CN105205732A - Risk assessment and maintenance method based on equipment risk characteristic model - Google Patents
Risk assessment and maintenance method based on equipment risk characteristic model Download PDFInfo
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- CN105205732A CN105205732A CN201510634395.5A CN201510634395A CN105205732A CN 105205732 A CN105205732 A CN 105205732A CN 201510634395 A CN201510634395 A CN 201510634395A CN 105205732 A CN105205732 A CN 105205732A
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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
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- 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
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- 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
The invention provides a risk assessment and maintenance method based on an equipment risk characteristic model. The risk assessment method includes the following steps that S1, the running states of equipment and a power grid in the running process of a transformer substation are detected and recorded, and whether the equipment and the power grid break down or not in the running states of themselves or not is detected and recoded; S2, according to the running states of the equipment and the power grid in the running process of the transformer substation and the historical record indicating whether the equipment and the power grid break down or not in the running states of themselves, the fault probability that the equipment breaks down is calculated, and the property loss degree of the equipment is calculated; S3, according to the fault probability and the property loss degree of the equipment and a security supply correction factor, the risk level of the equipment is calculated, wherein the risk level is equal to the number obtained by multiplying the fault probability by the property loss degree and the security supply correction factors. The risk assessment and maintenance method based on the equipment risk characteristic mode is accurate and timely and has practicability.
Description
Technical field
The present invention relates to transformer station field, particularly relate to a kind of risk assessment based on equipment Risk characteristic model and repair method.
Background technology
Tradition to the assessment of equipment Risk mainly according to the result of operational inspection, maintenance, maintenance, preventive trial and live testing (on-line monitoring), assay is carried out to each quantity of state index of reflection equipment health status, thus determine equipment state grade, and on this basis, the probability of malfunction that assessment apparatus breaks down and severity degree, determine that equipment faces with the risk that may cause.Though this methods of risk assessment has it scientific and advanced, also having some limitations property.First, the risk factors that this methods of risk assessment is considered are comprehensive not, the driving factors such as weather, people, equipment quality, and the influence factors such as reputation, economy, reliability, period or stable rank all do not include limit of consideration in; Secondly, the comprehensive evaluation of each risk indicator, with strong subjectivity, so just has a negative impact to the accuracy of risk evaluation result.
Summary of the invention
The present invention is directed to the risk factors that traditional methods of risk assessment considers comprehensive not, the driving factors such as weather, people, equipment quality, the influence factors such as reputation, economy, reliability, period or stable rank all do not include limit of consideration in; Meanwhile, the comprehensive evaluation of each risk indicator, with strong subjectivity, so just to the problem that the accuracy of risk evaluation result has a negative impact, proposes a kind of risk assessment based on equipment Risk characteristic model and repair method.
The technical scheme that the present invention proposes with regard to this technical matters is as follows:
The present invention proposes a kind of methods of risk assessment based on equipment Risk characteristic model, comprise the following steps:
Step S1, detection be recorded in the running status of equipment and electrical network in substation operation process, detect and record described equipment and whether described electrical network breaks down when respective running status;
Step S2, the historical record whether broken down when respective running status according to the running status of equipment and electrical network in substation operation process and equipment and electrical network, calculate the loss of assets degree of the probability of malfunction of described device fails, described equipment; And determine to protect power supply modifying factor according to the stable rank of current transformer substation; Wherein, there are the weighted mean of the expectation of the loss suffered by various fault in the loss of assets degree of equipment for this equipment;
Other corresponding relation of stationary level protecting power supply modifying factor and transformer station is as shown in table 1:
Other corresponding relation of stationary level of power supply modifying factor and transformer station protected by table 1
Step S3, probability of malfunction according to equipment, loss of assets degree and protect power supply modifying factor, the risk level of computing equipment; Wherein, risk level=probability of malfunction × loss of assets degree × guarantor powers modifying factor.
