Low-voltage residential electricity consumption fault risk assessment method and system

Abstract

The invention discloses a low-voltage residential electricity consumption fault risk assessment method and system, and relates to the technical field of risk assessment, and the method comprises the steps: constructing a fault risk assessment index system, and determining four first-level indexes and sixteen second-level indexes according to the fault risk assessment index system; determining the weight of each index in the assessment index system, carrying out the combined weight determination through a game theory to obtain a comprehensive weight, constructing a matter element extension model, and determining a to-be-tested matter element, a classic domain and a node domain according to the fault risk degree assessment index system and the corresponding index value; and calculating the correlation degree between the matter element and each risk degree grade, and judging the risk degree grade of the low-voltage residential electricity consumption fault according to a maximum correlation degree criterion. The problem that the fault risk degree cannot be graded and quantitatively evaluated is effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of hazard assessment, and particularly to a method and system for assessing the hazard degree of low-voltage residential electricity faults. Background Art

[0002] With the rapid economic development and the continuous improvement of people's living standards, various household appliances are more and more widely used in residential life, which also brings new challenges to electrical safety. There are various types of low-voltage residential electricity faults, including but not limited to leakage, short circuit, overload, poor contact, etc. These faults not only can cause damage to electrical equipment, but also may lead to serious consequences such as electrical fires and electric shock injuries. According to statistics, the occurrence probability of electrical fires is increasing continuously and has accounted for more than 30% of the total number of fires.

[0003] At present, for the research on hazard degree assessment, scholars at home and abroad have carried out a large number of studies, but mainly applied to fields such as medical treatment, geology, transportation, etc. The main methods include: analytic hierarchy process, fuzzy comprehensive evaluation method, fault tree analysis method, etc. These theoretical methods have achieved good results in safety prediction, analysis and evaluation, etc., but there are also certain limitations. For example, the determination of index weights is subjective and it is impossible to ensure the objectivity and credibility of the evaluation results.

[0004] So far, no relevant research has established a comprehensive index system and evaluation method to evaluate the hazard degree of low-voltage residential electricity faults. By evaluating the hazard degree of faults, potential safety risks and hazard factors can be detected early. This helps to identify the factors that may cause faults and accidents and take preventive measures to reduce the probability of risk occurrence. Summary of the Invention

[0005] The purpose of this part is to outline some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this part as well as in the abstract and title of the present application to avoid obscuring the purpose of this part, the abstract and the title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0006] In view of the above existing problems, the present invention is proposed.

[0007] Therefore, the present invention provides a method and system for assessing the hazard degree of low-voltage residential electricity faults, which can solve the problems mentioned in the background art.

[0008] To solve the above technical problems, the present invention provides the following technical solutions:

[0009] In a first aspect, the present invention provides a method for evaluating the risk degree of low-voltage residential power failures, which includes constructing an evaluation index system for the risk degree of failures, and determining four primary indicators and sixteen secondary indicators according to the evaluation index system for the risk degree of failures;

[0010] Determine the weights of each indicator in the evaluation index system, and obtain the comprehensive weight through combined weighting by game theory. At the same time, construct a matter-element extension model, and determine the matter-element to be measured, the classical domain, and the section domain according to the evaluation index system for the risk degree of failures and its corresponding index values;

[0011] Calculate the correlation degree between the matter-element and each risk degree level, and determine the risk degree level of the low-voltage residential power failure according to the maximum correlation degree criterion.

[0012] As a preferred solution of the method for evaluating the risk degree of low-voltage residential power failures according to the present invention, wherein: the construction of the evaluation index system for the risk degree of failures includes,

[0013] The four primary indicators and sixteen secondary indicators are determined according to the evaluation index system for the risk degree of failures;

[0014] The primary indicators include reliability indicators, equipment aging indicators, influence range indicators, and electrical quantity indicators;

[0015] The secondary indicators include, for the reliability indicator A, the mean time between failures A1, system availability A2, reliability A3 are subdivided; for the equipment aging indicator B, the equipment operation years B1, the wear degree of key components B2, the insulation aging index B3, the equipment performance degradation rate B4 are subdivided; for the influence range indicator C, the number of power outage users C1, the power outage area C2, the proportion of important loads affected C3, the degree of influence on the stability of the surrounding power grid C4 are subdivided; for the electrical quantity indicator D, the voltage deviation D1, frequency deviation D2, harmonic content D3, three-phase unbalance degree D4, power change rate D5 are subdivided.

