CN115879799A - Transformer substation electric energy quality analysis method - Google Patents

Abstract

The invention discloses a method for analyzing power quality of a substation, comprising: obtaining power quality data of a monitoring point in a substation according to a preset power quality index system, and discriminating the validity of the power quality data of a substation in each monitoring cycle to obtain effective monitoring of the monitoring point Period; calculate the average value of various data in the effective monitoring period of the monitoring point; calculate the single index quality of various data according to the average value of various data and the quality characteristics of the data; calculate the quality of various data according to the average value of various data Objective weight: obtain the importance degree information of various data, and calculate the subjective weight of various data according to the importance degree information; calculate the comprehensive power quality of the monitoring point according to the subjective weight, objective weight and single index quality of various data. The method of the invention can comprehensively and accurately analyze the power quality, and provide reliable data basis for the safe and economical operation of the power grid.

Description

A method for analyzing power quality in substation

Technical Field

The invention relates to the technical field of power system operation analysis, in particular to a method for analyzing power quality of a substation.

Background Art

With the access of special key loads such as electrified railways and wind power to the power grid, their inherent volatility and the use of rectifier power electronic equipment required for grid connection have brought power quality problems such as harmonics and three-phase imbalance to the power grid, worsening the level of single indicators of the power grid; at the same time, with the development of science and technology, the use of new equipment in industrial equipment and the use of smart home appliances by users, the demand of power users for electricity has shifted from power supply quantity to high-quality power supply. Therefore, power quality has received increasing attention from all parties. Power quality analysis can provide users with a reference for judging power supply quality, and can also provide guidance for the safe and economic operation of the power grid.

Power quality index data is mainly obtained through power quality monitoring devices. The amount of data uploaded by each monitoring point every day is certain. In actual applications, due to monitoring device failures or abnormal data uploads, the data actually received by each indicator during the monitoring period is incomplete. The existing analysis methods have a certain degree of randomness when selecting data, resulting in the comprehensive analysis results of power quality not being close to the actual level, and often can only roughly determine the power quality level of each monitoring point, and the analysis results are not detailed enough.

Summary of the invention

The purpose of the present invention is to provide a substation power quality analysis method, which can comprehensively and accurately analyze the power quality and provide a reliable data basis for the safe and economical operation of the power grid.

To achieve the above objectives, the technical solution adopted by the present invention is a substation power quality analysis method, comprising:

According to the preset power quality index system, the power quality data of the substation within at least one monitoring period at the monitoring point is obtained, wherein the power quality data of the substation includes multiple types of data such as voltage deviation, voltage total harmonic rate, three-phase unbalance index, long-term voltage flicker, and power factor;

According to the quantity of each type of data obtained in each monitoring period, the validity of the substation power quality data in each monitoring period is judged to obtain the effective monitoring period of the monitoring point;

Calculate the average value of various data within the effective monitoring period of the monitoring point;

Calculate the single indicator quality of each type of data based on the average value of each type of data and the quality characteristics of the data;

Calculate the objective weight of each type of data based on the average value of each type of data;

Obtaining importance information of various types of data, and calculating subjective weights of various types of data based on the importance information;

The comprehensive power quality of the monitoring point is calculated based on the subjective weight, objective weight and single indicator quality of various types of data.

In the above technical scheme, monitoring points can be set up at different locations in the substation, and the substation power quality monitoring device can be used to monitor the power quality data of various substations. The comprehensive power quality reference value of each monitoring point can be calculated through the above scheme, which is convenient for further analysis of power quality influencing factors for monitoring points with equivalent comprehensive power quality levels, providing a basis for improving power quality.

Optionally, judging the validity of the substation power quality data of each monitoring period according to the quantity of each type of data in each monitoring period obtained includes:

a1) Determine the data integrity threshold based on the data sampling frequency;

b1) for each type of data in each monitoring period, determine whether the number of data exceeds the data integrity threshold, if so, the corresponding type of data is valid data;

c1) For each monitoring period, if each type of data is valid data, then the monitoring period is a valid monitoring period, otherwise it is an invalid monitoring period.

