CN115879799A - Transformer substation electric energy quality analysis method - Google Patents
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Abstract
The invention discloses a transformer substation power quality analysis method, which comprises the following steps: acquiring substation power quality data of monitoring points according to a preset power quality index system, and judging the effectiveness of the substation power quality data of each monitoring period to obtain the effective monitoring period of the monitoring points; calculating the average value of various data in the effective monitoring period of the monitoring points; calculating the single index quality of each kind of data according to the average value of each kind of data and the quality characteristic of the data; calculating objective weights of various data according to the average value of the various data; acquiring importance degree information of each type of data, and calculating subjective weight of each type of data according to the importance degree information; and calculating the comprehensive power quality of the monitoring points according to the subjective weight, the objective weight and the single index quality of various data. The method can be used for comprehensively and accurately analyzing the power quality and providing a reliable data base for safe and economic operation of the power grid.
Description
Technical Field
The invention relates to the technical field of operation analysis of power systems, in particular to a transformer substation power quality analysis method.
Background
With the access of special key loads such as electrified railways and wind power into a power grid, inherent volatility and the use of rectification power electronic equipment required by grid connection bring power quality problems such as harmonic waves, three-phase imbalance and the like to the power grid, and the level of single indexes of the power grid is deteriorated; simultaneously along with the development of science and technology, the use of the novel equipment that industrial equipment adopted and user's intelligence domestic appliance, the demand of power consumer to the electric energy has been followed the power supply volume and has been changed to high-quality power supply. Therefore, the power quality is increasingly concerned in all aspects, and the power quality analysis can provide a reference basis for judging the power supply quality for users and also provide guidance for the safe and economic operation of a power grid.
The electric energy quality index data is mainly obtained through an electric energy quality monitoring device, the data volume uploaded by each monitoring point every day is certain, and in practical application, due to the fact that the monitoring device is in fault or the data are uploaded abnormally, the data actually received by each index in a monitoring period are incomplete. The existing analysis method has certain randomness when selecting data, so that the comprehensive analysis result of the power quality is not close to the real level, the power quality grade of each monitoring point can be determined only by roughness, and the analysis result is not fine enough.
Disclosure of Invention
The invention aims to provide a transformer substation power quality analysis method, which can comprehensively and accurately analyze the power quality and provide a reliable data base for safe and economic operation of a power grid.
In order to achieve the purpose, the invention adopts the technical scheme that the method for analyzing the electric energy quality of the transformer substation comprises the following steps:
according to a preset electric energy quality index system, acquiring electric energy quality data of a transformer substation in at least one monitoring period at a monitoring point, wherein the electric energy quality data of the transformer substation comprises various data in voltage deviation, total voltage harmonic rate, three-phase unbalance index, long-time voltage flicker and power factor;
according to the quantity of the obtained various data in each monitoring period, carrying out validity judgment on the power quality data of the transformer substation in each monitoring period to obtain the valid monitoring period of the monitoring point;
calculating the average value of various data in the effective monitoring period of the monitoring points;
calculating the single index quality of each kind of data according to the average value of each kind of data and the quality characteristic of the data;
calculating objective weights of various data according to the average value of the various data;
acquiring importance degree information of each type of data, and calculating subjective weight of each type of data according to the importance degree information;
and calculating the comprehensive power quality of the monitoring points according to the subjective weight, the objective weight and the single index quality of various data.
According to the technical scheme, the monitoring points can be arranged at different positions in the transformer substation, the transformer substation power quality monitoring device is used for monitoring power quality data of various transformer substations, the comprehensive power quality reference value of each monitoring point is obtained through calculation according to the scheme, further power quality influence factor analysis can be conveniently carried out on the monitoring points with the equivalent comprehensive power quality level, and a basis is provided for power quality improvement.
Optionally, the determining the validity of the power quality data of the substation in each monitoring period according to the number of the obtained various types of data in each monitoring period includes:
a1 Determining a data integrity threshold based on the data sampling frequency;
b1 For each type of data in each monitoring period, respectively judging whether the number of the data exceeds the data integrity threshold value, if so, the corresponding type of data is valid data;
c1 For each monitoring period, if each type of data is valid data, the monitoring period is a valid monitoring period, otherwise, the monitoring period is an invalid monitoring period.