[0016] As a preferred solution of the method for evaluating the risk degree of low-voltage residential power failures according to the present invention, wherein: the determination of the weights of each indicator in the evaluation index system includes that the weights of each indicator in the evaluation index system are obtained through combined weighting by game theory to obtain the comprehensive weight;

[0017] Use the G1 method to determine the subjective weight and the entropy weight method to determine the objective weight; the weight w corresponding to the mth indicator m The calculation formula is as follows:

[0018]

[0019] where m is the mth indicator, r iThe relative importance score for the i-th indicator, and the calculation formula for the weights of other indicators within the indicator set is as follows:

[0020] w n-1 = r n w n , n = m, m - 1,..., 3, 2

[0021] where i is the i-th indicator;

[0022] Determining the objective weight based on the entropy weight method includes collecting and organizing the data of each indicator of the evaluation object to form a decision matrix;

[0023] Suppose there are M objects and N indicators in the indicator system. Let the value of the j-th indicator of the i-th evaluation object be X ij , then this decision matrix of multiple indicators and multiple objects is used for calculation;

[0024] Standardize the original data according to the formula and calculate the indicator proportion matrix (X” ij ) m×n , and the calculation formula is as follows:

[0025]

[0026] where (X” ij ) m×n is the standardized indicator proportion; X ij is the value of the j-th indicator of the i-th evaluation object;

[0027] Calculate the information entropy E j of each indicator based on the standardized data, and the calculation formula is as follows:

[0028]

[0029] where E j is the entropy value of the j-th indicator;

[0030] Calculate the difference coefficient H j of each indicator according to the information entropy, and the calculation formula is as follows:

[0031] H j = 1 - E j

[0032] Calculate the objective weight w j of each indicator based on the difference coefficient, and the calculation formula is as follows:

[0033]

[0034] where w j is the objective weight of the j-th indicator.

[0035] As a preferred embodiment of the low-voltage residential electricity consumption fault risk assessment method of the present invention, wherein: the construction of the matter-element extension model includes

[0036] The matter-element extension model determines the matter-element to be measured, the classical domain, and the sectional domain according to the fault risk assessment index system and its corresponding index values;

[0037] Based on the matter-element theory and the extension set, a matter-element matrix to be measured, a classical domain matter-element matrix, and a sectional domain matter-element matrix are established;

[0038] The determined matter-element to be measured, the classical domain, and the sectional domain are transformed through the correlation function to obtain the correlation degree.

[0039] As a preferred embodiment of the low-voltage residential electricity consumption fault risk assessment method of the present invention, wherein: calculating the correlation degree between the matter-element and each risk level includes that the risk levels are divided into level I, level II, and level III;

[0040] Classical domain N j Is defined as Q j , C, V j , The total risk level Q defines its corresponding sectional domain N d As Q, C, V d , The calculation formula is as follows:

[0041]

[0042] Wherein, Q j Is the risk level, C is the risk level evaluation index set, and V j Is the value range of C with respect to Q j ;

[0043] For the total risk level Q, its corresponding sectional domain N is defined d As (Q, C, V d ), The calculation formula is as follows:

[0044]

[0045] Wherein, V d Is the value range of the risk level evaluation index set C with respect to the risk level domain Q of the electrical safety event, b di , a di Are the upper and lower limit values of the index set C at the total risk level.

[0046] As a preferred embodiment of the low-voltage residential electricity consumption fault risk assessment method of the present invention, wherein: calculating the correlation degree between the matter-element and each risk level includes calculating the correlation degree K of the third-level index with respect to each risk level j (C jp) The calculation formula is as follows:

[0047]

[0048] Among them, K j (C jp ) is the correlation degree of the third-level index with respect to each risk level. a jd and b jd are respectively the lower limit value and the upper limit value of the index set C at the risk level Q j . The correlation degree matrix K(C i ) of the secondary index with respect to each risk level. Based on the secondary index weight w i and the correlation degree matrix K(C i ) of the secondary index with respect to each risk level, the calculation formula for calculating the correlation degree matrix K(C) of the fault risk level is:

[0049]

[0050] Among them, w ip is the weight of the third-level index, and K(C ip ) = (K j (C ip )) is the correlation degree matrix of the third-level index with respect to each risk level.

[0051] As a preferred solution of the low-voltage residential electricity consumption fault risk assessment method described in the present invention, wherein: determining the risk level of the low-voltage residential electricity consumption fault according to the maximum correlation degree criterion includes comparing the correlation degrees under different risk levels and selecting the one with the largest correlation degree as the final risk level.

