CN113902314A - Expert knowledge fused power transmission line environment risk assessment method - Google Patents
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Abstract
The invention belongs to the technical field of power transmission line risk assessment, and particularly relates to a power transmission line environment risk assessment method fusing expert knowledge. In the data acquisition stage, the influence of multiple data sources and multiple factors on the environmental risk of the power transmission line is integrated, and a multi-metadata basis is provided for risk assessment. And then, combining expert knowledge and fuzzy mathematics to construct a multi-risk multi-dimensional disaster-causing factor knowledge base and a multi-dimensional disaster-causing factor membership function knowledge base, so that the purpose of multi-disaster environmental risk assessment of the power transmission line is achieved, and the universality of the method is improved. Finally, a multi-risk calculation method based on the multi-risk multi-dimensional disaster-causing factor knowledge base and the multi-dimensional disaster-causing factor membership function knowledge base is provided, the calculation method comprises BPA calculation, multi-source evidence fusion, risk probability generation and the like, the risk quantitative evaluation of the power transmission line is achieved, the uncertainty of input data and evaluation results can be flexibly processed, and the stability of the risk evaluation is improved.
Description
Technical Field
The invention belongs to the technical field of power transmission line risk assessment, and particularly relates to a power transmission line environment risk assessment method fusing expert knowledge.
Background
In China, due to the wide regional area, the power transmission lines are distributed all over the country, and the power transmission lines have the characteristics of multiple points, long lines, wide area and the like, and most of the power transmission lines are located in complex environments such as unmanned areas. The landform difference of the areas is large, the environment is severe, the natural disasters are serious, the lines are affected by disasters such as mountain fire, icing, ground disasters and thunder for a long time, the failure outage accidents occur frequently, and great loss is caused to the national economic development and the social life of people. Therefore, environmental risk assessment of the transmission line is imperative.
At present, the risk assessment of the power transmission line is mainly to assess the current state of the power transmission line by collecting meteorological information and operation condition information of a power grid, combining design data and applying methods such as an electrical geometric model method, an analytic hierarchy process, naive Bayes, a long-short term memory (LSTM) neural network and the like. Although the methods have certain advantages in the risk assessment and prediction effect of the power transmission line, the methods still have some limitations: firstly, an evaluation prediction model is established only aiming at a single risk, and the model is not strong in universality; secondly, only considering climate factors or single disaster factors and not integrating the influence of multiple factors on the risk of the power transmission line; thirdly, only input data with definite characteristic attributes are received, uncertainty of the input data cannot be flexibly processed, and processing of uncertainty of an evaluation result is lacked. .
Disclosure of Invention
In order to overcome the defects of the conventional power transmission line risk assessment technology and achieve the purpose of assessing various environmental risks of a power transmission line by using multi-source observation data, the invention provides a power transmission line environmental risk assessment method integrating expert knowledge. Firstly, the influence of multiple factors on the risk of the power transmission line is integrated by collecting multi-source data related to the operation of the power transmission line, and a multi-metadata basis is provided for risk assessment. Then, in order to realize the purpose of evaluating the risk of the multi-disaster environment of the power transmission line, a multi-dimensional disaster-causing factor knowledge base and a multi-dimensional disaster-causing factor membership function knowledge base in multiple risks are constructed on the basis of expert knowledge and fuzzy mathematics. Finally, a multi-risk evaluation calculation method based on the multi-risk multi-dimensional disaster-causing factor knowledge base and the multi-dimensional disaster-causing factor membership function knowledge base is provided, and the method comprises basic probability distribution function (BPA) calculation, multi-source evidence fusion, risk probability generation and the like. The method is suitable for multi-disaster environment risk assessment of the power transmission line, and can process uncertainty of input data and assessment results.