Optionally, the data integrity threshold is determined according to the data sampling frequency, and the formula is as follows:
in the formula, M represents a data integrity threshold, N represents the number of standard data sampled in a monitoring period, and is represented as:
wherein T represents the monitoring period time length, T 1 Representing a sampling time interval.
Optionally, the calculating the single-index quality of each type of data according to the average value of each type of data and the quality characteristics of the data includes:
a2 Determining data index types according to quality characteristics of various types of data, wherein the data index types comprise a very large index with the quality characteristic of the better index when the numerical value is larger, a very small index with the quality characteristic of the better index when the numerical value is smaller, and a region type index with the best index when the numerical value is in a certain region;
b2 For each type of data, calculating the quality of a single index according to the data index type and the data average value:
for the maximum index and the minimum index, the single index quality calculation formula is as follows:
in the formula (I), the compound is shown in the specification,
mean value of j-th class data, K, representing data index type being either maximum index or minimum index j 、k j Respectively represent the classIdeal values and threshold values of the data, wherein 60 represents qualified scores, 40 represents benchmark variation scores, and the qualified scores and the benchmark variation scores can be properly adjusted as required;
for interval type indexes, the single index quality calculation formula is as follows:
in the formula (I), the compound is shown in the specification,
average value of j-th class data indicating that data index type is interval type index, q j 、Q j Respectively representing the left and right boundaries of the ideal value of the data, f j 、F j Respectively represent the left and right boundaries of the jth interval type index threshold.
Optionally, the calculating the objective weight of each type of data according to the average value of each type of data includes:
a3 Average value of various types of data in the effective monitoring period of a plurality of monitoring points is standardized to obtain a standardized matrix composed of standardized data of various types of data in the effective monitoring period of the plurality of monitoring points:
in the formula, use
Represents the average value of the ith class data in the valid monitoring period of the jth monitoring point, then->
Represents->
Normalized result of (a), the normalized formula is:
wherein m is the number of monitoring points, and n is the type number of the considered substation electric energy quality data;
b3 ) find positive definite matrix H = a 1 T A 1 And carrying out normalization processing on the characteristic vector corresponding to the medium and maximum characteristic value to obtain a vector:
w'＝{w 1 '，w 2 '，…w n '}
c3 W therein) to 1 '，w 2 '，…w n ' as objective weights for n classes of data, respectively.
Optionally, the calculating the subjective weight of each type of data according to the importance information includes:
a4 According to the acquired importance information, sorting various data according to importance;
b4 For each kind of sorted data, calculating the relative importance degree R between adjacent index data types according to the importance degree information k K =2,3.., n, n is the number of data types;
c4 According to said relative degree of importance R) k Calculating the subjective weight w of each kind of data after sorting k ,k＝1,2,3,...,n。
Optionally, in b4, the relative importance degree R between adjacent index data types is calculated according to the importance degree information k The method comprises the following steps that for the kth class data which are sorted from large to small according to the importance degree, k is not equal to 1:
if the kth class data is as important as the kth-1 class data, then R k ＝1；
If class k data is slightly more important than class k-1 data, then R k ＝1.2；
If class k data is significantly more important than class k-1 data, then R k ＝1.4；
If class k data is more important than class k-1 data, then R k ＝1.6；
If the kth class data is extremely heavier than the kth-1 class dataTo then R k ＝1.8。
Optionally, in c4, the degree of relative importance R is determined k Calculating the subjective weight w of each kind of data after sorting k K =1,2, 3.., n, including:
c41 Calculates the subjective weight of the nth data with the relative importance degree ranked at the end, and the formula is:
c42 According to w) n And sequentially calculating the subjective weight of other various data, wherein the formula is as follows: w is a k-1 ＝R k w k 。
Optionally, the calculating the comprehensive power quality of the monitoring point according to the subjective weight, the objective weight and the single index quality of each type of data includes:
a5 According to the preset subjective weight coefficient and objective weight coefficient, determining the comprehensive weight of various data, wherein the formula is as follows:
W i ＝αw i +βw i '
in the formula, W i Integral weight, w, representing class i data i And w i Respectively the subjective weight and the objective weight of the ith data, and respectively alpha and beta are a subjective weight coefficient and an objective weight coefficient;
b5 According to the comprehensive weight and the single index quality of various types of data, calculating the comprehensive power quality of the monitoring point, wherein the formula is as follows:
in the formula, P i Indicating the integrated power quality of the ith monitoring point,
and the single index quality of the jth class of data in the effective monitoring period of the ith monitoring point is represented, and n is the number of the data types.