Optionally, the data integrity threshold is determined according to the data sampling frequency, and the formula is:

In the formula, M represents the data integrity threshold, and N represents the number of standard data sampled during the monitoring period, which can be expressed as:

Where T represents the length of the monitoring cycle, and _t1 represents the sampling time interval.

Optionally, the calculating of the single indicator 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 the data indicator type according to the quality characteristics of each type of data, wherein the data indicator type includes a very large indicator whose quality characteristic is that the larger the value, the better the indicator, a very small indicator whose quality characteristic is that the smaller the value, the better the indicator, and an interval indicator whose quality characteristic is that the indicator is best when the value is within a certain interval;

b2) For each type of data, calculate the quality of a single indicator based on the data indicator type and the data average value:

For very large and very small indicators, the single indicator quality calculation formula is:

表示数据指标类型为极大型指标或者极小型指标的第j类数据的平均值，K_j、k_j分别表示该类数据的理想值及阈值，60分表示合格分数，40表示基准变化分数，合格分数和基准变化分数可根据需要适当调整；In the formula,

It indicates the average value of the jth type of data whose data indicator type is extremely large indicator or extremely small indicator. K _j and k _j respectively represent the ideal value and threshold of this type of data. 60 points represent the passing score and 40 represents the benchmark change score. The passing score and benchmark change score can be adjusted appropriately according to needs.

For interval indicators, the single indicator quality calculation formula is:

表示数据指标类型为区间型指标的第j类数据的平均值，q_j、Q_j分别表示该类数据的理想值左右边界，f_j、F_j分别表示第j个区间型指标阈值左右边界。In the formula,

It represents the average value of the j-th type of data whose data indicator type is interval indicator. q _j and Q _j represent the left and right boundaries of the ideal value of this type of data respectively. f _j and F _j represent the left and right boundaries of the j-th interval indicator threshold respectively.

Optionally, the calculating the objective weight of each type of data according to the average value of each type of data includes:

a3) Standardize the average values of various types of data within the effective monitoring period of multiple monitoring points to obtain a standardized matrix composed of standardized data of multiple types of data within the effective monitoring period of multiple monitoring points:

表示第j个监测点的有效监测周期内第i类数据的平均值，则

表示

的标准化结果，标准化公式为：In the formula, use

represents the average value of the i-th type of data in the effective monitoring period of the j-th monitoring point, then

express

The standardized result is:

Where m is the number of monitoring points, n is the number of types of substation power quality data considered;

b3) Find the eigenvector corresponding to the maximum eigenvalue in the positive definite matrix H = A ₁ ^T A ₁ and normalize it to obtain the vector:

w'={w ₁ ', w ₂ ',... w _n '}

c3) Take w ₁ ', w ₂ ', ... w _n ' as the objective weights of n types of data respectively.

Optionally, the calculating of subjective weights of various types of data according to importance information includes:

a4) Sort the various types of data by importance based on the acquired importance information;

b4) for each type of data after sorting, the relative importance R _k between adjacent indicator data types is calculated according to the importance information, k=2, 3, ..., n, where n is the number of data types;

c4) According to the relative importance R _k , the subjective weight w _k of each type of data after sorting is calculated, k=1, 2, 3, ..., n.

Optionally, in b4, the method for calculating the relative importance R _k between adjacent indicator data types according to the importance information is, for the kth type of data sorted in descending order of importance, k≠1:

If the k-th category data is as important as the k-1-th category data, then R _k = 1;

If the kth class data is slightly more important than the k-1th class data, then R _k = 1.2;

If the k-th category data is significantly more important than the k-1-th category data, then R _k = 1.4;

If the k-th category data is more important than the k-1-th category data, then R _k = 1.6;

If the k-th category data is extremely important than the k-1-th category data, then R _k = 1.8.

Optionally, in c4, the calculation of the subjective weight w _k of each type of data after sorting according to the relative importance R _k , k=1,2,3,...,n, includes:

c41) Calculate the subjective weight of the nth category of data with the lowest relative importance. The formula is:

c42) Calculate the subjective weights of other types of data in turn according to w _n , the formula is: w _k-1 =R _k w _k .

