CN112101673B - Power grid development trend prediction method and system based on hidden Markov model - Google Patents
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Abstract
The invention discloses a power grid development trend prediction method and system based on a hidden Markov model. The method comprises the following steps: calculating a target index value corresponding to the target index type by adopting a principal component analysis method; determining a power grid development dynamic trend feature vector according to the target index value; solving parameters of a membership function model according to the power grid development dynamic trend feature vector and the optimization model and generating a membership function; substituting the power grid development dynamic trend feature vector into a membership function to generate a power grid development dynamic trend membership vector; generating a membership matrix according to the membership vector of the dynamic trend of the power grid development, and clustering the membership matrix; training the hidden Markov model according to the clustered membership matrix, and predicting the power grid development trend level by adopting a Viterbi algorithm according to the trained hidden Markov model. By adopting the method and the system provided by the invention, the development trend of the power grid can be reasonably judged so as to assist in adjusting various decision guides of the development and deployment of the power grid in the future.
Description
Technical Field
The invention relates to the technical field of power grid development trend prediction, in particular to a power grid development trend prediction method and system based on a hidden Markov model.
Background
With the maturation of new technologies such as artificial intelligence and the like and the improvement of the permeability of various new energy sources, the development brings great change to the power grid. The development state of the power grid is not reflected from the national construction, enterprise development, market construction, the power grid itself, and even from various perspectives of user service. The quantitative power grid development state is a basic work for smoothly developing power grid development diagnosis research, understanding the power grid development situation, evaluating the development dynamics and realizing the basis of power grid development trend prediction.
Based on power grid development diagnosis work, few methods for predicting and early warning power grid development trend are provided. In the dynamic process of accelerating development of the power grid, the power grid development result obtained by historical data evaluation shows a little delay for future development planning of the power grid, and the requirement of power grid development guidance cannot be met. Therefore, it is necessary to establish an effective prediction method to reasonably judge the power grid development trend so as to assist in adjusting decision guidance of future power grid development and deployment.
Disclosure of Invention
The invention aims to provide a power grid development trend prediction method and a power grid development trend prediction system based on a hidden Markov model, which can reasonably judge the power grid development trend so as to assist in adjusting various decision guides of future power grid development deployment.
In order to achieve the above object, the present invention provides the following solutions:
a power grid development trend prediction method comprises the following steps:
acquiring a target index set of a power grid development trend; the target index set comprises a plurality of target index types related to the power grid development trend, and each target index type comprises a plurality of basic indexes;
carrying out standardization processing on each basic index to obtain a standardized basic index value;
calculating a target index value corresponding to each target index type by adopting a principal component analysis method according to the standardized basic index value;
determining a power grid development dynamic trend feature vector corresponding to each target index type according to the target index values;
acquiring a power grid development trend early warning grade, and determining a membership function model according to the power grid development trend early warning grade;
solving parameters of the membership function model according to the power grid development dynamic trend feature vector and the optimization model, and generating a membership function according to the solved parameters; the optimization model is constructed according to fuzzy entropy;
substituting the power grid development dynamic trend feature vector into the membership function to generate a power grid development dynamic trend membership vector;
generating a membership matrix according to the power grid development dynamic trend membership vector, and clustering the membership matrix to obtain a clustered membership matrix;
training the hidden Markov model according to the clustered membership matrix to obtain a trained hidden Markov model;
and predicting the power grid development trend level by adopting a Viterbi algorithm according to the trained hidden Markov model.
Alternatively to this, the method may comprise,
the target index type specifically comprises:
development speed and scale, development safety and quality, development efficiency and benefit, and development operation and policy;
the basic indexes of the development speed and the scale specifically comprise:
GDP speed increasing, power supply installation speed increasing, load speed increasing, variable capacitance load ratio, line load ratio, household average distribution capacity, line length and interconnection rate;
the basic indexes of the development safety and quality specifically comprise:
n-1 passing rate, potential safety hazard number, power supply reliability, equipment availability coefficient, voltage qualification rate, equipment average life, automation coverage rate, cabling rate and intelligent ammeter coverage rate;
the basic indexes of the development efficiency and the benefit specifically comprise:
line maximum load rate, average load rate, transformer substation maximum load rate, line loss rate, number of business units per capita, power transformation capacity per capita, line length per capita, renewable energy access ratio, energy conservation and emission reduction, and power grid investment ratio;
the basic indexes of the development management and policy specifically comprise:
the unit power grid asset power load, the unit power grid asset sales amount, the net profit, the asset liability rate, the income growth rate and the purchase-sale spread increment ratio.