Optionally, the method further comprises: and determining the power quality grades of the corresponding monitoring points according to the comprehensive power quality of the monitoring points, wherein the power quality grades are divided into 5 grades of excellence, good, medium, poor and extremely poor. P i More than or equal to 90 represents that the quality of the electric energy is excellent grade, and is more than or equal to 80 and more than or equal to P i <90 represents that the quality of the electric energy is in good grade, and P is more than or equal to 70 i <80 represents that the power quality is in a medium level, and P is more than or equal to 60 i <70 indicates that the power quality is of a poor level, P i <And 60 represents a very poor quality of the power.
Advantageous effects
According to the invention, the reference value of the data integrity is determined according to the total theoretical data in the monitoring period, the effectiveness identification is carried out on the collected data, and then the effective monitoring period of each monitoring point is obtained, so that the phenomenon that the power quality result is not close to the true level due to data loss can be avoided. Meanwhile, the invention adopts an subjective and objective comprehensive weighting method to weight each data index, which not only reflects the importance degree of expert opinions on the electric energy quality analysis result, but also highlights the overall difference among data index objects, and avoids the situation that the electric energy quality index weight is too subjective or too objective by adopting a single weighting method. The method can obtain the power quality of different monitoring points and can further divide the power quality grade, can further evaluate the power quality condition and the power quality influence factors aiming at each monitoring point at the same power quality grade, enables the analysis result to be more precise, provides data reference for the construction and maintenance of the power line, and has higher applicability and popularization.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a schematic flow chart of one embodiment of the method of the present invention;
FIG. 3 is a schematic flow chart illustrating the validity determination of data according to the method of the present invention;
FIG. 4 is a schematic flow chart illustrating subjective weight determination using a sequence relationship method according to the present invention;
FIG. 5 is a schematic flow chart of objective weighting determination by the method of the present invention using a pull-down level method;
FIG. 6 is a schematic diagram illustrating a method for determining comprehensive weights according to the present invention;
FIG. 7 is a schematic diagram showing the calculation of comprehensive quality of monitoring points from various index data and weights in the method of the present invention;
fig. 8 is a schematic diagram of the power quality results of a plurality of monitoring points analyzed by the method of the present invention in an application example.
Detailed Description
The following further description is made in conjunction with the accompanying drawings and the specific embodiments.
Referring to fig. 1, the method for analyzing the quality of the electric energy of the transformer substation of the present invention includes:
acquiring substation power quality data in at least one monitoring period at a monitoring point according to a preset power quality index system;
according to the quantity of the obtained various data in each monitoring period, carrying out validity judgment on the power quality data of the transformer substation in each monitoring period to obtain the valid monitoring period of the monitoring point;
calculating the average value of various data in the effective monitoring period of the monitoring points;
calculating the single index quality of each kind of data according to the average value of each kind of data and the quality characteristic of the data;
calculating objective weights of various data according to the average value of the various data;
acquiring importance degree information of each type of data, and calculating subjective weight of each type of data according to the importance degree information;
and calculating the comprehensive power quality of the monitoring points according to the subjective weight, the objective weight and the single index quality of various data.
When the method is applied, monitoring points can be arranged on different line sides of the same voltage level, and the comprehensive power quality reference value of each monitoring point is obtained through calculation of the scheme, so that further power quality influence factor analysis can be conveniently carried out on the monitoring points with the equivalent comprehensive power quality level, and a basis is provided for power quality improvement.
Examples
This embodiment is described by taking a 10kV grid as an example. Referring to fig. 2, the present embodiment specifically involves the following steps.
1. Determining a power quality indicator system and indicator data acquisition
According to relevant national standards and experiences, the electric energy quality index system is formed by selecting voltage deviation, total voltage harmonic, three-phase unbalance, long-time voltage flicker and power factors.
For different voltage grades or lines, the index types of the electric energy quality index system can be adjusted according to actual conditions.
2. Validity determination of index data
The power quality index data can be acquired at the monitoring points through the power quality monitoring device, and 7 monitoring points are selected for explanation in this embodiment, referring to fig. 3, the sampling time interval of various data in the monitoring period and the monitoring period is firstly determined, in this embodiment, each monitoring point monitoring period is one week, data is collected every 15 minutes, and then the number N of the standard data collected by each index in the monitoring period is:
the number of data actually received by each index of 7 monitoring points is shown in the following table 1:
TABLE 1
The number of standard data collected by each index in the monitoring period can be calculated to be 672 according to the formula (1), and according to the data integrity threshold formula:
the data integrity threshold, i.e., M =336.
