Condition evaluation method for high-voltage circuit breaker

[0068] The present invention will be further described below with reference to the drawings and embodiments.

[0069] Such as figure 1 As shown, a method for evaluating the state of a high-voltage circuit breaker includes the following steps:

[0070] Step 1: Establish a set of judgment factors

[0071] High-voltage circuit breaker is a complex multi-factor system. Mechanical, electrical, and insulation factors are the main factors that affect the state of the circuit breaker. In addition, there are some factors that should be considered, such as working environment, appearance, and maintenance. Wait. Based on the system engineering point of view, considering the operating mechanism of the circuit breaker and related test items, selecting representative indicators, taking into account the rationality and comprehensiveness of the modeling, comprehensively establishing a high-voltage circuit breaker evaluation model, as shown in Table 1.

[0072] Table 1 Evaluation model of high voltage circuit breaker

[0073]

[0074] Step 2: Establish a rating set

[0075] The level of the judgment set needs to be divided according to the actual situation, and the judgment accuracy and the complexity of the operation need to be considered comprehensively. The model divides the operating state of high-voltage circuit breakers into 4 levels, which are excellent, good, medium, and poor, corresponding to the set V={V 1 , V 2 , V 3 , V 4 }.

[0076] Step 3: Determine the weight set of evaluation factors

[0077] The weight set can be regarded as the gray fuzzy relationship between the evaluation object and the evaluation factor. The model of the weight set reflects the importance of each evaluation factor in the overall evaluation in a quantitative form, and its gray part reflects the credibility of the evaluation . This model separately determines the modular and gray parts of the weight set. The modular part of the weight set is determined by the universal analytic hierarchy process; under the premise of ensuring the accuracy of the evaluation, the amount of calculation is minimized. This model assumes the same parent level The weights of the following evaluation factors are the same in the gray part, which is determined by the method of scoring by experts and taking the average value. Suppose there are n child evaluation factors under a certain parent level, namely a 1 ,a 2 ,...,A n.

[0078] (1) Determination of the weight set module

[0079] Analytical Hierarchy Process (AHP) is a system analysis method that combines qualitative analysis and quantitative analysis to analyze multi-objective, multi-criteria, and complex systems. It has been widely used in many fields. Use this method to determine Weight set module W i =(w 1 ,w 2 ,...,W n ).

[0080] (2) Determination of the gray part of the weight set

[0081] Based on the relative sufficiency of the risk data of evaluation factors at the same level, in order to simplify the amount of calculation, this model assumes that the weight sets of evaluation factors at the same parent level have the same gray part. Combining expert experience, the gray part of the weight set is determined by expert scoring and averaging method. The scoring standard is shown in Table 2.

[0082] Table 2 Gray scale scoring standards

[0083]

[0084] The specific method is as follows:

[0085] Evaluate factor a for n sub 1 ,a 2 ,...,A n , M experts respectively carry out comprehensive scoring, and the corresponding score is h 1 ,h 2 ,...,H m. In order to reduce the subjective bias of experts, remove a maximum value and a minimum value, and then calculate the average value, which corresponds to the sub-evaluation factor a under the parent level 1 ,a 2 ,...,A n The gray part of the weight set is:

[0086] v i = X j = 1 m h j - max ( h j ) - min ( h j ) m - 2 - - - ( 1 )

[0087] In the formula, i∈{1,2,3,...,n}, l represents the number of evaluation factors at the same level as the parent level.

[0088] In summary, construct the weight set matrix, the evaluation factor a under the parent level 1 ,a 2 ,...,A n The corresponding weight set module is {w 1 ,w 2 ,...,W n }, the gray part of the weight set is v i , The matrix representation of the weight set is as follows:

[0089] W ⊗ i ~ = [ ( w 1 , v i ) , ( w 2 , v i ) , · · · , ( w n , v i ) ] - - - ( 2 )

[0090] Step 4: Determine the gray fuzzy discriminant matrix

[0091] The gray fuzzy discrimination matrix is ​​composed of fuzzy parts and gray parts. The fuzzy part uses the fuzzy membership degree to represent the fuzzy relationship between the evaluation factors and the evaluation state, and the gray part uses the dot gray to represent the credibility of the corresponding fuzzy membership relationship. The specific method to determine the gray fuzzy discriminant matrix is ​​as follows:

[0092] (1) Determination of the fuzzy part

[0093] The status indicators of high-voltage circuit breakers include quantitative indicators and qualitative indicators. For quantitative indicators, this model uses a simple and highly applicable triangular membership function to determine the degree of membership; for qualitative indicators, this paper adopts the fuzzy statistical test method to obtain the degree of membership.

