CN112488475A - Method and system for differential evaluation of multi-level gridding reliability - Google Patents
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Abstract
The invention discloses a method and a system for differential evaluation of multilevel gridding reliability, wherein the method comprises the following steps: selecting an area to be evaluated and dividing a power supply zone; carrying out multi-level reliability evaluation on the partitions of different power supply reliability categories; performing horizontal-longitudinal multi-level comprehensive benchmarking mode differentiation evaluation; and optimizing the reliability transformation target partition according to the reliability differentiation comprehensive evaluation result of each power supply partition, and calculating a priority transformation index. According to the embodiment of the invention, the power supply subareas are adopted for transverse-longitudinal multi-level benchmarking evaluation, different evaluation objects are systematically compared, the power supply relation, weak links and reinforcement requirements of each level of power grid are comprehensively analyzed, and a reliability improvement strategy is formulated according to the regional differences of different regions.
Description
Technical Field
The invention relates to the field of power supply, in particular to a method and a system for differential evaluation of multi-level gridding reliability.
Background
The main task of the power distribution network is to undertake and meet the power supply requirements of users, wherein power supply reliability is an important index for measuring the continuous power supply capacity of the power distribution network for the users and is also an important assessment index of power enterprises, with the increase of economic development and high-tech enterprises, the requirements of the users on the power supply reliability are higher and higher, and the reliability management and control work of the power supply enterprises is also systematized, layered and refined gradually. On the premise of ensuring the power supply quality of the power system, the reliability evaluation of the power distribution network is an important work link for improving the modernization level of the power industry, the weak link of the power distribution network is found through the reliability evaluation of the power distribution network, main factors influencing the power supply reliability are found, then a specific power grid transformation scheme is provided aiming at existing problems, and the safety performance and the economic benefit of the power distribution network can be effectively increased.
Disclosure of Invention
The invention aims to solve the problems that in the prior art, hierarchical gridding power supply reliability differential evaluation is lacked, and dynamic and all-around management is difficult to implement for each grid area according to the principles of area level, area geographical layout, current situation management and the like. The invention provides a method and a system for multi-level gridding reliability differential evaluation, which adopt power supply partition horizontal-longitudinal multi-level benchmarking evaluation to deeply compare different evaluation objects, such as a power supply reliability comprehensive evaluation value, 5 key indexes of power supply reliability, and difference and average level of power supply reliability influence factors, comprehensively analyze power supply relations, weak links and reinforcement requirements of power grids at all levels, and determine a reliability improvement strategy according to regional characteristics of different power supply areas.
In order to solve the above technical problem, an embodiment of the present invention provides a method for differential evaluation of multi-level gridding reliability, where the method includes:
1-1, selecting an area to be evaluated and dividing a power supply partition;
1-2, carrying out multi-level reliability evaluation on partitions of different power supply reliability categories;
1-3, performing horizontal-longitudinal multi-level comprehensive benchmarking mode differentiation evaluation;
1-4, determining the optimal sequence of the reliability transformation target partition and the transformation index priority according to the reliability differentiation comprehensive evaluation result of each power supply partition.
1-1 to-be-evaluated area selection and power supply partition, comprising:
1-1-1, selecting an area to be evaluated according to five different levels of statistical calibers of city-region-station-line-point;
1-1-2, counting the historical power failure condition of an area to be evaluated, gridding the power distribution network according to the conditions of city positioning, economic development level, load property, load density and the like, and dividing the power distribution network into power supply partitions with different power supply reliability categories, such as full aperture, city center, urban area, town, rural area and the like.
1-2, performing multi-level reliability evaluation on power supply partitions of different power supply reliability categories, comprising:
1-2-1, selecting 5 key indexes from 33 power supply reliability indexes of a power supply system user power supply reliability evaluation regulation as a power supply reliability index system of the module, relating to a time index, a frequency index and an electric quantity index of a reliability core; the 5 key indexes comprise an average power failure frequency index of an SAIFI system, an average power failure duration index of the SAIDI system, an average power failure frequency index of a CAIFI user, an average power failure duration of the CAIDI user and an average power shortage amount of an AENS user.
1-2-2, extracting principal components capable of basically reflecting all index information by adopting a principal component analysis method, and calculating the weight of the power supply reliability index in the power supply partition so as to construct a power supply reliability comprehensive evaluation function to quantify the reliability evaluation value of the power supply partition.
1-2-3, constructing a power supply reliability influence factor system, and mining key influence factors from four dimensions of a power distribution network frame level, an equipment level, an operation technology level and an operation and maintenance management level.
1-2-4, performing sensitivity analysis on the power supply reliability index by using a multivariate linear regression analysis method, and solving the sensitivity of different types of power supply reliability influence factors on the power supply reliability index; by calculating the sensitivity of the power supply reliability influencing factor, the degree of influence on the power supply reliability influencing factor and the power supply reliability index can be determined.
