistency; when CR is more than or equal to 0.1, the judgment matrix needs to be adjusted and corrected to meet the condition that CR is less than 0.1, so that the consistency is satisfied. If the consistency condition is not met, the judgment process is readjusted until the consistency condition is met.
Step five: calculating the relative importance of each level of elements according to the third step
Figure BDA0002332521590000092
And carrying out consistency check in the fourth step; by calculating the comprehensive importance wz，
Figure BDA0002332521590000093
And prioritizing all the alternative schemes so as to select the optimal scheme to determine the risk alarm level.
The invention uses the Fuzzy Analytic Hierarchy Process (FAHP) theory to carry out state abnormity early warning on the relay protection equipment, takes a research object as a system, and carries out decision-making according to the analytical elements, the comparative judgment relative importance and the sequencing preferred thinking mode of the comprehensive importance. The method can well solve the problems of fuzziness and difficult quantization, and is suitable for solving various non-determinacy problems. By the method, real-time monitoring and abnormity analysis of the relay protection equipment and elements thereof are realized, and abnormity monitoring, early warning and the like of hidden dangers of the protection device are realized.
The foregoing is merely an example of the present invention and common general knowledge of known specific structures and features of the embodiments is not described herein in any greater detail. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.

Claims (6)

1. A protective equipment state early warning method based on an analytic hierarchy process is characterized in that: comprises the following steps of (a) carrying out,
the method comprises the following steps: establishing a hierarchical analysis structure model, collecting the obtained comprehensive relay protection fault information of the transformer substation, analyzing the protection states of relay protection equipment and elements thereof, combing all factors influencing the relay protection equipment and the elements thereof in the system, determining the cause-effect relationship among the factors, dividing risk early warning levels of all the factors influencing equipment abnormity, and establishing a multi-level hierarchical structure model;
step two: constructing a judgment matrix, comparing every two of the elements of the same layer with the elements of the previous layer as a criterion according to the multi-level hierarchical structure model established in the step one, determining the relative importance degree of the elements according to the evaluation scale, and finally establishing a fuzzy judgment matrix according to the relative importance degree;
step three: determining the rank ordering, and determining a feature vector by using a root method;
step four: checking the consistency of the sorting, and checking the consistency of the sorting through calculation;
step five: and determining the relative importance of each element, and performing priority sequencing on all the alternative schemes through the calculation of the comprehensive importance, so as to select the optimal scheme to determine the risk alarm level.
2. The analytic hierarchy process-based protective equipment state early warning method as claimed in claim 1, wherein: in the first step, the established hierarchical analysis structure model sequentially comprises a target layer, a criterion layer and a scheme layer from top to bottom; the target layer is used for giving a risk early warning level; the criterion layer is used for sequencing risk indexes affecting the health of the device and dividing the categories of the power grid stations according to actual operation conditions, and the sequencing of the risk indexes affecting the health of the device sequentially comprises the following steps: the method comprises the following steps that secondary equipment and a secondary circuit are abnormal, a channel is abnormal, a double-channel is abnormal, countermeasures are not executed, familial defects, overdue service and overdue undetected, and power grid stations are divided into key stations, secondary key stations and general stations according to the actual operation condition of a power grid; the scheme layer is used for dividing risk levels, the risk level alarms are divided into five levels, and the alarm is carried out from the first risk level to the fifth risk level.
3. The analytic hierarchy process-based protective equipment state early warning method as claimed in claim 1 or 2, wherein: the fuzzy judgment matrix establishing process in the second step is as follows:
(1) in fuzzy hierarchical analysis, when two factors are compared and judged, the importance degree of one factor to the other factor is quantitatively expressed, and the obtained fuzzy judgment matrix A is (a)ij) n × n, if the following properties are true, the fuzzy judgment matrix is obtained.
1)aii＝0.5,i＝1,2,…,n；
2)aij+aji＝1,i,j＝1,2,…,n；
(2) And (3) deducing a fuzzy complementary judgment matrix weight formula:
Figure FDA0002332521580000021
(3) and (3) consistency check for comparing and judging whether the weight values obtained by the formula (2) are reasonable or not, wherein when the deviation consistency is overlarge, the result of the calculation of the weight vector is used as a decision basis and is unreliable, so that a method for checking the consistency principle by using the compatibility of the fuzzy judgment matrix is deduced.
4. The analytic hierarchy process-based protective equipment state early warning method of claim 3, wherein: in the third step, the specific calculation steps of the feature vector are as follows:
(1) calculating the product of all elements of each row of the judgment matrix:
Figure FDA0002332521580000022
(2) b is obtainediRoot of cubic (n times)
Figure FDA0002332521580000023
(3) Normalized vector p ═ p (p)1,p2,...,pn)TLet us order
Figure FDA0002332521580000024
Then w is ═ w1,w2,...,wn)TI.e. the found feature vector, where the n components of w are the relative importance corresponding to the n elements.
5. The analytic hierarchy process-based protective equipment state early warning method of claim 4, wherein: in step four, the specific calculation process for checking the consistency of the sorting is as follows:
(1) calculating the maximum characteristic root of the judgment matrix:
Figure FDA0002332521580000025
wherein AW is the product of the judgment matrix A and the characteristic vector W, (AW)iIs the ith component of AW;
(2) finding a consistency index, where CI is ═ lambdam-n)/(n-1)；
(3) A random consistency index is obtained, where CR is CI/RI, and RI is an average random consistency ratio sequence.
6. The analytic hierarchy process-based protective equipment state early warning method of claim 5, wherein: in the fifth step, the specific calculation process of the comprehensive importance degree is as follows:
(1) calculating the relative importance of each level of elements according to the third step
Figure FDA0002332521580000026
And carrying out consistency check in the fourth step;
(2) calculating the comprehensive importance wz，
Figure FDA0002332521580000027
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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111913823A (en) * 2020-06-22 2020-11-10 成都飞机工业（集团）有限责任公司 Analytic hierarchy process based near-field test system fault analysis method
CN112053026A (en) * 2020-07-15 2020-12-08 中国人民解放军海军工程大学 Ship maintenance resource configuration verification method and device
CN112886038A (en) * 2020-12-23 2021-06-01 北京工业大学 Fuel cell fault diagnosis method
CN113466261A (en) * 2021-07-26 2021-10-01 鸿安（福建）机械有限公司 PCB board automatic checkout device
CN116884157A (en) * 2023-07-11 2023-10-13 中国人民解放军军事科学院系统工程研究院 Open-air base early warning system