In order to achieve the purpose, the invention adopts the following technical scheme:
a power transmission line environmental risk assessment method fusing expert knowledge, wherein the environmental risk comprises mountain fire, icing, ground disaster, thunder disaster, tree and bamboo and wind damage; as shown in fig. 1, the evaluation method includes the steps of:
s1, acquiring factor data related to environmental risks, including remote sensing monitoring d1And a sensor d2And d, manual inspection3And ground weather station d4Weather data service provider d5On-line equipment monitoring d6Wherein d islFor multiple factor data sets obtained from the ith data source, dl＝{Fl1n：Fl1，...，Flmn：Flm}，FlmnAs a data set dlName of the mth factor in (1), FlmAs a data set dlM is dlThe number of factors involved. All data sets obtained are denoted as D ═ D1，d2，d3，d4，d5，d6}。
Specifically, the remote sensing monitoring comprises monitoring data of mountain fire and ground disaster events, and specifically comprises time information, spatial information (longitude and latitude), intensity or grade of the events; the sensor comprises icing tension sensing, vibration sensing and smoke sensing data; the manual inspection and on-line monitoring equipment monitors the power transmission channel at regular time, and the collected factors comprise tree and bamboo cutting, tree and bamboo height, icing phenomenon and forest fire phenomenon grade data; the meteorological elements of the ground meteorological station and the meteorological data service provider comprise temperature, rainfall, wind speed, wind direction, humidity, ground flash intensity and time and space information (longitude and latitude).
Data set diFactor data in (1) is divided into three types: event class data, level class data, and continuity type data. Wherein, the event data is represented in the form of：{eventtype，eventtime，eventlat，eventlon，eventlevel}，eventtypeIs the event type; eventtimeIs the time of occurrence of the event; eventlatIs the latitude of the event; eventlonIs the longitude of the event; eventlevelIs the event level or intensity; the class data are: { F: level, F is an observation factor, level is the level of the factor, and the level is a specific value and is discrete data; the continuous data are: { F: value, F is the observation factor, value is the attribute value of the factor, which can be any value in a continuous range. Each data set diContains at least one type of factor data.
Taking the ground disaster as an example, relevant factors of the ground disaster comprise earthquake, heavy rainfall, hidden danger points of the ground disaster, mining blasting and quarrying. Wherein the earthquake is event data and is derived from d1(ii) a Heavy rainfall is continuous data from d4And d5(ii) a The ground disaster hidden trouble points belong to event data and are derived from d5(ii) a Mining blasting and quarrying belong to the event class of data and are derived from d3. The data input format of the disaster-causing factor is as follows:
wherein eq is1＝{earthquake，eqtime1，eqlat1，eqlon1，eqlevel1}；v41Is d4The amount of rainfall in a certain period of time; v. of51Is d5The amount of rainfall in a certain period of time; r is51＝{risklat1，risklon1Indicating that the ground disaster event happens at the longitude and latitude; e131Is d3Mine blasting event of (1), e131＝{destruction1，etime1，elat1，elon1，elevel1}；e231Is d3In quarrying, e231＝{destruction2，etime1，elat1，elon1，elevel1}。
And S2, constructing an expert knowledge base for power transmission line environmental risk assessment, wherein the expert knowledge base comprises a multi-risk multi-dimensional disaster-causing factor knowledge base and a multi-dimensional disaster-causing factor membership function knowledge base. Wherein a multi-risk multi-dimensional disaster-causing factor knowledge base is used for matching dlThe incidence relation between each factor and the 6 risks, and generates a corresponding rule set, which is marked as W1(ii) a The multidimensional disaster-causing factor membership function knowledge base has the effect of being based on W1The combination relationship between the intermediate factors and the risks, the construction rule and the configuration of the corresponding membership function type and parameter are marked as W2。
Defining a power transmission line environment risk assessment identification framework theta ═ { true, false, true or false }, wherein true represents the existence of risk, false represents the absence of risk, and true or false represents the unknown; true and false are unitary focal elements, true or false is a mixed focal element.
W is to be1Expressing by adopting a triad mode: (RT)i，Fljn，Vij). Wherein RT isiFor the ith environmental risk type, i ∈ [1,..., 6]；FljnFor the j and RT in the l data setiName of the associated factor, Flj∈dl；VijIs FljnResult in RTiRule set of occurrence, Vij＝{rule1，rule2，...，ruleHWherein rulehH is the number of rules.