1-3, performing differential evaluation on a transverse-longitudinal multilevel comprehensive benchmarking mode, wherein the differential evaluation comprises the following steps:
1-3-1, establishing a transverse-longitudinal benchmarking multi-level evaluation mode of a power supply partition, wherein the power supply reliability benchmarking evaluation is divided into two contents of transverse benchmarking and longitudinal benchmarking;
1-3-2, performing transverse power supply reliability comparison and evaluation, namely performing transverse comparison and analysis on the difference of power supply reliability indexes between a power supply partition to be evaluated and a target partition, or between the power supply partition and a power grid set standard, quantitatively grading the power supply partition to be evaluated based on the target partition or the power grid set standard, and determining the transformation priority and transformation index scheme of the power supply partition;
1-3-3, performing power supply reliability longitudinal benchmarking evaluation, namely, analyzing a power supply reliability improvement process of the optimal benchmarking and the power supply partition to be evaluated on a time scale, and assisting adjustment and improvement of power supply reliability improvement measures of the power supply partition to be evaluated.
1-4, determining a reliability priority transformation partition and indexes, comprehensively selecting a reliability transformation target partition and transformation indexes based on the reliability differentiation evaluation result of each power supply partition, determining the optimal sequence of the reliability transformation partition and the priority of the transformation indexes, and providing a reliability improvement strategy.
A system for power supply reliability differential evaluation comprises:
2-1, the power supply reliability gridding module is used for selecting a region to be evaluated and dividing power supply partitions;
the 2-2 multi-level evaluation module is used for carrying out multi-level power supply reliability management level grading on partitions of different power supply reliability categories;
2-3, the comprehensive benchmarking evaluation module is used for performing differential evaluation on a transverse-longitudinal multi-level comprehensive benchmarking mode;
and the 2-4 reliability improvement strategy module is used for optimizing the reliability improvement target partition and determining the improvement index priority.
The power supply reliability gridding module comprises: counting the area to be evaluated according to five different grades of city-area-station-line-point; and counting the historical power failure condition of the area to be evaluated, and gridding the power distribution network according to the conditions of city positioning, economic development level, load property, load density and the like, and dividing the power distribution network into power supply partitions of different power supply reliability categories.
The multi-level evaluation module comprises: key indexes are selected from 33 power supply reliability indexes of power supply reliability evaluation rules of users of the power supply system to serve as a power supply reliability index system of the module, and time indexes, frequency indexes and electric quantity indexes of a reliability core are related. Extracting principal components capable of reflecting all index information by adopting a principal component analysis method, calculating the weight of each power supply reliability index of the power supply partition to be evaluated, constructing a power supply reliability comprehensive evaluation function, and performing reliability scoring on the power supply partition to be evaluated; constructing a power supply reliability influence factor index system from four dimensions of a power distribution network frame level, an equipment level, an operation technology level and an operation and maintenance management level, and mining key influence factors of reliability indexes; and calculating the sensitivity of the power supply reliability influence factors of different types to the power supply reliability index by using multiple linear regression analysis, and determining the power supply reliability transformation priority of each power supply subarea according to the obtained sensitivity.
The comprehensive benchmarking evaluation module comprises: establishing a transverse-longitudinal benchmarking multi-level evaluation mode of the power supply subareas; performing transverse power supply reliability standard-matching evaluation, namely performing transverse comparison and analysis on the difference of power supply reliability indexes between a power supply partition to be evaluated and a target partition, or between the power supply partition and a power grid set standard, quantitatively grading the power supply partition to be evaluated based on the target partition or the power grid set standard, and determining the modification priority and the modification index scheme of the power supply partition; and performing longitudinal benchmarking evaluation on the power supply reliability, namely analyzing the power supply reliability improvement process of the optimal benchmarking and the power supply partition to be evaluated on the time scale, and assisting adjustment and improvement of power supply reliability improvement measures of the power supply partition to be evaluated.
The reliability promotion policy module includes: and comprehensively analyzing the priority of the reconstruction subareas and the reconstruction indexes based on the reliability differentiation evaluation results of the power supply subareas, and formulating a reliability improvement strategy.
According to the method and the system for the differential evaluation of the multi-level gridding reliability, provided by the embodiment of the invention, dynamic and all-around management is carried out on each grid partition according to the principles of regional level, regional geographical layout and the like; the provided power supply reliability influence factor system comprehensively covers multiple dimensions of a power grid, users and management, and the problem that multi-dimensional analysis is lacked in the existing reliability influence factor research is solved; the relevance of multidimensional influence factors such as a power distribution network reflecting the power supply reliability level, users, management and the like is visually quantized; the method comprises the steps of evaluating the benchmarks in a transverse-longitudinal multi-level mode by power supply subareas, deeply comparing different evaluation objects, reflecting power supply reliability evaluation indexes, differences and average levels of power supply reliability influence factors, comprehensively analyzing power supply relations, weak links and reinforcement requirements of power grids at all levels, and formulating a reliability improvement strategy according to regional differences of different grid areas.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for differential evaluation of multi-level gridding reliability.
Fig. 2 is a system of power supply reliability influencing factors.
Fig. 3 is a schematic diagram of the transverse benchmarking evaluation of the power supply reliability index.
FIG. 4 is a graphical illustration of the reliability influencing factor sensitivity and satisfaction area.