According to the data integrity threshold, the validity of the data of each monitoring period of each monitoring point is judged, as shown in fig. 3, for each type of data in each monitoring period, whether the number of the data exceeds the data integrity threshold 336 is judged, and if the number of the data exceeds the data integrity threshold, the corresponding type of data is valid data; for each monitoring period, if each type of data is valid data, the monitoring period is a valid monitoring period, otherwise, the monitoring period is an invalid monitoring period.
For the situation that the monitoring period obtained by any monitoring point is an invalid monitoring period, new monitoring period data needs to be obtained again, and validity judgment is carried out until the valid monitoring period of each monitoring point is obtained, as shown in table 2 below.
TABLE 2 number of data collected by 7 monitoring points in the effective monitoring period
3. Calculating the single index quality of each index data in the effective monitoring period of each monitoring point
Firstly, for each monitoring point, calculating the average value of various index data according to an index data average value calculation formula:
in the formula (2), the reaction mixture is,
means for representing the j-th index data, N j The number of data actually collected for the jth index data in the effective monitoring period is->
And g-th data value representing j-th index data.
Thus, the average value of various index data of 7 monitoring points can be obtained, as shown in the following table 3:
TABLE 3
According to the power quality characteristics of various index data, the data index types can be divided into 3 types: maximum type index, very small index, section index: the very large index means that the index value is larger, and the index is better; the ultra-small index means that the smaller the index value is, the better the index is; the interval index is the index that is best when the index value is in a certain interval.
Under normal conditions, the power grid power quality index should meet the national standard limit value or the user-defined standard threshold value, and the index is qualified at the moment. In order to quantify the power quality index, the invention establishes a power quality single-index quality function and visually reflects the quality degree of each index data.
The calculation formula of the quality scoring function of the ultra-large and ultra-small indexes is as follows (3):
in the formula, K j 、k j The ideal value and the threshold value of the jth type maximum or minimum index data are respectively represented, 60 represents a qualified score, and 40 represents a reference change score.
The calculation formula of the interval type index scoring function is as shown in formula (4):
in the formula, q j 、Q j Respectively representing the left and right boundaries of the ideal interval of the j-th interval type index data, f j 、F j Respectively representing the left and right boundaries of the threshold of the j-th interval type index data.
The voltage deviation is an interval index, and aiming at a 10kV power grid, the voltage deviation threshold value is +7% -10% according to the national standard and the requirement of a user, and the ideal interval is 0% -2%, so in the step (4), q is i ＝0，Q i ＝2％，f i ＝-10％，F i ＝7％。
The total harmonic rate of voltage is a very small index, and the voltage deviation threshold is 4% and the ideal value is 0% according to the national standard and the requirement of users, so K in the formula (3) j ＝0％、k j ＝4％。
The three-phase unbalance is a very small index, the voltage deviation threshold is 4% and the ideal value is 0% according to the national standard and the requirement of a user, so K in the formula (3) j ＝0％、k j ＝4％。
The long-time voltage flicker is a very small index, and the voltage deviation threshold is 1 and the ideal value is 0 according to the national standard and the requirement of a user, so K in the formula (3) j ＝0、k j ＝1。
The power factor is a maximum index, the voltage deviation threshold is 0.80 according to the national standard and the requirement of the user, the ideal value is 1, so K in the formula (3) j ＝1、k j ＝0.8。
The single-index quality scores of the various types of index data at the 7 monitoring points are as follows 4:
TABLE 4
4. Calculating objective weight of each kind of data according to average value of each kind of data
The embodiment determines the objective weight of various index data by adopting a grade pulling method, and comprises the following steps:
(4.1) carrying out standardization processing on the average value of various types of data in the effective monitoring period of the multiple monitoring points to obtain a standardization matrix A consisting of the standardized data of various types of data in the effective monitoring period of the multiple monitoring points 1 Wherein the standardized formula is:
in the formula (5), the reaction mixture is,
represents the average value, based on the class i data, of the valid monitoring period for the jth monitoring point>
Represents->
M is the number of monitoring points and n is the number of types of the considered substation power quality data, then the matrix A is standardized 1 Expressed as:
according to the formula, the average value of 5 types of index data of 7 monitoring points is used for constructing the comprehensive evaluation matrix of the power quality in the embodiment as follows:
preprocessing each index data in A to obtain a standardized matrix A 1 Expressed as:
(4.2) determining a positive definite matrix H = A 1 T A 1 The feature vector corresponding to the maximum feature value is normalized to obtain objective weight vector w' = { w of multi-class index data 1 '，w 2 '，…w n ' }, in this embodiment, the following can be calculated: w '= {0.213,0.179,0.222,0.197,0.189}, of which w' = 1 '，w 2 '，…w n ' Objective as 5-class index data respectivelyThe weight and the objective weight of 5-class index data are sequentially voltage deviation 0.213, total harmonic rate of voltage 0.179, three-phase unbalance 0.222, long-time voltage flicker 0.197 and power factor 0.189.