Optionally, the normalizing process is performed on each basic index to obtain a normalized basic index value, which specifically includes:
judging which type of the basic index is to obtain a first judging result;
if the first judging result is a maximum index, the basic index is subjected to standardization processing according to the following formula:
if the first judging result is a minimum index, the base index is normalized according to the following formula:
if the first judging result is a qualified index, the basic index is subjected to standardization processing according to the following formula:
in the method, in the process of the invention,represents the normalized basic index value, +.>The basic index value before conversion is represented, xi represents a safety threshold value for a qualified index, i represents a target index type, and j represents a basic index.
Optionally, calculating, according to the normalized basic index value, a target index value corresponding to each target index type by using a principal component analysis method, including:
within the full time sequence length, selecting the standardized basic index values in the same target index type to generate a sample matrix; the full-time sequence length is the length from the beginning year to the end year, the rows of the sample matrix represent the years, and the columns of the sample matrix represent the basic indexes;
calculating a correlation coefficient of each element in the sample matrix, and generating a correlation coefficient matrix according to the correlation coefficient;
solving a plurality of characteristic values of the correlation coefficient matrix, and arranging all the characteristic values in a sequence from big to small;
calculating the variance contribution rate of each characteristic value; the variance contribution rate of the characteristic values is the ratio of the selected characteristic values to the sum of all the characteristic values;
sequentially accumulating the variance contribution rates of the feature values from the variance contribution rate corresponding to the maximum feature value according to the order of the variance contribution rates from large to small, and taking the feature value corresponding to the accumulated variance contribution rate as a main component when the accumulated value of the variance contribution rate exceeds a preset contribution rate value for the first time;
taking the sum of products of the principal component and the variance contribution rate corresponding to the principal component as a target index value;
wherein,
the sample matrix is:
wherein X is a sample matrix, t is year, and X tj A normalized basic index value representing the jth basic index of the t-th year;
the target index value is:
I i ＝α 1 λ 1 +α 2 λ 2 +...+α m λ m
wherein I is i A is the target index value of the ith target index type m Represents the variance contribution rate, lambda corresponding to the mth principal component m Representing the mth principal component.
Optionally, determining, according to the target index value, a feature vector of a dynamic trend of power grid development corresponding to each target index type, specifically includes:
the index change rate f is calculated according to the following formula:
f＝I i (t+1)-I i (t)
calculating dynamic trend characteristics RES of power grid development according to the following formula:
wherein,
wherein I is i (t+1) represents the target index value of the t+1st year of the I-th target index type, I i (t) represents the target index value of the ith target index type in the t year, Δt represents the prediction step length, f (x) represents the line graph function of the target index value in time sequence, and g (x) represents the electricityA network development tie trend function, x represents a time variable;
generating a power grid development dynamic trend feature vector according to the index change rate and the dynamic trend feature of the power grid development;
wherein, the power grid development dynamic trend feature vector Q is as follows:
wherein RES is t Representing a dynamic trend characteristic of the grid development in the t-th year,indicating the index change rate in the t year.
Optionally, the determining the membership function model according to the early warning level of the power grid development trend specifically includes:
determining a membership function model according to the following formula:
in the formula, early warning is shown when the power grid development trend early warning level S=1, 2,3 and S=1, secondary early warning is shown when S=2, health is shown when S=3, and r is shown in the formula 1 (fet) represents the corresponding membership function when s=1, r 2 (fet) represents the corresponding membership function when s=2, r 3 (fet) represents the corresponding membership function when s=3, q= { fet 1 ,fet 2 ,...,fet t The fet represents the column vector of the characteristic vector Q of the dynamic trend of the power grid, a 1 Represent the firstA parameter, a 2 Representing a second parameter.
Optionally, the solving the parameters of the membership function model according to the feature vector of the dynamic trend of the power grid development and the optimization model, and generating the membership function according to the parameters obtained by solving specifically includes:
establishing an optimization model; the optimization model is as follows:
wherein u=1, 2,3, r 1 ＝r 1 (fet)，r 2 ＝r 2 (fet)，r 3 ＝r 3 (fet)，H(r 1 ,r 2 ,r 3 ) Represents an optimization function, k represents the total number of target index types, k=4, s (r u ) Is an intermediate function;
substituting the power grid development dynamic trend feature vector into the optimization model and then performing optimization operation to obtain parameters of the membership function model;
and generating a membership function according to the parameters of the membership function model.