Fig. 5 is a schematic diagram of longitudinal benchmarking evaluation of reliability indexes after/before power supply reliability modification.
FIG. 6 is a schematic diagram of longitudinal benchmarking evaluation of reliability influencing factors after/before power supply reliability modification.
Fig. 7 is a schematic diagram of the longitudinal benchmarking evaluation of the power supply reliability to-be-evaluated subarea/optimal marker post subarea.
Fig. 8 is a schematic structural diagram of a system for differential reliability evaluation in a multi-level grid.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Examples
Referring to fig. 1, fig. 1 is a schematic flow chart of a multi-level gridding reliability differentiation evaluation method.
As shown in fig. 1, a method for differential reliability evaluation in multi-level gridding includes:
1-1, selecting an area to be evaluated and dividing a power supply partition;
1-2, carrying out multi-level reliability evaluation on partitions of different power supply reliability categories;
1-3, performing horizontal-longitudinal multi-level comprehensive benchmarking mode differentiation evaluation;
1-4, determining the optimal sequence of the reliability transformation subareas and the transformation index priority according to the comprehensive evaluation result of the reliability differentiation of each power supply subarea.
1-1 to-be-evaluated area selection and power supply partition, comprising:
1-1-1, selecting an area to be evaluated according to five different hierarchical statistical apertures of city-region-station-line-point;
1-1-2, counting historical power failure conditions of different power supply partitions in a hierarchical level, gridding the power distribution network according to conditions such as urban positioning, economic development level, load property and load density, and dividing the power distribution network into power supply partitions of different power supply reliability categories. The power supply reliability partition standard adopted by the invention is based on the power distribution network planning and design technical guide, and the area to be evaluated is divided into power supply partitions (A +, A, B, C, D, E) of different grades according to the conditions of city positioning, economic development level, load property, load density and the like, and the specific partition standard is shown in table 1-1.
TABLE 1-1 Power supply region division TABLE
In the table, σ is the load density (MW/km) of the power supply region2). The area of the power supply area is generally not less than 5km2. When calculating the load density, the 110(66) kV special line load and the invalid power supply areas of mountains, gobi, deserts, water areas, forests and the like are deducted.
If the differentiated management of the power supply substations is implemented, the power supply substations are used as power supply partition division units. Analyzing the power supply reliability of different power supply partitions: the method comprises the following steps of calculating power supply reliability indexes, analyzing reliability influence factors on the network frame equipment level, the operation technology, the operation and maintenance management and the like; and multi-dimensional power supply reliability differential evaluation is realized so as to perform power supply reliability benchmarking evaluation.
1-2, performing multi-level reliability evaluation on partitions of different power supply reliability categories.
1-2-1, selecting 5 key indexes from 33 power supply reliability indexes of a power supply system user power supply reliability evaluation regulation as a power supply reliability index system of the module, relating to a time index, a frequency index and an electric quantity index of a reliability core; the 5 key indexes comprise an average power failure frequency index of an SAIFI system, an average power failure duration index of the SAIDI system, an average power failure frequency index of a CAIFI user, an average power failure duration of the CAIDI user and an average power shortage amount of an AENS user, and the specific formula is as follows:
(1) system Average power failure Frequency index SAIFI (System Average interruption Frequency index)
The index is the statistics of the system power supply interruption frequency, and is the average power failure frequency of each user powered by the system in each unit time. The total number of available user outages/total number of users in the power supply area is used to estimate:
(2) system Average power outage Duration index SAIDI (System Average interruption Duration index)
This indicator is the average duration of the power outage experienced by each user powered by the system over the course of a year. The total sum of the power failure duration of users/the total number of users in a power supply area in one year:
(3) user Average power failure Frequency index CAIFI (customer Average Interruption Frequency index)
The indicator is the average number of blackouts experienced per unit time by each customer affected by the blackout. The total number of times of power failure of users/the number of users affected by the power failure are calculated as follows:
(4) user Average power outage Duration index CAIDI (customer Average Interruption Duration index)
The index refers to the average power failure duration experienced by the users with power failure in one year, and is the sum of the power failure duration of the users in one year/the total number of the power failure users in the year:
(5) user average power shortage index AENS (average Energy Not Supplied index)
The index refers to the average power supply of each user due to power failure in a given time interval. Estimating the total power failure power shortage amount/total number of users in the power supply area in the statistical time length:
wherein λ isiIs the failure rate of load point i; n is a radical ofiThe number of users at the load point i; u shapeiIs the annual outage duration for load point i; piThe average load (kW) at load point i.
1-2-2, extracting principal components capable of basically reflecting all reliability index information by adopting a principal component analysis method, and calculating the weight of the power supply reliability index in the power supply partition so as to construct a power supply reliability comprehensive evaluation function to quantify the reliability evaluation value of the power supply partition. The weight of the power supply reliability index refers to the relative importance degree of the index in the comprehensive reliability evaluation, and the comprehensive power supply reliability of the power supply subarea is quantified mainly from three sides of the power failure times, the power failure time and the power shortage amount. The process of solving the power supply reliability index weight by the principal component analysis method is as follows:
an index matrix formed by m power supply reliability evaluation indexes of n objects to be evaluated (power supply partitions) is shown as a formula (6).
