CN112989695B - Switch cabinet state evaluation method considering importance of power grid nodes - Google Patents
Switch cabinet state evaluation method considering importance of power grid nodes Download PDFInfo
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- 238000011156 evaluation Methods 0.000 title claims abstract description 62
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- 238000013210 evaluation model Methods 0.000 claims abstract description 11
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- 238000004364 calculation method Methods 0.000 claims description 7
- 238000012937 correction Methods 0.000 claims description 6
- 238000007689 inspection Methods 0.000 claims description 5
- 230000007547 defect Effects 0.000 claims description 4
- 238000009666 routine test Methods 0.000 claims description 4
- 238000012360 testing method Methods 0.000 claims description 4
- 229910018503 SF6 Inorganic materials 0.000 claims description 3
- 238000009413 insulation Methods 0.000 claims description 2
- SFZCNBIFKDRMGX-UHFFFAOYSA-N sulfur hexafluoride Chemical compound FS(F)(F)(F)(F)F SFZCNBIFKDRMGX-UHFFFAOYSA-N 0.000 claims description 2
- 229960000909 sulfur hexafluoride Drugs 0.000 claims description 2
- 230000001052 transient effect Effects 0.000 claims description 2
- 238000012821 model calculation Methods 0.000 claims 1
- 238000012797 qualification Methods 0.000 claims 1
- 238000012423 maintenance Methods 0.000 abstract description 7
- 230000006870 function Effects 0.000 description 8
- 230000004927 fusion Effects 0.000 description 5
- 238000011161 development Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 238000007499 fusion processing Methods 0.000 description 2
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
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- G—PHYSICS
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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
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- 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
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
A switch cabinet state evaluation method considering importance of a power grid node relates to the technical field of switch cabinets. The method comprises the following specific steps: determining a monitoring information source and a state quantity of a switch cabinet; normalization processing of the multidimensional state quantity; establishing a layered state evaluation model; calculating the importance of the power grid nodes; correcting the evaluation result of the hierarchical model based on the importance of the nodes; and (5) establishing and solving a state quantity membership function model. Compared with other switch cabinet state evaluation methods, the technical scheme has the advantages that the two aspects of the operating state of the switch cabinet and the importance degree of the power grid node where the switch cabinet is located to the whole power system are taken as the basis for correcting the state evaluation of the switch cabinet, the limitation of the traditional state evaluation method of the switch cabinet is overcome, and the final evaluation result can be used as an important index for subsequently formulating the maintenance strategy.
Description
Technical Field
The invention relates to the field of switch cabinet state overhaul, in particular to a switch cabinet state assessment method.
Background
With the rapid development of social economy, the requirement of people on power supply reliability is higher and higher, and the switch cabinet is used as main electrical equipment in a power distribution network, and the running state of the switch cabinet is normal or not, so that the running effect of the whole power grid and the power consumption quality of vast users are directly related. The switch cabinet integrates various electrical elements, has a compact structure and small space gap, equipment is in a sealed and electrified state, and the switch cabinet is easy to generate partial discharge, abnormal temperature rise and the like under the operating condition of high voltage and high current after long-term operation. Therefore, the operation state of the switch cabinet is accurately evaluated, a targeted operation and maintenance strategy is made in time, and the method has important significance for ensuring the normal operation of the switch cabinet.
The existing switch cabinet state evaluation method mainly comprises the following steps that firstly, a state evaluation method based on a guide rule is adopted, state quantity is selected according to results of on-line monitoring, operation maintenance, tests and the like, then deduction is carried out according to deduction standards specified by the guide rule, and the state grade of the switch cabinet is determined according to the deduction standards, but only starting from equipment, the problems of large workload and low efficiency exist; currently, many researches are carried out on evaluation methods based on multi-information fusion, and the methods firstly select state quantities to establish an index system, then adopt a related method of information fusion to carry out the fusion of multi-type information, and finally obtain an evaluation result; the state evaluation method based on classification and regression is a state evaluation method based on big data analysis, and related methods of machine learning, such as a bayesian network, a support vector machine and the like, are widely adopted, but the defects of the methods are obvious, the reliability of electrical equipment such as a switch cabinet and the like is relatively high, the data of defects and fault states are deficient, training samples are insufficient, and the accuracy of the model is not ideal.
