Substation fire risk evaluation and analysis method based on cloud model

Abstract

The invention belongs to the technical field of transformer substation fire protection and disaster reduction, and particularly relates to a transformer substation fire risk evaluation and analysis method based on a cloud model, which comprises the following steps: establishing a transformer substation fire risk evaluation system with multi-stage evaluation indexes; establishing a graded fire-fighting strategy analysis suggestion library of each grade of evaluation indexes; calculating the comprehensive weight of each level of evaluation index; calculating cloud model characteristic parameters of indexes of each level by using a forward cloud generator according to score values of multiple experts on the lowest-layer evaluation indexes of the risk evaluation of the transformer substation, and then drawing evaluation cloud charts of different levels to obtain risk levels; and according to the characteristic parameters of the indexes at all levels, giving out fire-fighting suggestions in combination with a fire-fighting strategy analysis library. According to the invention, the current fire risk of the transformer substation can be evaluated, and suggestions of fire extinguishing strategies can be given.

Description

Technical Field

[0001] The present invention belongs to the technical field of fire protection and disaster reduction of substations, and specifically relates to a substation fire risk assessment and analysis method based on a cloud model. Background Art

[0002] The normal and stable operation of substations is an important link to ensure the orderly production and life. However, substation fires occur from time to time, which has a serious impact on the production of enterprises and people's lives in the power supply area. The operation of substations is affected by many factors, and unilateral evaluation indicators cannot reflect the overall fire risk process of substations, because the occurrence of various emergencies is not independent, but interconnected and mutually influential. The problem of how to couple multiple influencing factors to comprehensively consider the substation fire risk assessment has become prominent. A fire risk assessment method that can integrate various influencing factors of substation fires is proposed, and a fire fighting strategy is given on this basis, which is of great practical significance for improving the fire prevention capacity of substations. Summary of the invention

[0003] The purpose of the present invention is to provide a substation fire risk assessment and analysis method based on a cloud model, which uses a combined weight method to calculate the bottom-level comprehensive weight and a weighted average method to calculate the upper-level comprehensive indicator weight. A forward cloud generator is used to calculate a standard cloud model and evaluate the characteristic parameters of the cloud model. Firefighting suggestions are given based on the characteristic parameters, providing a reference for eliminating fire hazards in substations and providing firefighting strategies.

[0004] The present invention is achieved through the following technical solutions:

[0005] A substation fire risk assessment and analysis method based on a cloud model includes the following steps:

[0006] Step 1: Analyze the factors of substation fire and establish a substation fire risk assessment system with multi-level evaluation indicators;

[0007] Step 2: Based on the substation fire risk assessment index system and fire risk levels, establish a graded fire fighting strategy analysis and suggestion library for each level of assessment indicators;

[0008] Step 3: Sort the importance of the lowest-level indicators of the evaluation indicators, give the weight ratio of each indicator, calculate the subjective weight using the order relationship analysis method, calculate the objective weight of each indicator using the entropy weight method based on the lowest-level indicators scored by experts, and calculate the comprehensive weight using the combined weighting method;

[0009] Step 4: Use the weighted average method to calculate the comprehensive weight of the previous layer indicators in turn;

[0010] Step 5: According to the classification of substation fire risk levels, calculate the standard cloud characteristic parameters, and use the forward cloud generator to draw the substation fire risk assessment standard cloud model;

[0011] Step 6: Input the scores of multiple experts for the lowest level evaluation indicators of substation risk assessment;

[0012] Step 7: Based on the expert scores, the forward cloud generator is used to calculate the cloud model characteristic parameters of each level indicator;

[0013] Step 8: Draw evaluation cloud maps of different levels according to the characteristic parameters at each level to obtain the risk level;

[0014] Step 9: Based on the characteristic parameters of indicators at all levels and combined with the fire fighting strategy analysis library, fire fighting suggestions are given.

[0015] Preferably, the substation fire factors described in step 1 include safety management capability, equipment fire prevention capability, building fire resistance capability, fire safety facilities and rescue capability.