X＝(xji)n×m＝(X1,X2,...Xi,...,Xm) (6)
x＝{x1,x2,...,xi,...,xm} (7)
Wherein, X is an index matrix constructed by n multiplied by m index values; xiThe evaluation index is the ith index column vector in the index matrix, namely the vector formed by the ith evaluation indexes of the n evaluation objects; x is the number ofjiThe ith index value is the jth object to be evaluated; x is a set of indexes in a power supply reliability evaluation index system, namely the 5 indexes; x is the number ofiIs the ith index in the index set; m is the index number in the reliability evaluation index system, and the number is 5; and n is the number of the objects to be evaluated and is determined according to historical data of the power grid company.
In the reliability benchmarking evaluation index system, the five items of SAIFI, SAIDI, CAIFI, CAIDI and AENS are reverse indexes with the smaller index value, the better. The larger the index value is, the better the index value is, the forward index is, and in order to facilitate analysis and calculation, each reverse index is firstly normalized.
The forward indicator is normalized by equation (8):
forward and normalize the reverse indicators (SAIFI, SAIDI, CAIFI, CAIDI, AENS) using equation (9):
after the index forward and normalization processing, a normalized reliability index matrix (namely, normalization of formula (6)) shown as formula (10) is obtained.
And (3) carrying out dispersion standardization processing on the normalized index matrix X according to the formula (11) to obtain a standardized reliability index matrix shown as the formula (12).
According to the constructed standardized index matrix
And (3) solving a correlation coefficient matrix:
R＝(rij)m×m (13)
wherein the content of the first and second substances,
the covariance of the ith vector and the jth column vector in the normalized reliability index matrix;
and
the variances of the ith column vector and the jth column vector in the standardized reliability index matrix are respectively obtained; r isijReflects the index xiAnd index xjDegree of correlation of rijLarger values indicate higher correlation between indices, and when the majority rijWhen the value is more than or equal to 0.75, the requirement of reducing the dimension of the principal component is met.
The eigenvalue of the correlation coefficient matrix R is found from (λ E-a) x ═ 0, and the eigenvalue greater than 0 among them is selected to construct an eigenvalue set shown in the following equation.
λ＝{λ1,λ2,...,λk,...,λq} (15)
Wherein λ iskThe eigenvalue of the correlation coefficient matrix R is greater than zero, the q value is the number of eigenvalues greater than 0 (i.e. principal component), and the number of λ is defined1≥λ2≥...≥λk≥...≥λq>0。
The orthonormal eigenvector matrix a corresponding to the eigenvalue set λ is shown as the following equation.
From the principal component definition, the expression of the principal component after PCA on the normalized index matrix is shown in the following formula.
Wherein, ykAs a characteristic value λkThe corresponding principal component.
Principal component ykCorresponding characteristic value lambdakThe variance of the principal component is expressed by the following formulakThe contribution rate of the variance of (a) to the total variance is:
wherein, mukReflect ykContains the percentage of all index information.
As can be seen from the above equation, the contribution rates of the principal components decrease in sequence, where the first principal component variance contribution rate is the largest, and the cumulative variance contribution rates of the first d principal components are:
according to the principle of determining the main principal components by adopting the cumulative variance contribution rate, when the cumulative variance contribution rate mu is more than or equal to 80%, the first d principal components can basically reflect the information of m indexes, and the first d principal components are preliminarily determined to be the principal components playing the main role, but need to be further checked and determined.
To the standardized index matrix
Performing factor analysis under PCA method to obtain index x and principal component y shown in the following formulakThe associated load matrix.
Wherein the content of the first and second substances,
a principal component load matrix;
is the vector of the relevant load column of the index x and the principal component;
is an index xiWith principal component ykThe associated load value of (2).
Observation principal component load matrix
If the first d principal components and each index have higher related load values, the first d principal components can basically reflect the information of each index, and the principal components playing the main role can be finally determined to be the first d principal components.
From the above analysis, the first d principal components can basically reflect the information contained in the m indexes, and the weight of each index is determined by using the eigenvalue corresponding to the first d principal components and the element value of the orthonormal eigenvector matrix, as shown in the following formula.
Ωx＝(ωx1,ωx2,...,ωxi,...,ωxm)1×m (21)
Wherein, ω isxiThe weighted value of the ith index in the evaluation index system is obtained; omegaxThe method is used for evaluating a row vector formed by the weights of all indexes in an index system.
And according to the obtained index weight, combining the normalized index matrix in the formula (10) to obtain a power supply reliability comprehensive evaluation value matrix, wherein the power supply reliability comprehensive evaluation value matrix is as follows:
F＝Ωx(X*)T＝(f(1),...,f(j),...,f(n)) (23)
wherein, f (j) is the power supply reliability comprehensive evaluation index of the j-th evaluation object.
Equations (23) and (24) are used to obtain a power supply reliability comprehensive evaluation value of power supply reliability in three aspects of power failure time, power failure frequency and power supply shortage amount by comprehensively considering each reliability index and weight of the evaluation object. The power supply reliability assessment value represents the power supply reliability degree of the to-be-assessed partition under the consideration of all reliability indexes.