[0094] For quantitative indicators, considering the calculation accuracy and the amount of calculation comprehensively, this article first preprocesses the relevant raw data. Then, the widely used semi-trapezoidal distribution function and trigonometric function are used as the membership function, and the corresponding membership function distribution is determined according to the index type. For the smaller the better index, the evaluation level is V 1 The distribution function of adopts a small-scale decreasing half trapezoidal distribution (Equation 3), and the evaluation level is V 2 , V 3 The distribution function of uses the intermediate triangular distribution without loss of generality (Equation 4), and the evaluation level V 4 The distribution function of uses a relatively large ascending trapezoid distribution (Equation 5). For the bigger the better index, the judgement level is V 1 The distribution function of adopts a relatively large ascending semi-trapezoidal distribution (Equation 5), and the evaluation level is V 2 , V 3 The distribution function of still uses the intermediate triangular distribution without loss of generality (Equation 4), and the evaluation level V 4 The distribution function of adopts a small-scale reduced-half trapezoidal distribution (Equation 3). Finally, according to the relevant test specifications and expert experience, determine the parameters m contained in the corresponding distributions of different evaluation levels 1 , M 2 , M 3 , And bring the preprocessed data into the corresponding membership function to obtain the fuzzy membership μ ij.

[0095] μ ( x ) = 1 x ≤ m 1 m 2 - x m 2 - m 1 m 1 x ≤ m 2 0 m 2 x - - - ( 3 )

[0096] μ ( x ) = x - m 1 m 2 - m 1 m 1 x ≤ m 2 m 3 - x m 3 - m 2 m 2 ≤ x m 3 0 x ≤ m 1 , m 3 ≤ x - - - ( 4 )

[0097] μ ( x ) = 0 x ≤ m 1 x - m 1 m 2 - m 1 m 1 x ≤ m 2 1 m 2 x - - - ( 5 )

[0098] For qualitative indicators that are difficult to quantify, this model uses the fuzzy statistical test method to obtain the degree of membership. The specific method is: make an expert scoring questionnaire, and the expert will judge each evaluation factor based on experience, and tick the corresponding level of the scoring table , And then summarize the frequency of each evaluation factor corresponding to the grade, and normalize it to obtain the degree of membership of the corresponding grade. The formula is:

[0099]

[0100] (2) Determination of the gray part

[0101] When determining the fuzzy part, the amount of information collected by each evaluation factor is different, which will cause the uncertainty of the determined fuzzy relationship, and there are certain differences due to the sufficiency of information. Considering its impact on the overall evaluation, the gray part is introduced into the gray fuzzy relationship matrix, and a certain descriptive language is used to correspond to a certain gray scale range, as shown in Table 2. The determination of the gray part needs to select the value according to the sufficiency of the actual information. For each evaluation factor, this article uses the idea of ​​scoring an expert to find the average value to determine the corresponding gray value.

[0102] In summary, the gray fuzzy relationship matrix that determines the child evaluation factors under the parent level is:

[0103] R ⊗ i ~ = ( μ 11 , v 11 ) ( μ 12 , v 12 ) ( μ 13 , v 13 ) ( μ 14 , v 14 ) ( μ twenty one , v twenty one ) ( μ twenty two , v twenty two ) ( μ twenty three , v twenty three ) ( μ twenty four , v twenty four ) · · · · · · · · · · · · ( μ n 1 , v n 1 ) ( μ n 2 , v n 2 ) ( μ n 3 , v n 3 ) ( μ n 4 , v n 4 ) - - - ( 7 )

[0104] Step 5: Gray fuzzy comprehensive evaluation

[0105] The state evaluation of high-voltage circuit breakers is an analysis of the changing trend of the state of the circuit breaker. Actually, not all factors of the state evaluation can be accurately grasped. In order to retain as much evaluation information as possible, the M(·,+) operator is used for the modular operation and the M(⊙,+) operator is used for the gray operation. The result of the synthetic gray fuzzy comprehensive evaluation is:

[0106]

[0107] Where j∈{1,2,3,4},i∈{1,2,…,l}, l represents the number of evaluation factors at the same level as the parent level, Represents the weight set matrix; Represents the corresponding gray fuzzy discrimination matrix; w k , V i Is the weight of each evaluation index and the corresponding gray level; μ kj , V kj Is the membership value of the corresponding evaluation factor and the gray value of the corresponding point.

