CN110766320A - Method and device for evaluating operation safety of airport intelligent power grid - Google Patents
Method and device for evaluating operation safety of airport intelligent power grid Download PDFInfo
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Abstract
The invention discloses an airport intelligent power grid operation safety evaluation method, which comprises the following steps: determining an airport intelligent power grid operation safety evaluation index system; based on the airport intelligent power grid operation safety evaluation index system, obtaining an open-loop evaluation result of the airport intelligent power grid operation state through index normalization processing, index weight coefficient determination, evaluation model selection, multi-dimensional evaluation result integration, and quantitative ordering and issuing of evaluation results; and adjusting according to the index critical out-of-limit level, the index and running state consistency level and the index and operation instruction correlation degree based on the open-loop evaluation result of the running state of the airport intelligent power grid, and obtaining an adjustment value of the index weight coefficient through a fuzzy neural network algorithm. The invention can ensure the comprehensiveness and systematicness of the evaluation index, improve the flexibility and adaptability of the evaluation system and have good engineering application prospect.
Description
Technical Field
The invention relates to the technical field of intelligent power grids, in particular to an airport intelligent power grid operation safety evaluation method and device.
Background
At present, the smart grid technology is gradually popularized and used in airport power supply systems, particularly large airport power supply systems, and the application of the smart grid technology brings new opportunities for the stability and safety of the airport power supply systems. The intelligent power grid operation safety evaluation system is an important technical basis for guiding the safe, efficient and high-quality development of the airport intelligent power grid, and is an important basis for promoting the improvement of the airport operation efficiency, improving the airport operation safety level and solving the problem of microgrid control. The intelligent power grid operation safety evaluation system collects, processes, analyzes and evaluates the data of airport energy production and consumption, so that the airport energy system is monitored, controlled and operated to evaluate safety, and the purposes of energy conservation and efficiency improvement are achieved. Through the intelligent power grid operation safety evaluation index system, the system operation safety level can be improved, and the fine management of the airport intelligent power grid is realized.
In order to better utilize the smart grid technology in an airport, a corresponding management and control system and an operation safety evaluation method need to be researched, but research results in the field are mainly concentrated on the application aspect of the smart grid at home and abroad, and research on the aspects of operation safety evaluation standards and methods of the smart grid of the airport is still less. In view of the above, the invention provides an airport intelligent power grid operation safety evaluation method and device.
Disclosure of Invention
The method and the device for evaluating the operation safety of the airport intelligent power grid provided by the invention realize breakthrough in the aspect of constructing an airport intelligent power grid operation safety evaluation system.
The invention provides an airport intelligent power grid operation safety evaluation method which is characterized by comprising the following steps:
step 10, determining an airport intelligent power grid operation safety evaluation index system;
step 20, based on the airport intelligent power grid operation safety evaluation index system, obtaining an open-loop evaluation result of the airport intelligent power grid operation state through index normalization processing, index weight coefficient determination, evaluation model selection, multi-dimensional evaluation result integration, and quantitative ordering and issuing of evaluation results;
step 30, based on the open-loop evaluation result of the operation state of the airport intelligent power grid, adjusting according to the index critical out-of-limit level, the index and operation state consistency level and the index and operation instruction correlation degree, and obtaining an adjustment value of an index weight coefficient through a fuzzy neural network algorithm; the index critical out-of-limit level refers to the distance between a certain index and the corresponding running safety out-of-limit value, the index and running state consistency level refers to the conformity degree between the index comprehensive evaluation result and the actual running state of the system, and the index and operation instruction association degree adjustment refers to the association degree between the index and the operation instruction.
Preferably, the method for evaluating the operation safety of the airport smart grid provided by the invention comprises the following steps in step 10:
step 101, obtaining a system operation file of an airport intelligent power grid, wherein the system operation file comprises a system log, an abnormal alarm and an operation record;
102, statistically analyzing influence factors related to the operation safety of the airport intelligent power grid in the system operation file to form a rough airport intelligent power grid operation safety evaluation index factor set;
and 103, establishing an airport intelligent power grid operation safety evaluation index system by a principal component analysis method based on the rough airport intelligent power grid operation safety evaluation index factor set.
Preferably, the method for evaluating the operation safety of the airport smart grid provided by the invention, wherein the step 103 specifically comprises the following steps:
step 1031, standardizing original description information of the influence factors related to the operation safety of the airport intelligent power grid in the rough airport intelligent power grid operation safety evaluation index factor set to obtain the standardized influence factors related to the operation safety of the airport intelligent power grid;
step 1032, calculating a correlation matrix, specifically: calculating a correlation coefficient covariance matrix C of the influence factor matrix which is normalized in the step 1031 and related to the operation safety of the airport intelligent power grid by using a formula I:
in formula one, n represents the number of original operation safety influencing factors, cij(i, j is 1,2, …, n) represents the covariance coefficient between the influence factor i and the influence factor j related to the operation safety of the airport smart grid after standardization, and c is calculated by the formula twoij：
In formula two, m represents the number of statistical samples, xki、xkjRespectively representing the difference values of the standardized influence factors i and j related to the operation safety of the airport intelligent power grid and the mean value of the influence factors j during the kth statistical process;
step 1033, calculating eigenvalues and eigenvectors of the correlation coefficient covariance matrix C, specifically, calculating eigenvector λ of the matrix C, further obtaining n unitized eigenvectors corresponding to the eigenvector λ, forming a matrix α from the n unitized eigenvectors, and arranging α according to rows, wherein the arrangement basis is to ensure that eigenvalues of the matrix C corresponding to each row in α are ordered from large to small;
step 1034, calculating variance contribution degree and selecting principal components, specifically: from each eigenvalue λ in the eigenvector λiDetermining the contribution degree of each variance value according to the occupied proportion; the eigenvalue lambda in the eigenvector lambdaiSorting according to the sequence from big to small, selecting the first z eigenvalues in the eigenvector, and calculating the proportion Q of the sum of the z eigenvalues to the sum of all eigenvalueszWherein Q iszThe calculation formula of (2) is as follows:
in formula three, QzRepresents the sum of sample information represented by the first z principal components, when QzIf the sample information represented by the first z principal components is satisfied when a certain threshold is exceeded, the components corresponding to the z principal component eigenvalues are screened through the first z rows of the matrix α.