1-2-3, constructing a power supply reliability influence factor system, mining key influence factors from four dimensions of a power distribution network frame level, an equipment level, an operation technology level and an operation and maintenance management level, and further specifically analyzing power supply reliability improvement measures of the power distribution network, wherein the selected power supply reliability influence factors are shown in fig. 2.
1-2-4, performing sensitivity analysis on the power supply reliability index by using a multivariate linear regression analysis method, and solving the sensitivity of different types of power supply reliability influence factors on the power supply reliability index; by calculating the sensitivity of these power supply reliability influencing factors, a power supply reliability improvement scheme for a certain power supply reliability index can be provided. The method is characterized in that data such as historical fault reasons, fault times and equipment operation years of the power distribution network are counted, and key influence factors are mined from four dimensions of the power distribution network frame level, the equipment level, the operation technology level and the operation and maintenance management level.
The method can quickly and effectively analyze the correlation between a certain power supply reliability index and a plurality of influence factors by utilizing a multiple linear regression analysis method. Constructing a multivariate linear regression model between a power supply reliability influence factor Y (dependent variable) and a power supply reliability index X (independent variable) of the current evaluation power supply partition:
X＝Yβ+ε (25)
wherein, X ═ X1, X2, …, Xn ] T is n power supply partition sample values to be evaluated, Xn ═ X1n, X2n, …, X8n ] T is 5 reliability indexes of the nth partition sample to be evaluated after being subjected to forward normalization and normalization, and the reliability indexes of all samples are in consistent sequence; y ═ e, Y1, Y2, …, Ym ] is the reliability influencing factor matrix, and e ═ 1,1, …,1] T is the n × 1 order vector, Yi (i ═ 1,2, … … m) is the measured value or statistical value after normalization of the ith kind of reliability influencing factor; β ═ β 0, β 1, …, β 8] is a regression coefficient to be solved, β i (i ═ 1,2, … … 8) is corresponding to the power supply reliability influencing factor to solve the regression coefficient of the ith power supply reliability index; epsilon is a random error term representing many minor factors that affect the change in X, and epsilon-N (0, sigma 2). The regression coefficient is calculated by the least square method:
wherein the content of the first and second substances,
is the estimated regression coefficient. According to the estimated regression coefficient, the influence degree of the adjustment of the reliability influence factors on the power supply reliability index value can be judged through sensitivity analysis, so that an optimal targeted reliability index reconstruction scheme can be found out. The sensitivity is calculated as follows:
where Yi is the ith reliability influencing factor for sensitivity,
is the regression coefficient estimate for the ith reliability influencing factor. Thus, the importance of the power supply reliability influencing factor index can be determined so as to provide an effective reliability investment scheme.
1-3, carrying out differential evaluation on the transverse-longitudinal multilevel comprehensive benchmarking mode.
1-3-1, establishing a transverse-longitudinal benchmarking multi-level evaluation mode of a power supply partition; the power supply reliability benchmarking evaluation is divided into two contents of horizontal benchmarking and longitudinal benchmarking;
1-3-2, performing transverse power supply reliability comparison and evaluation, namely performing transverse comparison and analysis on the difference of power supply reliability indexes between a power supply partition to be evaluated and a target partition, or between the power supply partition and a power grid set standard, quantitatively grading the power supply partition to be evaluated based on the target partition or the power grid set standard, and determining the transformation priority and transformation index scheme of the power supply partition;
1-3-3, performing power supply reliability longitudinal benchmarking evaluation, namely, through analysis of power supply reliability improvement process of an optimal benchmarking and similar units on a time scale, providing auxiliary power supply partition power supply reliability improvement measures to be evaluated.
1-4, optimizing the reliability transformation target partition according to the reliability differentiation comprehensive evaluation result of each power supply partition, determining the transformation index priority, and providing accurate and effective power supply reliability promotion decision suggestions for the power supply reliability promotion optimization scheme.
1-4-1 transverse benchmarking result analysis method:
(1) the comprehensive evaluation indexes of the power supply reliability are aligned: and obtaining the reliability level condition of each partition by utilizing the comprehensive evaluation value of the reliability of each partition obtained by integrating 5 reliability indexes SAIFI, SAIDI, CAIFI, CAIDI and AENS. And power supply reliability evaluation values of all cities of the whole province or all power supply partitions in a certain city are obtained, and power supply reliability ranking of the power supply partitions is carried out according to the evaluation values, wherein the partition with the highest scoring ranking is the optimal benchmark partition, and the lower scoring partition has higher transformation priority.
(2) After a power supply partition (power supply partition to be evaluated) which is preferentially transformed is determined, the power supply reliability index of the partition and the optimal mark post partition are subjected to mark matching: and (3) carrying out forward normalization and normalization on the power supply subareas to be evaluated and 5 basic reliability indexes of the optimal marker post, and then carrying out mark alignment (figure 3). In the same grade, the maximum value of the numerical value is the highest reliability degree after the same index is subjected to forward normalization and normalization. Subtracting the minimum value from the maximum value, wherein the difference is the developable interval of the reliability index in the power supply grade, and specifying: the twenty percent in the front of the interval represents that the index difference is small, and the index reliability degree is considered to be good; eighty percent after the interval represents that the index difference is large, and the index is considered as a weak index.