[0052] In the second aspect, the present invention provides a low-voltage residential electricity consumption fault risk assessment system, which includes: an assessment system construction module, a weight assignment and integration module, and a risk level calculation module;

[0053] The assessment system construction module is used to construct a fault risk assessment index system and determine four primary indexes and sixteen secondary indexes according to the fault risk assessment index system;

[0054] The weight assignment and integration module is used to determine the weights of the indexes in the assessment index system, perform combined weight assignment through game theory to obtain the comprehensive weight, and construct a matter-element extension model, and determine the matter-element to be measured, the classical domain, and the joint domain according to the fault risk assessment index system and its corresponding index values;

[0055] The risk level calculation module is used to calculate the correlation degree between the matter-element and each risk level, and determine the risk level of the low-voltage residential electricity consumption fault according to the maximum correlation degree criterion.

[0056] In a third aspect, the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and: when the processor executes the computer program, the steps of the low-voltage residential electricity consumption fault risk assessment method are implemented.

[0057] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and: when the computer program is executed by a processor, the steps of the low-voltage residential electricity consumption fault risk assessment method are implemented.

[0058] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention provides a low-voltage residential electricity consumption fault risk assessment method based on game theory combined weighting, comprehensively considering reliability indicators, equipment aging indicators, influence range indicators, and electrical quantity indicators as four primary indicators and 17 secondary indicators, and establishing a low-voltage residential electricity consumption fault risk assessment index system. Then, the subjective weight of the indicators is determined by the G1 method, the objective weight of the indicators is determined by the entropy weight method, and game theory is used for combined weighting to eliminate the differences between the subjective qualitative and objective quantitative evaluations of the primary and secondary indicators. Next, the matter-element extension theory model is introduced to calculate the extension correlation degree of the matter-element level to be evaluated, and based on the principle of the largest correlation degree, the fault risk level evaluation is realized. Finally, the reliability and rationality of the evaluation method are verified by an example application. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0060] Figure 1 It is a method flow chart of a low-voltage residential electricity consumption fault risk assessment method and system provided by an embodiment of the present invention;

[0061] Figure 2 It is an internal structure diagram of a computer device of a low-voltage residential electricity consumption fault risk assessment method and system provided by an embodiment of the present invention;

[0062] Figure 3 It is a fault risk assessment index system diagram of a low-voltage residential electricity consumption fault risk assessment method and system provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0063] To make the above objects, features, and advantages of the present invention more comprehensible, the following will provide a detailed description of the specific embodiments of the present invention in conjunction with the accompanying drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0064] In the following description, many specific details are set forth to facilitate a thorough understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the spirit of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0065] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that excludes other embodiments.

[0066] Example 1, referring to Figure 1 - Figure 2 , which is the first embodiment of the present invention. This embodiment provides a method for evaluating the risk of low-voltage residential power failure, including:

[0067] This application can effectively solve the above-mentioned problems. Next, multiple embodiments will be used to elaborate in detail how to implement this method for evaluating the risk of low-voltage residential power failure;

[0068] Figure 1 The following shows a flowchart of a method for evaluating the risk of low-voltage residential power failure and its system, including:

[0069] S1: Construct an evaluation index system for the risk of failure, and determine four primary indicators and sixteen secondary indicators according to the evaluation index system for the risk of failure;

[0070] Furthermore, the primary indicators include reliability indicators, equipment aging indicators, influence range indicators, and electrical quantity indicators;

[0071] The secondary indicators include,

[0072] For the reliability indicator A, it is further divided into three secondary indicators: mean time between failures A1, system availability A2, and reliability A3;

[0073] For the equipment aging indicator B, it is further divided into four secondary indicators: equipment operation years B1, wear degree of key components B2, insulation aging index B3, and equipment performance degradation rate B4;

[0074] For the influence scope index C, four secondary indexes are further subdivided: the number of power outage users C1, the area of the power outage region C2, the proportion of important loads affected C3, and the degree of influence on the stability of the surrounding power grid C4.

[0075] For the electrical quantity index D, five secondary indexes are further subdivided: voltage deviation D1, frequency deviation D2, harmonic content D3, three-phase unbalance degree D4, and power change rate D5.

[0076] Furthermore, the specific descriptions of each secondary index are defined, providing detailed definitions and explanations for each secondary index, including

[0077] Mean time between failures A1: It measures the reliability of electrical equipment or systems by statistically counting the time intervals between failures. A longer mean time between failures means the equipment or system is more reliable and has a lower failure frequency.

[0078] System availability A2: It refers to the probability that the system operates normally at any given moment. High availability indicates that the system can supply power and operate normally for most of the time, with a lower risk level.

[0079] Reliability A3: It measures the probability that a system or equipment does not fail within a specified time. The higher the reliability, the lower the risk of electrical safety incidents.