Optionally, the clustering of the membership matrix to obtain a clustered membership matrix specifically includes:
clustering the membership matrix according to the following formula:
wherein E represents the sum of squares, r represents the cluster type, C represents the total number of clusters, and m represents the number of clusters classified into C r The power grid development dynamic trend membership vector, C r Representing the cluster center, M represents the membership matrix.
The invention also provides a power grid development trend prediction system, which comprises:
the data acquisition module is used for acquiring a target index set of the power grid development trend; the target index set comprises a plurality of target index types related to the power grid development trend, and each target index type comprises a plurality of basic indexes;
the standardized processing module is used for carrying out standardized processing on each basic index to obtain a standardized basic index value;
the principal component analysis module is used for calculating target index values corresponding to each target index type by adopting a principal component analysis method according to the normalized basic index values;
the feature vector determining module is used for determining a power grid development dynamic trend feature vector corresponding to each target index type according to the target index values;
the membership function model building module is used for obtaining the early warning grade of the power grid development trend and determining a membership function model according to the early warning grade of the power grid development trend;
the membership function generating module is used for solving parameters of the membership function model according to the power grid development dynamic trend feature vector and the optimization model and generating a membership function according to the solved parameters; the optimization model is constructed according to fuzzy entropy;
the membership vector generation module is used for substituting the power grid development dynamic trend feature vector into the membership function to generate a power grid development dynamic trend membership vector;
the clustering module is used for generating a membership matrix according to the membership vector of the power grid development dynamic trend, and clustering the membership matrix to obtain a clustered membership matrix;
the hidden Markov model training module is used for training the hidden Markov model according to the clustered membership matrix to obtain a trained hidden Markov model;
and the power grid development trend grade prediction module is used for predicting the power grid development trend grade by adopting a Viterbi algorithm according to the trained hidden Markov model.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a power grid development trend prediction method and a power grid development trend prediction system based on a hidden Markov model, wherein a principal component analysis method is adopted to calculate target index values corresponding to each target index type; according to the target index values, determining a power grid development dynamic trend feature vector corresponding to each target index type; solving parameters of a membership function model according to the power grid development dynamic trend feature vector and the optimization model, and generating a membership function according to the solved parameters; substituting the power grid development dynamic trend feature vector into a membership function to generate a power grid development dynamic trend membership vector; generating a membership matrix according to the membership vector of the dynamic trend of the power grid development, and clustering the membership matrix to obtain a clustered membership matrix; training the hidden Markov model according to the clustered membership matrix, predicting the power grid development trend grade by adopting a Viterbi algorithm according to the trained hidden Markov model, and reasonably judging the power grid development trend to assist in adjusting various decision guides of future power grid development deployment.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a method for predicting a power grid development trend based on a hidden Markov model in an embodiment of the present invention;
fig. 2 is a block diagram of a power grid development trend prediction system based on a hidden markov model in an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a power grid development trend prediction method and a power grid development trend prediction system based on a hidden Markov model, which can reasonably judge the power grid development trend so as to assist in adjusting various decision guides of future power grid development deployment.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Examples
Fig. 1 is a flowchart of a power grid development trend prediction method based on a hidden markov model in an embodiment of the present invention, as shown in fig. 1, a power grid development trend prediction method based on a hidden markov model includes:
step 101: acquiring a target index set of a power grid development trend; the target index set comprises a plurality of target index types related to the power grid development trend, and each target index type comprises a plurality of basic indexes.
The target index type specifically comprises:
development speed and scale, development safety and quality, development efficiency and benefit, and development operation and policy;
basic indexes of development speed and scale specifically include:
GDP speed increasing, power supply installation speed increasing, load speed increasing, variable capacitance load ratio, line load ratio, household average distribution capacity, line length and interconnection rate;
basic indexes of developing safety and quality specifically comprise:
n-1 passing rate, potential safety hazard number, power supply reliability, equipment availability coefficient, voltage qualification rate, equipment average life, automation coverage rate, cabling rate and intelligent ammeter coverage rate;
basic indexes of development efficiency and benefit specifically include:
line maximum load rate, average load rate, transformer substation maximum load rate, line loss rate, number of business units per capita, power transformation capacity per capita, line length per capita, renewable energy access ratio, energy conservation and emission reduction, and power grid investment ratio;
basic indexes of development management and policy specifically include:
the unit power grid asset power load, the unit power grid asset sales amount, the net profit, the asset liability rate, the income growth rate and the purchase-sale spread increment ratio.