Preferably, in the method for evaluating the operation safety of the airport smart grid, the fuzzy neural network algorithm in step 30 is specifically:
dividing an algorithm structure into an input layer, a membership function fuzzy layer, a fuzzy rule layer and an anti-fuzzy output layer;
layer 1 is an input layer for directly transferring input values to the next layer, and the input and output relationships of layer 1 are:
wherein,
respectively an input and an output of the node,superscript 1 denotes the number of layers, subscript i denotes the ith cell, and i<n, n is the number of input indexes;
the layer 2 is a membership function fuzzification layer, is used for fuzzifying variables input from the layer 1, and is specifically divided into the following steps: 5 fuzzy sets of extremely small, medium, large and extremely large are selected, a Gaussian function is selected as a membership function, and the input and output relations of the 2 nd layer are as follows:
in the formula five, the first and second groups,the input and the output of a j-th node corresponding to an ith input variable of a 2 nd layer of the fuzzy neural network, wherein j is 1,2,3,4, 5; m isij，σijMean and standard deviation of Gaussian membership function of j fuzzy node of i input variable, mij，σijAll are adjusted according to requirements;
representing membership of the ith input variable to the jth fuzzy set;
the 3 rd layer is a fuzzy rule layer, the number of nodes is a fuzzy rule number, and the input and output relations of the 3 rd layer are as follows:
in the formula six, wherein
The input and the output of the kth node in the 3 rd layer of the neural network are respectively;
the 4 th layer is an anti-fuzzy output layer and is used for de-blurring the fuzzy function, the output of the node is a linear combination corresponding to the rule obtained by the 3 rd layer, and the input and output relations of the 4 th layer are as follows:
in the formula seven, the first step,
is an input to the layer 4, and,
is the output of the 4 th layer, wherein m is the number of the indexes needing to be modified; omegamkIs the connection weight, ω, between layer 3 and layer 4mkAdjusting according to the requirement; and K is the number of nodes on the 3 rd layer.
Preferably, the method for evaluating the operation safety of the airport intelligent power grid, provided by the invention, defines a loss function E of the fuzzy neural network as follows:
in the formula eight, drTo a desired output value, yrIs the actual output value, yrIn the output layer
The value N is the number of times of neural network training;
using a loss function E vs. mij，σij，ωmkThe parameters are updated iteratively in a specific way:
where η is the learning rate, t is the number of learning iterations,
representing the operator of partial differentiation.
Preferably, the method for evaluating the operation safety of the airport smart grid provided by the invention, wherein the step 20 specifically comprises the following steps:
step 201, dividing indexes to be evaluated in an airport intelligent power grid operation safety evaluation index system into cost type indexes, benefit type indexes, fixed type indexes and interval type indexes according to the mutual relation among the index performance characteristics, the difference and the index values, and normalizing the cost type indexes, the benefit type indexes, the fixed type indexes and the interval type indexes into the same type of indexes which are called normalized indexes;
step 202, determining an index weight coefficient for the normalized index;
step 203, evaluating the operation safety state of the airport intelligent power grid by using a fuzzy comprehensive evaluation model;
step 204, determining a final quantitative evaluation index of the operation state of the airport intelligent power grid by using a comprehensive evaluation function;
and step 205, sequencing and displaying the final quantitative evaluation indexes of the operation state of the airport intelligent power grid, and displaying the change trend of the operation state of the airport intelligent power grid through comparison with historical data.
The invention provides an airport intelligent power grid operation safety evaluation device which is characterized by comprising the following components:
the safety evaluation index system acquisition device is used for determining an airport intelligent power grid operation safety evaluation index system;
the open-loop evaluation result acquisition device is used for acquiring an open-loop evaluation result of the operation state of the airport intelligent power grid through index normalization processing, index weight coefficient determination, evaluation model selection, multi-dimensional evaluation result integration, and quantitative sorting and issuing of the evaluation result based on the airport intelligent power grid operation safety evaluation index system;
the index weight coefficient adjustment value acquisition device is used for adjusting according to the index critical out-of-limit level, the index and running state consistency level and the index and operation instruction association degree based on the open loop evaluation result of the running state of the airport intelligent power grid, and obtaining the adjustment value of the index weight coefficient through a fuzzy neural network algorithm; the index critical out-of-limit level refers to the distance between a certain index and the corresponding running safety out-of-limit value, the index and running state consistency level refers to the conformity degree between the index comprehensive evaluation result and the actual running state of the system, and the index and operation instruction association degree adjustment refers to the association degree between the index and the operation instruction.
The invention provides an airport intelligent power grid operation safety evaluation system which is characterized by comprising the following components: memory, processor and computer program stored on the memory and executable on the processor, which when executed by the processor implements the method as described above.
A computer-readable storage medium according to the present invention, on which a computer program is stored, is characterized in that the program, when being executed by a processor, implements the method as described above.