Based on W1The combination relation of the intermediate factors and the risks is combined with expert knowledge and fuzzy mathematics to construct a rule structureh＝(Flj，Θ1s，Ph) Multidimensional disaster-causing factor membership function knowledge base W2The representative rule is:
IF condition1(Flj)and/or condition2(Flj)THENΘ1s，Ph
wherein rulehIs FljResult in RTiH rule of occurrence, ruleh∈Vij(ii) a condition1 is a rule set by an expert; fljIs a reaction of with FljnCorresponding j and RTiAssociated factor data, Flj∈dl；Θ1To identify a set of unary foci in the framework Θ, Θ1＝{true，false}，Θ1s∈Θ1；PhTo satisfy FljResulting in theta1sType of membership function occurring and parameter configuration, Ph＝{funch，param_1h，param_2h，...，param_khIn which funchParam _ k being a membership function typehIs the value of the kth parameter of the membership function, and k is the number of the membership function parameters. The membership function types comprise a triangular membership function, a Gaussian membership function, a semi-trapezoidal membership function and a ridge-shaped distribution membership function.
Taking disaster-causing factor earthquake in the ground disaster as an example, the input data is an earthquake event { earthquare, eqtime1，eqlat1，eqlon1，eqlevel1Converting the event data into { earthquare, eq by data preprocessingtime1，eqdist1，eqlevel1}。
At W2Middle, rule1Comprises the following steps:
IF eqtime1＜t0 and eqdist1＜dist0 THEN true，P1
wherein, t0Is a preset time threshold, dist0Is a preset spatial distance threshold.
The conflict resolution mode of the multi-dimensional disaster-causing factor membership function knowledge base is that when a plurality of contradictory rules exist, the most specific rule is excited.
S3, sequentially evaluating the risks of the power transmission line to be evaluated:
s31, according to W1The combined relation of the intermediate factor and the risk is obtained to evaluate the risk RTiFactor list Flist＝[Fl1n，Fl2n，...]，Fl1nIs the first data set leading to RTiName of the 1 st factor. According to the factor list FlistCorresponding factor data is acquired from step S1.
S32, according to W1The combination relationship of the middle factor and the rule set is obtained FlistA corresponding set of rules.
S33, for each rule in the step S32, inputting the factor data of S31 into the expert knowledge base W constructed in the step S22And according to rule matching, finding out the corresponding membership function type and parameter, and calculating the BPA of the one-dimensional focal element { true, false } of the target factor according to the membership function.
S34, calculating the BPA of the mixed focal elements { true or false } by adopting a geometric mean method, and normalizing the BPA of all the focal elements in the identification frame theta.
S35, synthesizing the BPA of the multi-dimensional disaster-causing factors of multiple data sources by adopting a Dempster rule in the DS evidence theory, and then converting the BPA into the probability of various risks to finish risk assessment.
Compared with the prior art, the method has the advantages that the influence of multiple data sources and multiple factors on the environmental risk of the power transmission line is integrated in the data acquisition stage, and a multi-metadata basis is provided for risk assessment. And then, combining expert knowledge and fuzzy mathematics to construct a multi-risk multi-dimensional disaster-causing factor knowledge base and a multi-dimensional disaster-causing factor membership function knowledge base, so that the purpose of multi-disaster environmental risk assessment of the power transmission line is achieved, and the universality of the method is improved. Finally, a multi-risk calculation method based on the multi-risk multi-dimensional disaster-causing factor knowledge base and the multi-dimensional disaster-causing factor membership function knowledge base is provided, the calculation method comprises BPA calculation, multi-source evidence fusion, risk probability generation and the like, the risk quantitative evaluation of the power transmission line is achieved, the uncertainty of input data and evaluation results can be flexibly processed, and the stability of the risk evaluation is improved.
Drawings
FIG. 1 is a logic sequence diagram of the present invention.
Detailed Description
The scheme of the invention is further described below:
the detailed steps of the invention are as follows:
s1, acquiring factor data related to 6 environmental risks such as mountain fire, icing, ground disaster, thunder disaster, tree and bamboo and wind disaster, and monitoring d by remote sensing1And a sensor d2And d, manual inspection3And ground weather station d4Weather data service provider d5On-line equipment monitoring d6Wherein d islFor multiple factor data sets obtained from the ith data source, dl＝{Fl1n：Fl1，...，Flmn：Flm}，FlmnAs a data set dlName of the mth factor in (1), FlmAs a data set dlM is dlThe number of factors involved. All data sets obtained are denoted as D ═ D1，d2，d3，d4，d5，d6}。
Specifically, the remote sensing monitoring comprises monitoring data of mountain fire and ground disaster events, and specifically comprises time information, spatial information (longitude and latitude), intensity or grade of the events; the sensor comprises icing tension sensing, vibration sensing and smoke sensing data; the manual inspection and on-line monitoring equipment monitors the power transmission channel at regular time, and the collected factors comprise tree and bamboo cutting, tree and bamboo height, icing phenomenon and forest fire phenomenon grade data; the meteorological elements of the ground meteorological station and the meteorological data service provider comprise temperature, rainfall, wind speed, wind direction, humidity, ground flash intensity and time and space information (longitude and latitude).