In the methods of risk assessment that the present invention is above-mentioned, the running status kind number of equipment is designated as p, and the m kind running status of equipment is designated as Bim, and m is less than or equal to p; Probability equipment being in m kind running status is designated as P (Bim), equipment is broken down under this m kind running status and is designated as Cm, and by equipment under this m kind running status and the probability broken down is designated as P (Cm), then the probability of malfunction P (BiC) of equipment is:
P(BiC)＝P(Bi1)P(C1)+P(Bi2)P(C2)+P(Bi3)P(C3)+…+P(Bim)P(Cm)+…+P(Bip)P(Rp)。
In the methods of risk assessment that the present invention is above-mentioned, cost allowance degree is designated as I1, load loss degree is designated as I2, power grid security degree of loss is designated as I3; The weight of cost allowance degree in loss of assets degree is designated as w1, the weight of load loss degree in loss of assets degree is designated as w2, the weight of power grid security degree of loss in loss of assets degree is designated as w3; The loss of assets degree I=w1 × I1+w2 × I2+w3 × I3 of equipment;
The computing method of w1, w2, w3 comprise the following steps:
Step S21, be configured to comparing matrix A:
Wherein, a
ijrepresent the comparative result of i-th factor relative to a jth factor; Wherein, the 1st factor is cost allowance degree, and the 2nd factor is load loss degree, and the 3rd factor is power grid security degree of loss;
Step S22, determine a by expert estimation
ij; Marking employing table 3 rule is carried out:
Table 3a
ijvalue scheme
Step S23, be calculated to be comparing the eigenvalue of maximum of matrix A and characteristic of correspondence vector thereof; The consistance of inspection pairwise comparison matrix A, if pairwise comparison matrix A has consistance, then described proper vector is weight vector, namely proper vector be w1, w2, w3}, wherein, w1+w2+w3=1; If pairwise comparison matrix A does not have consistance, then repeat step S22;
I1＝I11×POF11+I12×POF12+I13×POF13+I14×POF14+I15×POF15+I16×POF16；
Wherein, POF11 is especially big equipment breakdown loss probability, and I11 is especially big equipment breakdown penalty values; POF12 is substantial equipment causality loss probability; I12 is substantial equipment causality loss value; POF13 is category-A general device causality loss probability; I13 is category-A general device causality loss value; POF14 is category-B general device causality loss probability; I14 is category-B general device causality loss value; POF13 is equipment one class obstacle loss probability; I13 is equipment one class obstacle penalty values; POF16 is equipment two class obstacle loss probability; I16 is equipment two class obstacle penalty values; Further, the historical record that whether POF11, POF12, POF13, POF14, POF15 and POF16 all break down when its running status according to the running status of equipment in substation operation process and equipment calculates; Meanwhile, the value of I11, I12, I13, I14, I15 and I16 is as shown in table 5;
The value list of table 5I11, I12, I13, I14, I15 and I16
| Loss grade | Span |
| Especially big equipment breakdown I11 | 9 |
| Substantial equipment accident I12 | 6 |
| Category-A general device accident I13 | 3 |
| Category-B general device accident I14 | 2 |
| Equipment one class obstacle I15 | 1 |
| Equipment two class obstacle I16 | 0.5 |
Power grid security degree of loss I3=I31 × POF31+I32 × POF32+I33 × POF33+I34 × POF34+I35 × POF35+I36 × POF36
Wherein, POF31 is short-term load causality loss probability; I31 is short-term load causality loss value; POF32 attaches most importance to large power system accident loss probability; I32 attaches most importance to large power system accident penalty values; POF33 is the general power grid accident loss probability of category-A; I33 is the general power grid accident penalty values of category-A; POF34 is the general power grid accident loss probability of category-B; I34 is the general power grid accident penalty values of category-B; POF35 is electrical network one class obstacle loss probability; I35 is electrical network one class obstacle penalty values; POF36 is electrical network two class obstacle loss probability; I36 is electrical network two class obstacle penalty values; Further, the historical record that whether POF31, POF32, POF33, POF34, POF35 and POF36 all have an accident when its running status according to the running status of electrical network in substation operation process and electrical network calculates; Meanwhile, the value of I31, I32, I33, I34, I35 and I36 is as shown in table 6;
The value list of table 6I31, I32, I33, I34, I35 and I36
| Loss grade | Span |
| Short-term load accident I31 | 10 |
| Great power grid accident I32 | 7 |
| The general power grid accident I33 of category-A | 4 |
| The general power grid accident I34 of category-B | 3 |
| Electrical network one class obstacle I35 | 2 |
| Electrical network two class obstacle I36 | 1 |
Load loss degree I2=load loss score value × load loss probability;
Wherein, load loss score value obtains according to the load loss of electrical network different stage, and the corresponding relation of load loss score value and load loss is as shown shown in 7-table 9:
The score value that under table 7 the whole network level, different load loss is corresponding
The score value that under the level of table 8 provinces and regions, different load loss is corresponding
The score value that under the level of table 9 provinces and regions, different load loss is corresponding
Load loss probability P
2:
P
2＝n
x/N
x
Wherein, n
xfor occurring in the load loss event number of certain device type; N
xfor the device fails number of times of described device type, x is device type numbering.