[0016] The safety management capabilities include fire safety training, personnel quality development, resource management, emergency drills, and safety inspections;

[0017] The fire protection capability of the equipment includes oil pool factor, transformer factor, control room factor, capacitor room factor, and cable factor;

[0018] The fire resistance of the building includes building location factors, fixed load factors and regional segmentation factors;

[0019] The fire safety facilities include common fire extinguishing equipment, special fire extinguishing equipment, smoke prevention and exhaust facilities and automatic alarm equipment;

[0020] The rescue capabilities include external rescue forces, internal rescue forces and emergency command systems.

[0021] Preferably, the graded fire fighting strategy analysis suggestion library for evaluation indicators at each level in step 2 includes specific fire fighting strategy analysis suggestions for all underlying indicator factors in step 1 according to five risk levels of high risk, relatively high risk, medium risk, relatively low risk and low risk, as well as fire fighting strategy analysis suggestions for five risk levels of the five indicator factors of safety management capability, equipment fire prevention capability, building fire resistance capability, fire safety facilities and rescue capability described in step 1, as well as specific fire fighting strategy analysis suggestions for the five overall risk levels.

[0022] Preferably, the calculation of the comprehensive weight of the bottom-level indicators in step 3 includes:

[0023] The underlying subjective weight is calculated using the sequence relationship method, and the importance of each underlying indicator given by experts is ranked according to the evaluation indicators to obtain the importance matrix M n ; Assign weight ratios to the importance of the underlying evaluation indicators and obtain the weight ratio array R n ;

[0024] Calculate the subjective weight ω of the last indicator n :

[0025]

[0026] Calculate the subjective weights ω of other indicators n :

[0027] ω i-1 =R i-1 ω i

[0028] In the formula, i = 2-n; the subjective weight array of the underlying indicators ω is obtained (1) =(ω1,ω2,ω 33 ,···,ω n );

[0029] The entropy weight method is used to calculate the underlying objective weights, and the entropy value of each indicator is calculated based on the expert's underlying indicator scoring results:

[0030]

[0031] In the formula, X ij Score the importance of m third-level indicators for the ith expert. Then calculate the weight of each indicator:

[0032] V j =1+H j

[0033]

[0034] Where V j ∈[0,1], p ij The score of the i-th expert on the j-th indicator is obtained, and the objective weight array of the four underlying indicators ω is obtained. (2) =(ω1,ω2,ω3,···,ω n );

[0035] Then normalize the subjective weight array and the objective weight array, and use the combined weighting method to calculate the comprehensive weight of the underlying indicators:

[0036] ω (0) =θ1ω (1) +θ2ω (2)

[0037] In the formula, θ1+θ2=1.

[0038] Preferably, step 4 of calculating the comprehensive weight of the previous evaluation index includes:

[0039] The weighted average method is used to calculate the comprehensive weight of the previous layer of indicators:

[0040]

[0041] Where, X i is the expert's score on the lowest level indicator, n is the number of indicators at this level that this indicator contains, and the comprehensive weight indicator of this level is obtained;

[0042] Calculate in sequence until the highest level.

[0043] Preferably, step 5 is to classify the fire risk level of the substation and provide the standard cloud characteristic parameters (E x ,E n ,H e ), use the forward cloud generator to draw a cloud model diagram:

[0044] Enn=randn(1)*He+En

[0045] x(i)=randn(1)*Enn+Ex

[0046]

[0047] Where Ex is the expected value of cloud characteristic parameters, En is entropy, and He is super entropy.

[0048] Preferably, step 6 inputs the scoring values ​​of multiple experts for the lowest level evaluation indicators of substation risk assessment to construct a scoring array:

[0049]

[0050] Where, X ij is the score of the i-th expert on the j-th indicator.

[0051] Preferably, step 7 uses a forward cloud generator to calculate cloud model characteristic parameters of indicators at each level based on expert scoring, including:

[0052] Calculate the jth index evaluation cloud as C j =(E xj ,E nj ,H ej ):

[0053]

[0054]

[0055]

[0056] In the formula, S 2 is the sample variance: Get the evaluation characteristic parameters of the lowest level evaluation index (E x ,E n ,H e );

[0057] The lowest level evaluation index characteristic parameter (E x ,E n ,H e ) and the lowest level comprehensive weight ω obtained in step 3 (0) Bring in

[0058]

[0061] The calculated characteristic parameters of the previous evaluation index (E x ,E n ,H e );

[0062] Calculate sequentially until the highest layer, and obtain the evaluation characteristic parameters of each indicator at each layer and the comprehensive evaluation characteristic parameters.