[0080] Equipment operation years B1: The longer the equipment is used, the higher the degree of aging may be, and the probability of failure will also increase.

[0081] Wear degree of key components B2: The wear of key components will affect the performance and safety of the equipment. Severe wear may lead to equipment failure and increase the risk of electrical safety.

[0082] Insulation aging index B3: Insulation aging may lead to safety problems such as electrical leakage and short circuits. The higher the insulation aging index, the greater the risk of electrical safety incidents.

[0083] Equipment performance degradation rate B4: It reflects the change of equipment performance over time. A higher performance degradation rate may mean that the equipment is about to fail.

[0084] Number of power outage users C1: If an electrical safety incident causes a large number of users to lose power, the risk level will increase accordingly.

[0085] Area of power outage region C2: The larger the area of the power outage region, the more users and facilities are involved, and the higher the risk level of the electrical safety incident.

[0086] Proportion of important loads affected C3: If an electrical safety incident affects important loads such as hospitals and communication facilities, the risk level will increase significantly.

[0087] Degree of influence C4 on the stability of the surrounding power grid: If it causes problems such as voltage fluctuations and frequency changes in the surrounding power grid, it will increase the safety risk of the entire power grid and raise the danger level of power consumption safety incidents;

[0088] Voltage deviation D1: Excessive voltage deviation may cause equipment damage, reduce equipment life, or affect the normal operation of equipment. The greater the voltage deviation exceeds the normal range, the higher the danger level of power consumption safety incidents;

[0089] Frequency deviation D2: Frequency deviation will affect the speed of motors and the stability of the power system. Larger frequency deviation may cause equipment failures and power grid instability, increasing the danger of power consumption safety incidents;

[0090] Harmonic content D3: Harmonics will cause electrical equipment to heat up, increase losses, and reduce equipment performance. High harmonic content may lead to equipment damage and power consumption safety problems, raising the danger level;

[0091] Three-phase unbalance degree D4: Three-phase unbalance will cause motors to heat up, increase line losses, and affect the stability of the power system. Higher three-phase unbalance degree may increase the danger of power consumption safety incidents;

[0092] Rate of change of power D5: Rapid rates of change of active power and reactive power may cause power grid instability and equipment failures.

[0093] S2: Determine the weights of each index in the evaluation index system, and obtain the comprehensive weight through combination weighting using game theory. At the same time, construct a matter-element extension model, and determine the matter-element to be measured, the classical domain, and the joint domain according to the fault risk assessment index system and its corresponding index values;

[0094] Furthermore, using the G1 method to determine the subjective weight and the entropy weight method to determine the objective weight includes,

[0095] Assume the evaluation index set is {X 1 ,X 2 ,...X n}, and select the most important index in the index set, denoted as X’ 1 ; then continue to select the most important index from the remaining index set, denoted as X’ 2 , and so on, until the importance ranking of all the indexes in the index set is given;

[0096] Please ask experts to quantify the relative importance between indexes according to the following table. Among them, r n is the ratio of the importance of X’ n-1 to X’ n , that is,

[0097] r n =wn-1 / w n

[0098] where w n is the weight of the nth index, and w n-1 is the weight of the (n - 1)th index; the specific indexes are shown in Table 1,

[0099] Table 1 Index Importance Scoring Scale

[0100]

[0101]

[0102] According to the above steps, calculate the weights of the first-level indexes, second-level indexes, and third-level indexes respectively. Taking the calculation of the weight of a certain first-level index as an example, if the rational assignment of r n is given, then the weight w m corresponding to the mth index is as follows, and the calculation formula is as follows:

[0103]

[0104] After determining the single weight of a certain index, calculate the weights of other indexes in the same index set according to the formula. The calculation formula is as follows:

[0105] w n-1 = r n w n , n = m, m - 1,..., 3, 2

[0106] Similarly, calculate the weights of each layer of indexes; it should be noted that the above decision-making method can only determine the index weights under the decision-making of a single decision-maker. If there are t decision-makers participating in the decision-making, introduce the weight index L k of the kth decision-maker in the weight assignment process. The weight of the ith index under the decision-making of the kth decision-maker is , then the group decision-making result of the ith index is:

[0107]

[0108] Furthermore, determining the objective weight based on the entropy weight method includes collecting and organizing the data of each index of the evaluation object to form a decision matrix;

[0109] Suppose there are M objects (M 1 , M 2 ,..., M m ) and N indexes (N 1 , N 2 ,..., N n ) in the index system. Let the value of the jth index (j = 1, 2,..., n) of the ith evaluation object (i = 1, 2,..., m) be Xij , then the decision matrix for multiple indicators and multiple objects is as follows:

