CN113887925B - Optimal regulation and control decision method and system for electric power system - Google Patents
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Abstract
An optimal regulation decision method and system for electric power systems collect relevant data of regulation decision schemes of each electric power system; constructing a regulation decision index of the power system; converting the semantic evaluation information into an optimized fuzzy number; ranking the importance of the indexes, sorting the indexes according to the ranks, wherein the first index is the most important index, and the last index is the least important index; generating a relative importance matrix of the most important indicatorsRelative importance matrix to least important indexThe method comprises the steps of carrying out a first treatment on the surface of the Solving an importance weight matrix to obtain the final weight of each index; normalizing the data of the digital information index and the data of the semantic evaluation information index of each decision scheme; and determining an optimal decision scheme according to the normalization result and the weight. The invention can provide accurate decision for the regulation and control of the power system.
Description
Technical Field
The invention belongs to the field of power system dispatching operation, and particularly relates to a power system optimal regulation and control decision method and system.
Background
Along with the wide access of renewable energy sources to a power grid, the power grid is subjected to the influence caused by high uncertainty and intermittence, and in order to ensure the safe and reliable operation of the power grid, power grid dispatching personnel are required to have more accurate execution capacity. Therefore, effective evaluation of the regulation process of the regulator becomes important content of attention of the power enterprises.
At present, standardized evaluation of a regulating process of a scheduler is not involved, so that the scheduler cannot effectively form an effective assessment standard in the daily working process, and the operation effectiveness of the scheduler is difficult to improve. Furthermore, the high randomness and intermittence of the loads in the grid makes it difficult for the dispatcher to adapt to the current grid demand in a traditional mode of operation. Therefore, it is highly desirable to evaluate the standardization of the regulation and control process of the dispatcher, so as to effectively sense the standardization of the operation process of the dispatcher and provide support for the safe operation of the power grid.
The defect of the prior art document (CN 112488416A) of the optimal power supply path decision method and system for considering the multidimensional influence of power grid dispatching is that the document adopts a trapezoidal fuzzy number to represent uncertainty in the power grid dispatching process, the represented uncertainty is wider, and when the problem of power system regulation decision evaluation is faced, clear numbers defined by the trapezoidal fuzzy number and the meaning represented by the problem have larger access. In addition, in terms of decision, the technology file adopts the path of the maximum weighted sum product as the optimal power supply path, and it is difficult to effectively consider the influence caused by the self-evaluation information of each evaluation scheme.
Disclosure of Invention
In order to solve the defects existing in the prior art, the invention aims to provide an optimal regulation and control decision method and system for a power system.
The invention adopts the following technical scheme that the optimal regulation decision method of the power system comprises the following steps:
step 1, collecting relevant data of each power system regulation decision scheme;
step 2, constructing a power system regulation decision index according to the power system regulation decision scheme related data acquired in the step 1, wherein the power system regulation decision index comprises a semantic evaluation information index and a digital information index; converting the corresponding data of the semantic evaluation information index into semantic evaluation information;
step 3, converting the semantic evaluation information in the step 2 into fuzzy numbers;
step 4, grading the importance of the power system regulation decision indexes, sequencing the power system regulation decision indexes according to the grades, wherein the first index is the most important index, and the last index is the least important index;
step 5, determining the relative importance value of the most important index and the least important index and each other index respectively, and generating a relative importance matrix of the most important indexRelative importance matrix to least important index +. >
Step 6, relative importance matrix for the most important index of step 5Relative importance matrix to least important index +.>Solving to obtain the final weight of each power system regulation decision index;
step 7, normalizing the data of the digital information index and the data of the semantic evaluation information index of each decision scheme in the step 2;
and 8, determining an optimal decision scheme according to the normalization result in the step 7 and the weight in the step 6.
In step 1, the relevant data of each decision scheme includes the reply time of the dispatcher for the maintenance application ticket in the selected time period of the month, the probability of errors after the examination of the maintenance application ticket in the selected time period of the month, the types of errors in the dispatch operation instruction ticket in the selected time period of the month, the number of failed examination and dispatch operation instruction tickets in the selected time period of the month, the number of errors remained after the operation instruction ticket is pre-issued in the selected time period of the month, and the time period for dispatching the accident handling in the selected time period of the month when the decision is executed.
In step 2, the constructed power system regulation decision indexes comprise reply validity of the overhaul application ticket, accuracy of scheduled operation instruction ticket writing, checking failure rate of scheduled operation instruction ticket, accuracy of scheduled operation instruction ticket prefeeding, scheduled operation instruction ticket execution integrity and scheduled accident handling validity.
The reply validity of the overhaul application ticket refers to the reply time of the dispatcher for the overhaul application ticket, and is a semantic evaluation information index;
the checking error rate of the checking application ticket refers to the probability of error of the checking application ticket after checking in the time period selected in the current month, and is a digital information index;
the accuracy of the scheduled operation instruction ticket writing refers to the standard degree of the scheduled operation instruction ticket writing and is a semantic evaluation information index;
the failure rate of the audit scheduling operation instruction ticket refers to the ratio of the number of failed audit scheduling operation instruction tickets to the total number of failed audit scheduling operation instruction tickets in the time period selected in the current month, and is a digital information index;
the accuracy of dispatching the operation instruction ticket pre-issuing is determined according to the number of the reserved errors after the operation instruction ticket pre-issuing, and the operation instruction ticket pre-issuing is a semantic evaluation information index;
the execution integrity of the dispatching operation instruction ticket refers to the number of executed instructions in the dispatching operation instruction ticket, and is a semantic evaluation information index;
the effectiveness of the scheduling accident treatment refers to the time period for the scheduling accident treatment; and the semantic evaluation information index is used.
