CN115204615A - Disaster grading method and device based on Euclidean distance and grey correlation degree - Google Patents
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Abstract
The invention provides a disaster grading method and device based on Euclidean distance and grey correlation degree, wherein the method comprises the following steps: acquiring various grading indexes and corresponding historical data of a power system when a disaster event occurs; constructing an index evaluation matrix and a weighting matrix; selecting a maximum ideal index and a minimum index, and calculating a grey correlation degree and an Euclidean distance; calculating an ideal distance for representing the influence of the disaster event on each grading index of the power system according to the grey correlation degree and the Euclidean distance; and grading based on the ideal distance and the operation state judgment condition of the power system. Compared with the prior art, the accuracy of disaster event evaluation and analysis is improved, and reference is provided for restoring force evaluation of the power system.
Description
Technical Field
The invention relates to the field of power system safety planning operation, in particular to a disaster grading method and device based on Euclidean distance and grey correlation degree.
Background
Power systems are an important infrastructure for social security and economic life-lines. Ensuring safe and reliable supply of electric power and improving the restoring force of a power grid become the subject of wide attention at home and abroad. The resilience of the power system refers to the ability of the system to resist absorption and respond to recovery after being subjected to external extreme time, which is a necessary requirement for the development of the power system. Under the background that different types of extreme events increasingly threaten the safe operation of the power system, the influence of various extreme events on the power system is analyzed and researched, and the method is the key for improving the resilience of the power system. Since different events have different characteristics and affect the power system differently, it is necessary to rank the various extreme events that the power system may encounter. At present, an extreme event unified grading model comprehensively considering various extreme event dangerousness, power system damage conditions and power system restoring force levels does not exist, and the disaster event analysis and evaluation in the prior art is one-sided and lacks of comprehensive consideration.
Disclosure of Invention
The invention provides a disaster grading method and device based on Euclidean distance and grey correlation degree, which improve the accuracy of evaluation and analysis of disaster events.
In order to solve the above technical problem, an embodiment of the present invention provides a disaster classification method based on euclidean distance and gray correlation, including:
acquiring various types of grading indexes and historical data of the grading indexes of the power system when a disaster event occurs; the types of the grading indexes comprise a load type, a network frame type, an elastic resource type, a fault maintenance type and a weather type;
constructing an index evaluation matrix by taking each grading index as a row vector and historical data of each grading index as a column vector;
calculating to obtain a weight coefficient of each grading index according to the index evaluation matrix, and constructing a weighting matrix according to the weight coefficient of each grading index;
selecting a maximum ideal index and a minimum ideal index from the weighting matrix, and respectively calculating a first gray correlation degree and a second gray correlation degree according to the maximum ideal index, the minimum ideal index and the index evaluation matrix; respectively calculating a first Euclidean distance and a second Euclidean distance according to the maximum ideal index, the minimum ideal index and the weighting matrix;
calculating to obtain an ideal distance corresponding to each grading index one by one according to the first gray correlation degree, the first Euclidean distance, the second gray correlation degree and the second Euclidean distance; the ideal distance is used for representing the influence of the disaster event on each grading index of the power system;
and determining the grading result of the disaster event according to the ideal distance and by combining the operation state judgment condition of the power system.
As a preferred scheme, the calculating according to the index evaluation matrix to obtain the weight coefficient of each grading index, and constructing a weighting matrix according to the weight coefficient of each grading index specifically includes:
evaluating a matrix R = (R) according to the index ij ) n×m Meter for measuringCalculating the entropy H of the grading index i ：
Wherein r is ij For the ith row and jth column element of the index evaluation matrix, n is the number of grading indexes, m is the number of historical data of the grading indexes, i =1,2, …, n, j =1,2, …, m;
calculating the weight coefficient w of each grading index i ：
And constructing a weighting matrix according to the weight coefficient of each grading index:
T＝(t ij ) n×m ＝(w i r ij ) n×m ；
wherein T is the weighting matrix.
As a preferred scheme, the selecting a maximum ideal index and a minimum ideal index from the weighting matrix specifically includes:
selecting a maximum ideal index and a minimum ideal index according to the weighting matrix:
x j ＝max 1≤i≤n t ij ；
y j ＝min 1≤i≤n t ij ；
wherein x is j Is the maximum ideal index, y j J =1,2,.. M, the minimum ideal index.