(3) After weak reliability indexes of the power supply partitions to be evaluated are determined, power supply reliability influence factor benchmarking is carried out: according to the sensitivity (multiple linear regression coefficient beta) of each power supply reliability influence factor of the power supply partition to be evaluated on the weak reliability index of the partition and the influence factor index score, determining the distribution of the importance and satisfaction degree regional graph of each power supply reliability influence index of the power supply partition to be evaluated, and visualizing the power supply reliability influence index weak point and the promotion space of the power supply partition.
The satisfaction (normalized score) of each power supply reliability influence factor index is taken as an abscissa, and the sensitivity (degree of correlation between the influence factor index and the reliability index, obtained by equation (27)) of each index is taken as an ordinate, thereby drawing a graph of the satisfaction and sensitivity region of each influence factor index. FIG. 4 is a graphical illustration of the sensitivity and satisfaction area of the reliability influencing factors. Analyzing each power supply reliability influence factor of the object to be evaluated through regional graph distribution, and determining the improved ordering of the power supply reliability influence factors of the power supply subareas to be evaluated. The satisfaction (normalized score) of each power supply reliability influence factor index is taken as an abscissa, and the interval is (mu 1, mu 2); and the sensitivity of each power supply reliability influence factor index of the power supply subarea to be evaluated is taken as a vertical coordinate, and the interval is (beta 1, beta 2). Defining the index with the satisfied value larger than (mu 1+ mu 2)/2 as a satisfied index, otherwise as an unsatisfied index, and defining the index with the sensitivity larger than (beta 1+ beta 2)/2 as an important index, otherwise as a second important index; wherein the sizes of the mu 1, the mu 2, the beta 1 and the beta 2 are determined by the calculation values of the reliability influence factor indexes and the importance degree of the power supply subareas to be evaluated.
1-4-2, after corresponding reliability improvement measures are provided by determining the improvement priority of the power supply reliability influence factors, longitudinal benchmarking is carried out in the partition power supply reliability improvement process, and the feasibility of the improvement scheme is verified. The method comprises the following specific steps:
(1) and (3) longitudinally aligning after partition modification/before modification to be evaluated: and checking the reliability improvement effect on a time scale according to the reliability level after/before the transformation of the power supply partition to be evaluated as a longitudinal benchmarking object, adjusting related measures and improving the transformation scheme in the suggestion library.
The specific longitudinal alignment marking steps are as follows: calculating a power supply reliability index and a power supply reliability comprehensive evaluation value after a power supply partition to be evaluated implements a transformation scheme; and analyzing the difference between the reliability indexes after modification and before modification through the development curve of the power supply reliability data of the subarea to be evaluated, checking the effectiveness of the power supply reliability improvement route, improving the reliability improvement strategy and contributing to the experience reference of other power supply subareas. Fig. 5 and 6 are schematic diagrams of longitudinal benchmarking evaluation of reliability indexes and reliability influence factors after power supply reliability modification and before modification, respectively.
(2) And (3) partitioning to be evaluated/longitudinally aligning the optimal marker post: comparing the power supply reliability influence factor promotion trend (figure 7) of the optimal mark post on the time scale through the historical development curves of the power supply reliability data of the optimal mark post partition and the partition to be evaluated, absorbing the improved route of the reference mark post partition on the weak point, and adjusting and perfecting the reliability transformation scheme of the partition to be evaluated.
According to the method, dynamic and all-round management is carried out on each grid partition according to the principles of regional level, regional geographical layout and the like; the provided power supply reliability influence factor system comprehensively covers multiple dimensions of a power grid, users and management, and the problem that multi-dimensional analysis is lacked in the existing reliability influence factor research is solved; the relevance of multidimensional influence factors such as a power distribution network reflecting the power supply reliability level, users, management and the like is visually quantized; and transverse-longitudinal multi-level benchmarking evaluation of power supply partitions is adopted, different evaluation objects are deeply compared, the power supply relation, weak links and reinforcement requirements of each level of power grid are comprehensively analyzed, and a reliability improvement strategy is determined according to regional differences of different regions.
Fig. 8 is a schematic structural diagram of a system for differential reliability evaluation in a multi-level grid.
As shown in figure 8 of the drawings,
a system for power supply reliability differential evaluation comprises:
2-1, the power supply reliability gridding module is used for selecting a region to be evaluated and dividing power supply partitions;
the 2-2 multi-level evaluation module is used for carrying out multi-level power supply reliability management level grading on partitions of different power supply reliability categories;
2-3, the comprehensive benchmarking evaluation module is used for performing differential evaluation on a transverse-longitudinal multi-level comprehensive benchmarking mode;
and the 2-4 reliability improvement strategy module is used for optimizing the reliability improvement target partition and determining the improvement index priority.
2-1 power supply reliability gridding module, comprising: counting the area to be evaluated according to five different grades of city-area-station-line-point; and counting the historical power failure condition of the area to be evaluated, and gridding the power distribution network according to the conditions of city positioning, economic development level, load property, load density and the like, and dividing the power distribution network into power supply partitions of different power supply reliability categories.