Compared with the prior art, before the safety evaluation of the operation of the airport intelligent power grid is carried out, the evaluation indexes are simplified and sorted by a principal component analysis method, so that the comprehensiveness and systematicness of the evaluation indexes are ensured; after the open-loop evaluation result is generated, the flexibility, the adaptability and the like of the evaluation system are improved based on a weight coefficient adjusting mechanism of the fuzzy neural network algorithm, and the risk occurrence probability is comprehensively considered in the evaluation process, so that the method has a good engineering application prospect.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a flow chart of a method for evaluating the operation safety of an airport intelligent power grid according to the invention;
FIG. 2 is a schematic diagram of a fuzzy neural network-based weight coefficient correction method according to the present invention.
Detailed Description
Example one
Fig. 1 is a flowchart of an airport smart grid operation safety evaluation method according to the present invention. As shown in FIG. 1, the method divides the operation safety evaluation process of the airport intelligent power grid into two parts, namely an evaluation index system determination and operation state evaluation. Wherein, the part for determining the evaluation index system is used for establishing the index system required by the evaluation, and belongs to the preparation process of the operation safety evaluation; the operation state evaluation is used for realizing the evaluation of the operation safety of the airport intelligent power grid and can be subdivided into six processes of index normalization processing, weight coefficient determination, evaluation model selection, multi-dimensional evaluation result integration, quantitative ordering and issuing of evaluation results and evaluation index weight coefficient adjustment.
Specifically, the method comprises the following steps:
and step 10, determining an airport intelligent power grid operation safety evaluation index system.
In the part of determining the evaluation index system, the method simplifies a rough airport intelligent power grid operation safety evaluation index factor set through a principal component analysis method, and guarantees the comprehensiveness and systematicness of the proposed airport intelligent power grid operation safety evaluation index system.
As shown in fig. 1, the process of determining the safety evaluation index system for safe operation of the smart grid at the airport includes:
step 101, obtaining a system operation file of an airport intelligent power grid, wherein the system operation file comprises a system log, an abnormal alarm, an operation record, a planning and designing condition and the like.
And 102, statistically analyzing influence factors related to the operation safety of the airport intelligent power grid in the system operation file to form a rough airport intelligent power grid operation safety evaluation index factor set.
And 103, establishing an airport intelligent power grid operation safety evaluation index system by a principal component analysis method based on the rough airport intelligent power grid operation safety evaluation index factor set.
Wherein, step 103 specifically comprises:
and step 1031, standardizing the original description information of the influence factors related to the operation safety of the airport intelligent power grid in the rough airport intelligent power grid operation safety evaluation index factor set to obtain the standardized influence factors related to the operation safety of the airport intelligent power grid. Specifically, according to information such as system measuring points and interface description configuration, original description names of influence factors related to the operation safety of the airport intelligent power grid in system operation files such as system logs, abnormal alarms and operation records are unified, the original description names are subjected to standardized processing, description confusion is avoided, and linear independence among the influence factors is guaranteed. For example, for voltage problems affecting power supply safety, including voltage fluctuation, voltage flicker, voltage sudden rise and drop, and the like, the voltage problems have a certain correlation, and the described method is not uniform, thereby affecting the subsequent statistical analysis of influencing factors, and the voltage problems can be uniformly expressed as the system voltage qualified rate. In order to eliminate the influence of dimension difference, the Z-Score method is adopted to carry out standardization processing on each influence factor, such as unifying power meters (kilowatts and megawatts) with different magnitudes to the same magnitude.
Step 1032, calculate the correlation matrix. Calculating a correlation coefficient covariance matrix C of the influence factor matrix which is normalized in the step 1031 and related to the operation safety of the airport intelligent power grid by using a formula I:
in formula one, n represents the number of original operation safety influencing factors, cij(i, j is 1,2, …, n) represents the covariance coefficient between the influence factor i and the influence factor j related to the operation safety of the airport smart grid after standardization, and c is calculated by the formula twoij：
In formula two, m represents the number of statistical samples, xki、xkjAnd respectively representing the difference values of the standardized influence factors i and j related to the operation safety of the airport intelligent power grid and the mean value of the influence factors j during the kth statistical process.
And step 1033, calculating eigenvalues and eigenvectors of the correlation coefficient covariance matrix C, calculating eigenvector λ of the matrix C, further obtaining n unitized eigenvectors corresponding to the eigenvector λ, forming a matrix α by the n unitized eigenvectors, and arranging α in rows according to the basis that the eigenvalues of the matrix C corresponding to each row in α are ordered from large to small.
Step 1034, calculate variance contribution degree and select principal component. From each eigenvalue λ in the eigenvector λiAnd determining the contribution degree of each variance value according to the occupied proportion, wherein the contribution degree represents the information reflection degree of the principal component to the original sample, and the larger the contribution degree is, the more the sample information is reflected. The eigenvalue lambda in the eigenvector lambdaiSorting according to the sequence from big to small, selecting the first z eigenvalues in the eigenvector, and calculating the proportion Q of the sum of the z eigenvalues to the sum of all eigenvalueszWherein Q iszThe calculation formula of (2) is as follows:
Qzrepresenting the sum of the sample information represented by the first z principal components. When Q iszWhen a certain threshold (usually, the threshold is 0.8), which indicates that the sample information represented by the first z principal components has satisfied the requirement, the component corresponding to the characteristic value of the z principal components is screened through the first z rows of the matrix α, and the z factors are the simplified operation safety evaluation indexes.
Specifically, in the application of the airport smart grid, if the standardized influence factors related to the operation safety of the airport smart grid obtained in step 1031 include three influence factors, namely, a line current-carrying margin, a grid splitting number and a distributed power utilization rate, and there are 5 records for the three factors, that is, m is 5, the three factors may be expressed in a data set form as:
the first row of the data set represents records of current carrying margins of 5 lines, the second row represents records of the number of split pieces of 5 power grids, and the third row represents records of the utilization rate of 5 distributed power supplies.