As a preferred scheme, the calculating a first gray correlation degree and a second gray correlation degree according to the maximum ideal index, the minimum ideal index and the index evaluation matrix includes:
according to the index evaluation matrix and the maximum ideal index, constructing a first gray correlation matrix U:
U＝(u ij ) n×m ；
wherein x is j Is the maximum ideal index, r ij The method comprises the steps of taking the element in the ith row and the jth column of an index evaluation matrix, wherein rho is a preset resolution coefficient and the value range is 0-1;
calculating the first grey correlation degree:
wherein the content of the first and second substances,
the first grey correlation degree is in one-to-one correspondence with each grading index;
and constructing a second gray correlation matrix V according to the index evaluation matrix and the minimum ideal index:
V＝(v ij ) n×m ；
wherein, y j Is the minimum ideal index;
calculating the second gray color correlation degree:
wherein the content of the first and second substances,
the second gray color correlation degree is in one-to-one correspondence with each grading index;
the step of calculating a first Euclidean distance and a second Euclidean distance according to the maximum ideal index, the minimum ideal index and the weighting matrix respectively comprises the following specific steps:
calculating a first Euclidean distance according to the maximum ideal index and the weighting matrix:
wherein, the first and the second end of the pipe are connected with each other,
is the first Euclidean distance;
calculating a second Euclidean distance according to the minimum ideal index and the weighting matrix:
wherein the content of the first and second substances,
is the second euclidean distance.
As a preferred scheme, the calculating, according to the first gray correlation degree, the first euclidean distance, the second gray correlation degree, and the second euclidean distance, to obtain an ideal distance corresponding to each of the grading indexes one to one specifically is:
calculating a positive phase difference distance according to the first grey correlation degree and the second Euclidean distance:
calculating a negative phase difference distance according to the second gray color correlation degree and the first Euclidean distance:
and further calculating the ideal distance:
L i ＝N i /(M i +N i )；
wherein, M i Is a positive distance apart, N i By a negative distance, L i Is the ideal distance.
Preferably, before the step of constructing the index evaluation matrix by using each hierarchical index as a row vector and using historical data of each hierarchical index as a column vector, the method further comprises:
and performing per-unit treatment on each grading index in sequence according to the type of each grading index.
Preferably, the per-unit processing is performed on each classification index in sequence according to the type of each classification index, and specifically includes:
the elastic resource type comprises real-time residual electric quantity of the energy storage device, flexible controllable load response speed, a plurality of load indexes, a plurality of time indexes and a plurality of power indexes; the load class comprises a plurality of probability indexes and a plurality of load indexes; the weather class comprises a plurality of descriptive indexes; the fault maintenance class comprises a plurality of time indexes, a plurality of descriptive indexes and a plurality of number indexes;
when the grading index is the probability index in the load class or the index of the network frame class, performing no per unit operation;
when the grading index is the load index in the load class or the load index of the elastic resource class, performing per unit processing by taking the maximum load value of the power grid in the historical data as a reference;
when the grading index is the power index of the elastic resource class, per-unit processing is carried out by taking the corresponding installed total capacity as a reference value;
when the grading index is the real-time residual capacity of the energy storage devices in the elastic resource class, performing per unit processing by taking the sum of the maximum residual capacities of all the energy storage devices as a reference value;
when the grading index is the flexible controllable load response speed in the elastic resource class, performing per unit processing by taking the maximum demand side load response speed measured and calculated by a load regulation center as a reference;
when the grading index is the time index in the elastic resource class or the time index in the fault maintenance class, performing per-unit processing on the grading index by adopting an exponential conversion function;
when the grading index is the number index of the fault maintenance classes, per unit processing is carried out by taking the maximum number as a reference value;
and when the grading index is the descriptive index in the fault maintenance class or the descriptive index in the meteorological class, performing per-unit processing according to a preset calibration value.
Preferably, the determining, according to the ideal distance and in combination with the operating state determination condition of the power system, a classification result of the disaster event includes:
the grading of the disaster event comprises a primary early warning threshold, a secondary early warning threshold, a tertiary early warning threshold, a quaternary early warning threshold and a safety threshold;
the operation state judgment condition of the power system is determined by historical data samples, including absolute safety, absolute unsafe and indeterminable, and is determined according to the historical data samples;
and determining the grading of the disaster event based on the conditional probability according to the operation state judgment condition of the power system and the ideal distance so as to determine the grading result of the disaster event.
Correspondingly, the embodiment of the invention also provides a disaster grading device based on the Euclidean distance and the grey correlation degree, which comprises an acquisition module, a matrix construction module, a weighting module, a first calculation module, a second calculation module and a grading module; wherein the content of the first and second substances,
the acquisition module is used for acquiring various types of grading indexes and historical data of the grading indexes of the power system when a disaster event occurs; the types of the grading indexes comprise a load type, a network frame type, an elastic resource type, a fault maintenance type and a weather type;
the matrix construction module is used for constructing an index evaluation matrix by taking each grading index as a row vector and historical data of each grading index as a column vector;
the weighting module is used for calculating the weight coefficient of each grading index according to the index evaluation matrix and constructing a weighting matrix according to the weight coefficient of each grading index;
the first calculation module is used for selecting a maximum ideal index and a minimum ideal index from the weighting matrix, and respectively calculating a first gray correlation degree and a second gray correlation degree according to the maximum ideal index, the minimum ideal index and the index evaluation matrix; respectively calculating a first Euclidean distance and a second Euclidean distance according to the maximum ideal index, the minimum ideal index and the weighting matrix;
the second calculation module is used for calculating and obtaining ideal distances corresponding to the grading indexes one by one according to the first gray correlation degree, the first Euclidean distance, the second gray correlation degree and the second Euclidean distance; the ideal distance is used for representing the influence of the disaster event on each grading index of the power system;
and the grading module is used for determining a grading result of the disaster event according to the ideal distance and by combining the operation state judgment condition of the power system.