Step 102: and (3) carrying out standardization processing on each basic index to obtain a standardized basic index value.
Step 102, specifically includes:
and judging which type of index the basic index is, and obtaining a first judgment result.
{ GDP speed-up, power installation speed-up, load speed-up, average power distribution capacity, line length, interconnection rate, N-1 pass rate, power supply reliability } and the like belong to extremely large indexes; { potential safety hazard number, line loss rate, asset liability rate } and the like belong to extremely small indexes; { variable capacitance-to-load ratio, line capacitance-to-load ratio } and the like belong to the qualification index.
If the first judging result is a very large index, the basic index is standardized according to the following formula:
if the first judging result is a minimum index, the basic index is standardized according to the following formula:
if the first judging result is a qualified index, the basic index is subjected to standardization processing according to the following formula:
in the method, in the process of the invention,represents the normalized basic index value, +.>The basic index value before conversion is represented, xi represents a safety threshold value for a qualified index, i represents a target index type, and j represents a basic index.
Step 103: and calculating the target index value corresponding to each target index type by adopting a principal component analysis method according to the standardized basic index value.
Step 103, specifically includes:
within the full time sequence length, selecting the standardized basic index values in the same target index type to generate a sample matrix; the full-time sequence length is the length from the beginning year to the end year, the rows of the sample matrix represent the years, and the columns of the sample matrix represent the basic indexes;
calculating a correlation coefficient of each element in the sample matrix, and generating a correlation coefficient matrix according to the correlation coefficients;
solving a plurality of characteristic values of the correlation coefficient matrix, and arranging all the characteristic values in a sequence from big to small;
calculating the variance contribution rate of each characteristic value; the variance contribution rate of the characteristic values is the ratio of the selected characteristic values to the sum of all the characteristic values;
sequentially accumulating the variance contribution rates of the feature values from the variance contribution rate corresponding to the maximum feature value according to the order of the variance contribution rates from large to small, and taking the feature value corresponding to the accumulated variance contribution rate as a main component when the accumulated value of the variance contribution rate exceeds a preset contribution rate value for the first time;
taking the sum of products of the principal component and the variance contribution rate corresponding to the principal component as a target index value;
wherein,
the sample matrix is:
wherein X is a sample matrix, t is year, and X tj A normalized basic index value representing the jth basic index of the t-th year;
the target index value is:
I i ＝α 1 λ 1 +α 2 λ 2 +...+α m λ m
wherein I is i A is the target index value of the ith target index type m Represents the variance contribution rate, lambda corresponding to the mth principal component m Representing the mth principal component.
Step 104: and determining the power grid development dynamic trend feature vector corresponding to each target index type according to the target index values.
Step 104 specifically includes:
the index change rate f is calculated according to the following formula:
f＝I i (t+1)-I i (t)
calculating dynamic trend characteristics RES of power grid development according to the following formula:
wherein,
wherein I is i (t+1) represents the target index value of the t+1st year of the I-th target index type, I i (t)The method comprises the steps of representing a target index value of an ith target index type in a t year, wherein deltat represents a prediction step length, f (x) represents a line graph function of the target index value on time sequence, g (x) represents a power grid development tie trend function, and x represents a time variable.
And generating a power grid development dynamic trend feature vector according to the index change rate and the dynamic trend feature of the power grid development.
The power grid development dynamic trend feature vector Q is as follows:
wherein RES is t Representing a dynamic trend characteristic of the grid development in the t-th year,indicating the index change rate in the t year.
Step 105: and acquiring the early warning grade of the power grid development trend, and determining a membership function model according to the early warning grade of the power grid development trend.
Step 105 specifically includes:
the membership function model (choice of the kaolin membership function model) is determined according to the following formula:
in the formula, early warning is shown when the power grid development trend early warning level S=1, 2,3 and S=1, secondary early warning is shown when S=2, health is shown when S=3, and r is shown in the formula 1 (fet) represents the corresponding membership function when s=1, r 2 (fet) represents the corresponding membership function when s=2, r 3 (fet) represents the corresponding membership function, X, when s=3 K ＝{fet 1 ,fet 2 ,...,fet T The fet represents the column vector of the characteristic vector Q of the dynamic trend of the power grid, a 1 Representing a first parameter, a 2 Representing a second parameter.