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104077493A (en) * 2014-07-12 2014-10-01 东北电力大学 Method for constructing state evaluation index system of electric relaying protection system
CN106611099A (en) * 2015-10-16 2017-05-03 中国传媒大学 Program evaluation system and method based on analytic hierarchy process
CN108399287A (en) * 2018-02-06 2018-08-14 南通大学 Using the appraisal procedure of the machine tool beam design scheme of Fuzzy Level Analytic Approach
CN108985649A (en) * 2018-08-01 2018-12-11 国电联合动力技术有限公司 Timing ambiguity step analysis appraisal procedure based on wind power equipment anomaly analysis data
CN109829605A (en) * 2018-12-13 2019-05-31 国网浙江省电力有限公司经济技术研究院 Electricity power engineering Project Risk Evaluation based on Fuzzy AHP

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104077493A (en) * 2014-07-12 2014-10-01 东北电力大学 Method for constructing state evaluation index system of electric relaying protection system
CN106611099A (en) * 2015-10-16 2017-05-03 中国传媒大学 Program evaluation system and method based on analytic hierarchy process
CN108399287A (en) * 2018-02-06 2018-08-14 南通大学 Using the appraisal procedure of the machine tool beam design scheme of Fuzzy Level Analytic Approach
CN108985649A (en) * 2018-08-01 2018-12-11 国电联合动力技术有限公司 Timing ambiguity step analysis appraisal procedure based on wind power equipment anomaly analysis data
CN109829605A (en) * 2018-12-13 2019-05-31 国网浙江省电力有限公司经济技术研究院 Electricity power engineering Project Risk Evaluation based on Fuzzy AHP

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
商淼等: "AHP 与马尔科夫模型在封闭式组合电器状态评估中的应用", 《黑龙江电力》, vol. 40, no. 4, 31 August 2018 (2018-08-31), pages 300 - 301 *
张翠玲: "电力变压器综合评判和状态维修策略决策方法的研究", 《中国博士学位论文全文数据库-工程科技Ⅱ辑》, no. 3, 15 March 2017 (2017-03-15), pages 24 - 29 *

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111913823A (en) * 2020-06-22 2020-11-10 成都飞机工业（集团）有限责任公司 Analytic hierarchy process based near-field test system fault analysis method
CN112053026A (en) * 2020-07-15 2020-12-08 中国人民解放军海军工程大学 Ship maintenance resource configuration verification method and device
CN112886038A (en) * 2020-12-23 2021-06-01 北京工业大学 Fuel cell fault diagnosis method
CN112886038B (en) * 2020-12-23 2022-07-05 北京工业大学 Fuel cell fault diagnosis method
CN113466261A (en) * 2021-07-26 2021-10-01 鸿安（福建）机械有限公司 PCB board automatic checkout device
CN113466261B (en) * 2021-07-26 2023-04-07 鸿安（福建）机械有限公司 PCB board automatic checkout device
CN116884157A (en) * 2023-07-11 2023-10-13 中国人民解放军军事科学院系统工程研究院 Open-air base early warning system

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Application publication date: 20200519