As a preferred scheme, the weighting module calculates a weight coefficient of each grading index according to the index evaluation matrix, and constructs a weighting matrix according to the weight coefficient of each grading index, specifically:
the weighting module evaluates a matrix R = (R) according to the index ij ) n×m Calculating the entropy H of the grade index i ：
Wherein r is ij Is the jth row and jth column element of the index evaluation matrix, n is the number of grading indexes, m is the number of historical data of the grading indexes, i =1,2, …, n, j =1,2, …, m;
calculating the weight coefficient w of each grading index i ：
And constructing a weighting matrix according to the weight coefficient of each grading index:
T＝(t ij ) n×m ＝(w i r ij ) n×m ；
wherein T is the weighting matrix.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
the embodiment of the invention provides a disaster grading method and device based on Euclidean distance and grey correlation degree, wherein the method comprises the following steps: acquiring various types of grading indexes and historical data of the grading indexes of the power system when a disaster event occurs; the types of the grading indexes comprise a load type, a network frame type, an elastic resource type, a fault maintenance type and a weather type; constructing an index evaluation matrix by taking each grading index as a row vector and historical data of each grading index as a column vector; calculating to obtain a weight coefficient of each grading index according to the index evaluation matrix, and constructing a weighting matrix according to the weight coefficient of each grading index; selecting a maximum ideal index and a minimum ideal index from the weighting matrix, and respectively calculating a first gray correlation degree and a second gray correlation degree according to the maximum ideal index, the minimum ideal index and the index evaluation matrix; respectively calculating a first Euclidean distance and a second Euclidean distance according to the maximum ideal index, the minimum ideal index and the weighting matrix; calculating to obtain an ideal distance corresponding to each grading index one by one according to the first gray correlation degree, the first Euclidean distance, the second gray correlation degree and the second Euclidean distance; the ideal distance is used for representing the influence of the disaster event on each grading index of the power system; and determining the grading result of the disaster event according to the ideal distance and by combining the operation state judgment condition of the power system. Compared with the prior art, the ideal distance obtained by adopting the analysis method combining the Euclidean distance and the grey correlation degree does not need to be compared with the sequence to have a typical distribution rule, and the calculation is more intuitive and simpler.
Furthermore, the first gray correlation matrix and the second gray correlation matrix are determined based on the resolution coefficient, and the accuracy of the grading result of the power system is improved by combining the weight coefficient calculated based on the entropy and the constructed weighting matrix.
Furthermore, before the index evaluation matrix is constructed, different types of grading indexes are subjected to per unit processing, and the incommercity of the grading indexes is eliminated.
Drawings
FIG. 1: the invention provides a flow diagram of an embodiment of a disaster classification method based on Euclidean distance and grey correlation degree.
FIG. 2: the invention provides a structural schematic diagram of an embodiment of a disaster classification device based on Euclidean distance and grey correlation degree.
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 obtained by a person skilled in the art without inventive step based on the embodiments of the present invention, are within the scope of protection of the present invention.
The first embodiment is as follows:
referring to fig. 1, fig. 1 is a method for disaster classification based on euclidean distance and gray correlation according to an embodiment of the present invention, including steps S1 to S6, wherein,
the method comprises the following steps of S1, acquiring various types of grading indexes and historical data of the grading indexes of the power system when a disaster event occurs; the types of the grading indexes comprise a load type, a network frame type, an elastic resource type, a fault maintenance type and a weather type.
In this embodiment, 25 grading indexes are selected, and the grading indexes can be classified into a load class, a network frame class, an elastic resource class, a fault maintenance class and a weather class according to the index types. The load classes include, but are not limited to, a system load loss probability, a system load loss 10% probability, a system load loss expected value, a system 90% possible load loss value, a system load loss value, and the like, the grid classes include, but are not limited to, a line average load rate, a line maximum load rate, a transformer average load rate, a platform transformer maximum load rate, and the like, the elastic resource classes include, but are not limited to, a distributed photovoltaic power generation prediction, a distributed wind power generation prediction, an energy storage device online capacity, an energy storage device real-time remaining capacity (SOC), a flexible controllable load capacity, a flexible controllable load response speed, a diesel online capacity, a diesel available time prediction, and the like, the troubleshooting classes include, but are not limited to, a number of emergency power generation cars ready for use, a number of maintenance personnel ready for use, a remaining maintenance material reserve, a maintenance personnel/material in-place situation, a current equipment loss importance degree, and the meteorological classes include, but are not limited to, a meteorological disaster early warning level and a real-time severity degree, specifically refer to table 1:
TABLE 1 grading index example
The method comprises the steps of obtaining the above 25 grading indexes of the power system and corresponding historical data when a disaster event occurs. In practical application, addition or deletion can be performed according to requirements so as to meet the requirements of practical application scenes.