Then, through step 1032, a correlation matrix is calculated, and the data set is first normalized to:
the resulting matrix C is:
calculating the eigenvalue and the eigenvector of the matrix C, and respectively obtaining the arrangement result of the eigenvalue from large to small as follows: lambda [ alpha ]1＝7.5236，λ2＝1.1431，λ3The corresponding unitized feature vector α is:
through calculation, the proportion of the first two characteristic values in the sum of the characteristic values is as follows:
the main factors influencing the operation safety of the airport intelligent power grid, namely the line current-carrying margin and the power grid splitting number, can be screened out from the line current-carrying margin, the power grid splitting number and the distributed power supply utilization rate through the first two rows of the α matrix, so that the distributed power supply utilization rate is not considered in the construction of evaluation indexes influencing the operation safety of the airport intelligent power grid.
By analogy with the above method, the reduction of the set of influencing factors given in step 102 can be accomplished.
TABLE 1 airport Smart grid operation safety evaluation index System example
Table 1 shows an example of an airport smart grid operation safety evaluation index system, where the three-level index is the airport smart grid operation safety evaluation index system established by the principal component analysis method. Clustering is carried out according to the system to which each three-level index belongs and the data type source, and corresponding first-level indexes and second-level indexes can be constructed on the three-level indexes so as to be convenient for expression. The first-level indexes are divided according to planning related indexes and operation related indexes, and the planning related indexes (robustness indexes) comprise secondary indexes such as grid structures, power distribution network conveying capacity and system disaster resistance; the operation related indexes (reliability indexes) can be divided into secondary indexes such as power supply safety, power failure time, power failure loss and the like.
Therefore, according to the method, the factors influencing the safe operation of the airport intelligent power grid are counted based on the airport intelligent power grid system operation files such as system logs, abnormal alarms and operation records, the factors are classified and simplified by a principal component analysis method, an airport intelligent power grid operation safety evaluation index system is constructed, and the comprehensiveness and systematicness of evaluation indexes are guaranteed.
And step 20, based on the airport intelligent power grid operation safety evaluation index system, obtaining an open-loop evaluation result of the airport intelligent power grid operation state through index normalization processing, index weight coefficient determination, evaluation model selection, multi-dimensional evaluation result integration, and quantitative ordering and issuing of evaluation results.
In the operation state evaluation part, the method comprises open-loop evaluation processes of index normalization processing, weight coefficient determination, evaluation model selection, multi-dimensional evaluation result integration, quantitative ordering and issuing of evaluation results and the like, and the open-loop evaluation results are used for training the fuzzy neural network to realize a closed-loop adjustment process of the weight coefficients, so that the flexibility and the adaptability of the evaluation system are improved.
The method comprises the following steps:
step 201, dividing indexes needing to be evaluated in an airport intelligent power grid operation safety evaluation index system into cost type indexes, benefit type indexes, fixed type indexes and interval type indexes according to the mutual relation among the index performance characteristics, the difference and the index values, and normalizing the cost type indexes, the benefit type indexes, the fixed type indexes and the interval type indexes into the same type indexes. Specifically, indexes to be evaluated are classified to a certain extent according to related concepts, rules and connotations, quantitative indexes can be further classified into four categories, namely 'cost type', 'benefit type', 'fixed type' and 'interval type', according to the mutual relation among the performance characteristics, the differences and the index values of the indexes, and due to the fact that the types of the indexes are different and the incommercibility among the indexes is high, the indexes need to be subjected to normalization preprocessing before decision making.
The index pretreatment refers to the unified conversion of four different types of indexes including cost type, benefit type, fixed type and interval type into the same type of index.
The cost-type indexes are evaluation indexes with smaller values and better values, and for the cost-type indexes, the order is that
The 'benefit type' index refers to the evaluation index with the better value, and for the 'benefit type' index, the order is that
The fixed index is an evaluation index which is not too large or too small in value and is optimal when being stabilized at a certain fixed value, and for the fixed index, the order is that
The "interval type" index refers to a type of index whose value falls within a certain fixed interval as the best, and for the "interval type" index, the order is
Wherein a isijRepresenting scheme XiFor index YjIs detected by the measured values of (a) and (b),
denotes the jth index YjMaximum and minimum values of.
Is an index YjThe optimum stable value of (c). [ u ] of1j,u2j]Is an index YjThe optimum stability interval of (1).
After pretreatment, rij∈[0,1]. Pre-processed decision matrix R ═ (R)ij) m n is called normalized decision matrix, rijRepresenting scheme XiFor the index YjThe normalized index value of (1).
Step 202, determining an index weight coefficient for the normalized index. Specifically, the weighting coefficient is used for representing the contribution degree of the evaluation index to the evaluation target and the relative importance between the evaluation indexes, and each index in the evaluation system is weighted by a weighting method such as an entropy weight method and a standard deviation method.
And step 203, evaluating the operation safety state of the airport intelligent power grid by using a fuzzy comprehensive evaluation model. Specifically, considering that a distributed power supply in an airport intelligent power grid has the characteristics of volatility and intermittence, a fuzzy comprehensive evaluation model is used for comprehensively evaluating various operation states of the system, the fuzzy comprehensive evaluation model uses a level fuzzy subset method to quantify indexes of evaluated objects, and an evaluation subject is generally evaluated according to quantified data. The steps evaluated using this method were: determining an evaluation main body and a corresponding evaluation index system U; determining the evaluation grade, and constructing a comment set V on the basis of the evaluation grade; evaluating the evaluation index system U according to the grade of the comment to form an evaluation matrix D, wherein the element in the D indicates the membership degree of the evaluation index U to the comment v; and obtaining an evaluation result B according to a formula, wherein w is the weight of the evaluation index, and determining the evaluation grade of the evaluation subject according to the principle of the maximum membership degree according to the evaluation result.