In step 3, the conversion method for converting the semantic evaluation information into the fuzzy number comprises the following steps:
selecting a random number x for each piece of semantic evaluation information, wherein the random number is selected by the method that the more excellent the semantic evaluation information is, the larger the corresponding random number is;
For each random number x, its optimized fuzzy number isThe membership degree satisfies the following relation:
and is also provided with
Representing the upper set of intervals, i.e-> Representing the x-axis distance of the first vertex of the upper interval relative to the origin of coordinates; />Representing the x-axis distance of the second vertex of the upper interval relative to the origin of coordinates; />Representing the x-axis distance of the third vertex of the upper interval relative to the origin of coordinates; />Representing the y-axial distance of the second vertex of the upper interval relative to the origin of coordinates;
representing the lower interval set, i.e-> Representing the x-axis distance of the first vertex of the lower interval relative to the origin of coordinates; />Representing the x-axis distance of the second vertex of the lower interval relative to the origin of coordinates; />Representing the x-axis distance of the third vertex of the lower interval relative to the origin of coordinates; />Representing the y-axial distance of the second vertex of the lower interval relative to the origin of coordinates;
the selection is performed randomly by gaussian distribution.
In step 4, the number of grades and the score difference between each grade are set according to specific conditions; selecting the index with the highest score as the most important index and the index with the lowest score as the least important index; when the index is marked, only one index is needed for the most important index and only one index is needed for the least important index, and a plurality of indexes of other grades can be needed.
Step 5 comprises the following:
step 501, dividing the relative importance of the regulation decision index of the power system into k grade standards, respectively using different semantemes for the grade standards, and setting fuzzy numbers corresponding to the semantic descriptions of the grade standards;
step 502, determining the relative importance between the most important index and the least important index and each other index;
the importance of the most important index and the least important index relative to the least important index is the lowest grade of the grade standard;
every time the level of other indexes relative to the most important index is reduced by one level, the relative importance of the other indexes relative to the most important index is also reduced by one level;
every level of the other indexes relative to the level of the most important index rises by one level, the level of the relative importance of the other indexes relative to the most important index also rises by one level;
step 503, converting the relative importance between the most important index and the least important index of step 502 and each other index into a trapezoidal fuzzy number;
the magnitude of the trapezoidal fuzzy number is set according to the actual situation, and the setting principle is that each digit of the relatively important trapezoidal fuzzy number is smaller than the corresponding digit of the relatively important trapezoidal fuzzy number which is higher than the relatively important trapezoidal fuzzy number;
Step 504: constructing a relative importance matrix of the most important index using the trapezoidal blur number of step 503Relative importance matrix to least important index +.>
Wherein,matrix representing the representation of the trapezoidal blur number of the most important index relative to the importance of other indexes, +.>Ladder ambiguity number indicating the relative importance of the most important index to the nth index, +.>Matrix representing the representation of the trapezoidal blur number of the least significant index relative to the significance of the other indexes>Representing the relative importance of the nth index to the least important index.
In step 6, determining the trapezoidal fuzzy number corresponding to each index according to the importance level of the index in step 4; the setting principle is that each digit of the trapezoidal blur number of the index importance level is smaller than the corresponding digit of the trapezoidal blur number of the index importance level higher than the corresponding digit of the trapezoidal blur number of the index importance level.
The final weight of each power system regulation decision index is calculated as the following function:
minneeds to meet->
Wherein,the objective function of the model is solved for the weight, and the objective function is a set unknown variable and is also the trapezoidal fuzzy number solved by the model;
a ladder ambiguity number representing a level of importance of the most important indicator;
a ladder ambiguity number representing a j-th index importance level;
A ladder ambiguity number representing the relative importance of the most important index to the jth index; />A ladder ambiguity number representing a least significant indicator importance level;
a step of expressing the relative importance of the jth index to the least important index;
n represents the number of indexes;
the crisp number of the clear trapezoidal fuzzy number representing the importance level of the j index;
a first value of the number of trapezoidal ambiguities representing a j-th index importance level; from this, it is known that->The first value of the number of trapezoidal ambiguities for the j-th index importance level.
The method for solving the problems is as follows:
wherein a, b, c, d are four values corresponding to the number of trapezoidal ambiguities, a corresponds to a first value of the number of trapezoidal ambiguities, b corresponds to a second value of the number of trapezoidal ambiguities, c corresponds to a third value of the number of trapezoidal ambiguities, and d corresponds to a fourth value of the number of trapezoidal ambiguities, respectively.
In step 7, the normalization method of the semantic evaluation information index data is as follows:
wherein n is ij Representing the normalized result of the j index of the i decision scheme;representing the crisp number with the maximum fuzzy number of the j index in all decision schemes; />A crisp number representing the fuzzy number of the jth index of the ith decision scheme; c (C) 1 B, representing the index which is smaller and better as the fuzzy number corresponding to the semantic evaluation information in the semantic evaluation information index is clarified 1 The method comprises the steps of representing a better index which is larger after the fuzzy number corresponding to semantic evaluation information in semantic evaluation information indexes is clarified;
for optimized fuzzy numberIt can be converted into a sharp number using the following formula:
the normalization method of the digital information index data comprises the following steps:
wherein C is 2 B represents an index of which the number is smaller and the better in the index of the digital information 2 An index indicating that the larger the number is, the better the number is in the digital information index; r is (r) AIj Maximum data corresponding to the j-th index in all decision schemes are represented; r is (r) ij And the data corresponding to the j index of the i decision scheme is represented.
Step 8 includes the following:
step 801, calculating a decision matrix N:
where h represents the total number of decision schemes, n represents the number of indicators,the crisp number of the clear trapezoidal blur number representing the j index importance level is j=1, 2,3 … n; .