After obtaining the rating indexes of each type and the historical data of each rating index, before step S2, the method further includes: and performing per-unit treatment on each grading index in sequence according to the type of each grading index.
Optionally, the elastic resource class includes a plurality of load indexes, a plurality of time indexes, and a plurality of power indexes; the load class comprises a plurality of probability indexes and a plurality of load indexes; the weather class comprises a plurality of descriptive indexes; the fault repair class includes a plurality of time indicators, a plurality of descriptive indicators, and a plurality of number indicators.
Furthermore, when the grading index is a probability index (such as index 1: system load loss probability; index 2: 10% of system load loss probability) in the load classes or an index of the network frame class (index 6: average load rate of distribution lines; index 7: maximum load rate of distribution lines; index 8: average load rate of transformer in transformer area; index 9: maximum load rate of transformer in transformer area), the per-unit operation is not performed because the value range of the grading index is between 0 and 1.
When the grading index is a load index (index 3: expected value of system load loss; index 4: 90% load loss value of system; index 5: load loss value of system) in the load class or a load index (index 14: flexible controllable load capacity) of the elastic resource class, per-unit processing is performed by taking the maximum load value of the power grid in the historical data as a reference, specifically:
wherein, U i For per-unit grading index, S i Is the i-th ranking index before per unit, load max Is the maximum load value.
When the grading index is a power index of the elastic resource class (index 10: distributed photovoltaic power generation prediction; index 11: distributed wind power generation prediction; index 12: online capacity of an energy storage device; index 16: online capacity of diesel engine), per-unit processing is carried out by taking the corresponding total installed capacity as a reference value, specifically:
wherein, totalcapacity i Is the total installed capacity.
When the grading index is an index 13 in the elastic resource class: when the energy storage device has real-time remaining capacity, performing per unit processing by taking the sum of the maximum remaining capacities of all the energy storage devices as a reference value, specifically:
therein, SOC j Is the jth energy storage device.
When the grading index is the index 15 in the elastic resource class: when the flexible controllable load response speed is adopted, the per unit processing is carried out by taking the maximum demand side load response speed measured and calculated by the load regulation center as a reference, and the per unit processing specifically comprises the following steps:
wherein the content of the first and second substances,
the maximum demand side load response speed measured by the load regulation and control center.
When the grading index is a time index in the elastic resource class (index 17: diesel engine available time prediction) or a time index in the fault maintenance class (index 19: power generation vehicle available time prediction), because the value range is 0 to infinity, the grading index is subjected to per unit processing by adopting an exponential transfer function, specifically:
U i ＝1-exp(-S i ),i＝17,19。
when the grading index is the index of the number of the fault maintenance classes (index 18: the number of emergency power generation cars to be maintained; index 20: the number of maintenance personnel to be maintained; index 21: the remaining maintenance material reserves), per-unit processing is carried out by taking the maximum number under the normal condition as a reference value (for example, the maximum number of emergency power generation cars under the normal condition, the maximum number of maintenance personnel under the normal condition, and the maximum storage number of the material which is the most short under the normal condition), then:
wherein, number max,i Is the most excellentThe large number.
When the grading index is a descriptive index in the fault maintenance class (index 22: maintenance personnel/material in-place condition; index 23: current equipment loss importance degree) or a descriptive index in the weather class (25: real-time weather severity), performing per-unit processing according to preset calibration values in the table 2:
TABLE 2 Scale of descriptive indices
By implementing the embodiment of the application, up to 25 grading indexes are adopted, and each grading index covers the aspects of load, net rack, elastic resources, fault maintenance, weather and the like of the power system, so that a comprehensive index evaluation system is constructed; by conducting per unit processing on different types of grading indexes, the degree of importability of the grading indexes can be effectively eliminated.
And S2, constructing an index evaluation matrix by taking each grading index as a row vector and historical data of each grading index as a column vector.
In this embodiment, an index evaluation matrix having n ranking indexes and m history data is constructed from the ranking indexes and the history data of the ranking indexes:
R＝(r ij ) n×m ；
wherein i =1,2., n, j =1,2., m. r is a radical of hydrogen ij The ith row and jth column elements of the matrix are evaluated for the index.
And S3, calculating to obtain the weight coefficient of each grading index according to the index evaluation matrix, and constructing a weighting matrix according to the weight coefficient of each grading index.
Illustratively, the matrix R = (R) is evaluated according to the index ij ) n×m Calculating the entropy H of the grade index i ：
Wherein r is ij For the ith row and jth column element of the index evaluation matrix, n is the number of grading indexes, m is the number of historical data of the grading indexes, i =1,2, …, n, j =1,2, …, m;
calculating the weight coefficient w of each grading index i ：
And constructing a weighting matrix according to the weight coefficient of each grading index:
T＝(t ij ) n×m ＝(w i r ij ) n×m ；
wherein T is the weighting matrix. By implementing the embodiment of the application, the weight coefficient reflects the information quantity of the index, the larger the entropy weight is, the larger the contribution of the index to the comprehensive decision is, and the difference degree between the indexes can be intuitively and effectively reflected.