In the methods of risk assessment that the present invention is above-mentioned, the conforming step of inspection pairwise comparison matrix A comprises the following steps:
Step S231, be calculated to be coincident indicator CI to comparing matrix A,
Wherein, n is all eigenwert sums of pairwise comparison matrix A; λ is the eigenvalue of maximum of pairwise comparison matrix A;
Step 232, be calculated to be Consistency Ratio CR to comparing matrix A, CR=CI/RI; RI is 0.58;
Step 233, judge whether Consistency Ratio CR is less than 0.1, if so, then judge that pairwise comparison matrix A has consistance; If not, then judge that pairwise comparison matrix A does not have consistance.
The invention allows for a kind of repair method based on equipment Risk characteristic model, comprise the following steps:
Risk level threshold value is set;
Adopt the risk level of methods of risk assessment computing equipment described above, and judge whether described risk level is greater than described risk level threshold value, if so, then equipment described in major tune-up.
The risk of historical record to device fails whether the present invention breaks down when respective running status according to the running status of equipment and electrical network in substation operation process and equipment and electrical network is assessed.Like this, enriching constantly and upgrading along with historical record, the assessment carried out the risk of device fails is then more accurate and timely.The present invention also increases the consideration equipment that makes more and produces the factor of fault, as the situation of different weather environment and dissimilar equipment; On the basis of driving factors considering equipment failure, also consider the important factor in order such as reputation, load, safety, period or stable rank.The invention allows for a kind of repair method based on described appraisal procedure, on the basis of appraisal procedure accurately and timely, the equipment can with higher failure risk is changed timely, thus avoids larger loss.Risk assessment based on equipment Risk characteristic model of the present invention and repair method accurately and timely, have practicality.
Accompanying drawing explanation
Below in conjunction with drawings and Examples, the invention will be further described, in accompanying drawing:
Fig. 1 is the model schematic of the influence factor of the loss of assets degree of equipment.
Embodiment
The technical problem to be solved in the present invention is: the risk factors that existing methods of risk assessment is considered are comprehensive not, the driving factors such as weather, people, equipment quality, the influence factors such as reputation, economy, reliability, period or stable rank all do not include limit of consideration in; Further, the comprehensive evaluation of each risk indicator, with strong subjectivity, so just has a negative impact to the accuracy of risk evaluation result.The technical thought solved this technical problem that the present invention proposes is: increase and consider that the equipment that makes produces the factor of fault more, as the situation of different weather environment and dissimilar equipment; On the basis of driving factors considering equipment failure, also consider the important factor in order such as reputation, load, safety, period or stable rank.
In order to make technical purpose of the present invention, technical scheme and technique effect clearly, below in conjunction with specific embodiments and the drawings, the present invention is further elaborated.