[0063] Preferably, step 8 uses the characteristic parameters (E x ,E n ,H e ) is brought into the forward cloud generator, and the evaluation cloud model diagram is drawn in the same coordinate system as the standard cloud drawn in step 5, and the fire risk level is obtained by comparison.

[0064] Preferably, step 9 uses the characteristic parameters (E x ,E n ,H e ) x The values ​​are matched with the contents of the graded fire fighting strategy analysis suggestion library of each level of evaluation indicators in step 2 to obtain fire fighting strategy suggestions.

[0065] The benefit of the present invention lies in that the comprehensive weight of the evaluation index comprehensively considers the subjective weight and the objective weight, effectively improves the accuracy of the weight, and makes the evaluation result more objective; through the weighted average method, it is possible to calculate the weight of the evaluation index of the upper layer on the basis of more accurate clarity of the bottom-level score; the constructed graded fire-fighting strategy analysis and suggestion library of evaluation indicators at all levels gives fire-fighting suggestions at different levels, which provides a reference for substations to eliminate fire hazards and implement fire-fighting strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 The present invention is a flow chart of a substation fire risk assessment and analysis method based on a cloud model. DETAILED DESCRIPTION

[0067] The present invention will be further described below in conjunction with embodiments:

[0068] A substation fire risk assessment and analysis method based on a cloud model includes the following 9 steps:

[0069] Step 1: Analyze the fire factors of substations and establish a substation fire risk assessment system with two-level evaluation indicators; divide the fire factors of substations into 5 first-level evaluation indicators and 20 second-level evaluation indicators. The first-level evaluation indicators include safety management capabilities, equipment fire prevention capabilities, building fire resistance capabilities, fire safety facilities and rescue capabilities.

[0070] The secondary evaluation indicators include safety management capability factors including fire safety training, personnel quality training, resource management, emergency drills, and safety inspections; equipment fire prevention capability factors including oil pool factors, transformer factors, control room factors, capacitor room factors, and cable factors; building fire resistance factors including building location factors, fixed load factors, and area division factors; fire safety facility factors including commonly used fire extinguishing equipment, special fire extinguishing equipment, smoke prevention and exhaust facilities, and automatic alarm equipment; rescue capability factors include external rescue forces, internal rescue forces, and emergency command systems.

[0071] Step 2: Construct a graded fire fighting strategy analysis suggestion library for evaluation indicators at all levels, including specific fire fighting strategy analysis suggestions for all first-level evaluation indicator factors and second-level evaluation indicator factors in step 1 according to five risk levels of high risk, relatively high risk, medium risk, relatively low risk and low risk, as well as specific fire fighting strategy analysis suggestions for the overall five risk levels; for example, the fire fighting analysis suggestion for the automatic alarm equipment factor in the first-level indicator fire safety facility factor when it is at a higher risk level is to check all automatic alarm equipment and perform maintenance, repair and testing in a timely manner, and to appropriately increase the number and coverage density of automatic alarm equipment installations by comprehensively considering the building location factors.

[0072] Step 3: Calculate the comprehensive weight of the secondary evaluation indicators, use the sequence relationship method to calculate the underlying subjective weight, invite 10 experts to rank the importance of each underlying indicator given by the evaluation indicators, and obtain the importance matrix M n ; And assign weight ratios to the importance of the secondary evaluation indicators to obtain the weight ratio array R n ;

[0073] Calculate the subjective weight ω of the last indicator n :

[0074]

[0075] Calculate the subjective weights ω of other indicators n :

[0076] ω i-1 =R i-1 ω i

[0077] In the formula, i = 2-n; the subjective weight array of the secondary evaluation index ω is obtained (1) =(ω1,ω2,ω3,···,ω n );

[0078] The entropy weight method is used to calculate the secondary objective weights, and the entropy value of each indicator is calculated based on the expert's scoring results for the secondary indicators:

[0079]

[0080] Then calculate the weight of each indicator:

[0081]

[0082]

[0083] Where V j ∈[0,1], p ij The score of the i-th expert on the j-th indicator is obtained, and the objective weight array of the four underlying indicators ω is obtained. (2) =(ω1,ω2,ω3,···,ω n );

[0084] The combined weighting method is used to calculate the comprehensive weight of the underlying indicators:

[0085] ω (0) =θ1ω (1) +θ2ω (2)

[0086] In the formula, θ1+θ2=1.