2-2 a multi-level evaluation module comprising: key indexes are selected from 33 power supply reliability indexes of power supply reliability evaluation rules of users of the power supply system to serve as a power supply reliability index system of the module, and time indexes, frequency indexes and electric quantity indexes of a reliability core are related. Extracting principal components capable of reflecting all index information by adopting a principal component analysis method, calculating the weight of each power supply reliability index of the power supply partition to be evaluated, constructing a power supply reliability comprehensive evaluation function, and performing reliability scoring on the power supply partition to be evaluated; constructing a power supply reliability influence factor index system from four dimensions of a power distribution network frame level, an equipment level, an operation technology level and an operation and maintenance management level, and mining key influence factors of reliability indexes; and calculating the sensitivity of the power supply reliability influence factors of different types to the power supply reliability index by using multiple linear regression analysis, and determining the power supply reliability transformation priority of each power supply subarea according to the obtained sensitivity.
2-3 comprehensive benchmarking evaluation module, including: establishing a transverse-longitudinal benchmarking multi-level evaluation mode of the power supply subareas; performing transverse power supply reliability standard-matching evaluation, namely performing transverse comparison and analysis on the difference of power supply reliability indexes between a power supply partition to be evaluated and a target partition, or between the power supply partition and a power grid set standard, quantitatively grading the power supply partition to be evaluated based on the target partition or the power grid set standard, and determining the modification priority and the modification index scheme of the power supply partition; and performing longitudinal benchmarking evaluation on the power supply reliability, namely analyzing the power supply reliability improvement process of the optimal benchmarking and the power supply partition to be evaluated on the time scale, and assisting adjustment and improvement of power supply reliability improvement measures of the power supply partition to be evaluated.
2-4 a reliability-enhancement policy module, comprising: and comprehensively analyzing the priority of the reconstruction subareas and the reconstruction indexes based on the reliability differentiation evaluation results of the power supply subareas, and formulating a reliability improvement strategy.
According to the method and the system for the differentiated evaluation of the power supply reliability, provided by the embodiment of the invention, dynamic and all-around management is carried out on each grid partition according to the principles of regional level, regional geographical layout and the like; the provided power supply reliability influence factor system comprehensively covers multiple dimensions of a power grid, users and management, and the problem that multi-dimensional analysis is lacked in the existing reliability influence factor research is solved; the relevance of multidimensional influence factors such as a power distribution network reflecting the power supply reliability level, users, management and the like is visually quantized; the method comprises the steps of evaluating the power supply partition in a transverse-longitudinal multi-level mode, deeply comparing different evaluation objects such as a comprehensive evaluation value of power supply reliability, 5 indexes of power supply reliability, and difference and average level of power supply reliability influence factors, comprehensively analyzing power supply relations, weak links and reinforcement requirements of power grids at all levels, and determining a reliability improvement strategy according to regional differences of different regions.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable storage medium, and the storage medium may include: a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic or optical disk, or the like.
In addition, the method and the system for multi-level reliability differentiation evaluation provided by the embodiment of the present invention are described in detail above, a specific example should be adopted herein to explain the principle and the implementation manner of the present invention, and the description of the above embodiment is only used to help understanding the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (10)
1. A method for differential evaluation of multi-level gridding reliability is characterized by comprising the following steps:
1-1, selecting an area to be evaluated and dividing a power supply partition;
1-2, carrying out multi-level reliability evaluation on partitions of different power supply reliability categories;
1-3, performing horizontal-longitudinal multi-level comprehensive benchmarking mode differentiation evaluation;
1-4, optimizing the reliability transformation target partition according to the reliability differentiation comprehensive evaluation result of each power supply partition, and calculating a priority transformation index.
2. The method of claim 1, wherein the partition of the area to be evaluated and the power supply partition comprises:
1-1-1, selecting an area to be evaluated according to five different levels of statistical calibers of city-region-station-line-point;
1-1-2, counting the historical power failure condition of an area to be evaluated, gridding the power distribution network according to the conditions of city positioning, economic development level, load property, load density and the like, and dividing the power distribution network into power supply partitions of different power supply reliability categories.
3. The method of claim 1, wherein the multi-level reliability evaluation comprises:
1-2-1, selecting key indexes from 33 power supply reliability indexes of a power supply system user power supply reliability evaluation regulation as a power supply reliability index system of the module, wherein the key indexes relate to a time index, a frequency index and an electric quantity index of a reliability core;
1-2-2, extracting principal components capable of reflecting all index information by adopting a principal component analysis method, calculating the weight of each power supply reliability index of a power supply partition to be evaluated, constructing a power supply reliability comprehensive evaluation function, and performing reliability scoring on the power supply partition to be evaluated;
1-2-3, constructing a power supply reliability influence factor index system from four dimensions of a power distribution network frame level, an equipment level, an operation technology level and an operation and maintenance management level, and mining key influence factors of reliability indexes;
1-2-4, calculating the sensitivity of different types of power supply reliability influence factors to power supply reliability indexes by using multivariate linear regression analysis, and determining the power supply reliability transformation priority of each power supply subarea according to the obtained sensitivity.