S4, selecting a maximum ideal index and a minimum ideal index from the weighting matrix, and respectively calculating a first gray correlation degree and a second gray correlation degree according to the maximum ideal index, the minimum ideal index and the index evaluation matrix; and respectively calculating a first Euclidean distance and a second Euclidean distance according to the maximum ideal index, the minimum ideal index and the weighting matrix.
Preferably, the selecting a maximum ideal index and a minimum ideal index from the weighting matrix specifically includes:
selecting a maximum ideal index and a minimum ideal index according to the weighting matrix:
x j ＝max 1≤i≤n t ij ；
y j ＝min 1≤i≤n t ij ；
wherein x is j Is the maximum ideal index, y j J =1,2,.. M, is the minimum ideal indicator.
In this embodiment, the calculating a first gray correlation degree and a second gray correlation degree according to the maximum ideal index, the minimum ideal index, and the index evaluation matrix specifically includes:
according to the index evaluation matrix and the maximum ideal index, constructing a first gray correlation matrix U:
U＝(u ij ) n×m ；
wherein x is j Is the maximum ideal index, r ij The method comprises the steps of taking the element in the ith row and the jth column of an index evaluation matrix, wherein rho is a preset resolution coefficient and the value range is 0-1;
calculating the first gray correlation degree:
wherein the content of the first and second substances,
the first grey correlation degree is in one-to-one correspondence with each grading index;
and constructing a second gray correlation matrix V according to the index evaluation matrix and the minimum ideal index:
V＝(v ij ) n×m ；
wherein, y j Is the minimum ideal index;
calculating the second gray color correlation degree:
wherein the content of the first and second substances,
the second gray color correlation degree is in one-to-one correspondence with each grading index;
the calculating a first euclidean distance and a second euclidean distance according to the maximum ideal index, the minimum ideal index and the weighting matrix respectively specifically includes:
calculating a first Euclidean distance according to the maximum ideal index and the weighting matrix:
wherein the content of the first and second substances,
is the first Euclidean distance;
calculating a second Euclidean distance according to the minimum ideal index and the weighting matrix:
wherein the content of the first and second substances,
is the second euclidean distance.
S5, calculating to obtain ideal distances corresponding to the grading indexes one to one according to the first gray correlation degree, the first Euclidean distance, the second gray correlation degree and the second Euclidean distance; the ideal distance is used for representing the influence of the disaster event on each grading index of the power system.
As a preferred embodiment, the positive phase difference distance is calculated according to the first gray correlation and the second euclidean distance:
calculating a negative phase difference distance according to the second gray color correlation degree and the first Euclidean distance:
and further calculating the ideal distance:
L i ＝N i /(M i +N i )；
wherein, M i Is a positive distance apart, N i Are different by a negative distance, L i Is the ideal distance.
And sequencing according to the ideal distance from large to small, thereby obtaining the influence of the disaster event on each grading index of the power system and further reflecting the influence on the power system. The larger the ideal distance is, the less influence on the safe operation of the power system is caused in the situation, and the larger the influence is.
And S6, determining the grading result of the disaster event according to the ideal distance and by combining the operation state judgment condition of the power system.
Specifically, in this embodiment, according to the operation standard of the power system, the operation state corresponding to the history data sample of extreme weather or disaster, such as typhoon, thunderstorm, flood, etc., is determined to be "absolutely safe" or "absolutely unsafe", and is recorded.
And determining the grading result of the disaster event according to the ideal distance and by combining the operation state judgment condition of the power system, specifically:
the grading of the disaster event comprises a primary early warning threshold, a secondary early warning threshold, a tertiary early warning threshold, a quaternary early warning threshold and a safety threshold;
the operating state judgment condition of the power system is determined by a historical data sample, and comprises three conditions of absolute safety, absolute unsafe and incapability of determining, and is specifically determined according to the historical data sample;
determining the grading of the disaster event based on the condition probability according to the operation state judgment condition of the power system and the ideal distance so as to determine the grading result of the disaster event, specifically:
P{Λ i = absolutely unsafe | γ i ≤γ A }＝1；
P{Λ i = absolutely unsafe | γ A <γ i ≤γ II }＝α 1 ,0≤α 1 <1；
P{Λ i = absolute safety | γ i ≥γ Fourthly }＝1；
P{Λ i = absolute safety | γ III ≤γ i <γ Fourthly, the method }＝α 2 ,0≤α 2 <1；
The first-level early warning threshold value is a catastrophic influence, the second-level early warning threshold value is a severe influence, the third-level early warning threshold value is a general influence, the fourth-level early warning threshold value is a slight influence, and meanwhile the safety threshold value is also included. P is the conditional probability based on the occurrence of the event, e.g., P { A | B } is the probability of the event A occurring in the event of the occurrence of event B. Middle Λ of the formula i The condition (i.e., absolutely safe or absolutely unsafe) is determined for the operating condition of the ith sample. Gamma ray i Is the ideal distance for the ith sample. Gamma ray A 、γ II 、γ III 、γ Fourthly The method is characterized in that the method respectively corresponds to a primary early warning threshold, a secondary early warning threshold, a tertiary early warning threshold and a quaternary early warning threshold of the power system based on ideal distances, and is generally defined by scheduling personnel. And alpha is 1 And alpha 2 The probability is customized for the dispatcher accordingly.