Step 802, a decision matrix for each scheme is calculated,
wherein N is ij To sum the ith row in the decision matrix N, phi (a i ) Is the decision value of the i-th scheme.
Step 803, calculating a decision value of each decision scheme, and selecting a scheme with the largest decision value as a final decision scheme:
where h is the total number of decision schemes,for the smallest of all decision scheme decision values, For the largest decision value of all decision scheme decision values, ζ (A i ) Decision values for the ith scheme.
The invention also discloses an optimal regulation decision system based on the optimal regulation decision method of the power system, which comprises a data acquisition module, a regulation decision index construction module of the power system, a fuzzy number construction module, a semantic information conversion module, an index weight calculation module and a regulation degree scheme decision module of the power system;
the data acquisition module acquires relevant data of each decision scheme, wherein the relevant data comprises the length of reply time of a dispatcher for an overhaul application ticket, the probability of errors after the overhaul application ticket is checked in a time period selected in the month, the types of errors in a dispatch operation instruction ticket, the number of failed audit dispatch operation instruction tickets in the time period selected in the month, the number of errors reserved after the operation instruction ticket is issued, and the length of time for processing a dispatch accident when the decision is executed, and the data are input into the power system regulation decision index construction module;
the power system regulation decision index construction module constructs power supply path decision indexes including semantic indexes and non-semantic indexes, and obtains semantic evaluation information of the semantic indexes; the semantic evaluation information is input to a semantic information conversion module, and related data of non-semantic indexes is input to a normalization module;
The fuzzy number construction module constructs a fuzzy number according to the semantic index and inputs the fuzzy number to the semantic information conversion module;
the semantic information conversion module converts semantic information into fuzzy numbers, obtains the crisp number of each fuzzy number, and inputs the crisp number to the normalization module;
the normalization module normalizes the crisp number input by the semantic information conversion module and the related data of the non-semantic index respectively, and then inputs the crisp number and the related data of the non-semantic index into the power system regulation and control decision index construction module
The index weight calculation module firstly generates a relative importance matrix of the most important index and the non-most important index and a relative importance matrix of the least important index and the non-least important index, calculates the weight of each index according to the relative importance matrix model, and inputs the weight to the power system regulation and control decision index construction module;
the power system regulation and control decision index construction module calculates a decision value according to the normalized data and the weight, and selects a scheme with the maximum decision value as an optimal scheme.
The invention also discloses an optimal regulation decision system based on the optimal regulation decision method of the power system, which comprises a data acquisition module, a regulation decision index construction module of the power system, a fuzzy number construction module, a semantic information conversion module, an index weight calculation module and a regulation degree scheme decision module of the power system, wherein the data acquisition module acquires relevant data of each decision scheme, particularly, when the decision is executed, the time of a dispatcher replying to a maintenance application ticket, the probability of errors after the examination of the maintenance application ticket in a selected time period of the month, the types of errors in a dispatching operation instruction ticket, the number of failed examination and dispatching operation instruction tickets in the selected time period of the month, the number of reserved errors after the pre-issuing of the operation instruction ticket and the time for processing of a dispatching accident are input into the regulation decision index construction module of the power system;
The power system regulation decision index construction module constructs power supply path decision indexes including semantic indexes and non-semantic indexes, and obtains semantic evaluation information of the semantic indexes; the semantic evaluation information is input to a semantic information conversion module, and related data of non-semantic indexes is input to a normalization module;
the fuzzy number construction module constructs a fuzzy number according to the semantic index and inputs the fuzzy number to the semantic information conversion module;
the semantic information conversion module converts semantic information into fuzzy numbers, obtains the crisp number of each fuzzy number, and inputs the crisp number to the normalization module;
the normalization module normalizes the crisp number input by the semantic information conversion module and the related data of the non-semantic index respectively, and then inputs the crisp number and the related data of the non-semantic index into the power system regulation and control decision index construction module
The index weight calculation module firstly generates a relative importance matrix of the most important index and the non-most important index and a relative importance matrix of the least important index and the non-least important index, calculates the weight of each index according to the relative importance matrix model, and inputs the weight to the power system regulation and control decision index construction module;
the power system regulation and control decision index construction module calculates a decision value according to the normalized data and the weight, and selects a scheme with the maximum decision value as an optimal scheme.
Compared with the prior art, the method has the beneficial effects that the optimized fuzzy number is adopted to characterize the information of the regulation and control decision process of the power system, the fuzzy interval is reduced, more accurate evaluation information is obtained, and a basis is provided for the accurate evaluation of the regulation and control decision of the power system. In addition, in the decision process, the final evaluation result is calculated after the evaluation information in each evaluation scheme is fully considered, so that the obtained result is not influenced by the index weight only, and the defects in the prior art file are overcome.
Drawings
FIG. 1 is a flow chart of an optimal regulation decision method for an electric power system according to the present invention.
Detailed Description
The present application is further described below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical solutions of the present invention and are not intended to limit the scope of protection of the present application.