[0108] The result of comprehensive evaluation by evaluation factors at the same level as the parent level The gray fuzzy discriminant matrix of high-level evaluation factors is shown in formula (9). Combine the corresponding weight set to perform gray fuzzy calculation, and so on, layer by layer calculation to obtain the comprehensive evaluation result of the high-voltage circuit breaker operating state

[0109]

[0110] For the processing of the evaluation results, this model uses a combination of the inner product method and the principle of maximum membership degree for processing. Assumption b i Yes The i-th vector, let d i = 1-v i , Where v i Represents the gray value, then d i Means b i Credibility. Let b i =(μ i ,d i ), the comprehensive evaluation Can be calculated by b i To determine the size of, simplified to solve b i The norm of is as follows.

[0111] | | b i | | = [ b i , b i ] - - - ( 10 )

[0112] Where [b i ,b i ] Is the vector b i The inner product. Finally, the operating state of the high-voltage circuit breaker is obtained using the principle of maximum membership.

[0113] In order to verify the effectiveness and feasibility of the application of this algorithm in high-voltage circuit breaker condition assessment, this paper analyzes a certain SF 6 The circuit breaker has carried out grey fuzzy comprehensive assessment. The main technical parameters of this circuit breaker are shown in Table 3 and Table 4.

[0114] Table 3 3AP1FG SF 6 Main technical parameters of circuit breaker

[0115]

[0116] Table 4 SF 6 Circuit breaker gas pressure gauge (20℃)

[0117]

[0118] The different periods of closing and opening obtained from preventive tests are 1.65ms and 1.73ms respectively; the just opening speed and just closing speed are 2.4m/s and 2.1m/s respectively; the opening and closing DC resistances are 90Ω and 125Ω respectively . The circuit breaker has been in operation for 13 years, and the cumulative number of breaking times is 472. The lowest operating voltages of the opening and closing coils are 63V and 70V respectively; the main circuit resistance is 27μΩ. The gas pressure in the insulating medium is 0.55MPa, the humidity is 163ppm, and the insulation resistance of the primary circuit to the ground is 8000MΩ.

[0119] As can be seen from Table 1, this article mainly considers four factors: mechanical characteristics, electrical characteristics, insulation characteristics and other factors (environment, appearance, etc.). The specific verification process is as follows:

[0120] Step 1: Determine the weight set of the mechanical characteristics sub-factors and the gray fuzzy discriminant matrix, taking the time parameter as an example.

[0121] (1) Determination of time parameter weight set

[0122] First of all, determine its mold part. The time parameters under mechanical characteristics include different closing periods, different opening periods and other factors. Experts conduct pairwise comparisons based on experience to obtain a discriminant matrix [1 1/2 3; 2 1 5; 1 /31/5 1], find the maximum characteristic root λ max =3.0037, and the corresponding feature vector W i '=(0.463, 0.871, 0.164), normalize to get W i = (0.309, 0.582, 0.109), the random consistency ratio of the discriminant matrix CR = 0.0032 <0.1, with satisfactory consistency. Therefore, the time parameter weight set module W under the mechanical characteristics i = (0.309, 0.582, 0.109); Secondly, for the credibility of its model, 7 experts scored based on experience, and scored values ​​{0.2, 0.4, 0.3, 0.3, 0.1, 0.3, 0.5}, based on experts The method of scoring and averaging to obtain the gray part V i = 0.3; in summary In the same way, the weight set of the speed parameter and the DC resistance of the opening and closing coil can be obtained, as shown in Table 5.