The factor data in data set di is classified into three types: event class data, level class data, and continuity type data. The event data expression form is as follows: { eventtype，eventtime，eventlat，eventlon，eventlevel}，eventtypeIs the event type; eventtimeIs the time of occurrence of the event; eventlatIs the latitude of the event; eventlonIs the longitude of the event; eventlevelIs the event level or intensity; the class data are: { F: level, F is an observation factor, level is the level of the factor, and the level is a specific value and is discrete data; the continuous data are: { F: value, F is the observation factor, value is the attribute value of the factor, which can be any value in a continuous range. Each data set diContains at least one type of factor data.
Taking the ground disaster as an example, relevant factors of the ground disaster comprise earthquake, heavy rainfall, hidden danger points of the ground disaster, mining blasting and quarrying. Wherein the earthquake is event data and is derived from d1(ii) a Heavy rainfall is continuous data from d4And d5(ii) a The ground disaster hidden trouble points belong to event data and are derived from d5(ii) a Mining blasting and quarrying belong to the event class of data and are derived from d3. The data input format of the disaster-causing factor is as follows:
wherein eq is1＝{earthquake，eqtime1，eqlat1，eqlon1，eqlevel1}；v41Is d4The amount of rainfall in a certain period of time; v. of51Is d5The amount of rainfall in a certain period of time; r is51＝{risklat1，risklon1Indicating that the ground disaster event happens at the longitude and latitude; e131Is d3Mine blasting event of (1), e131＝{destruction1，etime1，elat1，elon1，elevel1}；e231Is d3In quarrying, e231＝{destruction2，etime1，elat1，elon1，elevel1}。
And S2, constructing an expert knowledge base for power transmission line environmental risk assessment, wherein the expert knowledge base comprises a multi-risk multi-dimensional disaster-causing factor knowledge base and a multi-dimensional disaster-causing factor membership function knowledge base. Wherein a multi-risk multi-dimensional disaster-causing factor knowledge base is used for matching dlCorrelation between each factor in the set and the 6 risksAnd generates a corresponding set of rules, denoted as W1(ii) a The multidimensional disaster-causing factor membership function knowledge base has the effect of being based on W1The combination relationship between the intermediate factors and the risks, the construction rule and the configuration of the corresponding membership function type and parameter are marked as W2。
Defining a power transmission line environment risk assessment identification framework theta ═ { true, false, true or false }, wherein true represents the existence of risk, false represents the absence of risk, and true or false represents the unknown; true and false are unitary focal elements, true or false is a mixed focal element.
W is to be1Expressing by adopting a triad mode: (RT)i，Fljn，Vij). Wherein RT isiFor the ith environmental risk type, i ∈ [1,..., 6]；FljnFor the j and RT in the l data setiName of the associated factor, Flj∈dl；VijIs FljnResult in RTiRule set of occurrence, Vij＝{rule1，rule2，...，ruleHWherein rulehH is the number of rules.
Based on W1The combination relation of the intermediate factors and the risks is combined with expert knowledge and fuzzy mathematics to construct a rule structureh＝(Flj，Θ1s，Ph) Multidimensional disaster-causing factor membership function knowledge base W2The representative rule is:
IF condition1(Flj)and/or condition2(Flj)THENΘ1s，Ph
wherein rulehIs FljResult in RTiH rule of occurrence, ruleh∈Vij(ii) a condition1 is a rule set by an expert; fljIs a reaction of with FljnCorresponding j and RTiAssociated factor data, Flj∈dl；Θ1To identify a set of unary foci in the framework Θ, Θ1＝{true，false}，Θ1s∈Θ1；PhTo satisfy FljResulting in theta1sType of membership function occurring and parameter configuration, Ph＝{funch，param_1h，param_2h，...，param_khIn which funchParam _ k being a membership function typehIs the value of the kth parameter of the membership function, and k is the number of the membership function parameters. The membership function types comprise a triangular membership function, a Gaussian membership function, a semi-trapezoidal membership function and a ridge-shaped distribution membership function.