Step 106: solving parameters of a membership function model according to the power grid development dynamic trend feature vector and the optimization model, and generating a membership function according to the solved parameters; and constructing an optimization model according to the fuzzy entropy.
Step 106, specifically includes:
domain of design and discussion X K ＝{fet 1 ,fet 2 ,...,fet T State field r= { R } 1 ,r 2 ,r 3 Then from the Fuzzy Entropy (FEI) as a measure of ambiguity, an optimization model can be built:
establishing an optimization model; the optimization model is as follows:
wherein u=1, 2,3, r 1 ＝r 1 (fet)，r 2 ＝r 2 (fet)，r 3 ＝r 3 (fet)，H(r 1 ,r 2 ,r 3 ) Represents an optimization function, k represents the total number of target index types, k=4, s (r u ) Is an intermediate function;
substituting the power grid development dynamic trend feature vector into an optimization model, and then performing optimization operation to obtain parameters of a membership function model;
and generating a membership function according to the parameters of the membership function model.
Step 107: substituting the power grid development dynamic trend feature vector into the membership function to generate a power grid development dynamic trend membership vector.
Step 108: generating a membership matrix according to the membership vector of the dynamic trend of the power grid development, and clustering the membership matrix to obtain the clustered membership matrix.
Step 108 specifically includes:
clustering the membership matrix according to the following formula:
wherein E represents the sum of squares, r represents the cluster type, C represents the total number of clusters, and m represents the number of clusters classified into C r The power grid development dynamic trend membership vector, C r And (3) representing a clustering center, wherein M represents a membership matrix, M is a 4*3 matrix, and the rows of the matrix M represent target index types.
Step 109: training the hidden Markov model according to the clustered membership matrix to obtain a trained hidden Markov model.
Step 110: and predicting the power grid development trend grade by adopting a Viterbi algorithm according to the trained hidden Markov model.
And inputting a sequence to be predicted, and calculating a power grid development trend grade sequence by using a dimension bit algorithm.
Analyzing the weak links of the power grid development by combining the trend grade discrimination results and the index values; and the development trend parameters are used for participating in power grid development diagnosis and evaluation work with different meanings, so as to assist in realizing power grid situation awareness and comprehensive power grid evaluation work.
Fig. 2 is a block diagram of a power grid development trend prediction system based on a hidden markov model in an embodiment of the present invention. As shown in fig. 2, a power grid development trend prediction system based on a hidden markov model includes:
the data acquisition module 201 is configured to acquire a target index set of a power grid development trend; the target index set comprises a plurality of target index types related to the power grid development trend, and each target index type comprises a plurality of basic indexes.
The normalization processing module 202 is configured to perform normalization processing on each base index to obtain a normalized base index value.
The principal component analysis module 203 is configured to calculate, according to the normalized basic index value, a target index value corresponding to each target index type by using a principal component analysis method.
The feature vector determining module 204 is configured to determine a power grid development dynamic trend feature vector corresponding to each target index type according to the target index values.
The membership function model building module 205 is configured to obtain a power grid development trend early warning level, and determine a membership function model according to the power grid development trend early warning level.
The membership function generating module 206 is configured to solve parameters of a membership function model according to the dynamic trend feature vector of the power grid development and the optimization model, and generate a membership function according to the solved parameters; and constructing an optimization model according to the fuzzy entropy.
The membership vector generation module 207 is configured to substitute the power grid development dynamic trend feature vector into a membership function to generate a power grid development dynamic trend membership vector.
And the clustering module 208 is configured to generate a membership matrix according to the membership vector of the dynamic trend of the power grid, and cluster the membership matrix to obtain a clustered membership matrix.
The hidden markov model training module 209 is configured to train the hidden markov model according to the clustered membership matrix to obtain a trained hidden markov model.
The power grid trend level prediction module 210 is configured to predict the power grid trend level by using a viterbi algorithm according to the trained hidden markov model.
For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The power grid development trend prediction method and system based on the hidden Markov model provided by the invention can evaluate the future development trend of the power grid based on the current basic index and model, develop power grid planning in advance for the power grid department, and provide effective support for weak links.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In summary, the present description should not be construed as limiting the invention.