[0087] Step 4: Calculate the comprehensive weight of the first-level evaluation index using the weighted average method:

[0088]

[0089] Where, X i is the expert's score on the secondary evaluation index, n is the number of the lowest level indicators included in the primary evaluation index, and the comprehensive weight of the primary evaluation index is obtained.

[0090] Step 5: According to the fire risk level classification of substations, five risk level standard cloud feature parameters (E x ,E n,H e ):

[0091]

[0092] Draw a cloud model diagram using the forward cloud generator:

[0093] Enn=randn(1)*He+En

[0094] x(i)=randn(1)*Enn+Ex

[0095]

[0096] Where Ex is the expected value of cloud characteristic parameters, En is entropy, and He is super entropy.

[0097] Step 6: Enter the scores of the five experts for the secondary evaluation indicators of substation risk assessment and construct a score array:

[0098]

[0099] Step 7: Based on the expert rating array R, use the forward cloud generator to calculate the cloud model characteristic parameters (E x ,E n ,H e ), calculate the jth index evaluation cloud as C j =(E xj ,E nj ,H ej ):

[0100]

[0101]

[0102]

[0103] In the formula, S 2 is the sample variance: The evaluation characteristic parameters of the secondary evaluation index (E x ,E n ,H e );

[0104] The secondary evaluation index characteristic parameter (E x ,E n ,H e ) and the secondary comprehensive weight ω obtained in step 3 (0) Bring in

[0105]

[0108] Calculate the characteristic parameters of the first-level evaluation index (Ex ,E n ,H e );

[0109] The characteristic parameter of the first-level evaluation index (E x ,E n ,H e ) and the first-level evaluation index comprehensive weight calculated in step 4 are brought into the formula for calculating the first-level evaluation index characteristic parameter to obtain the comprehensive evaluation index characteristic parameter (E x ,E n ,H e ).

[0110] Step 8: Use the characteristic parameters (E x ,E n ,H e ) is brought into the forward cloud generator, and the evaluation cloud model diagram is drawn in the same coordinate system as the standard cloud drawn in step 5, and the fire risk level is obtained by comparison.

[0111] Step 9: Use the characteristic parameters (E x ,E n ,H e ) x The values ​​are matched with the contents of the graded fire fighting strategy analysis suggestion library of each level of evaluation indicators in step 2 to obtain fire fighting strategy suggestions.

[0112] For those skilled in the art, according to the teachings of the present invention, without departing from the principles and spirit of the present invention, changes, modifications, substitutions and variations made to the implementation methods are still within the scope of protection of the present invention.

Claims

1. A method for substation fire risk assessment and analysis based on cloud model, characterized in that: The following steps are involved: Step 1: Analyze the factors of substation fire and establish a substation fire risk assessment system with multi-level evaluation indicators; Step 2: Based on the substation fire risk assessment index system and fire risk levels, establish a graded fire fighting strategy analysis and suggestion library for each level of assessment indicators; Step 3: Sort the importance of the lowest-level indicators of the evaluation indicators, give the weight ratio of each indicator, calculate the subjective weight using the order relationship analysis method, calculate the objective weight of each indicator using the entropy weight method based on the lowest-level indicators scored by experts, and calculate the comprehensive weight using the combined weighting method; Step 4: Use the weighted average method to calculate the comprehensive weight of the previous layer indicators in turn; Step 5: According to the classification of substation fire risk levels, calculate the standard cloud characteristic parameters, and use the forward cloud generator to draw the substation fire risk assessment standard cloud model; Step 6: Input the scores of multiple experts for the lowest level evaluation indicators of substation risk assessment; Step 7: Based on the expert scores, the forward cloud generator is used to calculate the cloud model characteristic parameters of each level indicator; Step 8: Draw evaluation cloud diagrams of different levels according to the characteristic parameters at each level to obtain the risk level; Step 9: Based on the characteristic parameters of indicators at all levels and combined with the fire fighting strategy analysis library, fire fighting suggestions are given.