The method for analyzing the electric energy quality weakens the weight of the total harmonic rate and the power factor of the voltage, strengthens the flicker weight of the long-term voltage, emphasizes the objective fact and enables the electric energy quality analysis result to be closer to the real level.
5. Calculating subjective weight of each index data
In this embodiment, subjective weights of various types of index data are determined by using a sequence relation method, which includes the following specific steps.
(5.1) determining an index order relational expression according to the opinions of experts or decision makers:
in this embodiment, the order relationship of the 5 types of index data is:
(5.2) determining the relative importance degree R between adjacent index data in the order relation k 。
Degree of relative importance R between adjacent indices k The rational judgment value of (c) can be determined by referring to the following table 5:
TABLE 5
From this, it can be further determined that the relative importance of the 5 indicators is:
index ratio>
Index (I)Significantly, then R2=1.4, ` is present>
Index and->
Equally important is the index R3=1, ` is>
Index ratio>
If the index is of slight importance, R4=1.2, ` is greater than or equal to `>
Index ratio
Index is slightly important, then R5=1.2
(5.3) calculating subjective weighting according to relative importance degree
By w k To represent
Subjective weighting of the corresponding type of index data, expert evaluation index->
And/or>
Ratio of degree of importance R k The rational judgment formula is as follows:
it can therefore be determined first
I.e. the expert in the embodimentSubjective weighting w of index data type considered least important 5 The method comprises the following steps:
variant w according to formula (8) k-1 ＝R k w k The subjective weights of other types of index data can be further calculated and obtained as follows: w is a 4 ＝0.141×1.2＝0.169，w 3 ＝0.169×1.2＝0.203，w 2 ＝0.203×1＝0.203，w 1 ＝0.203×1.4＝0.284。
The subjective weights of the 5 types of index data obtained by the method are as follows in sequence: voltage deviation 0.284, total harmonic rate of voltage 0.203, three-phase unbalance 0.169, long-time voltage flicker 0.141 and power factor 0.203.
6. Calculating the comprehensive weight of various index data according to the subjective weight and the objective weight
In the embodiment, the subjective and objective comprehensive weighting method is adopted to weight each data index, so that the situation that the power quality index weight is too subjective or too objective by adopting a single weighting method is avoided.
The calculation formula of the comprehensive weight of the ith index data is as follows:
W i ＝αw i +βw i ′ (10)
in the formula (10), α is an index subjective weight coefficient, β is an index objective weight coefficient, and the values of the two coefficients can be adjusted as required. In this example, when both are set to 0.5, the subjective weight, objective weight, and comprehensive weight of the 5-type index data calculated according to equation (10) are shown in table 6 below:
TABLE 6
| Index (I) | Subjective weighting | Objective weight | Composite weight |
| Deviation of voltage | 0.284 | 0.213 | 0.249 |
| Total harmonic ratio of voltage | 0.203 | 0.179 | 0.191 |
| Degree of unbalance of three phases | 0.169 | 0.222 | 0.195 |
| Long time voltage flicker | 0.141 | 0.197 | 0.169 |
| Power factor | 0.203 | 0.189 | 0.196 |
7. Calculating the comprehensive power quality of each monitoring point
According to the single index quality shown in the table 3 obtained by the third part of calculation and the comprehensive weight shown in the table 6 obtained by the sixth part of calculation, the comprehensive power quality of each monitoring point can be obtained by calculation, and the formula is as follows:
in the formula, P i Indicating the integrated power quality of the ith monitoring point,
the single index quality of the jth class data in the effective monitoring period of the ith monitoring point is represented, n is the number of the data types, namely 5 in the embodiment, and according to the formula (11), the comprehensive power quality result of 7 monitoring points refers to fig. 8.