The present invention proposes a kind of methods of risk assessment based on equipment Risk characteristic model, comprise the following steps:
Step 100, detect and be recorded in the running status of equipment and electrical network in substation operation process, detect and record described equipment and whether described electrical network breaks down when respective running status;
Step 200, the historical record whether broken down when respective running status according to the running status of equipment and electrical network in substation operation process and equipment and electrical network, calculate the loss of assets degree of the probability of malfunction of described device fails, described equipment; And determine to protect power supply modifying factor according to the stable rank of current transformer substation; Wherein, there are the weighted mean of the expectation of the loss suffered by various fault in the loss of assets degree of equipment for this equipment;
Other corresponding relation of stationary level protecting power supply modifying factor and transformer station is as shown in table 1:
Other corresponding relation of stationary level of power supply modifying factor and transformer station protected by table 1
In this step, the running status kind number of equipment is designated as p, wherein, the m kind running status of equipment is designated as Bim, and here, m is less than or equal to p; Probability equipment being in m kind running status is designated as P (Bim), equipment is broken down under this m kind running status and is designated as Cm, and the probability that equipment breaks down under this m kind running status is designated as P (Cm), then equipment probability of malfunction P (BiC)=P (Bi1) P (C1)+P (Bi2) P (C2)+P (Bi3) P (C3)+... + P (Bim) P (Cm)+... + P (Bip) P (Rp);
Preferably, the present embodiment is according to the regulation of " south electric network equipment state evaluates detailed rules and regulations ", and the running status of equipment is divided into normal condition, attention state, abnormality, severe conditions, and like this, p is 4.Meanwhile, by determining a sampling time interval in equipment history run, and in this sampling time interval, determine that equipment is in the probability of often kind of running status; Simultaneously by step 100 recorded data, determine the probability that equipment breaks down under often kind of running status, thus the equipment that calculates be in different running status under and the probability broken down; By recording and calculating, this probability is as shown in table 2:
Under table 2 equipment is in different running status and the probability broken down
Further, in this step, device fails can cause damage, and Fig. 1 shows the model schematic of the influence factor of the loss of assets degree of equipment.Be summed up, the influence factor of the loss of assets degree of equipment comprises cost allowance degree, load loss degree, power grid security degree of loss, Environmental Pollution Loss degree and personal safety degree of loss.Here, Environmental Pollution Loss and personal safety loss are all small probability events for any transformer station, and its probability occurred trends towards 0.
Cost allowance degree is designated as I1, load loss degree is designated as I2, power grid security degree of loss is designated as I3.
The weight of cost allowance degree in loss of assets degree is designated as w1, the weight of load loss degree in loss of assets degree is designated as w2, the weight of power grid security degree of loss in loss of assets degree is designated as w3.
The then loss of assets degree I=w1 × I1+w2 × I2+w3 × I3 of equipment;
Here, the computing method of w1, w2, w3 comprise the following steps:
Step 210, be configured to comparing matrix A:
Wherein, a
ijrepresent the comparative result of i-th factor relative to a jth factor; Wherein, the 1st factor is cost allowance degree, and the 2nd factor is load loss degree, and the 3rd factor is power grid security degree of loss.
Step 220, determine a by expert estimation
ij; Marking employing table 3 rule is carried out:
Table 3a
ijvalue scheme
Step 230, be calculated to be comparing the eigenvalue of maximum of matrix A and characteristic of correspondence vector thereof; The consistance of inspection pairwise comparison matrix A, if pairwise comparison matrix A has consistance, then described proper vector is weight vector, namely proper vector be w1, w2, w3}, wherein, w1+w2+w3=1; If pairwise comparison matrix A does not have consistance, then repeat step 220.
In this step, the conforming step of pairwise comparison matrix A is checked to comprise the following steps:
Step 231, be calculated to be coincident indicator CI to comparing matrix A,
Wherein, n is all eigenwert sums of pairwise comparison matrix A; λ is the eigenvalue of maximum of pairwise comparison matrix A.