At present, the state evaluation of the switch cabinet is based on the state quantity of the switch cabinet, and the node position of the switch cabinet in the power distribution network is not considered.
Disclosure of Invention
The problem to be solved by the invention is to introduce the importance of the power grid node where the switch cabinet is located into the state evaluation of the switch cabinet as the correction of the evaluation result, so that the state quantity of the switch cabinet is combined with the importance of the power grid node where the switch cabinet is located.
The invention provides a switch cabinet state evaluation method considering power grid node importance, which comprises the following specific steps:
1) determining a monitoring information source and a state quantity;
in order to avoid the over-subjectivity of the state quantity selection of the switch cabinet, the state quantity monitored by the switch cabinet is determined according to a Q/GDW645-2011 distribution network equipment state evaluation guide rule and a southern power grid company-Guangdong power grid-equipment state evaluation and risk evaluation technology guide rule.
Further: with the development of sensor technology, information sources are mainly obtained in four modes of family information, online monitoring, operation inspection and routine test;
further: the state quantity of the switch cabinet is divided into five parts, namely equipment information, insulating property, hexafluoroation annual leakage rate, mechanical property and current carrying capacity, and the lower surface of each part corresponds to the corresponding state quantity;
further: the state grades are divided into five grades in order to reflect the accuracy of the result: a (excellent), B (excellent), C (medium), D (poor) and E (poor);
2) normalization processing of the multidimensional state quantity;
because the data types of the various state quantities are different, normalization processing is adopted, a half-ridge model is used for evaluating the quantitative state quantities, and a proportional model is used for the operation years of the switch cabinet;
further: the half-ridge model is divided into a rising half-ridge model and a falling half-ridge model. The former is used for evaluating a state quantity with a larger numerical value and the latter is used for evaluating a state quantity with a smaller numerical value, and the formulas are respectively as follows:
wherein a and b represent upper and lower score thresholds, respectively.
Further: for the fusion of the operation years, multiplying by a coefficient KTThe formula is as follows:
KT(100-years of operation x 0.3)/100
Further: the determination of the state quantity scoring threshold value is based on corresponding national standard and industry standard.
Further: after the evaluation scores of the state quantities are obtained, normalization processing is carried out on the evaluation scores in order to facilitate the subsequent data fusion process: the result is uniformly divided by 100 and transformed into the [0,1] interval.
3) Establishing a layered state evaluation model;
taking the state evaluation components selected in the step 1) as parent layers, taking the state quantity corresponding to each evaluation component as sub-layers, determining each state evaluation result by the sub-layers, and taking the average value of the state score values of all the sub-layers as the state evaluation score value of the parent layer when the state score value of each sub-layer is greater than 0.7; and if not, taking the minimum value in the sub-layer state score values as the state evaluation score value of the parent layer.
4) Calculating the importance of the power grid nodes;
the power network can be regarded as being composed of nodes and branches, and the positions of the fulcrums in the power grid are different, and the load capacity of the fulcrums is different, so that the positions of the nodes of the fulcrums and the load capacity of the fulcrums can be used as two indexes for measuring the importance of the power grid nodes.
Further: the positions of the grid nodes are divided into three types. The first type is a structural node, which is characterized in that the node is positioned on a main line of a power grid, and particularly when the main line has a fault, the node can carry out load transfer through the switch cabinet, and the switch cabinet also has the function of serving as a tie switch; the second type is an important load node, and the node is responsible for switching on and off important loads in the power distribution network, such as large-scale precision instruments with higher power supply requirements, main operation equipment of an offshore platform and the like; the third type of nodes are edge nodes, and the third type of nodes are mainly responsible for supplying power to living facilities and the like and can bear the influence caused by short-time power failure maintenance;
further: the load capacity of the power grid nodes is measured according to the percentage of the node load capacity in the rated capacity of the switch cabinet, and is divided into three grades, wherein the load capacity is less than 50%, 50% -75% and more than 75%;
further: the importance of the power grid node is determined by two factors, namely the node position and the node load, and the calculation formula is as follows:
Cn＝0.7Ln+0.3Pn
wherein C isnRepresenting the node importance correction coefficient for correcting the evaluation result of the switch cabinet state, LnAs a node position importance parameter, PnIs a node load parameter.