Step 1, collecting relevant data of each power system regulation decision scheme;
the relevant data of each decision scheme comprises the length of the reply time of a dispatcher to the maintenance application ticket in the selected time period of the month, the probability of errors after the examination of the maintenance application ticket in the selected time period of the month, the types of errors in the dispatch operation instruction ticket book in the selected time period of the month, the number of failed examination and dispatch operation instruction tickets in the selected time period of the month, the number of errors reserved after the operation instruction ticket is pre-issued in the selected time period of the month and the time period for dispatching the accident treatment in the selected time period of the month when the decision is executed;
In the invention, the related data are collected through a DMS and PMS system;
step 2, constructing a power system regulation decision index according to the power system regulation decision scheme related data acquired in the step 1, wherein the power system regulation decision index comprises a semantic evaluation information index and a digital information index; converting the corresponding data of the semantic evaluation information index into semantic evaluation information;
the indexes constructed by the invention are shown in table 1:
TABLE 1 Power System Regulation decision index
Wherein, semantic evaluation information indexes are A1, A3, A5, A6 and A7;
the semantic evaluation information can be classified according to actual conditions by a person skilled in the art, and the more the classification is, the higher the precision is; in the present embodiment, the semantic evaluation information is classified into 5 levels, corresponding to "very high", "general", "low", and "very low", respectively, from good to bad;
the reply validity A1 of the overhaul application ticket refers to the length of the reply time of the dispatcher to the overhaul application ticket, and is a semantic evaluation information index.
When the ticket application reply time is the first time t 1 When the corresponding semantic evaluation information is 'very high', the application ticket reply time is the second time t 2 When the corresponding semantic evaluation information is "high", the ticket application reply time is the third time t 3 When the corresponding semantic evaluation information is "general", the application ticket is repliedThe time is the fourth time t 4 When the corresponding semantic evaluation information is "low", the ticket application reply time is the fifth time t 5 When the corresponding semantic evaluation information is 'very low';
preferably, t 1 ≤5min，5min<t 2 ≤15min，15min<t 3 ≤30min，30min<t 4 ≤45min，45min<t 5 ；
The checking error rate A2 of the checking application ticket refers to the probability of error of the checking application ticket after checking in the time period selected in the current month, and is a digital information index.
The accuracy A3 of the scheduled operation instruction ticket writing refers to the standard degree of the scheduled operation instruction ticket writing and is a semantic evaluation information index; the specification degree is determined by the error types in the dispatch operation instruction ticket; error types include grammar errors, writing errors, number filling errors, and time filling errors; when the scheduling operation instruction ticket has no error, the corresponding semantic evaluation is 'very high'; when any error occurs in the scheduling operation instruction ticket, the corresponding semantic evaluation is high; when any two errors occur in the scheduling operation instruction ticket, the corresponding semantic evaluation is general; when any three errors occur in the scheduling operation instruction ticket, the corresponding semantic evaluation is low; when more than three errors occur in the scheduling operation instruction ticket, the corresponding semantic evaluation is 'very low'.
The audit scheduling operation instruction ticket non-passing rate A4 refers to the ratio of the number of the audit scheduling operation instruction tickets which do not pass to the total number of the audit scheduling operation instruction ticket in the time period selected in the current month, and is a digital information index;
and the accuracy A5 of the dispatching operation instruction ticket pre-issuing is determined according to the number of the reserved errors after the operation instruction ticket pre-issuing, and is a semantic evaluation information index. When the dispatching operation instruction ticket is pre-issued, no error is found, the semantic evaluation is 'very high', when an error is found after the dispatching operation instruction ticket is pre-issued, the semantic evaluation is 'high', when 2 errors are found after the dispatching operation instruction ticket is pre-issued, the semantic evaluation is 'general', when 3 errors are found after the dispatching operation instruction ticket is pre-issued, the semantic evaluation is 'low', when more than 3 errors are found after the dispatching operation instruction ticket is pre-issued, the semantic evaluation is 'very low';
the execution integrity A6 of the scheduling operation instruction ticket refers to the number of instructions to be executed in the scheduling operation instruction ticket, and is a semantic evaluation information index. When all the instructions in the dispatch operation instruction ticket are executed, the semantic evaluation is 'high', when one instruction in the dispatch operation instruction ticket is not executed, the semantic evaluation is 'normal', when two instructions in the dispatch operation instruction ticket are not executed, the semantic evaluation is 'low', when three instructions in the dispatch operation instruction ticket are not executed, the semantic evaluation is 'low', when the instructions in the dispatch operation instruction ticket are more than three instructions are not executed.
The effectiveness A7 of the scheduling accident treatment refers to the time period used for the scheduling accident treatment, and is a semantic evaluation information index. If the processing time t is less than or equal to 5min, the corresponding semantic evaluation is 'very high', and if 5min is less than or equal to 10min, the corresponding semantic evaluation is 'high', and if 10min is less than or equal to t and less than or equal to 15min, the corresponding semantic evaluation is 'general', and if 15min is less than or equal to t and less than or equal to 20min, the corresponding semantic evaluation is 'low', and if 20min is less than or equal to t and less than or equal to 25min, the corresponding semantic evaluation is 'very low'.