Step 232, look into the random index RI that following table obtains pairwise comparison matrix A,
The value scheme of table 4 random index RI
Wherein, Q is the exponent number of pairwise comparison matrix A; In the present embodiment, Q is 3.Then the Consistency Ratio CR to comparing matrix A is calculated to be, CR=CI/RI;
Step 233, judge whether Consistency Ratio CR is less than 0.1, if so, then judge that pairwise comparison matrix A has consistance; If not, then judge that pairwise comparison matrix A does not have consistance.
Further, the present embodiment also to cost allowance degree I1, load loss degree be I2, the influence factor of power grid security degree of loss I3 investigates respectively.
Here, I1=I11 × POF11+I12 × POF12+I13 × POF13+I14 × POF14+I15 × POF15+I16 × POF16
Wherein, POF11 is especially big equipment breakdown loss probability, and I11 is especially big equipment breakdown penalty values; POF12 is substantial equipment causality loss probability; I12 is substantial equipment causality loss value; POF13 is category-A general device causality loss probability; I13 is category-A general device causality loss value; POF14 is category-B general device causality loss probability; I14 is category-B general device causality loss value; POF13 is equipment one class obstacle loss probability; I13 is equipment one class obstacle penalty values; POF16 is equipment two class obstacle loss probability; I16 is equipment two class obstacle penalty values; Further, the historical record that whether POF11, POF12, POF13, POF14, POF15 and POF16 all break down when its running status according to the running status of equipment in substation operation process and equipment calculates; Preferably, POF11, POF12, POF13, POF14, POF15 and POF16 can in described sampling time intervals, and the historical record whether broken down when its running status according to the running status of equipment in substation operation process and equipment calculates.
Meanwhile, the value of I11, I12, I13, I14, I15 and I16 is as shown in table 5.
The value list of table 5I11, I12, I13, I14, I15 and I16
| Loss grade | Span |
| Especially big equipment breakdown I11 | 9 |
| Substantial equipment accident I12 | 6 |
| Category-A general device accident I13 | 3 |
| Category-B general device accident I14 | 2 |
| Equipment one class obstacle I15 | 1 |
| Equipment two class obstacle I16 | 0.5 |
Further, power grid security degree of loss I3=I31 × POF31+I32 × POF32+I33 × POF33+I34 × POF34+I35 × POF35+I36 × POF36
Wherein, POF31 is short-term load causality loss probability; I31 is short-term load causality loss value; POF32 attaches most importance to large power system accident loss probability; I32 attaches most importance to large power system accident penalty values; POF33 is the general power grid accident loss probability of category-A; I33 is the general power grid accident penalty values of category-A; POF34 is the general power grid accident loss probability of category-B; I34 is the general power grid accident penalty values of category-B; POF35 is electrical network one class obstacle loss probability; I35 is electrical network one class obstacle penalty values; POF36 is electrical network two class obstacle loss probability; I36 is electrical network two class obstacle penalty values.Further, the historical record that whether POF31, POF32, POF33, POF34, POF35 and POF36 all have an accident when its running status according to the running status of electrical network in substation operation process and electrical network calculates; Preferably, POF31, POF32, POF33, POF34, POF35 and POF36 can in described sampling time intervals, and the historical record whether had an accident when its running status according to the running status of electrical network in substation operation process and electrical network calculates.
Meanwhile, the value of I31, I32, I33, I34, I35 and I36 is as shown in table 6.
The value list of table 6I31, I32, I33, I34, I35 and I36
| Loss grade | Span |
| Short-term load accident I31 | 10 |
| Great power grid accident I32 | 7 |
| The general power grid accident I33 of category-A | 4 |
| The general power grid accident I34 of category-B | 3 |
| Electrical network one class obstacle I35 | 2 |
| Electrical network two class obstacle I36 | 1 |
Further, according to point Distribution value that expert in CHINA SOUTHERN POWER power-management and communication centre in November, 2009 " the power grid security risk quantification method of assessment " provides, by the whole network level, Hongkong and Macro's level, provincial, and municipal level with prefecture-levelly provide load loss score value respectively, as shown in table 7-table 9.