[0123] Table 5 Weight set of sub-factors of mechanical characteristics

[0124]

[0125] (2) Determination of the gray fuzzy discriminant matrix of time parameters

[0126] First, determine the fuzzy part, and use the triangular membership function to find the degree of membership for the different periods of closing and opening. Because the magnitude of data in different periods of closing and opening is 1, which meets the calculation accuracy, the original data is directly used here, that is, the preprocessing function f(x)=x. Select the corresponding distribution function according to the index that belongs to the larger the better, and determine the parameter m contained in the corresponding distribution of different evaluation levels according to relevant test specifications and expert experience 1 , M 2 , M 3 , The membership functions of different periods of closing and opening are obtained, as shown in Table 6,

[0127] Table 6 Membership functions for different periods of closing and opening

[0128]

[0129]

[0130] And bring in the known data to obtain the corresponding degree of membership. For other factors, the fuzzy statistical test method is used to obtain the degree of membership as [0.3 0.5 0.2 0]. In summary, the fuzzy part is 0.35 0.65 0.325 0.217 0 0.54 0.73 0.487 0.3 0.5 0.2 0 .

[0131] Second, determine the gray part. According to the time parameter, different periods of closing are excellent. 7 experts scored {0.6,0.5,0.4,0.4,0.4,0.2,0.3} according to the sufficiency of the data and experience, and the corresponding point gray value was 0.4 . In the same way, the point gray value of the corresponding subordinate level of each index factor is obtained.

[0132] In summary, the grey fuzzy discriminant matrix of time parameters is shown in Table 7.

[0133] Table 7 Sub-factor discrimination matrix of mechanical characteristics

[0134]

[0135] In the same way, the gray fuzzy discrimination matrix of the speed parameter and the DC resistance of the opening and closing coil can be obtained.

[0136] The gray fuzzy comprehensive evaluation is used to comprehensively evaluate the relevant sub-evaluation factors, and the evaluation results obtained from formula (8) are as follows:

[0137]

[0138]

[0139]

[0140] Step 2: The comprehensive evaluation results of the sub-factors calculated in Step 1 constitute the gray fuzzy discrimination matrix of the mechanical characteristics, as follows:

[0141]

[0142] The same can be obtained The determination of the weight set of the sub-evaluation factors of the operating state of the circuit breaker is shown in step 1, and the weight set and the discrimination matrix of the sub-evaluation factors of the operating state of the circuit breaker are shown in Table 8 and Table 9.

[0143] Table 8 The weight set of the sub-factors of the circuit breaker operating status

[0144]

[0145] Table 9 Sub-factor discrimination matrix of circuit breaker operating status

[0146]

[0147]

[0148] The gray fuzzy comprehensive evaluation is performed on the relevant evaluation factors, and the evaluation results are as follows:

[0149]

[0150]

[0151]

[0152]

[0153] Step 3: Perform grey fuzzy comprehensive evaluation on the operating state of high-voltage circuit breakers, and process the evaluation results.

[0154] The comprehensive evaluation results of the sub-factors calculated in step 2 constitute the gray fuzzy discrimination matrix of the operating state of the high-voltage circuit breaker, as follows:

[0155]

[0156] Combine The grey fuzzy comprehensive evaluation is carried out, and the grey fuzzy evaluation result is obtained as

[0157]

[0158] Take the norm of the gray fuzzy comprehensive evaluation result to get

[0159] ||b 1 ||=0.9879,||b 2 ||=1.0405,||b 3 ||=1.0475,||b 4 ||=1.0139. Combined with the principle of maximum membership, the operating state of the high-voltage circuit breaker is finally determined to be "medium". It can be seen from the actual operating data that some values ​​have deviated from the factory value or the optimal value, and there is a tendency of deterioration. The actual operating condition is: the transmission mechanism is jammed, which causes the closing speed to decrease or refuse to close. If judged based only on the degree of membership, the operating state of the circuit breaker is "good", which is quite deviated from the actual operating state. The reason is that the gray level corresponding to the degree of membership is relatively large, that is, the degree of membership is less reliable . It can be seen that the conclusion drawn based on the grey fuzzy comprehensive evaluation model is more effective and credible.

[0160] Although the specific embodiments of the present invention are described above with reference to the accompanying drawings, they do not limit the protection scope of the present invention. Those skilled in the art should understand that on the basis of the technical solutions of the present invention, those skilled in the art do not need to pay creative work Various modifications or variations that can be made are still within the protection scope of the present invention.