Claims (2)
1. The utility model provides a power grid development trend prediction method which is characterized by comprising the following steps:
acquiring a target index set of a power grid development trend; the target index set comprises a plurality of target index types related to the power grid development trend, and each target index type comprises a plurality of basic indexes;
carrying out standardization processing on each basic index to obtain a standardized basic index value;
calculating a target index value corresponding to each target index type by adopting a principal component analysis method according to the standardized basic index value;
determining a power grid development dynamic trend feature vector corresponding to each target index type according to the target index values;
acquiring a power grid development trend early warning grade, and determining a membership function model according to the power grid development trend early warning grade;
solving parameters of the membership function model according to the power grid development dynamic trend feature vector and the optimization model, and generating a membership function according to the solved parameters; the optimization model is constructed according to fuzzy entropy;
substituting the power grid development dynamic trend feature vector into the membership function to generate a power grid development dynamic trend membership vector;
generating a membership matrix according to the power grid development dynamic trend membership vector, and clustering the membership matrix to obtain a clustered membership matrix;
training the hidden Markov model according to the clustered membership matrix to obtain a trained hidden Markov model;
according to the trained hidden Markov model, predicting the power grid development trend level by adopting a Viterbi algorithm;
the target index type specifically comprises:
development speed and scale, development safety and quality, development efficiency and benefit, and development operation and policy;
the basic indexes of the development speed and the scale specifically comprise:
GDP speed increasing, power supply installation speed increasing, load speed increasing, variable capacitance load ratio, line load ratio, household average distribution capacity, line length and interconnection rate;
the basic indexes of the development safety and quality specifically comprise:
n-1 passing rate, potential safety hazard number, power supply reliability, equipment availability coefficient, voltage qualification rate, equipment average life, automation coverage rate, cabling rate and intelligent ammeter coverage rate;
the basic indexes of the development efficiency and the benefit specifically comprise:
line maximum load rate, average load rate, transformer substation maximum load rate, line loss rate, number of business units per capita, power transformation capacity per capita, line length per capita, renewable energy access ratio, energy conservation and emission reduction, and power grid investment ratio;
the basic indexes of the development management and policy specifically comprise:
the unit power grid asset power supply load, the unit power grid asset sales amount, the net profit, the asset liability rate, the income increasing rate and the purchase-sale price difference increment ratio;
the step of carrying out standardization processing on each basic index to obtain a standardized basic index value, which specifically comprises the following steps:
judging which type of the basic index is to obtain a first judging result;
if the first judging result is a maximum index, the basic index is subjected to standardization processing according to the following formula:
if the first judging result is a minimum index, the base index is normalized according to the following formula:
if the first judging result is a qualified index, the basic index is subjected to standardization processing according to the following formula:
in the method, in the process of the invention,represents the normalized basic index value, +.>The basic index value before conversion is represented, xi represents a safety threshold value aiming at a qualified index, i represents a target index type, and j represents a basic index;
calculating a target index value corresponding to each target index type by adopting a principal component analysis method according to the normalized basic index value, wherein the method specifically comprises the following steps:
within the full time sequence length, selecting the standardized basic index values in the same target index type to generate a sample matrix; the full-time sequence length is the length from the beginning year to the end year, the rows of the sample matrix represent the years, and the columns of the sample matrix represent the basic indexes;
calculating a correlation coefficient of each element in the sample matrix, and generating a correlation coefficient matrix according to the correlation coefficient;
solving a plurality of characteristic values of the correlation coefficient matrix, and arranging all the characteristic values in a sequence from big to small;
calculating the variance contribution rate of each characteristic value; the variance contribution rate of the characteristic values is the ratio of the selected characteristic values to the sum of all the characteristic values;
sequentially accumulating the variance contribution rates of the feature values from the variance contribution rate corresponding to the maximum feature value according to the order of the variance contribution rates from large to small, and taking the feature value corresponding to the accumulated variance contribution rate as a main component when the accumulated value of the variance contribution rate exceeds a preset contribution rate value for the first time;
taking the sum of products of the principal component and the variance contribution rate corresponding to the principal component as a target index value;
wherein,
the sample matrix is:
wherein X is a sample matrix, t is year, and X tj A normalized basic index value representing the jth basic index of the t-th year;