4. The method according to claim 1, wherein the transverse-longitudinal multi-level comprehensive label matching mode differential evaluation comprises:
1-3-1, establishing a transverse-longitudinal benchmarking multi-level evaluation mode of a power supply partition;
1-3-2, performing transverse power supply reliability comparison and evaluation, namely performing transverse comparison and analysis on the difference of power supply reliability indexes between a power supply partition to be evaluated and a target partition, or between the power supply partition and a power grid set standard, quantitatively grading the power supply partition to be evaluated based on the target partition or the power grid set standard, and determining the transformation priority and transformation index scheme of the power supply partition;
1-3-3, performing power supply reliability longitudinal benchmarking evaluation, namely, analyzing a power supply reliability improvement process of the optimal benchmarking and the power supply partition to be evaluated on a time scale, and assisting adjustment and improvement of power supply reliability improvement measures of the power supply partition to be evaluated.
5. The method of claim 1, wherein the reliability improvement partition and indicator determination comprises:
and comprehensively analyzing the priority of the reconstruction subareas and the reconstruction indexes based on the reliability differentiation evaluation results of the power supply subareas, and formulating a reliability improvement strategy.
6. A system for multi-level gridding reliability differential evaluation is characterized in that the system comprises:
the power supply reliability gridding module is used for selecting a region to be evaluated and dividing a power supply partition;
the multi-level evaluation module is used for carrying out multi-level power supply reliability management level grading on partitions of different power supply reliability categories;
the comprehensive benchmarking evaluation module is used for performing differential evaluation on a transverse-longitudinal multi-level comprehensive benchmarking mode;
and the reliability promotion strategy module is used for determining the optimal sequence of the reliability transformation target partition and the transformation index priority.
7. The method of claim 6, wherein the power reliability gridding module comprises:
counting the area to be evaluated according to five different grades of city-area-station-line-point;
and counting the historical power failure condition of the area to be evaluated, and gridding the power distribution network according to the conditions of city positioning, economic development level, load property, load density and the like, and dividing the power distribution network into power supply partitions of different power supply reliability categories.
8. The method of claim 6, wherein the multi-level evaluation module comprises:
selecting key indexes from 33 power supply reliability indexes of a power supply system user power supply reliability evaluation regulation as a power supply reliability index system of the module, wherein the key indexes relate to a time index, a frequency index and an electric quantity index of a reliability core;
extracting principal components capable of reflecting all index information by adopting a principal component analysis method, calculating the weight of each power supply reliability index of the power supply partition to be evaluated, constructing a power supply reliability comprehensive evaluation function, and performing reliability scoring on the power supply partition to be evaluated;
constructing a power supply reliability influence factor index system from four dimensions of a power distribution network frame level, an equipment level, an operation technology level and an operation and maintenance management level, and mining key influence factors of reliability indexes;
and calculating the sensitivity of the power supply reliability influence factors of different types to the power supply reliability index by using multiple linear regression analysis, and determining the power supply reliability transformation priority of each power supply subarea according to the obtained sensitivity.
9. The method of claim 6, wherein the comprehensive benchmarking module comprises:
establishing a transverse-longitudinal benchmarking multi-level evaluation mode of the power supply subareas;
performing transverse power supply reliability standard-matching evaluation, namely performing transverse comparison and analysis on the difference of power supply reliability indexes between a power supply partition to be evaluated and a target partition, or between the power supply partition and a power grid set standard, quantitatively grading the power supply partition to be evaluated based on the target partition or the power grid set standard, and determining the modification priority and the modification index scheme of the power supply partition;
and performing longitudinal benchmarking evaluation on the power supply reliability, namely analyzing the power supply reliability improvement process of the optimal benchmarking and the power supply partition to be evaluated on the time scale, and assisting adjustment and improvement of power supply reliability improvement measures of the power supply partition to be evaluated.
10. The method of claim 6, wherein the reliability-enhancement policy module comprises:
and comprehensively analyzing the priority of the reconstruction subareas and the reconstruction indexes based on the reliability differentiation evaluation results of the power supply subareas, and formulating a reliability improvement strategy.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109214717A (en) * | 2018-10-24 | 2019-01-15 | 国网江西省电力有限公司经济技术研究院 | Power distribution network power grid evaluation method and its evaluation system |
| CN110490471A (en) * | 2019-08-23 | 2019-11-22 | 广西电网有限责任公司电力科学研究院 | A kind of remodeling method of distribution network reliability differentiation |
-
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109214717A (en) * | 2018-10-24 | 2019-01-15 | 国网江西省电力有限公司经济技术研究院 | Power distribution network power grid evaluation method and its evaluation system |
| CN110490471A (en) * | 2019-08-23 | 2019-11-22 | 广西电网有限责任公司电力科学研究院 | A kind of remodeling method of distribution network reliability differentiation |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113762728A (en) * | 2021-08-13 | 2021-12-07 | 国网能源研究院有限公司 | Hierarchical reliability management system and method for power distribution network |
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