CN111525589B - Reactive voltage support capability assessment method for multi-partition power grid - Google Patents

Reactive voltage support capability assessment method for multi-partition power grid Download PDF

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CN111525589B
CN111525589B CN202010422482.5A CN202010422482A CN111525589B CN 111525589 B CN111525589 B CN 111525589B CN 202010422482 A CN202010422482 A CN 202010422482A CN 111525589 B CN111525589 B CN 111525589B
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reactive
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partition
power grid
voltage
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CN111525589A (en
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管益斌
周挺
徐贤
罗凯明
李海峰
刘盛松
刘林
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State Grid Jiangsu Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/16—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by adjustment of reactive power
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/30—Reactive power compensation

Abstract

The invention provides a reactive voltage support capability assessment method for a multi-partition power grid, which mainly comprises the steps of selecting key assessment index data of the reactive voltage support capability of the multi-partition power grid; establishing a multi-partition power grid reactive voltage support capability evaluation index system; carrying out fuzzy analysis on the data to be evaluated by using a fuzzy membership function and a dynamic time warping algorithm, and calculating the similarity between the data to be evaluated and a reference sample after fuzzy processing; and determining the reactive voltage support capability of the multi-partition power grid according to the similarity of the data to be evaluated and the reference data. The method establishes an index system capable of accurately representing the reactive voltage support capability of the multi-partition power grid, applies a fuzzy theory and a dynamic time warping algorithm to the evaluation of the reactive voltage support capability of the power grid partitions, can accurately evaluate the reactive support capability of the power grid partitions, and provides data support for the provincial dispatching AVC system.

Description

Reactive voltage support capability assessment method for multi-partition power grid
Technical Field
The invention relates to the field of power grid layered and partitioned reactive power coordination control, in particular to a reactive voltage support capability assessment method for a multi-partitioned power grid.
Background
The power grid layering and partitioning technology is mainly used for avoiding the defects caused by the stability of an electromagnetic looped network to a power system. The layered and partitioned reactive power coordination control of the power grid is mainly realized by adopting an automatic voltage control system (AVC). The automatic voltage control system collects power grid operation data in real time through the data collection and monitoring control system, optimizes voltage reactive power from a global angle, and sends control signals to each reactive power regulation device through remote equipment. However, the system still has the problems of low real-time data accuracy, incomplete reactive power optimization method and lack of effective data support at present.
The power grid layered and partitioned reactive power coordination control requires realization of reactive power local balance in partitions, reactive power interconnection interaction among the partitions and overall reactive power optimization of a power grid. The AVC system adopts a distributed control principle, takes a provincial dispatching AVC system as a control center, takes the AVC system of each subarea as a substation, and AVC subsystems of a power plant, a transformer substation and the like exist in each subarea to form a closed-loop control system with up-down multi-level coordination. For the layered partition architecture of the power grid, the reactive local balance state of each partition and the reactive interconnection interaction state between the partitions are very important. The provincial dispatching AVC system needs to obtain accurate and effective voltage reactive power balance conditions from each sub-system, so as to timely perform overall reactive power optimization, send out control commands and facilitate adjustment of each sub-system. Therefore, the development of the evaluation research oriented to the reactive voltage supporting capability of the multi-partition power grid has important significance on the layered and partitioned reactive power coordination control of the power grid. To realize the evaluation of the reactive voltage support capability of the power grid subareas, the following problems need to be solved: 1) how to establish an index system for multi-partition power grid reactive voltage support capability evaluation; 2) and (5) discovering an accurate and rapid evaluation algorithm.
Disclosure of Invention
In order to make up for the deficiency of the prior art, the invention provides a reactive voltage support capability evaluation method for a multi-partition power grid.
The method comprises the following steps:
step 1: selecting the reactive voltage support capability evaluation index data of the multi-partition power grid;
the establishment of the evaluation data considers the characteristics of the whole architecture of the layered and partitioned power grid, the relationship among subsystems of all levels of AVC and the control of an AVC reactive power coordination optimization closed-loop system. Aiming at the close relation between voltage and reactive power, the reactive power local balance principle and the mutual backup among the power grid subareas, the evaluation data has practical significance for evaluating the reactive voltage support capability of the power grid subareas. The evaluation data includes: monitoring node voltage of a power grid, on-load tap positions of voltage-regulating transformers in partitions, reactive capacity in the partitions, reactive capacity of interconnected partitions, and partition reactive power formulated by an AVC system according to global reactive power optimization.