the target index value is:
I i ＝α 1 λ 1 +α 2 λ 2 +...+α m λ m
wherein I is i A is the target index value of the ith target index type m Represents the variance contribution rate, lambda corresponding to the mth principal component m Represents the mth principal component;
according to the target index value, determining a power grid development dynamic trend feature vector corresponding to each target index type, wherein the power grid development dynamic trend feature vector specifically comprises:
calculating the index change rate according to the following formula
Calculating dynamic trend characteristics RES of power grid development according to the following formula:
wherein,
wherein I is i (t+1) represents the target index value of the t+1st year of the I-th target index type, I i (t) represents a target index value of the ith target index type in the t year, Δt represents a prediction step length, f (x) represents a line graph function of the target index value on a time sequence, g (x) represents a power grid development tie trend function, and x represents a time variable;
generating a power grid development dynamic trend feature vector according to the index change rate and the dynamic trend feature of the power grid development;
wherein, the power grid development dynamic trend feature vector Q is as follows:
wherein RES is t Representing a dynamic trend characteristic of the grid development in the t-th year,index change rate indicating the t year;
the determining the membership function model according to the power grid development trend early warning level specifically comprises the following steps:
determining a membership function model according to the following formula:
in the formula, early warning is shown when the power grid development trend early warning level S=1, 2,3 and S=1, secondary early warning is shown when S=2, health is shown when S=3, and r is shown in the formula 1 (fet) represents the corresponding membership function when s=1, r 2 (fet) represents the corresponding membership function when s=2, r 3 (fet) represents the corresponding membership function when s=3, q= { fet 1 ,fet 2 ,...,fet t The fet represents the column vector of the characteristic vector Q of the dynamic trend of the power grid, a 1 Representing a first parameter, a 2 Representing a second parameter;
solving parameters of the membership function model according to the power grid development dynamic trend feature vector and the optimization model, and generating a membership function according to the solved parameters, wherein the method specifically comprises the following steps:
establishing an optimization model; the optimization model is as follows:
wherein u=1, 2,3, r 1 ＝r 1 (fet)，r 2 ＝r 2 (fet)，r 3 ＝r 3 (fet)，H(r 1 ,r 2 ,r 3 ) Represents an optimization function, k represents the total number of target index types, k=4, s (r u ) Is an intermediate function;
substituting the power grid development dynamic trend feature vector into the optimization model and then performing optimization operation to obtain parameters of the membership function model;
generating a membership function according to the parameters of the membership function model;
clustering the membership matrix to obtain a clustered membership matrix, wherein the clustering method specifically comprises the following steps of:
clustering the membership matrix according to the following formula:
wherein E represents the sum of squares, r represents the cluster type, C represents the total number of clusters, and m represents the number of clusters classified into C r The power grid development dynamic trend membership vector, C r Representing the cluster center, M represents the membership matrix.
2. A power grid development trend prediction system constructed according to the power grid development trend prediction method of claim 1, comprising:
the data acquisition module is used for acquiring a target index set of the power grid development trend; the target index set comprises a plurality of target index types related to the power grid development trend, and each target index type comprises a plurality of basic indexes;
the standardized processing module is used for carrying out standardized processing on each basic index to obtain a standardized basic index value;
the principal component analysis module is used for calculating target index values corresponding to each target index type by adopting a principal component analysis method according to the normalized basic index values;
the feature vector determining module is used for determining a power grid development dynamic trend feature vector corresponding to each target index type according to the target index values;
the membership function model building module is used for obtaining the early warning grade of the power grid development trend and determining a membership function model according to the early warning grade of the power grid development trend;
the membership function generating module is used for solving parameters of the membership function model according to the power grid development dynamic trend feature vector and the optimization model and generating a membership function according to the solved parameters; the optimization model is constructed according to fuzzy entropy;
the membership vector generation module is used for substituting the power grid development dynamic trend feature vector into the membership function to generate a power grid development dynamic trend membership vector;
the clustering module is used for generating a membership matrix according to the membership vector of the power grid development dynamic trend, and clustering the membership matrix to obtain a clustered membership matrix;
the hidden Markov model training module is used for training the hidden Markov model according to the clustered membership matrix to obtain a trained hidden Markov model;
and the power grid development trend grade prediction module is used for predicting the power grid development trend grade by adopting a Viterbi algorithm according to the trained hidden Markov model.
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