Taking disaster-causing factor earthquake in the ground disaster as an example, the input data is an earthquake event { earthquare, eqtime1，eqlat1，eqlon1，eqlevel1Converting the event data into { earthquare, eq by data preprocessingtime1，eqdist1，eqlevel1}。
At W2Middle, rule1Comprises the following steps:
IF eqtime1＜t0 and eqdist1＜dist0 THEN true，P1
wherein, t0Is a preset time threshold, dist0Is a preset spatial distance threshold, P1＝{func1，param_11，param_21}。
The conflict resolution mode of the multi-dimensional disaster-causing factor membership function knowledge base is that when a plurality of contradictory rules exist, the most specific rule is excited, and the judgment basis is that the specific rule processes more information than the general rule.
S3, sequentially evaluating the risks of the power transmission line to be evaluated:
s31, according to W1The combined relation of the intermediate factor and the risk is obtained to evaluate the risk RTiFactor list Flist＝[Fl1n，Fl2n，...]，Fl1nIs the first data set leading to RTiName of the 1 st factor. According to the factor list FlistCorresponding factor data is acquired from step S1.
S32, according to W1The combination relationship of the middle factor and the rule set is obtained FlistA corresponding set of rules.
S33, for each rule in the step S32, inputting the factor data of S31 into the expert knowledge base W constructed in the step S22And according to rule matching, finding out the corresponding membership function type and parameter, and calculating the BPA of the one-dimensional focal element { true, false } of the target factor according to the membership function.
Taking the disaster-causing factor earthquake of the ground disaster as an example, according to the rules
IF eqtime1＜t0 and eqdist1＜dist0 THEN true，P1
Finding out the corresponding membership function type and parameter P1＝{func1，param_11，param_21BPA, BPA of the monadic focus true of the seismic event, respectivelytrue＝func1(param_11，param_21)；
Likewise, according to the rules
IF eqtime1≥t0 and eqdist1≥dist0 THENfalse，P2
Find out the corresponding membership function type and parameter P2 ═ { func2，param_12，param_22BPA, BPA of the single focal element false of the seismic event, respectivelyfalse＝func2(param_12，param_22)。
S34, calculating the BPA of the mixed focal elements { true or false } by adopting a geometric mean method, and normalizing the BPA of all the focal elements in the identification frame theta.
Taking earthquake factors of ground disaster risks as an example, the mixed focal elements
Upon normalization, BPA 'is generated that identifies all of the focal elements of framework Θ'true，BPA′fazse，BPA′mix∈[0，1]。
S35, synthesizing the BPA of the multi-dimensional disaster-causing factors of multiple data sources by adopting a Dempster rule in the DS evidence theory, and then converting the BPA into the probability of various risks to finish risk assessment.
The invention provides a power transmission line environmental risk assessment method integrating expert knowledge, which aims to assess various risks simultaneously and improve the universality of the method; meanwhile, the uncertainty of input data and an evaluation result can be flexibly processed, and the accuracy and stability of risk evaluation are improved.
In the prior art, the risk assessment of the power transmission line only aims at single risk to establish an assessment model, the method provided by the invention integrates expert knowledge and fuzzy mathematics, and can assess various risks in the operation of the power transmission line by constructing a multi-risk multi-dimensional disaster-causing factor knowledge base and a multi-dimensional disaster-causing factor membership function knowledge base, so that the universality is strong. In the prior art, influence of climate factors or single disaster factors is generally considered, influence of multiple factors on the risk of the power transmission line is not integrated, and various data related to operation of the power transmission line are collected and contain abundant disaster factors, so that an abundant data basis is provided for risk assessment. The risk assessment calculation method based on the expert knowledge base and the DS evidence theory not only realizes quantitative assessment of the transmission line risk, but also can flexibly process the uncertainty of the input data and the assessment result, and improves the stability of the risk assessment.