Step 2: establishing a multi-partition power grid reactive voltage support capability evaluation index system;
the index system is formed by carrying out mean value and normalization processing on the evaluation data provided in the step 1, and comprises the following steps: the method comprises the steps of main network subarea voltage margin, subarea on-load transformer tap adjustable margin indexes, subarea reactive power margin indexes and subarea reactive power deviation margin indexes. The detailed formulation formula of each index is as follows:
(1) main network partition voltage margin index
Figure GDA0003278048880000021
Wherein the content of the first and second substances,
Figure GDA0003278048880000022
in the formula, N is the number of the nodes of the partition; u shapeiIs the voltage of the i-th node, Ui,crThe critical safety voltage of the ith node when the voltage collapses.
(2) Partition on-load transformer tap adjustable margin index
Figure GDA0003278048880000023
Wherein the content of the first and second substances,
Figure GDA0003278048880000024
in the formula, NtNumber of on-load tap changers for zoning, Ui,admFor the margin of the adjustable voltage of the ith on-load tap changer, Ui,adFor the total regulation range of the ith on-load tap changer, i.e. tap percentage, DeltaUi,adThe residual adjustable range of the ith on-load tap changing transformer, namely the percentage of the residual adjustable taps.
(3) Index of partitioned reactive margin
Figure GDA0003278048880000031
In the formula, QLRepresenting zone real-time reactive capacity, Qi,GmaxMaximum adjustable reactive power N for normal or phase-in operation of the ith power plant unitGRepresenting the number of power plants in the zone; qi,recIndicating the capacity of the respective reactive power compensator, NrRepresenting the number of reactive power compensation devices in the partition; qi,PmaxIs the maximum reactive power, N, that each interconnection zone can providePIndicating the number of partitions interconnected with the partition;
(4) partition reactive power deviation margin index
Figure GDA0003278048880000032
Wherein Q isAVCThe partition reactive power Q established for the upper AVC system according to the global reactive power regulationATotal reactive capacity provided for the partition.
And step 3: a multi-partition power grid reactive voltage support capability evaluation method;
and (4) evaluating the physical quantity of the steps by combining a fuzzy membership function and a dynamic time warping algorithm. The method comprises the following two steps:
step 3.1: simultaneously fuzzifying the evaluation data and the reference data by using a Gaussian membership function;
as the Gaussian membership function can calculate the fuzzy membership for the condition that the data to be evaluated fluctuates on both sides of the reference data, the Gaussian membership function is adopted for fuzzification. Each index corresponds to fiveFuzzy subsets, respectively f1Is equal to [. strong]、f2Is ═ strong]、f3General (1)]、f4Not good at]、f5Is ═ weak]The Gaussian membership function is as follows:
Figure GDA0003278048880000033
wherein b is the value of each reference data, different indexes correspond to different b values, meanwhile, different fuzzy subsets have different b values, and σ represents the difference of membership degrees of different data fuzziness, namely the width of a gaussian curve, which is 0.1699. And x is data to be evaluated and reference data.
Step 3.2: calculating the similarity between the reference data and the data to be evaluated by using a dynamic time warping algorithm;
and dynamically planning the data sequence to be evaluated and each reference data sequence by the dynamic time warping algorithm, and calculating the distance of the dynamic warping path so as to determine the reference data series which is most similar to the data sequence to be evaluated. The specific calculation method is as follows:
step 3.2.1: calculating the distance between each element of the sequence to be evaluated and the reference sequence to form a distance matrix;
suppose that the sequence to be evaluated and the reference sequence are X ═ X (X), respectively1,x2,…,xm)，Y＝(y1,y2,…,yn) Then, first, without considering the three constraints, the distances d of all the points in the two sequences are calculated△：
Figure GDA0003278048880000041
Wherein d isijIs the Euclidean distance between the ith element of the sequence to be evaluated with the length of m and the jth element of the reference sequence with the length of n.