8. Evaluating the power quality grade of each monitoring point
After the seventh part calculates the comprehensive power quality of each monitoring point, the embodiment may further perform further analysis according to the comprehensive power quality result, for example, by analyzing the data of the monitoring points with similar comprehensive power quality scores, inferring power quality influence factors, and the like. The embodiment also carries out power quality grade evaluation on each monitoring point according to the comprehensive power quality result.
In the rating evaluation, the power quality ratings were classified into excellent, good, medium, poor and very poor 5 ratings. P i More than or equal to 90 represents that the quality of the electric energy is excellent grade, and is more than or equal to 80 and more than or equal to P i <90 represents that the power quality is in good grade, and is more than or equal to 70 and less than or equal to P i <80 represents that the power quality is in a medium level, and P is more than or equal to 60 i <70 indicates that the power quality is of a poor level, P i <And 60 represents a very poor quality of the power.
In fig. 8, the total evaluation score of the power quality of the 6 th monitoring point is 92.97, which is an excellent level, the evaluation scores of the monitoring points 2 and 5 are 81.26 and 84.23, which are good levels, the evaluation scores of the monitoring points 3 and 7 are medium levels, the evaluation score of the monitoring point 4 is a poor level, and the evaluation score of the monitoring point 1 is a poor level.
In the same level, the evaluation of the monitoring point 2 is higher than that of the monitoring point 5, the evaluation of the monitoring point 7 is higher than that of the monitoring point 3, and the comparison in the same level shows that the power quality levels of the monitoring points 2 and 7 are better, so that the factors influencing the power quality difference between the two monitoring points can be further analyzed, the power quality analysis is further refined, a data base is provided for line maintenance, and the safe and stable operation of the line is ensured.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. A transformer substation electric energy quality analysis method is characterized by comprising the following steps:
according to a preset electric energy quality index system, acquiring electric energy quality data of a transformer substation in at least one monitoring period at a monitoring point, wherein the electric energy quality data of the transformer substation comprises various data in voltage deviation, total voltage harmonic rate, three-phase unbalance index, long-time voltage flicker and power factor;
according to the quantity of the obtained various data in each monitoring period, carrying out validity judgment on the power quality data of the transformer substation in each monitoring period to obtain the valid monitoring period of the monitoring point;
calculating the average value of various data in the effective monitoring period of the monitoring points;
calculating the single index quality of each kind of data according to the average value of each kind of data and the quality characteristic of the data;
calculating objective weights of various data according to the average value of the various data;
acquiring importance degree information of each type of data, and calculating subjective weight of each type of data according to the importance degree information;
and calculating the comprehensive power quality of the monitoring points according to the subjective weight, the objective weight and the single index quality of various data.
2. The method as claimed in claim 1, wherein the determining the validity of the substation power quality data in each monitoring period according to the obtained quantity of each type of data in each monitoring period comprises:
a1 Determining a data integrity threshold based on the data sampling frequency;
b1 For each type of data in each monitoring period, respectively judging whether the number of the data exceeds the data integrity threshold value, if so, the corresponding type of data is valid data;
c1 For each monitoring period, if each type of data is valid data, the monitoring period is a valid monitoring period, otherwise, the monitoring period is an invalid monitoring period.
3. The method of claim 2, wherein the data integrity threshold is determined based on a data sampling frequency by the formula:
in the formula, M represents a data integrity threshold, N represents the number of standard data sampled in a monitoring period, and is represented as:
wherein T represents the monitoring period time length, T 1 Representing a sampling time interval.
4. The method of claim 1, wherein calculating the single index quality of each type of data according to the average value of each type of data and the quality characteristics of the data comprises:
a2 Determining data index types according to quality characteristics of various types of data, wherein the data index types comprise an extremely large index with the quality characteristic of being the index with better index when the numerical value is larger, an extremely small index with the quality characteristic of being the index with better index when the numerical value is smaller, and an interval type index with the best index when the numerical value is in a certain interval;
b2 For each type of data, calculating the quality of a single index according to the data index type and the data average value:
for the maximum index and the minimum index, the single index quality calculation formula is as follows:
in the formula (I), the compound is shown in the specification,
mean value of j-th class data, K, representing data index type being either maximum index or minimum index j 、k j Respectively representing an ideal value and a threshold value of the data, 60 represents a qualified score, and 40 represents a benchmark change score;
for interval type indexes, the single index quality calculation formula is as follows:
in the formula (I), the compound is shown in the specification,
mean value of j-th class data representing interval type of data index, q j 、Q j Respectively representing the left and right boundaries of the ideal value of the data, f j 、F j Respectively representing the left and right boundaries of the jth interval type index threshold.