Claims (1)
1. A power transmission line environmental risk assessment method fusing expert knowledge, wherein the environmental risk comprises mountain fire, icing, ground disaster, thunder disaster, tree and bamboo and wind damage; the method is characterized by comprising the following steps:
s1, acquiring factor data related to environmental risks, including remote sensing monitoring d1And a sensor d2And d, manual inspection3And ground weather station d4Weather data service provider d5On-line equipment monitoring d6Is established from the l-thMultiple factor data set d obtained by data sourcel，dl＝{Fl1n：Fl1，...，Flmn：Flm}，FlmnAs a data set dlName of the mth factor in (1), FlmAs a data set dlM is dlThe number of factors involved. All data sets obtained are denoted as D ═ D1，d2，d3，d4，d5，d6}；
S2, constructing a power transmission line environmental risk assessment expert knowledge base, wherein the power transmission line environmental risk assessment expert knowledge base comprises a multi-risk multi-dimensional disaster-causing factor knowledge base and a multi-dimensional disaster-causing factor membership function knowledge base; wherein a multi-risk multi-dimensional disaster-causing factor knowledge base is used for matching dlThe incidence relation between each factor and the 6 risks, and generates a corresponding rule set, which is marked as W1(ii) a The multidimensional disaster-causing factor membership function knowledge base has the effect of being based on W1The combination relationship between the intermediate factors and the risks, the construction rule and the configuration of the corresponding membership function type and parameter are marked as W2；
Defining a power transmission line environment risk assessment identification framework theta ═ { true, false, true or false }, wherein true represents the existence of risk, false represents the absence of risk, and true or false represents the unknown; true and false are unitary focal elements, true or false is a mixed focal element;
w is to be1Expressing by adopting a triad mode: (RT)i，Fljn，Vij) (ii) a Wherein RT isiFor the ith environmental risk type, i ∈ [1,..., 6]；FljnFor the j and RT in the l data setiName of the associated factor, Flj∈dl；VijIs FljnResult in RTiRule set of occurrence, Vij＝{rule1，rule2，...，ruleHWherein rulehIs a rule, H is the number of the rules;
based on W1The combination relation of the intermediate factors and the risks is combined with expert knowledge and fuzzy mathematics to construct a rule structureh＝(Flj，Θ1s，Ph) Is/are as followsMultidimensional disaster-causing factor membership function knowledge base W2The representative rule is:
IF condition1(Flj)and/or condition2(Flj)THENΘ1s，Ph
wherein rulehIs FljResult in RTiH rule of occurrence, ruleh∈Vij(ii) a condition1 is a rule set by an expert; fljIs a reaction of with FljnCorresponding j and RTiAssociated factor data, Flj∈dl；Θ1To identify a set of unary foci in the framework Θ, Θ1＝{true，false}，Θ1s∈Θ1；PhTo satisfy FljResulting in theta1sType of membership function occurring and parameter configuration, Ph＝{funch，param_1h，param-2h，...，param_khIn which funchParam _ k being a membership function typehTaking the value of the kth parameter of the membership function, wherein k is the number of the membership function parameters; the membership function types comprise a triangular membership function, a Gaussian membership function, a semi-trapezoidal membership function and a ridge-shaped distribution membership function;
s3, sequentially evaluating the risks of the power transmission line to be evaluated:
s31, according to W1The combined relation of the intermediate factor and the risk is obtained to evaluate the risk RTiFactor list Flist＝[Fl1n，Fl2n，…]，Fl1nIs the first data set leading to RTiAccording to factor list FlistAcquiring corresponding factor data from step S1;
s32, according to W1The combination relationship of the middle factor and the rule set is obtained FlistA corresponding rule set;
s33, for each rule in the step S32, inputting the factor data of S31 into the expert knowledge base W constructed in the step S22According to rule matching, finding out the corresponding membership function type and parameter, and calculating the unitary focal element { true, fa of the target factor according to the membership functionBPA of ls };
s34, calculating the BPA of the mixed focal elements { true or false } by adopting a geometric mean method, and carrying out normalization processing on the BPA of all the focal elements in the identification frame theta;
s35, synthesizing the BPA of the multi-dimensional disaster-causing factors of multiple data sources by adopting a Dempster rule in the DS evidence theory, and then converting the BPA into the probability of various risks to finish risk assessment.
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