Step 3, converting the semantic evaluation information in the step 2 into fuzzy numbers;
selecting a random number x for each piece of semantic evaluation information, wherein the random number is selected by the method that the random number corresponding to the semantic evaluation information changes along with the quality degree of the semantic evaluation information; the better the semantic evaluation information is, the larger the corresponding random number is; namely, the semantic evaluation information is 'very high', and the corresponding random number is the largest; the semantic evaluation information is "high", which corresponds to the second largest random number, and so on;
for each random number x, it optimizes the fuzzy number asThe membership degree satisfies the following relation:
and is also provided with
Representing the upper set of intervals, i.e-> Representing the x-axis distance of the first vertex of the upper interval relative to the origin of coordinates; / >Representing the x-axis distance of the second vertex of the upper interval relative to the origin of coordinates; />Representing the x-axis distance of the third vertex of the upper interval relative to the origin of coordinates; />Representing the y-axial distance of the second vertex of the upper interval relative to the origin of coordinates;
representing the lower interval set, i.e-> Representing the x-axis distance of the first vertex of the lower interval relative to the origin of coordinates; />Representing the second vertex of the lower interval relative to the origin of coordinatesIs the x-axis distance of (2); />Representing the x-axis distance of the third vertex of the lower interval relative to the origin of coordinates; />Representing the y-axial distance of the second vertex of the lower interval relative to the origin of coordinates;
randomly selecting through Gaussian distribution;
in this example, the converted values are shown in table 2:
table 2 optimized fuzzy number conversion table
Step 4, grading the importance of the power system regulation decision indexes, sequencing the power system regulation decision indexes according to the grades, wherein the first index is the most important index, and the last index is the least important index;
the scheduler classifies the importance of the indexes according to the actual demands, sorts the indexes according to the grades, determines the first ranking as the most important index, and finally ranks as the least important index;
The number of the grades and the score difference between each grade are set according to specific conditions; selecting the index with the highest score as the most important index and the index with the lowest score as the least important index; when the index is marked, only one index is needed to be marked, and a plurality of indexes are needed to be marked;
in this embodiment, the number of importance levels is 5, and is 5,4,3,2,1, which correspond to the most important index, the second important index, the third important index, the fourth important index, and the least important index, respectively;
wherein, the most important index is the effectiveness A7 of the dispatching accident handling, and the least important index is the accuracy A3 of the dispatching operation instruction ticket writing;
step 5, determining the relative importance value of the most important index and the least important index and each other index respectively, and generating a relative importance matrix of the most important indexRelative importance matrix to least important index +.>
The method specifically comprises the following steps:
step 501, dividing the relative importance of the regulation decision index of the power system into k grade standards, respectively using different semantemes for the grade standards, and setting fuzzy numbers corresponding to the semantic descriptions of the grade standards; in this embodiment, the relative importance is divided into five criteria of "relative importance", "weak importance", "moderate importance", "strong importance" and "very unimportant", the more the level criteria are divided, the higher the accuracy is, and the person skilled in the art can divide the index relative importance according to the actual requirement;
Step 502, determining the relative importance between the most important index and the least important index and each other index;
the importance of the most important index and the least important index relative to the least important index is the lowest grade of the grade standard, and in the embodiment, the importance is 'relative importance';
every time the level of other indexes relative to the most important index is reduced by one level, the relative importance of the other indexes relative to the most important index is also reduced by one level;
every level of the other indexes relative to the level of the most important index rises by one level, the level of the relative importance of the other indexes relative to the most important index also rises by one level;
step 503, converting the relative importance between the most important index and the least important index of step 502 and each other index into a trapezoidal fuzzy number;
the size of the trapezoidal blur number can be set according to actual conditions by a person skilled in the art, and the setting principle is that each digit of the trapezoidal blur number with relative importance is smaller than the corresponding digit of the trapezoidal blur number with relative importance which is higher than the trapezoidal blur number with relative importance;
step 504: constructing a relative importance matrix of the most important index using the trapezoidal blur number of step 503Relative importance matrix to least important index +.>
Wherein,matrix representing the representation of the trapezoidal blur number of the most important index relative to the importance of other indexes, +. >Ladder ambiguity number indicating the relative importance of the most important index to the nth index, +.>Matrix representing the representation of the trapezoidal blur number of the least significant index relative to the significance of the other indexes>Representing the relative importance of the nth index to the least important index.
In this embodiment, the importance level is represented by the semantic evaluation information as shown in table 3:
TABLE 3 significance level description and associated ladder ambiguity number conversion
/>
Those skilled in the art will recognize that the number of the trapezoidal blur in the present embodiment is only a preferred embodiment, and those skilled in the art can select the value of the number of the trapezoidal blur according to the actual situation.
Step 6, relative importance matrix for the most important index of step 5Relative importance matrix to least important index +.>And solving to obtain the final weight of each power system regulation decision index.
Determining the corresponding trapezoidal fuzzy number of each index according to the importance level of the index in the step 4; the size of the trapezoidal fuzzy number can be set according to actual conditions by a person skilled in the art, and the setting principle is that each digit of the trapezoidal fuzzy number of the index importance level is smaller than the corresponding digit of the trapezoidal fuzzy number of the index importance level higher than the corresponding digit of the trapezoidal fuzzy number of the index importance level;
In the present embodiment, the corresponding values of the trapezoidal blur number of the index importance level are shown in table 3.
The function of solving the index weight is as follows:
minneeds to meet->
Wherein,the objective function of the model is solved for the weight, and the objective function is a set unknown variable and is also the trapezoidal fuzzy number solved by the model;
a ladder ambiguity number representing a level of importance of the most important indicator;
a ladder ambiguity number representing a j-th index importance level;
a ladder ambiguity number representing the relative importance of the most important index to the jth index; />A ladder ambiguity number representing a least significant indicator importance level;
a step of expressing the relative importance of the jth index to the least important index;
n represents the number of indexes;
the crisp number of the clear trapezoidal fuzzy number representing the importance level of the j index;
a first value of the number of trapezoidal ambiguities representing a j-th index importance level; from this, it is known that->Ladder ambiguity for the j-th index importance levelA first value of the number; for the trapezoidal blur number (5, 6,7, 8), the first value is 5, the second value is 6, the third value is 7, and the fourth value is 8;
the method for sharpening comprises the following steps:
wherein a, b, c, d are four values corresponding to the number of trapezoidal ambiguities, a corresponds to a first value of the number of trapezoidal ambiguities, b corresponds to a second value of the number of trapezoidal ambiguities, c corresponds to a third value of the number of trapezoidal ambiguities, and d corresponds to a fourth value of the number of trapezoidal ambiguities; for "very important" ladder blur numbers (5, 6,7, 8), a is 5, b is 6, c is 7,d is 8;
Step 7, normalizing the data of the digital information index and the data of the semantic evaluation information index of each decision scheme in the step 2,
the normalization method of the semantic evaluation information index data comprises the following steps:
wherein n is ij Representing the normalized result of the j index of the i decision scheme;representing the crisp number with the maximum fuzzy number of the j index in all decision schemes; />Friability representing the fuzzy number of the jth index of the ith decision scheme,/for the jth index>A fuzzy number representing the j index of the i decision scheme; c (C) 1 B, representing the index which is smaller and better as the fuzzy number corresponding to the semantic evaluation information in the semantic evaluation information index is clarified 1 And the higher the fuzzy number corresponding to the semantic evaluation information in the semantic evaluation information index is, the better the index is. One skilled in the art would need to make decisions based on the role of the metrics in the decision scheme; in this embodiment, all semantic evaluation information indexes are B 1 。
For optimized fuzzy numberIt can be converted into a sharp number using the following formula:
the normalization method of the digital information index data comprises the following steps:
wherein C is 2 B represents an index of which the number is smaller and the better in the index of the digital information 2 The larger the number is, the better the index is. r is (r) AIj Maximum data representing the j-th index in all decision schemes, e.g. r for index audit schedule operation instruction ticket failure rate A4 AIj Representing the maximum ratio of the number of failed audit and dispatch operation instruction tickets to the total number in the time period selected by the current month in all decision schemes; r is (r) ij Data corresponding to a j index of the i decision scheme is represented; in this embodiment, A 2 And A is a 4 Are all better indicators of the smaller the number.