And 204, determining a final quantitative evaluation index of the operation state of the airport intelligent power grid by using a comprehensive evaluation function. Specifically, the process can be expressed in a mathematical language where m evaluation index values { x } are knownjIn the case of (j ═ 1,2, …, m), a comprehensive evaluation function is constructed
Wherein w is an index weight vector, and w is (w)1,w2,…,wm)TSatisfy the following requirements
And y is the final quantitative evaluation index of the operation state of the airport intelligent power grid.
And step 205, sequencing and displaying the final quantitative evaluation indexes of the operation state of the airport intelligent power grid, and comparing the final quantitative evaluation indexes with historical data to display the change trend of the operation state of the airport intelligent power grid, so that the bottleneck restricting the improvement of the operation safety of the system is obtained, and finally, the operation and maintenance personnel are helped to comprehensively master the operation state of the system.
And step 30, based on the open-loop evaluation result of the operation state of the airport intelligent power grid, adjusting according to the index critical out-of-limit level, the index and operation state consistency level and the index and operation instruction correlation degree, and obtaining the adjustment value of the index weight coefficient through a fuzzy neural network algorithm.
As shown in fig. 2, in the present invention, after a single open-loop evaluation is completed, the existing weighting coefficients are adjusted by using the fuzzy neural network algorithm according to the critical out-of-limit level of the indicator, the consistency level of the indicator and the operating state, the association degree adjustment of the indicator and the operating instruction, and other factors, so that the evaluation system can meet the objective requirement of the system operation.
The index critical out-of-limit level refers to the distance between an index and the corresponding operation safety out-of-limit value, when the index approaches or exceeds the operation safety out-of-limit value, the weight of the index is increased, the out-of-limit behavior of the main index is pre-warned, the out-of-limit index is prevented from being covered due to the fact that the weight is small, and 'report missing' under the condition that the index is out-of-limit is prevented. For example: under normal conditions, the load rate has little influence on the system operation safety, a smaller weight is given to the load rate, and when the load rate is possibly seriously deviated from a normal value, the comprehensive evaluation result of the airport intelligent power grid operation safety is still at a safety level and cannot reflect the real operation condition of the system. When the load rate is critical or exceeds the safety limit, the weight coefficient of the load rate should be properly adjusted through a weight coefficient feedback algorithm.
The consistency level of the indexes and the running state reflects the conformity degree between the comprehensive evaluation result of the indexes and the actual running state of the system, and when the comprehensive evaluation result of the indexes is smaller but the running safety state of the system is not changed, the weight of the corresponding indexes is reduced, so that the condition of 'false alarm' of the running safety indexes is prevented. For example: when a smart grid in a certain area of an airport is in isolated island operation (only a micro power supply is used for supplying power, and the smart grid is not connected with a large grid), due to the lack of support of the large grid, the two indexes of the voltage qualification rate and the frequency qualification rate of a system in the area are poor, but the system in isolated island operation can still run safely, and the weight of the two indexes of the voltage qualification rate and the frequency qualification rate of the system is reduced through a weight coefficient feedback algorithm.
The relevance degree of the index and the operation instruction is adjusted to indicate the relevance degree between the index and the operation instruction, and the weight of the index is adjusted, so that certain selectivity is provided between the setting of the weight of the index and the running state of the system. For example: when a certain island operation area in a smart grid of a airport needs to be converted from grid to grid, starting and stopping of certain micro power sources and conversion of operation modes are involved, so that indexes such as distributed power source/energy storage/micro grid capacity, system voltage qualification rate, system frequency qualification rate and the like fluctuate, but the system still runs safely at the moment. Then the weight coefficient of the affected item should be adjusted through a weight coefficient feedback algorithm according to the grid-connected and off-grid control command.
The fuzzy neural network method adopted by the invention divides the algorithm structure into an input layer, a membership function fuzzy layer, a fuzzy rule layer and an anti-fuzzy output layer. The meaning and effect of each layer are expressed as:
the layer 1 is an input layer which is responsible for directly transmitting an input value to the next layer, and the input and output relations of the layer are as follows:
wherein,
input and output, respectively, of a node (the superscript "1" denotes the number of layers, the subscript "i" denotes the ith cell, and i<n, n is the number of input indexes), the input can adopt an index critical out-of-limit level, an index and running state consistency level, and an index and operation instruction association degree adjustment index, and if n is 3.
The 2 nd layer is a membership function fuzzification layer, and fuzzifies input variables. The layer has 15 nodes in total, and the function of the layer is to fuzzify an input signal. Using a Gaussian function to perform fuzzification processing on the input variable of the 1 st layer, and specifically comprising the following steps: 5 fuzzy sets which are extremely small, medium, large and extremely large. Considering the advantages of the Gaussian membership function in processing non-binary input and space mapping, selecting the Gaussian function as the membership function, and then the layer 2 input and output relations are as follows:
in the formula:
j is the input and output of the j-th node corresponding to the ith input variable of the 2 nd layer of the neural network, and j is 1,2,3,4, 5; m isij，σijIs the ith input variableMean and standard deviation of Gaussian membership functions of j fuzzy nodes, mij，σijAll can be adjusted according to requirements.
It represents the membership of the ith input variable to the jth fuzzy set.
The 3 rd layer is a fuzzy rule layer, the number of nodes is a fuzzy rule number, and the input and output relations are as follows:
wherein
Respectively, the input and output of the kth node in layer 3 of the neural network.