5) Correcting the evaluation result of the hierarchical model based on the importance of the nodes;
the score value of the hierarchical evaluation model calculated in the step 3) and the node importance C calculated in the step 4) are comparednAnd multiplying to obtain the corrected state evaluation score value.
6) Establishing and solving a state quantity membership function model;
substituting the result of the hierarchical evaluation model modified in the step 5), namely the score values of all parts into the membership function of each state grade, and determining the state grade of the equipment according to the maximum membership principle, wherein the membership function formula is as follows:
class A
Grade B
Grade C
Grade D
Grade E
Compared with the existing switch cabinet state evaluation scheme, the switch cabinet state evaluation method considering the importance degree of the power grid nodes has the following gain effects: according to the traditional state evaluation of single-source information, only the operation inspection information or test information of equipment is considered, part of obtained data has no timeliness, along with the development of an online monitoring technology, an evaluation method based on multi-dimensional information fusion can enrich information sources, and an evaluation model can have higher robustness and accuracy. Meanwhile, the method solves the problem that the traditional evaluation method lacks consideration on the overall operation condition of the power grid, introduces the concept of node importance into the evaluation as a correction of an evaluation result, and can take the evaluation result as an important index for subsequently formulating a maintenance strategy.
Drawings
FIG. 1 is a flow chart of the present invention
FIG. 2 is a hierarchical evaluation model of the switch cabinet
Detailed Description
The following further describes embodiments of the present invention with reference to the drawings.
The invention relates to a switch cabinet state evaluation method considering grid node importance, which comprises the following steps as shown in figure 1:
1. determining a monitoring information source and a state quantity;
in order to obtain the multidimensional data of the switch cabinet, the monitoring information sources are family information, online monitoring, operation inspection and routine tests respectively. And dividing the state quantities into five parts according to the characteristics of the state quantities, wherein each part at least comprises one state quantity, and the corresponding state quantities are as follows:
there are two state quantities for family information acquisition: the handover test yield and the family defect rate are respectively marked as F1 and F2;
the state quantity obtained by on-line monitoring is three: the voltage value of the local discharge monitoring transient state ground, the voltage value of the local discharge monitoring ultrasonic wave and the temperature of the conductive connection point are respectively marked as PD1, PD2 and TR;
the state quantity obtained by the operation inspection is two: the operating years and the readings of the sulfur hexafluoride gas pressure indicator are respectively recorded as Y, SF;
the routine test obtains five state quantities: the insulation resistance value, the grounding resistance value, the opening operation TIME, the closing operation TIME and the main loop direct current resistance are respectively marked as R1, R2, TIME1, TIME2 and R3;
2. carrying out multi-dimensional state quantity normalization processing;
because the data types of the state quantities are different, normalization processing is adopted, a half-ridge model is used for evaluating quantitative state quantities, and a proportional model is used for fusing the operation years of the switch cabinet;
further: the half-ridge model is divided into a rising half-ridge model and a falling half-ridge model. The former is used for evaluating a state quantity with a larger numerical value and the latter is used for evaluating a state quantity with a smaller numerical value, and the formulas are respectively as follows:
wherein a and b represent upper and lower score thresholds, respectively.
For the operating life, multiplying by a factor KTThe formula is as follows:
KT(100-years of operation x 0.3)/100
After the evaluation scores of the state quantities are obtained, normalization processing is carried out on the evaluation scores in order to facilitate the subsequent data fusion process: the result is uniformly divided by 100 and transformed into the [0,1] interval.