Step 3.2.2: planning a dynamic path;
first, a vector W ═ W (W)1,…,wk,…,wR) A dynamic warping path is represented and,and establishing a mapping relation:
wk＝dij (9)
wherein the length of the vector W satisfies
max(m,n)≤R≤m+n-1 (10)
Because the order of arrangement of elements in two sequences to be compared is the same, it is not necessary to calculate the cumulative distance for each path, and in order to find the optimal path more quickly, three constraint conditions that the optimal dynamic warping path must satisfy are given:
a) boundary condition
Since the order of the two sequence elements to be compared is inherent, dynamic path warping must be done from element (x)1,y1) Starting with element (x)m,yn) And (6) ending.
b) Monotonicity
When two sequences are compared, the comparison needs to be carried out in one direction, and cross comparison cannot be carried out. Namely:
for wk＝dij，wk+1＝dstShould satisfy
i≤s,j≤t (11)
Wherein d isstIs the Euclidean distance between the s-th element of the sequence to be evaluated with the length of m and the t-th element of the reference sequence with the length of n.
c) Continuity of
The distance between two points in the dynamically planned path must be the distance between two adjacent points, and comparison across points is not possible. Namely: for wk＝dij，wk+1＝dstShould satisfy
s-i≤1,t-j≤1 (12)
Step 3.2.3: minimum dynamic warping path distance;
the minimum dynamic warping path distance is represented by an accumulated distance D (i, j), and can be obtained on the premise that three constraints are satisfied:
D(i,j)＝dij+min{D(i-1,j),D(i-1,j-1),D(i,j-1)} (13)
and 4, step 4: determining the reactive voltage support capacity of the power grid subareas;
and 3, obtaining the dynamic regular path distance between the data to be evaluated and each reference data, wherein the minimum dynamic regular path distance represents that the evaluation sequence is most similar to the corresponding reference sequence, and the grade corresponding to the reference sequence is the grade corresponding to the reactive voltage support capability of the multi-partition power grid.
Compared with the closest prior art, the invention has the beneficial effects that:
(1) according to the invention, from the perspective of regional reactive power local balance and regional reactive power interconnection interactive support, the close relation between reactive power and voltage is considered, a set of index system capable of reflecting the regional reactive power voltage support capability is established, and effective data support is provided for reactive power coordination control of the existing AVC system.
(2) The method integrates the fuzzy theory and the dynamic time warping algorithm to evaluate the reactive voltage supporting capability of the multi-partition power grid, effectively inhibits the ill-condition warping of the dynamic warping path, enables the evaluation speed to be faster, facilitates the AVC coordination control system to carry out optimization control on the overall reactive power in time, and improves the stability of the power system.
Drawings
FIG. 1 is a flow chart of the present invention.
Fig. 2 is a system block diagram employed in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
As shown in fig. 1, the method for evaluating the reactive voltage support capability of a multi-partition grid according to the present invention specifically includes the following steps:
step 1: selecting reactive voltage support capability evaluation index data of the multi-partition power grid;
the establishment of the evaluation data considers the characteristics of the whole architecture of the layered and partitioned power grid, the relationship among subsystems of all levels of AVC and the control of an AVC reactive power coordination optimization closed-loop system. Aiming at the close relation between voltage and reactive power, the reactive power local balance principle and the mutual backup among the power grid subareas, the evaluation data has practical significance for evaluating the reactive voltage support capability of the power grid subareas. The evaluation data includes: monitoring node voltage of a power grid, on-load tap positions of voltage-regulating transformers in partitions, reactive capacity in the partitions, reactive capacity of interconnected partitions, and partition reactive power formulated by an AVC system according to global reactive power optimization.
Step 2: establishing a multi-partition power grid reactive voltage support capability evaluation index system;
the index system is formed by carrying out mean value and normalization processing on the evaluation data provided in the step 1, and comprises the following steps: the method comprises the steps of main network subarea voltage margin, subarea on-load transformer tap adjustable margin indexes, subarea reactive power margin indexes and subarea reactive power deviation margin indexes. The detailed formulation formula of each index is as follows:
(1) main network partition voltage margin index
Figure GDA0003278048880000061
Wherein the content of the first and second substances,
Figure GDA0003278048880000062
in the formula, N is the number of the nodes of the partition; u shapeiIs the voltage of the ith node, Ui,crThe critical safety voltage of the ith node when the voltage collapses.
(2) Partition on-load transformer tap adjustable margin index
Figure GDA0003278048880000063
Wherein the content of the first and second substances,
Figure GDA0003278048880000071
in the formula, NtNumber of on-load tap changers for zoning, Ui,admFor the margin of the adjustable voltage of the ith on-load tap changer, Ui,adFor the total regulation range of the ith on-load tap changer, i.e. tap percentage, DeltaUi,adThe residual adjustable range of the ith on-load tap changing transformer, namely the percentage of the residual adjustable taps.