The score value that under table 7 the whole network level, different load loss is corresponding
The score value that under the level of table 8 provinces and regions, different load loss is corresponding
The score value that under the level of table 9 provinces and regions, different load loss is corresponding
Further, load loss probability P
2:
P
2＝n
x/N
x
Wherein, n
xfor occurring in the load loss event number of certain device type; N
xfor the device fails number of times of described device type, x is device type numbering.
Load loss degree I2=load loss score value × load loss probability;
Step 300, probability of malfunction according to equipment, loss of assets degree and protect power supply modifying factor, the risk level of computing equipment; Wherein, risk level=probability of malfunction × loss of assets degree × guarantor powers modifying factor.
The invention allows for a kind of repair method based on equipment Risk characteristic model, comprise the following steps:
Risk level threshold value is set;
Adopt as the risk level of above-mentioned methods of risk assessment computing equipment, and judge whether described risk level is greater than described risk level threshold value, if so, then equipment described in major tune-up.
In the present embodiment, the historical record whether risk level threshold value breaks down when respective running status according to the running status of equipment and electrical network in substation operation process and equipment and electrical network is determined to obtain, greatly can reduce the frequency that device fails causes power grid accident.
The risk of historical record to device fails whether the present invention breaks down when respective running status according to the running status of equipment and electrical network in substation operation process and equipment and electrical network is assessed.Like this, enriching constantly and upgrading along with historical record, the assessment carried out the risk of device fails is then more accurate and timely.The present invention also increases the consideration equipment that makes more and produces the factor of fault, as the situation of different weather environment and dissimilar equipment; On the basis of driving factors considering equipment failure, also consider the important factor in order such as reputation, load, safety, period or stable rank.The invention allows for a kind of repair method based on described appraisal procedure, on the basis of appraisal procedure accurately and timely, the equipment can with higher failure risk is changed timely, thus avoids larger loss.Risk assessment based on equipment Risk characteristic model of the present invention and repair method accurately and timely, have practicality.
Should be understood that, for those of ordinary skills, can be improved according to the above description or convert, and all these improve and convert the protection domain that all should belong to claims of the present invention.
Claims (5)
1. based on a methods of risk assessment for equipment Risk characteristic model, it is characterized in that, comprise the following steps:
Step S1, detection be recorded in the running status of equipment and electrical network in substation operation process, detect and record described equipment and whether described electrical network breaks down when respective running status;
Step S2, the historical record whether broken down when respective running status according to the running status of equipment and electrical network in substation operation process and equipment and electrical network, calculate the loss of assets degree of the probability of malfunction of described device fails, described equipment; And determine to protect power supply modifying factor according to the stable rank of current transformer substation; Wherein, there are the weighted mean of the expectation of the loss suffered by various fault in the loss of assets degree of equipment for this equipment;
Other corresponding relation of stationary level protecting power supply modifying factor and transformer station is as shown in table 1:
Other corresponding relation of stationary level of power supply modifying factor and transformer station protected by table 1
Step S3, probability of malfunction according to equipment, loss of assets degree and protect power supply modifying factor, the risk level of computing equipment; Wherein, risk level=probability of malfunction × loss of assets degree × guarantor powers modifying factor.