Step 8, determining an optimal decision scheme according to the normalization result in the step 7 and the weight in the step 6;
step 801, calculating a decision matrix N:
where h represents the total number of decision schemes, n represents the number of indicators,the number of crisp steps in which the number of trapezoidal blur representing the j-th index importance level is clarified is j=1, 2,3 … n.
Step 802, a decision matrix for each scheme is calculated,
wherein N is ij Is the sum of the ith row in the decision matrix N. Phi (A) i ) Is the decision value of the i-th scheme.
Step 803, calculating a decision value of each decision scheme, and selecting a scheme with the largest decision value as a final decision scheme:
where h is the total number of decision schemes,for the smallest of all decision scheme decision values,for the largest decision value of all decision scheme decision values, ζ (A i ) Decision values for the ith scheme.
The invention also discloses an optimal regulation and control decision system of the power system by utilizing the optimal regulation and control decision method of the power system, which comprises a data acquisition module, a regulation and control decision index construction module of the power system, a fuzzy number construction module, a semantic information conversion module, an index weight calculation module and a regulation and control degree scheme decision module of the power system;
the data acquisition module acquires relevant data of each decision scheme, wherein the relevant data comprises the length of reply time of a dispatcher for an overhaul application ticket, the probability of errors after the overhaul application ticket is checked in a time period selected in the month, the types of errors in a dispatch operation instruction ticket, the number of failed audit dispatch operation instruction tickets in the time period selected in the month, the number of errors reserved after the operation instruction ticket is issued, and the length of time for processing a dispatch accident when the decision is executed, and the data are input into the power system regulation decision index construction module;
the power system regulation decision index construction module constructs power supply path decision indexes including semantic indexes and non-semantic indexes, and obtains semantic evaluation information of the semantic indexes; the semantic evaluation information is input to a semantic information conversion module, and related data of non-semantic indexes is input to a normalization module;
The fuzzy number construction module constructs a fuzzy number according to the semantic index and inputs the fuzzy number to the semantic information conversion module;
the semantic information conversion module converts semantic information into fuzzy numbers, obtains the crisp number of each fuzzy number, and inputs the crisp number to the normalization module;
the normalization module normalizes the crisp number input by the semantic information conversion module and the related data of the non-semantic index respectively, and then inputs the crisp number and the related data of the non-semantic index into the power system regulation and control decision index construction module
The index weight calculation module firstly generates a relative importance matrix of the most important index and the non-most important index and a relative importance matrix of the least important index and the non-least important index, calculates the weight of each index according to the relative importance matrix model, and inputs the weight to the power system regulation and control decision index construction module;
the power system regulation and control decision index construction module calculates a decision value according to the normalized data and the weight, and selects a scheme with the maximum decision value as an optimal scheme.
The following evaluation is performed on the regulation and control process of the dispatcher at different moments, and table 4 shows the corresponding semantic evaluation values and values of the regulation and control decision indexes of the power system in three schemes:
Table 4 evaluation index of each decision scheme
First, a contrast index matrix is obtained, A 7 A is the most important index 3 And obtaining a contrast matrix for the least important index. The importance description of each index and the conversion of the relevant trapezoidal fuzzy number are shown in a table 3; the relative importance of each index is shown in table 5:
table 5 index weight contrast matrix
The weights of the indexes are calculated, and the specific results are shown in Table 6:
TABLE 6 weight of each index
The decision matrix is then transformed with the triangular blur number pairs as shown in table 7:
table 7 converted decision information
The index was normalized and each process was evaluated, and finally, the normalized results of the regulatory process at different times were obtained as shown in table 8.
TABLE 8 weight of each index
The final evaluation result is therefore: scheme 2 > scheme 1 > scheme 3
While the applicant has described and illustrated the embodiments of the present invention in detail with reference to the drawings, it should be understood by those skilled in the art that the above embodiments are only preferred embodiments of the present invention, and the detailed description is only for the purpose of helping the reader to better understand the spirit of the present invention, and not to limit the scope of the present invention, but any improvements or modifications based on the spirit of the present invention should fall within the scope of the present invention.