Optionally, the comprehensive power quality of the monitoring point is calculated according to the subjective weight, objective weight and single indicator quality of various types of data, including:

a5) Determine the comprehensive weight of each type of data based on the preset subjective weight coefficient and objective weight coefficient. The formula is:

_Wi = _αwi + _βwi '

In the formula, _Wi represents the comprehensive weight of the i-th category data, _Wi and _Wi ' are the subjective weight and objective weight of the i-th category data, α and β are the subjective weight coefficient and objective weight coefficient, respectively;

b5) According to the comprehensive weight of various data and the quality of single indicators, the comprehensive power quality of the monitoring point is calculated. The formula is:

表示第i个监测点的有效监测周期中第j类数据的单指标质量，n为数据种类数量。Where _Pi represents the comprehensive power quality of the ith monitoring point,

It represents the single indicator quality of the j-th type of data in the effective monitoring period of the ith monitoring point, and n is the number of data types.

Optionally, the method further includes: determining the power quality level of the corresponding monitoring point according to the comprehensive power quality of the monitoring point, wherein the power quality level is divided into five levels: excellent, good, medium, poor and extremely poor. _{P i} ≥ 90 indicates that the power quality is excellent, 80 ≤ _{P i} < 90 indicates that the power quality is good, 70 ≤ _{P i} < 80 indicates that the power quality is medium, 60 ≤ _{P i} < 70 indicates that the power quality is poor, and P _i < 60 indicates that the power quality is extremely poor.

Beneficial Effects

The present invention determines the data integrity reference value based on the total amount of theoretical data within the monitoring period, identifies the validity of the collected data, and then obtains the effective monitoring period of each monitoring point, which can avoid the power quality results not being close to the actual level due to data loss. At the same time, the present invention adopts a subjective and objective comprehensive weighting method to weight each data indicator, which not only reflects the importance of expert opinions to the power quality analysis results, but also highlights the overall differences between the data indicator objects, avoiding the use of a single weighting method to make the power quality indicator weight too subjective or too objective. Using the method of the present invention, the power quality of different monitoring points can be obtained and the power quality level can be further divided. For each monitoring point at the same power quality level, the power quality and power quality influencing factors can be further evaluated, making the analysis results more refined, and providing data reference for power line construction and maintenance, with high applicability and promotion.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic flow diagram of the method of the present invention;

FIG2 is a schematic flow chart of a specific embodiment of the method of the present invention;

FIG3 is a schematic diagram showing a flow chart of determining the validity of data in the method of the present invention;

FIG4 is a schematic diagram showing a flow chart of determining subjective weights using the sequence relationship method in the method of the present invention;

FIG5 is a schematic diagram showing a flow chart of determining objective weights using the grade separation method in the method of the present invention;

FIG6 is a schematic diagram of a method for determining a comprehensive weight in the method of the present invention;

FIG7 is a schematic diagram showing the calculation of the comprehensive quality of monitoring points by various indicator data and weights in the method of the present invention;

FIG8 is a schematic diagram showing power quality results of multiple monitoring points obtained by analyzing the method of the present invention in an application example.

DETAILED DESCRIPTION

The invention is further described below with reference to the accompanying drawings and specific embodiments.

Referring to FIG1 , the power quality analysis method of a substation of the present invention comprises:

According to the preset power quality index system, obtain the power quality data of the substation within at least one monitoring cycle at the monitoring point;

According to the quantity of each type of data obtained in each monitoring period, the validity of the substation power quality data in each monitoring period is judged to obtain the effective monitoring period of the monitoring point;

Calculate the average value of various data within the effective monitoring period of the monitoring point;

Calculate the single indicator quality of each type of data based on the average value of each type of data and the quality characteristics of the data;

Calculate the objective weight of each type of data based on the average value of each type of data;

Obtaining importance information of various types of data, and calculating subjective weights of various types of data based on the importance information;

The comprehensive power quality of the monitoring point is calculated based on the subjective weight, objective weight and single indicator quality of various types of data.

When applied, monitoring points can be set up on different line sides of the same voltage level, and the comprehensive power quality reference value of each monitoring point can be calculated through the above scheme, which is convenient for further analysis of power quality influencing factors for monitoring points with equivalent comprehensive power quality levels, providing a basis for improving power quality.