2. methods of risk assessment according to claim 1, is characterized in that, the running status kind number of equipment is designated as p, and the m kind running status of equipment is designated as Bim, and m is less than or equal to p; Probability equipment being in m kind running status is designated as P (Bim), equipment is broken down under this m kind running status and is designated as Cm, and by equipment under this m kind running status and the probability broken down is designated as P (Cm), then the probability of malfunction P (BiC) of equipment is:
P(BiC)＝P(Bi1)P(C1)+P(Bi2)P(C2)+P(Bi3)P(C3)+…+P(Bim)P(Cm)+…+P(Bip)P(Rp)。
3. methods of risk assessment according to claim 1, is characterized in that, cost allowance degree is designated as I1, and load loss degree is designated as I2, and power grid security degree of loss is designated as I3; The weight of cost allowance degree in loss of assets degree is designated as w1, the weight of load loss degree in loss of assets degree is designated as w2, the weight of power grid security degree of loss in loss of assets degree is designated as w3; The loss of assets degree I=w1 × I1+w2 × I2+w3 × I3 of equipment;
The computing method of w1, w2, w3 comprise the following steps:
Step S21, be configured to comparing matrix A:
Wherein, a
ijrepresent the comparative result of i-th factor relative to a jth factor; Wherein, the 1st factor is cost allowance degree, and the 2nd factor is load loss degree, and the 3rd factor is power grid security degree of loss;
Step S22, determine a by expert estimation
ij; Marking employing table 3 rule is carried out:
Table 3a
ijvalue scheme
Step S23, be calculated to be comparing the eigenvalue of maximum of matrix A and characteristic of correspondence vector thereof; The consistance of inspection pairwise comparison matrix A, if pairwise comparison matrix A has consistance, then described proper vector is weight vector, namely proper vector be w1, w2, w3}, wherein, w1+w2+w3=1; If pairwise comparison matrix A does not have consistance, then repeat step S22;
I1＝I11×POF11+I12×POF12+I13×POF13+I14×POF14+I15×POF15+I16×POF16；
Wherein, POF11 is especially big equipment breakdown loss probability, and I11 is especially big equipment breakdown penalty values; POF12 is substantial equipment causality loss probability; I12 is substantial equipment causality loss value; POF13 is category-A general device causality loss probability; I13 is category-A general device causality loss value; POF14 is category-B general device causality loss probability; I14 is category-B general device causality loss value; POF13 is equipment one class obstacle loss probability; I13 is equipment one class obstacle penalty values; POF16 is equipment two class obstacle loss probability; I16 is equipment two class obstacle penalty values; Further, the historical record that whether POF11, POF12, POF13, POF14, POF15 and POF16 all break down when its running status according to the running status of equipment in substation operation process and equipment calculates; Meanwhile, the value of I11, I12, I13, I14, I15 and I16 is as shown in table 5;
The value list of table 5I11, I12, I13, I14, I15 and I16
Power grid security degree of loss I3=I31 × POF31+I32 × POF32+I33 × POF33+I34 × POF34+I35 × POF35+I36 × POF36
Wherein, POF31 is short-term load causality loss probability; I31 is short-term load causality loss value; POF32 attaches most importance to large power system accident loss probability; I32 attaches most importance to large power system accident penalty values; POF33 is the general power grid accident loss probability of category-A; I33 is the general power grid accident penalty values of category-A; POF34 is the general power grid accident loss probability of category-B; I34 is the general power grid accident penalty values of category-B; POF35 is electrical network one class obstacle loss probability; I35 is electrical network one class obstacle penalty values; POF36 is electrical network two class obstacle loss probability; I36 is electrical network two class obstacle penalty values; Further, the historical record that whether POF31, POF32, POF33, POF34, POF35 and POF36 all have an accident when its running status according to the running status of electrical network in substation operation process and electrical network calculates; Meanwhile, the value of I31, I32, I33, I34, I35 and I36 is as shown in table 6;
The value list of table 6I31, I32, I33, I34, I35 and I36
Load loss degree I2=load loss score value × load loss probability;
Wherein, load loss score value obtains according to the load loss of electrical network different stage, and the corresponding relation of load loss score value and load loss is as shown shown in 7-table 9:
The score value that under table 7 the whole network level, different load loss is corresponding
The score value that under the level of table 8 provinces and regions, different load loss is corresponding
The score value that under the level of table 9 provinces and regions, different load loss is corresponding
Load loss probability P
2:
P
2＝n
x/N
x
Wherein, n
xfor occurring in the load loss event number of certain device type; N
xfor the device fails number of times of described device type, x is device type numbering.