(3) Partition reactive margin index
Figure GDA0003278048880000072
In the formula, QLRepresenting zone real-time reactive capacity, Qi,GmaxIndicating the maximum adjustable reactive power, N, for the ith power plant unit in normal or in-phase operationGRepresenting the number of power plants in the zone; qi,recIndicating the capacity of the respective reactive power compensator, NrRepresenting the number of reactive power compensation devices in the partition; qi,PmaxIs the maximum reactive power, N, that each interconnection zone can providePIndicating the number of partitions interconnected with the partition;
(4) partition reactive power deviation margin index
Figure GDA0003278048880000073
Wherein Q isAVCThe partition reactive power Q established for the upper AVC system according to the global reactive power regulationATotal reactive capacity provided for the partition.
And step 3: a multi-partition power grid reactive voltage support capability evaluation method;
and (4) evaluating the physical quantity of the steps by combining a fuzzy membership function and a dynamic time warping algorithm. The method comprises the following two steps:
step 3.1: simultaneously fuzzifying the evaluation data and the reference data by using a Gaussian membership function;
as the Gaussian membership function can calculate the fuzzy membership for the condition that the data to be evaluated fluctuates on both sides of the reference data, the Gaussian membership function is adopted for fuzzification. Each index corresponds to five fuzzy subsets, f1Is ═ strong]、f2Is ═ strong]、f3General (1)]、f4Not good at]、f5Is ═ weak]The Gaussian membership function is as follows:
Figure GDA0003278048880000074
wherein b is the value of each reference data, different indexes correspond to different b values, meanwhile, different fuzzy subsets have different b values, and σ represents the difference of membership degrees of different data fuzziness, namely the width of a gaussian curve, which is 0.1699. And x is data to be evaluated and reference data.
Step 3.2: calculating the similarity between the reference data and the data to be evaluated by using a dynamic time warping algorithm;
and dynamically planning the data sequence to be evaluated and each reference data sequence by the dynamic time warping algorithm, and calculating the distance of the dynamic warping path so as to determine the reference data series which is most similar to the data sequence to be evaluated. The specific calculation method is as follows:
step 3.2.1: calculating the distance between each element of the sequence to be evaluated and the reference sequence to form a distance matrix;
suppose that the sequence to be evaluated and the reference sequence are X ═ X (X), respectively1,x2,…,xm)，Y＝(y1,y2,…,yn) Then, first, without considering the three constraints, the distances d of all the points in the two sequences are calculated△：
Figure GDA0003278048880000081
Wherein d isijIs the Euclidean distance between the ith element of the sequence to be evaluated with the length of m and the jth element of the reference sequence with the length of n。
Step 3.2.2: planning a dynamic path;
first, a vector W ═ W (W)1,…,wk,…,wR) Expressing the dynamic warping path and establishing a mapping relation:
wk＝dij (9)
wherein the length of the vector W satisfies
max(m,n)≤R≤m+n-1 (10)
Since the order of arrangement of the two sequence elements to be compared is the same, it is not necessary to calculate the cumulative distance for each path, and in order to find the optimal path more quickly, three constraints that the optimal dynamic warping path must satisfy are given:
(a) boundary condition
Since the order of the two sequence elements to be compared is inherent, dynamic path warping must be done from element (x)1,y1) Starting with element (x)m,yn) And (6) ending.
(b) Monotonicity
When two sequences are compared, the comparison needs to be carried out in one direction, and cross comparison cannot be carried out. Namely:
for wk＝dij，wk+1＝dstShould satisfy
i≤s,j≤t (11)
Wherein d isstIs the Euclidean distance between the s-th element of the sequence to be evaluated with the length of m and the t-th element of the reference sequence with the length of n.
(c) Continuity of
The distance between two points in the dynamically planned path must be the distance between two adjacent points, and comparison across points is not possible. Namely: for wk＝dij，wk+1＝dstShould satisfy
s-i≤1,t-j≤1 (12)
Step 3.2.3: minimum dynamic warping path distance;
the minimum dynamic warping path distance is represented by an accumulated distance D (i, j), and can be obtained on the premise that three constraints are satisfied:
D(i,j)＝dij+min{D(i-1,j),D(i-1,j-1),D(i,j-1)} (13)
and 4, step 4: determining the reactive voltage support capacity of the power grid subareas;
and 3, obtaining the dynamic regular path distance between the data to be evaluated and each reference data, wherein the minimum dynamic regular path distance represents that the evaluation sequence is most similar to the corresponding reference sequence, and the grade corresponding to the reference sequence is the grade corresponding to the reactive voltage support capability of the multi-partition power grid.