Example

This embodiment is described by taking a 10 kV power grid as an example. Referring to FIG2 , this embodiment specifically involves the following steps.

1. Determine the power quality index system and obtain index data

According to relevant national standards and experience, this embodiment selects voltage deviation, voltage total harmonics, three-phase unbalance, long-term voltage flicker and power factor to form a power quality index system.

For different voltage levels or lines, the types of indicators that constitute the power quality indicator system can be adjusted according to actual conditions.

2. Determination of the validity of indicator data

The power quality index data can be obtained at the monitoring point through the power quality monitoring device. In this embodiment, 7 monitoring points are selected for illustration. Referring to FIG3 , the monitoring period and the sampling time interval of various data in the monitoring period are first determined. In this embodiment, the monitoring period of each monitoring point is one week, and data is collected every 15 minutes. Then, the number of standard data N collected for each indicator in the monitoring period is:

The actual number of data received for each indicator at the 7 monitoring points is shown in Table 1:

Table 1

则数据完整度阈值即M＝336。From formula (1), it can be calculated that the number of standard data collected for each indicator during the monitoring period is 672. According to the data integrity threshold formula:

Then the data integrity threshold is M=336.

According to the data integrity threshold, the validity of the data of each monitoring point and each monitoring period is judged. As shown in Figure 3, for each type of data in each monitoring period, it is judged whether the number of data exceeds the data integrity threshold 336. If it exceeds, 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 it is an invalid monitoring period.

In the case where the monitoring cycle obtained at any monitoring point is an invalid monitoring cycle, it is necessary to re-acquire new monitoring cycle data and perform validity determination until a valid monitoring cycle for each monitoring point is obtained, as shown in Table 2 below.

Table 2 Number of data collected by 7 monitoring points during the effective monitoring period

3. Calculate the single indicator quality of each indicator data within the effective monitoring period of each monitoring point

First, for each monitoring point, the average value of each indicator data is calculated according to the indicator data average value calculation formula:

表示第j类指标数据的平均值，N_j为有效监测周期内第j类指标数据实际收集的数据个数，

表示第j类指标数据的第g个数据值。In formula (2),

represents the average value of the j-th indicator data, _Nj is the number of data actually collected for the j-th indicator data within the effective monitoring period,

Represents the g-th data value of the j-th category indicator data.

So far, the average values of various indicator data of the 7 monitoring points can be obtained, as shown in Table 3 below:

Table 3

According to the power quality characteristics of various indicator data, data indicator types can be divided into three categories: maximum indicator, minimum indicator, and interval indicator: Maximum indicator means that the larger the indicator value, the better the indicator; minimum indicator means that the smaller the indicator value, the better the indicator; interval indicator means that the indicator value is within a certain interval and the indicator is best.

Under normal circumstances, the power quality index of the power grid should meet the national standard limit or the user-defined standard threshold, which means that the index is qualified. In order to quantify the power quality index, the present invention establishes a single index quality function of power quality to intuitively reflect the quality of each index data.

The calculation formula of the quality scoring function of the extremely large and extremely small indicators is as follows:

Where _Kj and _kj represent the ideal value and threshold of the jth type of extremely large or extremely small indicator data, 60 points represent the passing score, and 40 represents the benchmark change score.

The calculation formula of interval index scoring function is as follows:

Where q _j and Q _j represent the left and right boundaries of the ideal interval of the j-th interval indicator data, and f _j and F _j represent the left and right boundaries of the threshold of the j-th interval indicator data.

Voltage deviation is an interval indicator. For a 10 kV power grid, according to national standards and user requirements, the voltage deviation threshold is +7% to -10%, and the ideal interval is 0%-2%. Therefore, in (4) above, _qi = 0, _Qi = 2%, _fi = -10%, and _Fi = 7%.

The voltage total harmonic rate is an extremely small index. According to national standards and user requirements, the voltage deviation threshold is 4%, and the ideal value is 0%. Therefore, in the above formula (3), K _j = 0%, k _j = 4%.

The three-phase unbalance is an extremely small index. According to national standards and user requirements, the voltage deviation threshold is 4%, and the ideal value is 0%. Therefore, in the above formula (3), K _j = 0%, k _j = 4%.