Claims (11)
1. The optimal regulation and control decision method for the electric power system is characterized by comprising the following steps of:
step 1, collecting relevant data of each power system regulation decision scheme;
step 2, constructing a power system regulation decision index according to the power system regulation decision scheme related data acquired in the step 1, wherein the power system regulation decision index comprises a semantic evaluation information index and a digital information index; converting the corresponding data of the semantic evaluation information index into semantic evaluation information;
the decision indexes comprise reply validity of the overhaul application ticket, accuracy of scheduled operation instruction ticket writing, failure rate of checking scheduled operation instruction ticket, accuracy of scheduled operation instruction ticket prefeeding, scheduled operation instruction ticket execution integrity and scheduled accident handling validity;
step 3, converting the semantic evaluation information in the step 2 into fuzzy numbers;
the conversion method comprises the following steps:
selecting a random number x for each piece of semantic evaluation information, wherein the random number is selected by the method that the more excellent the semantic evaluation information is, the larger the corresponding random number is;
for each random number x, its optimized fuzzy number isThe membership degree satisfies the following relation:
And is also provided with
Representing the upper set of intervals, i.e->Representing the x-axis distance of the first vertex of the upper interval relative to the origin of coordinates; />Representing the x-axis distance of the second vertex of the upper interval relative to the origin of coordinates; />Representing the x-axis distance of the third vertex of the upper interval relative to the origin of coordinates; />Representing the y-axial distance of the second vertex of the upper interval relative to the origin of coordinates;
representing the lower interval set, i.e->Representing the x-axis distance of the first vertex of the lower interval relative to the origin of coordinates; />X-axis distance representing the second vertex of the lower interval relative to the origin of coordinatesSeparating; />Representing the x-axis distance of the third vertex of the lower interval relative to the origin of coordinates; />Representing the y-axial distance of the second vertex of the lower interval relative to the origin of coordinates;
randomly selecting through Gaussian distribution;
step 4, grading the importance of the power system regulation decision indexes, sequencing the power system regulation decision indexes according to the grades, wherein the first index is the most important index, and the last index is the least important index;
step 5, determining the relative importance value of the most important index and the least important index and each other index respectively, and generating a relative importance matrix of the most important indexRelative importance matrix to least important index +. >
Step 6, relative importance matrix for the most important index of step 5Relative importance matrix to least important indexSolving to obtain the final weight of each power system regulation decision index;
step 7, normalizing the data of the digital information index and the data of the semantic evaluation information index of each decision scheme in the step 2;
and 8, determining an optimal decision scheme according to the normalization result in the step 7 and the weight in the step 6.
2. The power system optimal regulation decision method of claim 1, wherein:
in the step 1, the relevant data of each decision scheme comprises the reply time of the dispatcher for the maintenance application ticket in the selected time period of the month, the probability of errors after the examination of the maintenance application ticket in the selected time period of the month, the types of errors in the dispatch operation instruction ticket in the selected time period of the month, the number of failed examination and dispatch operation instruction tickets in the selected time period of the month, the number of errors remained after the operation instruction ticket is pre-issued in the selected time period of the month, and the time for dispatching the accident treatment in the selected time period of the month when the decision is executed.
3. The power system optimal regulation decision method of claim 2, wherein:
The reply validity of the overhaul application ticket refers to the reply time of the dispatcher for the overhaul application ticket, and is a semantic evaluation information index;
the checking error rate of the checking application ticket refers to the probability of error of the checking application ticket after checking in the time period selected in the current month, and is a digital information index;
the accuracy of the scheduled operation instruction ticket writing refers to the standard degree of the scheduled operation instruction ticket writing and is a semantic evaluation information index;
the failure rate of the audit scheduling operation instruction ticket refers to the ratio of the number of failed audit scheduling operation instruction tickets to the total number of failed audit scheduling operation instruction tickets in the time period selected in the current month, and is a digital information index;
the accuracy of dispatching the operation instruction ticket pre-issuing is determined according to the number of the reserved errors after the operation instruction ticket pre-issuing, and the operation instruction ticket pre-issuing is a semantic evaluation information index;
the execution integrity of the dispatching operation instruction ticket refers to the number of executed instructions in the dispatching operation instruction ticket, and is a semantic evaluation information index;
the effectiveness of the scheduling accident treatment refers to the time period for the scheduling accident treatment; and the semantic evaluation information index is used.
4. The power system optimal regulation decision method of claim 1, wherein:
in the step 4, the number of grades and the score difference between each grade are set according to specific conditions; selecting the index with the highest score as the most important index and the index with the lowest score as the least important index; when the index is marked, only one index is needed to be marked on the most important index and only one index is needed to be marked on the least important index, and a plurality of indexes are needed to be marked on the other grades.
5. The power system optimal regulation decision method according to claim 1 or 4, wherein:
the step 5 comprises the following steps:
step 501, dividing the relative importance of the regulation decision index of the power system into k grade standards, respectively using different semantemes for the grade standards, and setting fuzzy numbers corresponding to the semantic descriptions of the grade standards;
step 502, determining the relative importance between the most important index and the least important index and each other index;
the importance of the most important index and the least important index relative to the least important index is the lowest grade of the grade standard;
every time the level of other indexes relative to the most important index is reduced by one level, the relative importance of the other indexes relative to the most important index is also reduced by one level;
every level of the other indexes relative to the level of the most important index rises by one level, the level of the relative importance of the other indexes relative to the most important index also rises by one level;
step 503, converting the relative importance between the most important index and the least important index of step 502 and each other index into a trapezoidal fuzzy number;
the magnitude of the trapezoidal fuzzy number is set according to the actual situation, and the setting principle is that each digit of the relatively important trapezoidal fuzzy number is smaller than the corresponding digit of the relatively important trapezoidal fuzzy number which is higher than the relatively important trapezoidal fuzzy number;
Step 504: constructing a relative importance matrix of the most important index using the trapezoidal blur number of step 503Relative importance matrix to least important index +.>
Wherein,matrix representing the representation of the trapezoidal blur number of the most important index relative to the importance of other indexes, +.>Ladder ambiguity number indicating the relative importance of the most important index to the nth index, +.>Matrix representing the representation of the trapezoidal blur number of the least significant index relative to the significance of the other indexes>Representing the relative importance of the nth index to the least important index.