Example 2
1) Partitioned grid evaluation data
Fig. 2 is a diagram of an IEEE2 zone 4 machine system, which extracts evaluation data from a power monitoring system and an AVC system, including: monitoring node voltage of a power grid, tap positions of on-load tap-changing transformers in subareas, reactive capacity of interconnected subareas, and subarea reactive power formulated by an AVC system according to global reactive power optimization. The evaluation data is calculated according to the expressions (1) to (6) to obtain the index-corresponding data value. The index data to be evaluated is shown in table 1.
TABLE 1 two-partition evaluation data index values
Figure GDA0003278048880000102
2) Index value corresponding to reference data
The reference sample data obtained after calculating according to equations (1) to (6) and performing weight assignment on each index according to the original reference data is shown in table 2:
table 2 reference sample data
Figure GDA0003278048880000101
3) Multi-partition power grid reactive voltage support capability assessment method
The minimum bending distances of the two partitions corresponding to the five subsets can be obtained by applying a gaussian membership function and a dynamic time bending algorithm program, and are shown in the following table 3:
TABLE 3 similarity of reference data and evaluation data
Minimum bending distance High strength Is stronger In general terms Is weaker Weak (weak)
Region 1 1.6550 0.5244 2.2008 3.6921 3.9842
Region 2 2.1752 2.0615 1.9883 2.2998 3.5793
4) Evaluation results
As can be seen from table 3, the minimum bending distance between the area 1 and the reference data 2 is the smallest, that is, the area 1 data is the most similar to the stronger reference data, and it can be found that the reactive voltage supporting capability of the area 1 is stronger. Similarly, the reactive voltage capability of zone 2 is general.
While the invention has been described in terms of its preferred embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention.

Claims (1)

1. A reactive voltage support capability assessment method for a multi-partition power grid is characterized by comprising the following steps:
step 1: selecting reactive voltage support capability evaluation index data of the multi-partition power grid;
the establishment of the evaluation data takes the characteristics of the whole architecture of the layered partitions of the power grid, the relationship among subsystems of all levels of AVC (automatic voltage control) and the control of an AVC reactive power coordination optimization closed-loop system into consideration; aiming at the close relation between voltage and reactive power, the reactive power local balance principle and the mutual standby among the power grid subareas, the evaluation data has practical significance for evaluating the reactive voltage support capacity of the power grid subareas; the evaluation data includes: monitoring node voltage of a power grid, on-load tap positions of voltage-regulating transformers in partitions, reactive capacity in the partitions and reactive capacity of interconnected partitions, wherein an AVC system is used for formulating partition reactive power according to global reactive power optimization;
step 2: establishing a multi-partition power grid reactive voltage support capability evaluation index system;
the index system is formed by carrying out mean value and normalization processing on the evaluation data provided in the step 1, and comprises the following steps: voltage margin of a main network subarea, an adjustable margin index of a subarea on-load transformer tap joint, a subarea reactive power margin index and a subarea reactive power deviation margin index; the detailed formulation formula of each index is as follows:
(1) main network partition voltage margin index
Figure FDA0003278048870000011
Wherein the content of the first and second substances,
Figure FDA0003278048870000012
in the formula, N is the number of the nodes of the partition; u shapeiIs the voltage of the i-th node, Ui,crThe critical safety voltage of the ith node when the voltage collapses;
(2) partition on-load transformer tap adjustable margin index
Figure FDA0003278048870000013
Wherein the content of the first and second substances,
Figure FDA0003278048870000014
in the formula, NtNumber of on-load tap changers for zoning, Ui,admFor the margin of the adjustable voltage of the ith on-load tap changer, Ui,adFor the total regulation range of the ith on-load tap changer, i.e. tap percentage, DeltaUi,adThe residual adjustable range of the ith on-load tap changing transformer is the percentage of the residual adjustable tap;
(3) partition reactive margin index
Figure FDA0003278048870000021