The 4 th layer is an anti-fuzzy output layer, the node is used for carrying out anti-fuzzy function, the output of the node is a linear combination corresponding to the control rule obtained by the 3 rd layer, and the relation between the input and the output is as follows:
wherein,
is an input to the layer 4, and,
is the output of the 4 th layer, wherein m is the output number, namely the number of the index weights required to be modified; omegamkIs the connection weight between layer 3 and layer 4, and ωmkCan be adjusted according to the requirements; and K is the number of nodes on the 3 rd layer. After passing through the 4-layer neural network, the important index weight correction quantity can be obtained. In the layer 4 output layer, each neural node represents an index weight correction amount of the evaluation weight for correcting the weight coefficient of the specific index.
The loss function E defining the fuzzy neural network is:
wherein: drTo a desired output value, yrIs the actual output value, yrI.e. in the output layer
The value, N, is the number of times the neural network training is performed.
The loss function E can be used for mij，σij，ωmkThe parameters are updated iteratively in a specific way:
where η is the learning rate, t is the number of learning iterations,
representing the operator of partial differentiation.
In a complex airport smart grid system, the weight of the system operational safety influencing factors is difficult to be expressed by a fixed value. By using the weight system feedback algorithm, the fuzzy neural network is trained by using massive airport intelligent power grid operation record information. And then, the trained fuzzy neural network model is used for adjusting the weight coefficient of the evaluation index according to the critical out-of-limit level of the index, the consistency level of the index and the running state, the adjustment of the association degree of the index and the operation instruction and other factors, so that the accuracy and the flexibility of the whole algorithm are improved.
Example two
The invention also provides an airport intelligent power grid operation safety evaluation device, which comprises: the safety evaluation index system acquisition device is used for determining an airport intelligent power grid operation safety evaluation index system; the open-loop evaluation result acquisition device is used for acquiring an open-loop evaluation result of the operation state of the airport intelligent power grid through index normalization processing, index weight coefficient determination, evaluation model selection, multi-dimensional evaluation result integration, and quantitative sorting and issuing of the evaluation result based on the airport intelligent power grid operation safety evaluation index system; the index weight coefficient adjustment value acquisition device is used for adjusting according to the index critical out-of-limit level, the index and running state consistency level and the index and operation instruction association degree based on the open loop evaluation result of the running state of the airport intelligent power grid, and obtaining the adjustment value of the index weight coefficient through a fuzzy neural network algorithm; the index critical out-of-limit level refers to the distance between a certain index and the corresponding running safety out-of-limit value, the index and running state consistency level refers to the conformity degree between the index comprehensive evaluation result and the actual running state of the system, and the index and operation instruction association degree adjustment refers to the association degree between the index and the operation instruction.
EXAMPLE III
The invention also provides an airport intelligent power grid operation safety evaluation system, which is characterized by comprising the following components: memory, processor and computer program stored on the memory and executable on the processor, which when executed by the processor implements the method as described above.
Example four
The invention also proposes a computer-readable storage medium, on which a computer program is stored, characterized in that the program, when executed by a processor, implements the method as described above.
It should be understood that the above-mentioned embodiments are merely preferred examples of the present invention, and not restrictive, but rather, all the changes, substitutions, alterations and modifications that come within the spirit and scope of the invention as described above may be made by those skilled in the art, and all the changes, substitutions, alterations and modifications that fall within the scope of the appended claims should be construed as being included in the present invention.
Claims (9)
1. An airport intelligent power grid operation safety evaluation method is characterized by comprising the following steps:
step 10, determining an airport intelligent power grid operation safety evaluation index system;
step 20, based on the airport intelligent power grid operation safety evaluation index system, obtaining an open-loop evaluation result of the airport intelligent power grid operation state through index normalization processing, index weight coefficient determination, evaluation model selection, multi-dimensional evaluation result integration, and quantitative ordering and issuing of evaluation results;
step 30, based on the open-loop evaluation result of the operation state of the airport intelligent power grid, adjusting according to the index critical out-of-limit level, the index and operation state consistency level and the index and operation instruction correlation degree, and obtaining an adjustment value of an index weight coefficient through a fuzzy neural network algorithm; the index critical out-of-limit level refers to the distance between a certain index and the corresponding running safety out-of-limit value, the index and running state consistency level refers to the conformity degree between the index comprehensive evaluation result and the actual running state of the system, and the index and operation instruction association degree adjustment refers to the association degree between the index and the operation instruction.
2. The airport smart grid operation safety evaluation method of claim 1, wherein step 10 specifically comprises the steps of:
step 101, obtaining a system operation file of an airport intelligent power grid, wherein the system operation file comprises a system log, an abnormal alarm and an operation record;
102, statistically analyzing influence factors related to the operation safety of the airport intelligent power grid in the system operation file to form a rough airport intelligent power grid operation safety evaluation index factor set;
and 103, establishing an airport intelligent power grid operation safety evaluation index system by a principal component analysis method based on the rough airport intelligent power grid operation safety evaluation index factor set.