[0110]

[0111] Standardize the original data according to the formula and calculate the index weight matrix. The calculation formula is as follows:

[0112]

[0113] Calculate the standardized value X’ of the i-th evaluation object under the j-th indicator ij , and the calculation formula is as follows:

[0114]

[0115] Obtain the index weight matrix. The calculation formula is as follows:

[0116]

[0117] Calculate the information entropy of each indicator based on the standardized data. The calculation formula is as follows:

[0118]

[0119] Among them, is greater than zero, X” ij ∈[0,1], E j ∈[0,1];

[0120] Calculate the difference coefficient of each indicator according to the information entropy. Given that E j is the entropy value of the j-th indicator, let H j =1 - E j , then H j is the difference coefficient of the j-th indicator. The larger E j is, the smaller H j is, indicating that the difference between indicators is smaller and the evaluation effect on objects is also smaller; calculate the objective weight of each indicator according to the difference coefficient. The calculation formula is as follows:

[0121]

[0122] According to the properties of entropy weights, it can be known that: the entropy of the evaluation object is inversely proportional to its entropy weight. The larger the entropy, the smaller the entropy weight, and it satisfies 0 ≤ W j ≤1, ∑W j =1. When the values of each evaluation object on indicator j are all the same, the entropy value is the maximum value of 1 at this time, and the entropy weight is 0, indicating that this indicator does not provide useful information to the decision maker, and the decision maker can consider canceling this indicator.

[0123] Furthermore, the combined weighting by game theory includes

[0124] Combining the weights calculated by the G1 method and the entropy weight method to construct a basic weight vector set and setting the linear combination weight coefficient; Suppose there are L methods to determine the combined weight, then:

[0125]

[0126] where u k is the constructed basic weight vector set, is the linear combination weight coefficient;

[0127] By minimizing the deviation between the weights obtained by different methods, optimize the combination coefficients to obtain the most ideal weight value;

[0128] To determine the optimal combination coefficient, the deviation between u and u k can be minimized, and the L weight combination coefficients in the formula are optimized to obtain the most ideal weight value in u. Let the objective function be

[0129]

[0130] According to the matrix differential property and the first-order derivative condition of optimization, the calculation formula is as follows:

[0131]

[0132] For After normalization, determine the weight u * of the evaluation index combined weighting, that is

[0133] Get a more balanced comprehensive weight.

[0134] Furthermore, determine the measured matter element, classical domain and joint domain according to the fault hazard degree evaluation index system and the corresponding index values;

[0135] Based on the matter element theory and the extension set, establish the measured matter element matrix, classical domain matter element matrix, and joint domain matter element matrix;

[0136] Convert the determined measured matter element, classical domain and joint domain through the correlation function to obtain the correlation degree.

[0137] S3: Calculate the correlation degree between the matter element and each hazard degree level, and determine the hazard degree level of the low-voltage residential power failure according to the maximum correlation degree criterion.

[0138] Furthermore, determine the fault hazard degree level division, including,

[0139] According to the "Regulations on Emergency Disposal and Investigation and Handling of Electric Power Safety Accidents" (Decree No. 599 of the State Council) and other regulatory documents, papers, and expert experiences, the risk levels of low-voltage residential power failures are divided into three levels: Level I (safe), Level II (low risk), and Level III (high risk).

[0140] Furthermore, determining the classical domain and the section domain includes

[0141] The classical domain N j is defined as

[0142]

[0143] where Q j is the risk level divided for power safety incidents, C is the risk level evaluation index set, and V j is the value range of C with respect to Q j , and b ij , a ij are the upper and lower limits of the index set C at the risk level of Q j ;

[0144] For the total risk level Q, define its corresponding section domain N d as (Q, C, V d ), specifically

[0145]

[0146] where V d is the value range of the risk level evaluation index set C with respect to the risk level domain Q of power safety incidents, and b di , a di are the upper and lower limits of the index set C at the total risk level.

[0147] Furthermore, construct the matter-element matrix to be evaluated. The matter-element Ni to be evaluated is defined as

[0148]

[0149] where R i is the i-th secondary index for the risk evaluation of power safety incidents, and C i ={C i1 , C i2 ,..., C im} is the tertiary index set of R i , and v ip is the value of the tertiary index C i of R i .