2. The method for substation fire risk assessment and analysis based on cloud model according to claim 1 is characterized in that: The substation fire factors described in step 1 include safety management capabilities, equipment fire prevention capabilities, building fire resistance capabilities, fire safety facilities and rescue capabilities.

3. The method for substation fire risk assessment and analysis based on cloud model according to claim 1 is characterized in that: The graded fire fighting strategy analysis suggestion library for each level of evaluation indicators in step 2 includes specific fire fighting strategy analysis suggestions for all underlying indicator factors in step 1 according to five risk levels of high risk, relatively high risk, medium risk, relatively low risk and low risk, as well as fire fighting strategy analysis suggestions for five risk levels of the five indicator factors of safety management capability, equipment fire prevention capability, building fire resistance capability, fire safety facilities and rescue capability mentioned in step 1, as well as specific fire fighting strategy analysis suggestions for the five overall risk levels.

4. The method for substation fire risk assessment and analysis based on cloud model according to claim 1 is characterized in that: The calculation of the comprehensive weight of the bottom-level indicators in step 3 includes: The underlying subjective weight is calculated using the sequence relationship method, and the importance of each underlying indicator given by experts is ranked according to the evaluation indicators to obtain the importance array M n ; Assign weight ratios to the importance of the underlying evaluation indicators and obtain the weight ratio array R n ; Calculate the subjective weight ω of the last indicator n : Calculate the subjective weights ω of other indicators n : oh i-1 =R i-1 oh i In the formula, i = 2-n; the subjective weight array of the underlying indicators ω is obtained (1) =(ω1,ω2,ω3,···,ω n ); The entropy weight method is used to calculate the underlying objective weights, and the entropy value of each indicator is calculated based on the expert's underlying indicator scoring results: In the formula, X ij Score the importance of m third-level indicators for the ith expert. Then calculate the weight of each indicator: V j =1+H j Where V j ∈[0,1], p ij The score of the i-th expert on the j-th indicator is obtained, and the objective weight array of the four underlying indicators ω is obtained. (2) =(ω1,ω2,ω3,···,ω n ); Then normalize the subjective weight array and the objective weight array, and use the combined weighting method to calculate the comprehensive weight of the underlying indicators: Use the combined weighting method to calculate the comprehensive weight of the underlying indicators: oh (0) =θ1ω (1) +θ2ω (2) In the formula, θ1+θ2=1.

5. The method for substation fire risk assessment and analysis based on cloud model according to claim 1 is characterized in that: Step 4: Calculation of the comprehensive weight of the previous evaluation index, including: The weighted average method is used to calculate the comprehensive weight of the previous layer of indicators: Where, X i is the expert's score on the lowest level indicator, n is the number of indicators at this level that this indicator contains, and the comprehensive weight indicator of this level is obtained; Calculate in sequence until the highest level.

6. The method for substation fire risk assessment and analysis based on cloud model according to claim 1 is characterized in that: Step 9 uses the characteristic parameters (E x ,E n ,H e ) x The values ​​are matched with the contents of the graded fire fighting strategy analysis suggestion library of each level of evaluation indicators in step 2 to obtain fire fighting strategy suggestions.

7. The substation fire safety management capability factor according to claim 2, characterized in that: Including fire safety training, personnel quality development, resource management, emergency drills, and safety inspections.

8. The fire protection capability factor of substation fire equipment according to claim 2, characterized in that: Including oil pool factors, transformer factors, control room factors, capacitor room factors, and cable factors.

9. The fire resistance factor of substation fire building according to claim 2, characterized in that: Includes building location factors, fixed load factors and area segmentation factors.

10. The fire safety facility factor of a substation according to claim 2, characterized in that: Including common fire-fighting equipment, special fire-fighting equipment, smoke prevention and exhaust facilities and automatic alarm equipment.

11. The substation fire rescue capability factor according to claim 2, characterized in that: Including external rescue forces, internal rescue forces and emergency command systems.

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