In the formula, QLRepresenting zone real-time reactive capacity, Qi,GmaxIndicating the maximum adjustable reactive power, N, for the ith power plant unit in normal or in-phase operationGRepresenting the number of power plants in the zone; qi,recIndicating the capacity of the respective reactive power compensator, NrRepresenting the number of reactive power compensation devices in the partition; qi,PmaxAre respective interconnected partitionsMaximum reactive power that can be provided, NPIndicating the number of partitions interconnected with the partition;
(4) partition reactive power deviation margin index
Figure FDA0003278048870000022
Wherein Q isAVCThe partition reactive power Q established for the upper AVC system according to the global reactive power regulationATotal reactive capacity provided for the partition;
and 3, step 3: a multi-partition power grid reactive voltage support capability evaluation method;
and (3) evaluating the physical quantity of the steps by combining a fuzzy membership function and a dynamic time warping algorithm, wherein the method comprises the following two steps:
step 3.1: simultaneously fuzzifying the evaluation data and the reference data by using a Gaussian membership function;
since the Gaussian membership function can calculate the fuzzy membership for the fluctuation of the data to be evaluated on both sides of the reference data, the Gaussian membership function is adopted for fuzzification; each index corresponds to five fuzzy subsets, f1Is ═ strong]、f2Is ═ strong]、f3General (1)]、f4Not good at]、f5Is equal to [ weak]The Gaussian membership function is as follows:
Figure FDA0003278048870000023
b is the value of each reference data, different indexes correspond to different b values, meanwhile, different b values of fuzzy subsets are different, sigma represents the difference of membership degrees obtained by fuzzy of different data, namely the width of a Gaussian curve, and 0.1699 is taken; x is data to be evaluated and reference data;
step 3.2: calculating the similarity between the reference data and the data to be evaluated by using a dynamic time warping algorithm;
the dynamic time warping algorithm dynamically plans the data sequence to be evaluated and each reference data sequence, and calculates the distance of a dynamic warping path, thereby determining the reference data series which is most similar to the data sequence to be evaluated; the specific calculation method is as follows:
step 3.2.1: calculating the distance between each element of the sequence to be evaluated and the reference sequence to form a distance matrix;
suppose that the sequence to be evaluated and the reference sequence are X ═ X (X), respectively1,x2,…,xm)，Y＝(y1,y2,…,yn) Then, first, without considering the three constraints, the distances d of all the points in the two sequences are calculated△：
Figure FDA0003278048870000031
Wherein d isijThe Euclidean distance between the ith element of the sequence to be evaluated with the length of m and the jth element of the reference sequence with the length of n;
step 3.2.2: planning a dynamic path;
first, a vector W ═ W (W)1,…,wk,…,wR) Expressing the dynamic warping path and establishing a mapping relation:
wk＝dij (9)
wherein the length of the vector W satisfies
max(m,n)≤R≤m+n-1 (10)
Because the order of arrangement of elements in two sequences to be compared is the same, it is not necessary to calculate the cumulative distance for each path, and in order to find the optimal path more quickly, three constraint conditions that the optimal dynamic warping path must satisfy are given:
a) boundary condition
Since the order of the two sequence elements to be compared is inherent, dynamic path warping must be done from the element (x)1,y1) Starting with element (x)m,yn) Finishing;
b) monotonicity
When two sequences are compared, the two sequences need to be compared in one direction, and cross comparison cannot be performed; namely:
for wk＝dij，wk+1＝dstShould satisfy
i≤s,j≤t (11)
Wherein d isstThe Euclidean distance between the s element of the sequence to be evaluated with the length of m and the t element of the reference sequence with the length of n;
c) continuity of
The distance between two points in the dynamic planning path must be the distance between two adjacent points, and the two points cannot be compared across the points; namely: for wk＝dij，wk+1＝dstShould satisfy
s-i≤1,t-j≤1 (12)
Step 3.2.3: minimum dynamic warping path distance;
the minimum dynamic warping path distance is represented by an accumulated distance D (i, j), and can be obtained on the premise that three constraints are satisfied:
D(i,j)＝dij+min{D(i-1,j),D(i-1,j-1),D(i,j-1)} (13)
and 4, step 4: determining the reactive voltage support capacity of the power grid subareas;
and 3, obtaining the dynamic regular path distance between the data to be evaluated and each reference data, wherein the minimum dynamic regular path distance represents that the evaluation sequence is most similar to the corresponding reference sequence, and the grade corresponding to the reference sequence is the grade corresponding to the reactive voltage support capability of the multi-partition power grid.
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