The determination of the state quantity scoring threshold value is based on corresponding national standard and industry standard. As shown in table 1:
TABLE 1
Substituting the state quantities except the operation age in the step 1 and the threshold value in the table 1 into the half-ridge model, dividing the result by 100, and performing normalization processing to obtain the results corresponding to the state quantities, wherein the results are respectively as follows: FF1, FF2, FPD1, FPD2, FTR, FSF, FR1, FR2, FTIME1, FTIME2, FR 3.
Will operateSubstituting the state quantity of the age into the proportional model to obtain KT(Y)。
3. Establishing a layered state evaluation model;
as shown in fig. 2, the state evaluation components selected in step 1 are used as parent layers, the state quantity corresponding to each evaluation component is used as a sub-layer, each state evaluation result is determined by the sub-layer, and when the state score values of the sub-layers are all larger than 0.7, the state evaluation score value of the parent layer is the average value of the state score values of all the sub-layers; and if not, taking the minimum value in the sub-layer state score values as the state evaluation score value of the parent layer. Substituting the result obtained in the step 2 into the layered state evaluation model, wherein the result is as follows: [ FS1 FS2 FS3 FS4 FS5 ].
4. Calculating the importance of the power grid nodes;
the power network can be regarded as being composed of nodes and branches, and because the positions of the fulcrums in the power grid are different, and the load capacity of the fulcrums is different, the positions of the nodes of the fulcrums and the load capacity of the fulcrums are used as two indexes for measuring the importance of the power grid nodes.
The positions of the grid nodes are divided into three types. The first type is a structural node, which is characterized in that the node is located in a main line of a power grid, and particularly when the main line has a fault, the node can carry out load transfer through the switch cabinet, and the switch cabinet also serves as a tie switch; the second type is an important load node, and the node is responsible for switching on and off important loads in the power distribution network, such as large-scale precision instruments with higher power supply requirements, main operation equipment of an offshore platform and the like; the third type of nodes are edge nodes, and the third type of nodes are mainly responsible for supplying power to living facilities and the like and can bear the influence caused by short-time power failure maintenance;
the load capacity of the power grid nodes is measured according to the percentage of the node load capacity in the rated capacity of the switch cabinet, and is divided into three grades, wherein the load capacity is less than 50%, 50% -75% and more than 75%;
two parameter selection principles of measuring the importance of the power grid node where the switch cabinet is located and the load capacity are shown in table 2:
TABLE 2
The calculation formula is as follows:
Cn＝0.7Ln+0.3Pn
wherein C isnRepresenting the node importance correction coefficient for correcting the evaluation result of the switch cabinet state, LnAs a node position importance parameter, PnIs a node load parameter. C1 is obtained by calculation according to the node position and the load capacity of the switch cabinet;
5. correcting the evaluation result of the hierarchical model based on the importance of the nodes;
and (4) multiplying the hierarchical evaluation model score value [ FS1 FS2 FS3 FS4 FS5] obtained in the step (3) with the node importance degree C1 obtained in the step (4) to obtain a corrected state evaluation score value [ FS11 FS12 FS13 FS14 FS15 ].
6. Establishing and solving a state quantity membership function model;
substituting the result of the hierarchical evaluation model corrected in the step 5, namely the score value [ FS11 FS12 FS13 FS14 FS15] of each component into a membership function of each state grade, wherein the formula of the membership function is as follows:
class A
Grade B
Grade C
Grade D
Grade E
Obtaining a membership matrix:
according to the maximum membership rule, taking the maximum value of each row (taking the first row as an example): MAX [ FS11A FS11B FS11C FS11D FS11E ], the state corresponding to the maximum value is the state of the component.
Then multiplying the minimum value in the score values of all the parts by the calculation result K of the proportional model of the operation ageT(Y), finally substituting into the membership function to obtain the overall state evaluation grade membership matrix of the switch cabinet as follows: [ FSA FSB FSC FSD FSE]Taking MAX FSA FSB FSC FSD FSE according to the maximum membership rule]The state corresponding to the maximum value is the overall state of the switch cabinet.