Correspondingly, the embodiment of the invention also provides a disaster grading device based on the Euclidean distance and the grey correlation degree, which comprises an acquisition module 101, a matrix construction module 102, a weighting module 103, a first calculation module 104, a second calculation module 105 and a grading module 106; wherein, the first and the second end of the pipe are connected with each other,
the acquiring module 101 is configured to acquire various types of grading indexes and historical data of the grading indexes of the power system when a disaster event occurs; the types of the grading indexes comprise a load type, a network frame type, an elastic resource type, a fault maintenance type and a weather type;
the matrix construction module 102 is configured to construct an index evaluation matrix by using each hierarchical index as a row vector and using historical data of each hierarchical index as a column vector;
the weighting module 103 is configured to calculate a weight coefficient of each hierarchical index according to the index evaluation matrix, and construct a weighting matrix according to the weight coefficient of each hierarchical index;
the first calculating module 104 is configured to select a maximum ideal indicator and a minimum ideal indicator from the weighting matrix, and calculate a first gray correlation degree and a second gray correlation degree according to the maximum ideal indicator, the minimum ideal indicator, and the indicator evaluation matrix; respectively calculating a first Euclidean distance and a second Euclidean distance according to the maximum ideal index, the minimum ideal index and the weighting matrix;
the second calculating module 105 is configured to calculate and obtain an ideal distance corresponding to each of the grading indexes one to one according to the first gray correlation degree, the first euclidean distance, the second gray correlation degree, and the second euclidean distance; the ideal distance is used for representing the influence of the disaster event on each grading index of the power system;
the grading module 106 is configured to determine a grading result of the disaster event according to the ideal distance and by combining with the operating state determination condition of the power system.
As a preferred scheme, the weighting module 103 calculates a weight coefficient of each grading index according to the index evaluation matrix, and constructs a weighting matrix according to the weight coefficient of each grading index, specifically:
the weighting module 103 evaluates the matrix R = (R) according to the index ij ) n×m Calculating the entropy H of the grade index i ：
Wherein r is ij For the ith row and jth column element of the index evaluation matrix, n is the number of grading indexes, m is the number of historical data of the grading indexes, i =1,2, …, n, j =1,2, …, m;
calculating the weight coefficient w of each grading index i ：
And constructing a weighting matrix according to the weight coefficient of each grading index:
T＝(t ij ) n×m ＝(w i r ij ) n×m ；
wherein T is the weighting matrix.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
the embodiment of the invention provides a disaster grading method and a device based on Euclidean distance and grey correlation degree, wherein the method comprises the following steps: acquiring various types of grading indexes and historical data of the grading indexes of the power system when a disaster event occurs; the types of the grading indexes comprise a load type, a network frame type, an elastic resource type, a fault maintenance type and a weather type; constructing an index evaluation matrix by taking each grading index as a row vector and historical data of each grading index as a column vector; calculating to obtain a weight coefficient of each grading index according to the index evaluation matrix, and constructing a weighting matrix according to the weight coefficient of each grading index; selecting a maximum ideal index and a minimum ideal index from the weighting matrix, and respectively calculating a first gray correlation degree and a second gray correlation degree according to the maximum ideal index, the minimum ideal index and the index evaluation matrix; respectively calculating a first Euclidean distance and a second Euclidean distance according to the maximum ideal index, the minimum ideal index and the weighting matrix; calculating to obtain an ideal distance corresponding to each grading index one by one according to the first gray correlation degree, the first Euclidean distance, the second gray correlation degree and the second Euclidean distance; the ideal distance is used for representing the influence of the disaster event on each grading index of the power system; and determining the grading result of the disaster event according to the ideal distance and by combining the operation state judgment condition of the power system. Compared with the prior art, the ideal distance obtained by adopting the analysis method combining the Euclidean distance and the grey correlation degree does not need to be compared with the sequence to have a typical distribution rule, and the calculation is more intuitive and simpler.
Furthermore, the first gray correlation matrix and the second gray correlation matrix are determined based on the resolution coefficient, and the accuracy of the grading result of the power system is improved by combining the weight coefficient calculated based on the entropy and the constructed weighting matrix.
Furthermore, before the index evaluation matrix is constructed, different types of grading indexes are subjected to per unit processing, and the incommercity of the grading indexes is eliminated.