3. The airport smart grid operation security evaluation method of claim 2, wherein step 103 specifically comprises the steps of:
step 1031, standardizing original description information of the influence factors related to the operation safety of the airport intelligent power grid in the rough airport intelligent power grid operation safety evaluation index factor set to obtain the standardized influence factors related to the operation safety of the airport intelligent power grid;
step 1032, calculating a correlation matrix, specifically: calculating a correlation coefficient covariance matrix C of the influence factor matrix which is normalized in the step 1031 and related to the operation safety of the airport intelligent power grid by using a formula I:
in formula one, n represents the number of original operation safety influencing factors, cij(i, j is 1,2, …, n) represents the covariance coefficient between the influence factor i and the influence factor j related to the operation safety of the airport smart grid after standardization, and c is calculated by the formula twoij：
In formula two, m represents the number of statistical samples, xki、xkjRespectively representing the difference values of the standardized influence factors i and j related to the operation safety of the airport intelligent power grid and the mean value of the influence factors j during the kth statistical process;
step 1033, calculating eigenvalues and eigenvectors of the correlation coefficient covariance matrix C, specifically, calculating eigenvector λ of the matrix C, further obtaining n unitized eigenvectors corresponding to the eigenvector λ, forming a matrix α from the n unitized eigenvectors, and arranging α according to rows, wherein the arrangement basis is to ensure that eigenvalues of the matrix C corresponding to each row in α are ordered from large to small;
step 1034, calculating variance contribution degree and selecting principal components, specifically: from each eigenvalue λ in the eigenvector λiDetermining the contribution degree of each variance value according to the occupied proportion; the eigenvalue lambda in the eigenvector lambdaiSorting according to the sequence from big to small, and selecting the first z eigenvalues in the eigenvectorCalculating the proportion Q of the sum of the z eigenvalues to the sum of all eigenvalueszWherein Q iszThe calculation formula of (2) is as follows:
in formula three, QzRepresents the sum of sample information represented by the first z principal components, when QzIf the sample information represented by the first z principal components is satisfied when a certain threshold is exceeded, the components corresponding to the z principal component eigenvalues are screened through the first z rows of the matrix α.
4. The airport smart grid operation safety evaluation method of claim 1, wherein the fuzzy neural network algorithm in step 30 is specifically:
dividing an algorithm structure into an input layer, a membership function fuzzy layer, a fuzzy rule layer and an anti-fuzzy output layer;
layer 1 is an input layer for directly transferring input values to the next layer, and the input and output relationships of layer 1 are:
wherein,
input and output of the node, respectively, the superscript 1 denoting the number of layers, the subscript i denoting the ith cell, and i<n, n is the number of input indexes;
the layer 2 is a membership function fuzzification layer, is used for fuzzifying variables input from the layer 1, and is specifically divided into the following steps: 5 fuzzy sets of extremely small, medium, large and extremely large are selected, a Gaussian function is selected as a membership function, and the input and output relations of the 2 nd layer are as follows:
in the formula five, the first and second groups,
the input and the output of a j-th node corresponding to an ith input variable of a 2 nd layer of the fuzzy neural network, wherein j is 1,2,3,4, 5; m isij，σijMean and standard deviation of Gaussian membership function of j fuzzy node of i input variable, mij，σijAll are adjusted according to requirements;
representing membership of the ith input variable to the jth fuzzy set;
the 3 rd layer is a fuzzy rule layer, the number of nodes is a fuzzy rule number, and the input and output relations of the 3 rd layer are as follows:
in the formula six, wherein
The input and the output of the kth node in the 3 rd layer of the neural network are respectively;
the 4 th layer is an anti-fuzzy output layer and is used for de-blurring the fuzzy function, the output of the node is a linear combination corresponding to the rule obtained by the 3 rd layer, and the input and output relations of the 4 th layer are as follows:
in the formula seven, the first step,
is an input to the layer 4, and,
is the output of layer 4Wherein m is the number of the index weights required to be modified; omegamkIs the connection weight, ω, between layer 3 and layer 4mkAdjusting according to the requirement; and K is the number of nodes on the 3 rd layer.
5. The airport smart grid operational safety assessment method of claim 4, wherein: the loss function E defining the fuzzy neural network is:
in the formula eight, drTo a desired output value, yrIs the actual output value, yrIn the output layer
The value N is the number of times of neural network training;
using a loss function E vs. mij，σij，ωmkThe parameters are updated iteratively in a specific way:
where η is the learning rate, t is the number of learning iterations,
representing the operator of partial differentiation.
6. The airport smart grid operation security evaluation method of claim 1, wherein step 20 comprises the steps of:
step 201, dividing indexes to be evaluated in an airport intelligent power grid operation safety evaluation index system into cost type indexes, benefit type indexes, fixed type indexes and interval type indexes according to the mutual relation among the index performance characteristics, the difference and the index values, and normalizing the cost type indexes, the benefit type indexes, the fixed type indexes and the interval type indexes into the same type of indexes which are called normalized indexes;
step 202, determining an index weight coefficient for the normalized index;
step 203, evaluating the operation safety state of the airport intelligent power grid by using a fuzzy comprehensive evaluation model;
step 204, determining a final quantitative evaluation index of the operation state of the airport intelligent power grid by using a comprehensive evaluation function;
and step 205, sequencing and displaying the final quantitative evaluation indexes of the operation state of the airport intelligent power grid, and displaying the change trend of the operation state of the airport intelligent power grid through comparison with historical data.
7. An airport smart grid operation safety evaluation device, characterized in that the device includes:
the safety evaluation index system acquisition device is used for determining an airport intelligent power grid operation safety evaluation index system;
the open-loop evaluation result acquisition device is used for acquiring an open-loop evaluation result of the operation state of the airport intelligent power grid through index normalization processing, index weight coefficient determination, evaluation model selection, multi-dimensional evaluation result integration, and quantitative sorting and issuing of the evaluation result based on the airport intelligent power grid operation safety evaluation index system;
the index weight coefficient adjustment value acquisition device is used for adjusting according to the index critical out-of-limit level, the index and running state consistency level and the index and operation instruction association degree based on the open loop evaluation result of the running state of the airport intelligent power grid, and obtaining the adjustment value of the index weight coefficient through a fuzzy neural network algorithm; the index critical out-of-limit level refers to the distance between a certain index and the corresponding running safety out-of-limit value, the index and running state consistency level refers to the conformity degree between the index comprehensive evaluation result and the actual running state of the system, and the index and operation instruction association degree adjustment refers to the association degree between the index and the operation instruction.