[0150] Furthermore, calculate the correlation degrees between the tertiary indexes and each risk level, including

[0151] Introduce the concept of distance in the matter-element extension theory and calculate the value v of the third-level index ip and the classical domain V j , the section domain V d The distances ρ(v ip , V j ), ρ(v ip , V d ) between them are calculated according to the following formulas:

[0152]

[0153] Accordingly, calculate the correlation degree K j (C jp ) of the third-level index with respect to each risk level, and the calculation formula is as follows:

[0154]

[0155] The correlation degree matrix K(C i ) of the second-level index with respect to each risk level, that is,

[0156]

[0157] where w ip is the weight of the third-level index, and K(C ip ) = (K j (C ip )) is the correlation degree matrix of the third-level index with respect to each risk level;

[0158] Based on the weight w i of the second-level index and the correlation degree matrix K(C i ) of the second-level index with respect to each risk level, calculate the correlation degree matrix K(C) of the fault risk level, and the calculation formula is as follows:

[0159]

[0160] Furthermore, determining the risk level of low-voltage residential electricity consumption faults includes

[0161] According to the maximum correlation criterion, if K j (C) = max K(C), {j = 1, 2,..., m}, then it is determined that the risk level of the object to be evaluated is level j.

[0162] Furthermore, this embodiment also provides a low-voltage residential electricity consumption fault risk assessment system, including:

[0163] An evaluation system construction module, a weight assignment and integration module, and a risk level calculation module;

[0164] The evaluation system construction module is used to construct a fault risk assessment index system, and determine four first-level indicators and sixteen second-level indicators according to the fault risk assessment index system;

[0165] The weight assignment and integration module is used to determine the weights of the indicators in the evaluation index system, and perform combined weight assignment through game theory to obtain the comprehensive weight. At the same time, a matter-element extension model is constructed, and the matter-element to be measured, the classical domain and the joint domain are determined according to the fault risk assessment index system and its corresponding index values;

[0166] The risk level calculation module is used to calculate the correlation degree between the matter-element and each risk level, and determine the risk level of the low-voltage residential electricity consumption fault according to the maximum correlation degree criterion.

[0167] This embodiment also provides a computer device, which can be a terminal, and its internal structure diagram can be as Figure 2 shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a carrier network, NFC (Near Field Communication) or other technologies. When the computer program is executed by the processor, it implements a method for evaluating the risk of low-voltage residential electricity consumption faults. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, a trackball or a touchpad set on the shell of the computer device, or an external keyboard, a touchpad or a mouse, etc.

[0168] This embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by the processor, the following steps are implemented:

[0169] Construct a fault risk assessment index system, and determine four first-level indicators and sixteen second-level indicators according to the fault risk assessment index system;

[0170] Determine the weights of the indicators in the evaluation index system, and perform combined weight assignment through game theory to obtain the comprehensive weight. At the same time, a matter-element extension model is constructed, and the matter-element to be measured, the classical domain and the joint domain are determined according to the fault risk assessment index system and its corresponding index values;

[0171] Calculate the correlation degree between the matter element and each risk level, and determine the risk level of the low-voltage residential power failure according to the maximum correlation degree criterion.

[0172] Example 2, refer to Figure 1 - Figure 2 , which is the second embodiment of the present invention. This embodiment provides a method for evaluating the risk level of low-voltage residential power failures. In order to verify the beneficial effects of the present invention, scientific demonstrations are carried out through economic benefit calculations and simulation experiments.

[0173] Step 1: Construct an evaluation index system for fault risk level, including 4 first-level indicators and 16 second-level indicators, as shown in the appendix Figure 2 as follows;

[0174] Step 2: Determine the subjective weights of the indicators based on the G1 method, the objective weights of the indicators based on the entropy weight method, and use game theory for combined weighting;

[0175] Step 3: Construct a matter-element extension model, and the specific method is as follows:

[0176] 1) Determine the matter element to be measured, the classical domain, and the joint domain according to the evaluation index system for fault risk level and the corresponding index values;

[0177] 2) Based on the matter-element theory and the extension set as the theoretical basis, establish a matter-element matrix to be measured, a classical domain matter-element matrix, and a joint domain matter-element matrix according to the determined matter element to be measured, classical domain, and joint domain;

[0178] 3) Obtain the correlation degree by transforming the determined matter element to be measured, classical domain, and joint domain through the correlation function;

[0179] Step 4: Determine the risk level of low-voltage residential power failures according to the maximum correlation degree criterion.

[0180] To illustrate the beneficial effects of the present invention, a certain low-voltage residential household Z is evaluated and analyzed.

[0181] Construct an index system: The index system is as shown in the appendix Figure 2 as follows

[0182] Determine the index weights

[0183] Determine the subjective weights of the indicators by the G1 method

[0184] Invite 5 experts to rank the relative importance of each second-level indicator. The index importance scoring scale is shown in Table 1 of the technical solution. By determining the group decision-making results of the 5 experts, the final results of the weights of each indicator are shown in Table 3.