The above-mentioned embodiments are provided to further explain the objects, technical solutions and advantages of the present invention in detail, and it should be understood that the above-mentioned embodiments are only examples of the present invention and are not intended to limit the scope of the present invention. It should be understood that any modifications, equivalents, improvements and the like, which come within the spirit and principle of the invention, may occur to those skilled in the art and are intended to be included within the scope of the invention.
Claims (10)
1. A disaster grading method based on Euclidean distance and grey correlation degree is characterized by comprising the following steps:
acquiring various types of grading indexes and historical data of the grading indexes of the power system when a disaster event occurs; the types of the grading indexes comprise a load type, a network frame type, an elastic resource type, a fault maintenance type and a weather type;
constructing an index evaluation matrix by taking each grading index as a row vector and historical data of each grading index as a column vector;
calculating to obtain a weight coefficient of each grading index according to the index evaluation matrix, and constructing a weighting matrix according to the weight coefficient of each grading index;
selecting a maximum ideal index and a minimum ideal index from the weighting matrix, and respectively calculating a first gray correlation degree and a second gray correlation degree according to the maximum ideal index, the minimum ideal index and the index evaluation matrix; respectively calculating a first Euclidean distance and a second Euclidean distance according to the maximum ideal index, the minimum ideal index and the weighting matrix;
calculating to obtain an ideal distance corresponding to each grading index one by one according to the first gray correlation degree, the first Euclidean distance, the second gray correlation degree and the second Euclidean distance; the ideal distance is used for representing the influence of the disaster event on each grading index of the power system;
and determining the grading result of the disaster event according to the ideal distance and by combining the operation state judgment condition of the power system.
2. The disaster classification method based on euclidean distance and gray correlation as claimed in claim 1, wherein the weighting coefficients of each classification index are calculated according to the index evaluation matrix, and a weighting matrix is constructed according to the weighting coefficients of each classification index, specifically:
evaluating a matrix R = (R) according to the index ij ) n×m Calculating the entropy H of the grade index i ：
Wherein r is ij For the ith row and jth column element of the index evaluation matrix, n is the number of grading indexes, m is the number of historical data of the grading indexes, i =1,2, …, n, j =1,2, …, m;
calculating a weight coefficient w of each grading index i ：
And constructing a weighting matrix according to the weight coefficient of each grading index:
T＝(t ij ) n×m ＝(w i r ij ) n×m ；
wherein T is the weighting matrix.
3. The method as claimed in claim 2, wherein the method for grading disasters based on euclidean distance and gray correlation includes selecting a maximum ideal index and a minimum ideal index from the weighting matrix, and specifically includes:
selecting a maximum ideal index and a minimum ideal index according to the weighting matrix:
x j ＝max 1≤i≤n t ij ；
y j ＝min 1≤i≤n t ij ；
wherein x is j Is the maximum ideal index, y j J =1,2,.. M, is the minimum ideal indicator.
4. The method as claimed in claim 2, wherein the first gray correlation degree and the second gray correlation degree are respectively calculated according to the maximum ideal index, the minimum ideal index and the index evaluation matrix, and specifically:
according to the index evaluation matrix and the maximum ideal index, constructing a first gray correlation matrix U:
U＝(u ij ) n×m ；
wherein x is j Is the maximum ideal index, r ij The method comprises the steps of taking the element in the ith row and the jth column of an index evaluation matrix, wherein rho is a preset resolution coefficient and the value range is 0-1;
calculating the first gray correlation degree:
wherein the content of the first and second substances,
the first grey correlation degree is in one-to-one correspondence with each grading index;
and constructing a second gray correlation matrix V according to the index evaluation matrix and the minimum ideal index:
V＝(v ij ) n×m ；
wherein, y j Is the minimum ideal index;
calculating the second gray color correlation degree:
wherein the content of the first and second substances,
the second gray color correlation degree is in one-to-one correspondence with each grading index;
the step of calculating a first Euclidean distance and a second Euclidean distance according to the maximum ideal index, the minimum ideal index and the weighting matrix respectively comprises the following specific steps:
calculating a first Euclidean distance according to the maximum ideal index and the weighting matrix:
wherein, the first and the second end of the pipe are connected with each other,
is the first Euclidean distance;
calculating a second Euclidean distance according to the minimum ideal index and the weighting matrix:
wherein, the first and the second end of the pipe are connected with each other,
is the second euclidean distance.
5. The disaster classification method based on euclidean distance and gray degree as claimed in claim 4, wherein said calculating to obtain the ideal distance corresponding to each classification index one by one according to the first gray degree, the first euclidean distance, the second gray degree and the second euclidean distance specifically comprises:
calculating the positive phase difference distance according to the first grey correlation degree and the second Euclidean distance:
calculating a negative phase difference distance according to the second gray color correlation degree and the first Euclidean distance:
and then calculating the ideal distance:
L i ＝N i /(M i +N i )；
wherein M is i Is a positive distance apart, N i Are different by a negative distance, L i Is the ideal distance.