The switch cabinet state evaluation method considering the importance of the power grid nodes is a specific embodiment of the invention, has the substantial characteristics and progress of the invention, not only considers the state quantity of the switch cabinet, but also combines the influence factors of the importance of the power grid nodes, and the evaluation result can be used for guiding the formulation of the subsequent maintenance sequence. It will be appreciated by persons skilled in the art that the above embodiments are illustrative only and not intended to be limiting, and that changes and modifications may be made to the above embodiments without departing from the true spirit of the invention and the scope of the appended claims.
Claims (8)
1. A switch cabinet state evaluation method considering grid node importance degree is characterized by comprising the following steps:
1) monitoring an information source and determining state quantity, wherein the information source is a channel for acquiring state evaluation data, further determining state quantity information required by the state evaluation of the switch cabinet, and dividing the state quantities into five parts according to the characteristics of the state quantities, and each part comprises a corresponding state quantity;
2) normalization processing of multidimensional state quantities, wherein due to different data types of the state quantities, normalization processing is adopted, a half-ridge model is used for evaluating quantitative state quantities, normalization processing is carried out on the operation years of a switch cabinet, and a proportional model is adopted;
3) establishing a layered state evaluation model, namely taking the five parts selected in the step 1) as a parent layer, taking the state quantity corresponding to each evaluation part as a sub-layer, determining each state evaluation result by the sub-layer, and obtaining the score value of the parent layer according to the score value of each sub-layer;
4) calculating the importance of the nodes of the power grid, namely taking each switch cabinet as a node in the power grid, taking two factors of the position of each node and the load born as indexes for measuring the importance of the nodes, and calculating to obtain the value of the importance of the nodes;
5) modifying the hierarchical evaluation result model obtained in the step 3) based on the node importance calculation result obtained in the step 4);
6) establishing and solving a state quantity membership function model, substituting the result obtained after correction in the step 5), namely the corrected score values of all parts into the membership function of each state grade, and determining the state grade of the equipment according to the maximum membership principle.
2. The method according to claim 1, wherein the information source includes four types of modes, namely family information, online monitoring, operation inspection and routine test.
3. The method for evaluating the state of the switch cabinet according to claim 1, wherein the method is divided into five parts, and each state part and the corresponding state quantity are as follows:
the device information includes the following three: the operation age, the qualification rate of the handover test and the family defect rate;
the insulating properties include the following four: partial discharge monitoring transient earth voltage value, partial discharge monitoring ultrasonic wave value, insulation resistance value and grounding resistance value;
SF6the gas comprises one of: the annual leakage rate of sulfur hexafluoride gas;
the mechanical properties include the following two: switching-off operation time and switching-on operation time;
the current carrying capacity includes the following two: the direct current resistance of the main loop and the temperature of the conductive connection point.
4. The method for evaluating the state of the switch cabinet according to claim 1, wherein the state grade is divided into five grades in order to show the accuracy of the result: a represents excellent, B represents good, C represents medium, D represents poor and E represents poor.
5. The method as claimed in claim 1, wherein the normalization process has a mid-half-ridge model calculation formula as follows:
for the state quantity with higher evaluation value, the normalization formula is as follows:
for the state quantity which is better as the evaluation value is smaller, the normalization formula is as follows:
wherein a and b represent upper and lower score thresholds, respectively.
6. The method for evaluating the state of the switch cabinet according to claim 1, wherein the proportional model for normalizing the operation years has the following calculation formula:
KT(100-years of operation x 0.3)/100
Wherein, KTThe coefficient obtained after the operation age normalization.
7. The method for evaluating the state of the switch cabinet according to claim 1, wherein the calculation formula of the importance of the grid node is as follows:
Cn＝0.7Ln+0.3Pn
wherein C isnRepresenting the node importance correction coefficient for correcting the evaluation result of the switch cabinet state, LnAs a node position importance parameter, PnIs a node load parameter.
8. The method for evaluating the state of the switch cabinet according to claim 1, wherein the membership function of each level is:
class A
Grade B
Grade C
Grade D
Grade E
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