4. methods of risk assessment according to claim 3, is characterized in that, the conforming step of inspection pairwise comparison matrix A comprises the following steps:
Step S231, be calculated to be coincident indicator CI to comparing matrix A,
Wherein, n is all eigenwert sums of pairwise comparison matrix A; λ is the eigenvalue of maximum of pairwise comparison matrix A;
Step 232, be calculated to be Consistency Ratio CR to comparing matrix A, CR=CI/RI; RI is 0.58;
Step 233, judge whether Consistency Ratio CR is less than 0.1, if so, then judge that pairwise comparison matrix A has consistance; If not, then judge that pairwise comparison matrix A does not have consistance.
5., based on a repair method for equipment Risk characteristic model, comprise the following steps:
Risk level threshold value is set;
Adopt the risk level of methods of risk assessment computing equipment as described in claim 1-4 any one, and judge whether described risk level is greater than described risk level threshold value, if so, then equipment described in major tune-up.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107766947A (en) * | 2017-08-21 | 2018-03-06 | 国家电网公司 | A kind of determination method and apparatus of substation equipment maintenance mode |
| WO2019218875A1 (en) * | 2018-05-14 | 2019-11-21 | 深圳市联软科技股份有限公司 | Single network equipment risk assessment method and system |
| CN110782162A (en) * | 2019-10-25 | 2020-02-11 | 贵州电网有限责任公司 | Equipment risk assessment method in distribution network equipment state evaluation |
| CN110866216A (en) * | 2019-10-22 | 2020-03-06 | 陕西省地方电力（集团）有限公司 | Intelligent management method and system for overhaul of transformer substation equipment |
| CN112418638A (en) * | 2020-11-18 | 2021-02-26 | 贵州电网有限责任公司 | Early warning system and early warning method for operation and maintenance risks of DC power supply system for station |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103164819A (en) * | 2013-03-29 | 2013-06-19 | 广东电网公司 | Power equipment safety detection method and device |
| CN103630776A (en) * | 2013-11-19 | 2014-03-12 | 中国南方电网有限责任公司 | Evaluation method for fault probability of power equipment |
| CN104299116A (en) * | 2014-11-14 | 2015-01-21 | 国家电网公司 | Quantitative evaluation method for security risk of operation of power network |
| CN104392391A (en) * | 2014-11-14 | 2015-03-04 | 国家电网公司 | Power grid running safety risk quantification method |
| CN104484837A (en) * | 2015-01-04 | 2015-04-01 | 国家电网公司 | Power distribution network risk evaluation comprehensive quantizing method |
-
2015
- 2015-09-28 CN CN201510634395.5A patent/CN105205732A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103164819A (en) * | 2013-03-29 | 2013-06-19 | 广东电网公司 | Power equipment safety detection method and device |
| CN103630776A (en) * | 2013-11-19 | 2014-03-12 | 中国南方电网有限责任公司 | Evaluation method for fault probability of power equipment |
| CN104299116A (en) * | 2014-11-14 | 2015-01-21 | 国家电网公司 | Quantitative evaluation method for security risk of operation of power network |
| CN104392391A (en) * | 2014-11-14 | 2015-03-04 | 国家电网公司 | Power grid running safety risk quantification method |
| CN104484837A (en) * | 2015-01-04 | 2015-04-01 | 国家电网公司 | Power distribution network risk evaluation comprehensive quantizing method |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107766947A (en) * | 2017-08-21 | 2018-03-06 | 国家电网公司 | A kind of determination method and apparatus of substation equipment maintenance mode |
| WO2019218875A1 (en) * | 2018-05-14 | 2019-11-21 | 深圳市联软科技股份有限公司 | Single network equipment risk assessment method and system |
| CN110866216A (en) * | 2019-10-22 | 2020-03-06 | 陕西省地方电力（集团）有限公司 | Intelligent management method and system for overhaul of transformer substation equipment |
| CN110782162A (en) * | 2019-10-25 | 2020-02-11 | 贵州电网有限责任公司 | Equipment risk assessment method in distribution network equipment state evaluation |
| CN112418638A (en) * | 2020-11-18 | 2021-02-26 | 贵州电网有限责任公司 | Early warning system and early warning method for operation and maintenance risks of DC power supply system for station |
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