6. The method for grading disasters according to any one of claims 1 to 5, wherein before constructing the index evaluation matrix, the method further comprises, with each grading index as a row vector and historical data of each grading index as a column vector, the method further comprising:
and performing per-unit processing on each grading index in sequence according to the type of each grading index.
7. The disaster classification method based on euclidean distance and grey correlation as claimed in claim 6, wherein the classification indexes are per unit in sequence according to their types, specifically:
the elastic resource class comprises real-time residual electric quantity of the energy storage device, flexible controllable load response speed, a plurality of load indexes, a plurality of time indexes and a plurality of power indexes; the load class comprises a plurality of probability indexes and a plurality of load indexes; the weather class comprises a plurality of descriptive indexes; the fault maintenance class comprises a plurality of time indexes, a plurality of descriptive indexes and a plurality of number indexes;
when the grading index is the probability index in the load class or the index of the network frame class, no per-unit operation is carried out;
when the grading index is the load index in the load class or the load index of the elastic resource class, performing per unit processing by taking the maximum load value of the power grid in the historical data as a reference;
when the grading index is the power index of the elastic resource class, per-unit processing is carried out by taking the corresponding installed total capacity as a reference value;
when the grading index is the real-time residual capacity of the energy storage devices in the elastic resource class, performing per unit processing by taking the sum of the maximum residual capacities of all the energy storage devices as a reference value;
when the grading index is the flexible controllable load response speed in the elastic resource class, performing per unit processing by taking the maximum demand side load response speed measured and calculated by a load regulation center as a reference;
when the grading index is the time index in the elastic resource class or the time index in the fault maintenance class, performing per-unit processing on the grading index by adopting an exponential conversion function;
when the grading index is the number index of the fault maintenance classes, per unit processing is carried out by taking the maximum number as a reference value;
and when the grading index is the descriptive index in the fault maintenance class or the descriptive index in the meteorological class, performing per-unit processing according to a preset calibration value.
8. The method according to any one of claims 1 to 5, wherein the determining of the grading result of the disaster event according to the ideal distance and the determination of the operating state of the power system is specifically:
the grading of the disaster event comprises a primary early warning threshold, a secondary early warning threshold, a tertiary early warning threshold, a quaternary early warning threshold and a safety threshold;
determining the operation state judgment condition of the power system by historical data samples, wherein the operation state judgment condition comprises absolute safety, absolute safety and incapability of determining and is determined according to the historical data samples;
and determining the grading of the disaster event based on the conditional probability according to the operation state judgment condition of the power system and the ideal distance so as to determine the grading result of the disaster event.
9. A disaster grading device based on Euclidean distance and grey correlation degree is characterized by comprising an acquisition module, a matrix construction module, a weighting module, a first calculation module, a second calculation module and a grading module; wherein the content of the first and second substances,
the acquisition module is used for acquiring various types of grading indexes and historical data of the grading indexes of the power system when a disaster event occurs; the types of the grading indexes comprise a load type, a network frame type, an elastic resource type, a fault maintenance type and a weather type;
the matrix construction module is used for constructing an index evaluation matrix by taking each grading index as a row vector and historical data of each grading index as a column vector;
the weighting module is used for calculating the weight coefficient of each grading index according to the index evaluation matrix and constructing a weighting matrix according to the weight coefficient of each grading index;
the first calculation module is used for selecting a maximum ideal index and a minimum ideal index from the weighting matrix, and respectively calculating a first gray correlation degree and a second gray correlation degree according to the maximum ideal index, the minimum ideal index and the index evaluation matrix; respectively calculating a first Euclidean distance and a second Euclidean distance according to the maximum ideal index, the minimum ideal index and the weighting matrix;
the second calculation module is used for calculating and obtaining ideal distances corresponding to the grading indexes one by one according to the first gray correlation degree, the first Euclidean distance, the second gray correlation degree and the second Euclidean distance; the ideal distance is used for representing the influence of the disaster event on each grading index of the power system;
and the grading module is used for determining a grading result of the disaster event according to the ideal distance and by combining the operation state judgment condition of the power system.
10. The disaster classification device based on euclidean distance and gray correlation as claimed in claim 9, wherein the weighting module calculates the weighting coefficient of each classification index according to the index evaluation matrix, and constructs a weighting matrix according to the weighting coefficient of each classification index, specifically:
the weighting module evaluates a matrix R = (R) according to the index ij ) n×m Calculating the entropy H of the grade index i ：
Wherein r is ij Is the jth row and jth column element of the index evaluation matrix, n is the number of grading indexes, m is the number of historical data of the grading indexes, i =1,2, …, n, j =1,2, …, m;
calculating a weight coefficient w of each grading index i ：
And constructing a weighting matrix according to the weight coefficient of each grading index:
T＝(t ij ) n×m ＝(w i r ij ) n×m ；
wherein T is the weighting matrix.
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