[0185] Table 3 Calculation results of G1 method index weights

[0186]

[0187] Determining the objective weight of indicators by the entropy weight method

[0188] The present invention collects the device index data of the household user and the data such as the influence range and electrical quantity recorded when the power consumption accident occurs, and performs standardized processing on the original data matrix composed of various index factors by using a formula to obtain a standardized matrix, and then calculates the information entropy and objective weight of each evaluation index, as shown in Table 4.

[0189] Table 4 Calculation results of entropy weight method index weights

[0190]

[0191]

[0192] Determining combined weights by game theory

[0193] The G1 method and the entropy weight method are used to combine weights by using the idea of game theory to obtain the game theory combined weights, and the results are shown in Table 5.

[0194] Table 5 Calculation results of game theory weights

[0195]

[0196] According to the divided Z fault risk levels and value ranges of low-voltage residential household users, the classical domain and the nodal domain are determined. Multiple experts evaluate the risk of secondary indicators according to the risk value standard, and the average value is obtained to

[0197] obtain the initial risk values of the indicators, which are (4.200, 2.300, 3.100; 2.600, 1.500; 2.000, 4.000, 3.600; 3.400, 2.200, 1.700, 1.000, 0.500, 1.500; 2.600, 3.100). Based on this, the matter element to be evaluated is established, and the correlation degree of the secondary indicators with respect to each risk level is calculated.

[0198] Taking A1 as an example, the initial risk value of A1 is 4.2, then the distance between the initial risk of A1 and the classical domain is:

[0199] ρ(v 1 ,V 1 ) = 1.2

[0200] ρ(v 1 ,V 2 ) = -1.2

[0201] ρ(v 1 ,V 3 ) = 1.8

[0202] The distance between the initial risk level A1 and the extension domain:

[0203] ρ(v 1 ,V d ) = -4.2

[0204] Calculate the correlation degree of this index with respect to each risk level:

[0205]

[0206] Similarly, the correlation degrees of other primary and secondary indicators with respect to each risk level can be obtained. According to the maximum correlation criterion, the risk levels are determined. The correlation degrees of the Z fault risk levels of low-voltage residential household users are shown in Table 6.

[0207] Table 6 Z risk correlations and risk levels of low-voltage residential household users

[0208]

[0209] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not restrictive. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

[0210] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages. For example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript.

[0211] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of processes and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate for implementing the process Figure 1one or more processes and / or blocks Figure 1 means for the functions specified in one or more blocks

[0212] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction means that implements the functions in the process Figure 1 one or more processes and / or blocks Figure 1 the functions specified in one or more blocks

[0213] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus, such that a series of operational steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions in the process Figure 1 one or more processes and / or blocks Figure 1 the steps of the functions specified in one or more blocks

[0214] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present application

[0215] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these modifications and variations

Claims

1. A method for assessing the risk of low-voltage residential power failure, characterized by: include, Construct a fault risk assessment index system, and determine four first-level indicators and sixteen second-level indicators based on the fault risk assessment index system; Determine the weight of each indicator in the evaluation index system, and combine and weight them through game theory to obtain a comprehensive weight, and at the same time construct a matter-element extension model, and determine the matter-element to be tested, the classical domain and the node domain according to the fault risk evaluation index system and its corresponding index value; The correlation between the matter-element and each danger level is calculated, and the danger level of the low-voltage residential power failure is determined according to the maximum correlation criterion.

2. The method for assessing the risk of low-voltage residential power failure according to claim 1, characterized in that: The construction of the fault risk assessment index system includes: The four primary indicators and sixteen secondary indicators are determined according to the fault risk assessment indicator system; The first-level indicators include reliability indicators, equipment aging indicators, impact range indicators and electrical quantity indicators; The secondary indicators include, for the reliability index A, the mean time between failures A1, system availability A2, and reliability A3; for the equipment aging index B, the equipment operating life B1, the degree of wear of key components B2, the insulation aging index B3, and the equipment performance degradation rate B4; for the impact range index C, the number of power outage users C1, the area of ​​the power outage area C2, the proportion of important loads affected C3, and the degree of impact on the stability of the surrounding power grid C4 are subdivided; for the electrical quantity index D, the voltage deviation D1, the frequency deviation D2, the harmonic content D3, the three-phase imbalance D4, and the power change rate D5 are subdivided.