8. An airport smart grid operation safety evaluation system is characterized in that the system comprises: memory, processor and computer program stored on the memory and executable on the processor, which when executed by the processor implements the method of any one of claims 1 to 6.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-6.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112000556A (en) * | 2020-07-06 | 2020-11-27 | 广州西山居世游网络科技有限公司 | Method and device for displaying downtime of client program and readable medium |
| CN112668888A (en) * | 2020-12-30 | 2021-04-16 | 中国电力科学研究院有限公司 | Method and system for evaluating comprehensive state of relay protection device |
| CN112862266A (en) * | 2021-01-19 | 2021-05-28 | 昆明理工大学 | Coal mine accident emergency plan evaluation model construction method based on fuzzy decision |
| CN114154780A (en) * | 2021-10-19 | 2022-03-08 | 港华能源投资有限公司 | Evaluation method and device, electronic equipment and related product |
| CN114204554A (en) * | 2021-12-10 | 2022-03-18 | 国网江苏省电力有限公司 | Method for evaluating regulation and control operation safety for intelligent power grid system |
| CN114384916A (en) * | 2022-01-13 | 2022-04-22 | 华中科技大学 | Adaptive decision-making method and system for off-road vehicle path planning |
| CN114462898A (en) * | 2022-04-12 | 2022-05-10 | 交通运输部规划研究院 | Road planning management method and system based on geographic information |
| CN117949886A (en) * | 2024-03-27 | 2024-04-30 | 国网山西省电力公司营销服务中心 | Intelligent regulation and control method and system for transformer calibrator, electronic equipment and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103679544A (en) * | 2012-11-27 | 2014-03-26 | 江苏省电力公司南京供电公司 | Comprehensive assessment method for running of intelligent power distribution network |
| CN104252686A (en) * | 2014-08-15 | 2014-12-31 | 国家电网公司 | Determination method for power grid safety aggregative indicators |
| CN108108875A (en) * | 2017-11-29 | 2018-06-01 | 国家电网公司 | A kind of distribution Running State fuzzy synthetic appraisement method based on cooperative game method and trapezoidal cloud model |
| CN109523101A (en) * | 2017-09-18 | 2019-03-26 | 张丽蓉 | A kind of distribution Running State fuzzy synthetic appraisement method |
| DE102018103996A1 (en) * | 2018-02-22 | 2019-08-22 | Innogy Se | Reduction of harmonics in power grids |
-
2019
- 2019-10-23 CN CN201911010376.XA patent/CN110766320A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103679544A (en) * | 2012-11-27 | 2014-03-26 | 江苏省电力公司南京供电公司 | Comprehensive assessment method for running of intelligent power distribution network |
| CN104252686A (en) * | 2014-08-15 | 2014-12-31 | 国家电网公司 | Determination method for power grid safety aggregative indicators |
| CN109523101A (en) * | 2017-09-18 | 2019-03-26 | 张丽蓉 | A kind of distribution Running State fuzzy synthetic appraisement method |
| CN108108875A (en) * | 2017-11-29 | 2018-06-01 | 国家电网公司 | A kind of distribution Running State fuzzy synthetic appraisement method based on cooperative game method and trapezoidal cloud model |
| DE102018103996A1 (en) * | 2018-02-22 | 2019-08-22 | Innogy Se | Reduction of harmonics in power grids |
Non-Patent Citations (3)
| Title |
|---|
| 曹建芳: "《大规模场景图像的情感语义分析若干关键技术研究》", 31 January 2018 * |
| 郭京福等: "《民族地区特色产业论》", 30 June 2006 * |
| 麻兴斌 等: "《电网运行的可靠性、适应性和经济性研究》", 31 August 2014, 山东大学出版社 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112000556A (en) * | 2020-07-06 | 2020-11-27 | 广州西山居世游网络科技有限公司 | Method and device for displaying downtime of client program and readable medium |
| CN112000556B (en) * | 2020-07-06 | 2023-04-28 | 广州西山居网络科技有限公司 | Client program downtime display method and device and readable medium |
| CN112668888A (en) * | 2020-12-30 | 2021-04-16 | 中国电力科学研究院有限公司 | Method and system for evaluating comprehensive state of relay protection device |
| CN112862266A (en) * | 2021-01-19 | 2021-05-28 | 昆明理工大学 | Coal mine accident emergency plan evaluation model construction method based on fuzzy decision |
| CN112862266B (en) * | 2021-01-19 | 2022-08-09 | 昆明理工大学 | Coal mine accident emergency plan evaluation model construction method based on fuzzy decision |
| CN114154780A (en) * | 2021-10-19 | 2022-03-08 | 港华能源投资有限公司 | Evaluation method and device, electronic equipment and related product |
| CN114204554A (en) * | 2021-12-10 | 2022-03-18 | 国网江苏省电力有限公司 | Method for evaluating regulation and control operation safety for intelligent power grid system |
| CN114384916A (en) * | 2022-01-13 | 2022-04-22 | 华中科技大学 | Adaptive decision-making method and system for off-road vehicle path planning |
| CN114462898A (en) * | 2022-04-12 | 2022-05-10 | 交通运输部规划研究院 | Road planning management method and system based on geographic information |
| CN117949886A (en) * | 2024-03-27 | 2024-04-30 | 国网山西省电力公司营销服务中心 | Intelligent regulation and control method and system for transformer calibrator, electronic equipment and storage medium |
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