5. The method of claim 1, wherein said calculating objective weights for each class of data based on an average of each class of data comprises:
a3 Normalizing the average value of various types of data in the effective monitoring period of the multiple monitoring points to obtain a normalized matrix consisting of the normalized data of various types of data in the effective monitoring period of the multiple monitoring points:
in the formula, use
Represents the average value of the ith class data in the valid monitoring period of the jth monitoring point, then->
Represents->
The normalization formula is:
wherein m is the number of monitoring points, and n is the type number of the considered substation electric energy quality data;
b3 ) find positive definite matrix H = a 1 T A 1 And carrying out normalization processing on the characteristic vector corresponding to the medium and maximum characteristic values to obtain a vector:
w'＝{w 1 '，w 2 '，…w n '}
c3 W therein) to 1 '，w 2 '，…w n ' as objective weights for n classes of data, respectively.
6. The method of claim 1, wherein said calculating subjective weights for each type of data based on importance information comprises:
a4 According to the acquired importance information, sorting various data according to importance;
b4 For each kind of sorted data, calculating the type of the adjacent index data according to the importance degree informationRelative degree of importance R between k K =2,3,.., n, n is the number of data types;
c4 According to said relative degree of importance R) k Calculating the subjective weight w of each kind of data after sorting k ,k＝1,2,3,...,n。
7. The method as claimed in claim 6, wherein in b4, the relative degree of importance R between adjacent index data types is calculated based on the degree of importance information k The method comprises the following steps that for the kth class data which are sorted from large to small according to the importance degree, k is not equal to 1:
if the kth class data is as important as the kth-1 class data, then R k ＝1；
If the kth class data is slightly more important than the kth-1 class data, then R k ＝1.2；
If class k data is significantly more important than class k-1 data, then R k ＝1.4；
If class k data is more important than class k-1 data, then R k ＝1.6；
If class k data is extremely important than class k-1 data, then R k ＝1.8。
8. The method of claim 6, wherein in c4, said relative degree of importance R is a function of k Calculating the subjective weight w of each kind of data after sorting k K =1,2, 3.., n, including:
c41 Calculates the subjective weight of the nth data with the relative importance degree ranked at the end, and the formula is:
c42 According to w) n And sequentially calculating subjective weights of other various data, wherein the formula is as follows: w is a k-1 ＝R k w k 。
9. The method of claim 1, wherein calculating the integrated power quality of the monitoring point according to the subjective weight, the objective weight and the single index quality of each type of data comprises:
a5 According to preset subjective weight coefficients and objective weight coefficients, determining comprehensive weights of various types of data, wherein the formula is as follows:
W i ＝αw i +βw i '
in the formula, W i Integral weight, w, representing class i data i And w i ' are subjective weight and objective weight of the i-th class data respectively, and alpha and beta are subjective weight coefficient and objective weight coefficient respectively;
b5 According to the comprehensive weight and the single index quality of various types of data, calculating the comprehensive power quality of the monitoring point, wherein the formula is as follows:
in the formula, P i Indicating the integrated power quality of the ith monitoring point,
and the quality of a single index of the j-th class of data in the effective monitoring period of the ith monitoring point is represented, and n is the number of the data types.
10. The method of claim 1, further comprising: and determining the power quality grades of the corresponding monitoring points according to the comprehensive power quality of the monitoring points, wherein the power quality grades are divided into 5 grades of excellence, good, medium, poor and extremely poor. P i More than or equal to 90 represents that the quality of the electric energy is excellent grade, and is more than or equal to 80 and more than or equal to P i <90 represents that the power quality is in good grade, and is more than or equal to 70 and less than or equal to P i <80 represents that the power quality is in a medium level, and P is more than or equal to 60 i <70 indicates that the power quality is of a poor level, P i <And 60 represents a very poor quality of the power.
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