6. The power system optimal regulation decision method of claim 5, wherein:
in the step 6, determining the trapezoidal fuzzy number corresponding to each index according to the importance level of the index in the step 4; the setting principle is that each digit of the trapezoidal blur number of the index importance level is smaller than the corresponding digit of the trapezoidal blur number of the index importance level higher than the corresponding digit of the trapezoidal blur number of the index importance level.
7. The power system optimal regulation decision method of claim 6, wherein:
the final weight of each power system regulation decision index is calculated as the following function:
needs to meet->
Wherein,the objective function of the model is solved for the weight, and the objective function is a set unknown variable and is also the trapezoidal fuzzy number solved by the model;
A ladder ambiguity number representing a level of importance of the most important indicator;
a ladder ambiguity number representing a j-th index importance level;
a ladder ambiguity number representing the relative importance of the most important index to the jth index; />A ladder ambiguity number representing a least significant indicator importance level;
a step of expressing the relative importance of the jth index to the least important index;
n represents the number of indexes;
the crisp number of the clear trapezoidal fuzzy number representing the importance level of the j index;
a first value of the number of trapezoidal ambiguities representing a j-th index importance level; from this, it is known that->The first value of the number of trapezoidal ambiguities for the j-th index importance level.
8. The power system optimal regulation decision method of claim 7, wherein:
the method for solving the problems is as follows:
wherein a, b, c, d are four values corresponding to the number of trapezoidal ambiguities, respectively.
9. The power system optimal regulation decision method of claim 1, wherein:
in the step 7, the normalization method of the semantic evaluation information index data is as follows:
wherein n is ij Representing the normalized result of the j index of the i decision scheme;representing the crisp number with the maximum fuzzy number of the j index in all decision schemes; / >A crisp number representing the fuzzy number of the jth index of the ith decision scheme; c (C) 1 B, representing the index which is smaller and better as the fuzzy number corresponding to the semantic evaluation information in the semantic evaluation information index is clarified 1 The method comprises the steps of representing a better index which is larger after the fuzzy number corresponding to semantic evaluation information in semantic evaluation information indexes is clarified;
for optimized fuzzy numberIt is converted into a sharp number using the following formula:
the normalization method of the digital information index data comprises the following steps:
wherein C is 2 B represents an index of which the number is smaller and the better in the index of the digital information 2 An index indicating that the larger the number is, the better the number is in the digital information index; r is (r) AIj Maximum data corresponding to the j-th index in all decision schemes are represented; r is (r) ij And the data corresponding to the j index of the i decision scheme is represented.
10. The power system optimal regulation decision method of claim 9, wherein:
the step 8 comprises the following steps:
step 801, calculating a decision matrix N:
where h represents the total number of decision schemes, n represents the number of indicators,the crisp number of the clear trapezoidal blur number representing the j index importance level is j=1, 2,3 … n;
step 802, a decision matrix for each scheme is calculated,
Wherein N is ij To sum the ith row in the decision matrix N, phi (a i ) Is the decision value of the ith scheme;
step 803, calculating a decision value of each decision scheme, and selecting a scheme with the largest decision value as a final decision scheme:
where h is the total number of decision schemes,for the smallest of all decision scheme decision values,for the largest decision value of all decision scheme decision values, ζ (A i ) Decision values for the ith scheme.
11. The optimal regulation decision system of the optimal regulation decision method of the power system according to any one of claims 1 to 10, comprising a data acquisition module, a power system regulation decision index construction module, a fuzzy number construction module, a semantic information conversion module, an index weight calculation module and a power system regulation degree scheme decision module, wherein the optimal regulation decision system is characterized in that:
the data acquisition module acquires relevant data of each decision scheme, wherein the relevant data comprises the length of reply time of a dispatcher for an overhaul application ticket, the probability of errors after the overhaul application ticket is checked in a time period selected in the month, the types of errors in a dispatch operation instruction ticket book, the number of failed audit dispatch operation instruction tickets in the time period selected in the month, the number of errors reserved after the operation instruction ticket is issued, and the time length for processing a dispatch accident when the decision is executed, and the data are input into the power system regulation decision index construction module;
The power system regulation and control decision index construction module constructs power supply path decision indexes including semantic indexes and non-semantic indexes, and obtains semantic evaluation information of the semantic indexes; the semantic evaluation information is input to a semantic information conversion module, and related data of non-semantic indexes is input to a normalization module;
the fuzzy number construction module constructs a fuzzy number according to the semantic index and inputs the fuzzy number to the semantic information conversion module;
the semantic information conversion module converts semantic information into fuzzy numbers, obtains the crisp number of each fuzzy number, and inputs the crisp number to the normalization module;
the normalization module normalizes the crisp number input by the semantic information conversion module and the related data of the non-semantic index respectively, and then inputs the crisp number and the related data of the non-semantic index into the power system regulation decision index construction module
The index weight calculation module firstly generates a relative importance matrix of the most important index and the non-most important index and a relative importance matrix of the least important index and the non-least important index, calculates the weight of each index according to the relative importance matrix model, and inputs the weight to the power system regulation decision index construction module;
And the power system regulation and control decision index construction module calculates a decision value according to the normalized data and the weight, and selects a scheme with the maximum decision value as an optimal scheme.
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