3. The method for assessing the risk of low-voltage residential power failure according to claim 2, characterized in that: Determining the weight of each indicator in the evaluation indicator system includes combining and weighting each indicator in the evaluation indicator system through game theory to obtain a comprehensive weight; The G1 method is used to determine the subjective weight and the entropy weight method is used to determine the objective weight; the weight w corresponding to the mth indicator m The calculation formula is as follows: Among them, m is the mth index, r i is the relative importance score of the ith indicator, and the calculation formula for the weights of other indicators in the same indicator set is: w n-1 =r n w n ,n=m,m-1,...,3,2 Among them, i is the i-th indicator; Determining objective weights based on the entropy weight method includes collecting and organizing the data of various indicators of the evaluation object to form a decision matrix; Assume that there are M objects and N indicators in the indicator system, and let the jth indicator value of the i-th evaluation object be X ij , then this multi-index and multi-object decision matrix is ​​used for calculation; The original data is standardized according to the formula, and the index weight matrix (X i,j ,) m×n , the calculation formula is as follows: Among them, (X i,j ,) m×n is the standardized index proportion; X ij Take the value of the jth indicator of the i-th evaluation object; Calculate the information entropy E of each indicator based on the standardized data j , the calculation formula is as follows: Among them, E j is the entropy value of the jth indicator; Calculate the difference coefficient H of each indicator based on information entropy j , the calculation formula is as follows: H j =1-E j Calculate the objective weight w of each indicator based on the difference coefficient j , the calculation formula is as follows: Among them, w j is the objective weight of the jth indicator.

4. The method for assessing the risk of low-voltage residential power failure according to claim 3, characterized in that: The constructing of the matter-element extension model comprises: The matter-element extension model determines the matter-element to be tested, the classical domain and the node domain according to the fault risk assessment index system and its corresponding index value; Based on matter-element theory and extension set, the matter-element matrix to be tested, the matter-element matrix of classical domain and the matter-element matrix of node domain are established; The determined object element, classical domain and section domain to be tested are transformed through correlation function to obtain the correlation degree.

5. The method for evaluating the risk of low-voltage residential power failure according to claim 4, characterized in that: The calculating of the correlation between the matter-element and each danger level includes dividing the danger level into level I, level II and level III; Classical Domain N j Defined as Q j , C, V j The total hazard level Q defines its corresponding section N d Q, C, V d , the calculation formula is as follows: Among them, Q j is the hazard level, C is the hazard level evaluation index set, V j For C about Q j The value range of For the total hazard level Q, define the corresponding section N d is (Q, C, V d ), the calculation formula is as follows: Among them, V d is the value range of the hazard level evaluation index set C for the hazard level domain Q of electrical safety events, b di 、a di It is the upper and lower limit values ​​of the total hazard level of indicator set C.

6. The method for evaluating the risk of low-voltage residential power failure according to claim 5, characterized in that: The calculation of the correlation between the matter-element and each hazard level includes calculating the correlation K of the three-level index with respect to each hazard level. j (C jp ) is calculated as: Among them, K j (C jp ) is the correlation degree of the three-level indicators with respect to each hazard level, a jd , b jd They are respectively the indicator set C at the risk level Q j The lower and upper limits of the secondary indicators and the correlation matrix K(C i ), based on the secondary indicator weight w i and the correlation matrix K(C i ), the calculation formula for calculating the fault risk level correlation matrix K(C) is: Among them, w ip is the weight of the third-level indicator, K(C ip )=(K j (C ip )) is the correlation matrix of the three-level indicators for each hazard level.

7. The method for assessing the risk of low-voltage residential power failure according to claim 6, characterized in that: Determining the danger level of the low-voltage residential power failure according to the maximum correlation criterion includes comparing the correlations at different danger levels and selecting the maximum correlation as the final danger level.

8. A low-voltage residential electricity failure risk assessment system, based on the low-voltage residential electricity failure risk assessment method according to any one of claims 1 to 7, characterized in that: Including, assessment system building module, empowerment comprehensive module and hazard level calculation module; The evaluation system construction module is used to construct a fault risk evaluation index system, and four first-level indicators and sixteen second-level indicators are determined according to the fault risk evaluation index system; The weighted comprehensive module is used to determine the weight of each indicator in the evaluation index system, and to combine and weight the indicators through game theory to obtain the comprehensive weight, and to construct a matter-element extension model, and to determine the matter-element to be tested, the classical domain and the node domain according to the fault risk evaluation index system and its corresponding index value; The danger level calculation module is used to calculate the correlation between the matter-element and each danger level, and determine the danger level of the low-voltage residential power failure according to the maximum correlation criterion.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for assessing the risk of low-voltage residential power failure described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for assessing the risk of low-voltage residential power failure described in any one of claims 1 to 7 are implemented.

Images