Long-term voltage flicker is an extremely small indicator. According to national standards and user requirements, the voltage deviation threshold is 1, and the ideal value is 0. Therefore, in the above formula (3), K _j = 0, k _j = 1.

The power factor is a very large index. According to national standards and user requirements, the voltage deviation threshold is 0.80, and the ideal value is 1. Therefore, in the above formula (3), K _j = 1, k _j = 0.8.

The single indicator quality scores of various indicator data at the 7 monitoring points are as follows in Table 4:

Table 4

4. Calculate the objective weight of each type of data based on the average value of each type of data

This embodiment adopts the method of spreading out the grades to determine the objective weights of various indicator data, including the following steps:

(4.1) The average values of various types of data within the effective monitoring period of multiple monitoring points are standardized to obtain a standardized matrix A ₁ composed of standardized data of multiple types of data within the effective monitoring period of multiple monitoring points, wherein the standardized formula is:

表示第j个监测点的有效监测周期内第i类数据的平均值，

表示

的标准化结果，m为监测点数量，n为所考虑的变电站电能质量数据的种类数量，则标准化矩阵A₁表示为：In formula (5),

It represents the average value of the i-th type of data in the effective monitoring period of the j-th monitoring point,

express

The standardized result of , m is the number of monitoring points, n is the number of types of substation power quality data considered, then the standardized matrix _A1 is expressed as:

According to the above formula, in this embodiment, the average value of 5 types of indicator data at 7 monitoring points is used to construct a comprehensive power quality evaluation matrix as follows:

After preprocessing the index data in A, the standardized matrix A ₁ is obtained, which is expressed as:

(4.2) The eigenvector corresponding to the maximum eigenvalue of the positive definite matrix H = A ₁ ^T A ₁ is calculated and normalized to obtain the objective weight vector w' = {w ₁ ', w ₂ ', ... w _n '} of the multiple categories of index data. In this embodiment, it can be calculated that: w' = {0.213, 0.179, 0.222, 0.197, 0.189}, where w ₁ ', w ₂ ', ... w _n ' are respectively used as the objective weights of the five categories of index data. The objective weights of the five categories of index data are voltage deviation 0.213, voltage total harmonic rate 0.179, three-phase unbalance 0.222, long-term voltage flicker 0.197, and power factor 0.189.

The grading method adopted in this embodiment weakens the weight of the total harmonic rate of voltage and the power factor, strengthens the weight of long-term voltage flicker, emphasizes objective facts, and can make the power quality analysis results closer to the actual level.

5. Calculation of subjective weights of various indicator data

This embodiment adopts the order relationship method to determine the subjective weights of various indicator data, and the specific steps are as follows.

(5.1) Determine the index sequence relationship based on the opinions of experts or decision makers:

In this embodiment, the order relationship of the five types of indicator data is:

(5.2) Determine the relative importance R _k between adjacent indicator data in the order relationship.

The rational judgment value of the relative importance degree R _k between adjacent indicators can be determined by referring to the following Table 5:

Table 5

指标比

指标明显重要，则R2＝1.4，

指标与

指标一样重要，则R3＝1，

指标比

指标稍微重要，则R4＝1.2，

指标比

指标稍微重要，则R5＝1.2From this, the relative importance of the five indicators can be further determined as follows:

Index ratio

The indicator is obviously important, then R2＝1.4,

Indicators and

The indicators are equally important, then R3＝1,

Index ratio

The indicator is slightly important, then R4＝1.2,

Index ratio

The indicator is slightly important, then R5＝1.2

(5.3) Calculate subjective weights based on relative importance

对应的指标数据类型的主观权重，专家评价指标

与

的重要程度之比R_k的理性判断式为：Expressed in w _k

Corresponding subjective weights of indicator data types, expert evaluation indicators

and

The rational judgment formula of the importance ratio R _k is:

即本实施例中专家意见认为最不重要的指标数据类型的主观权重w₅，为：Therefore, we can first determine

That is, the subjective weight w ₅ of the indicator data type that experts consider to be the least important in this embodiment is:

According to the variant form of formula (8) w _k-1 =R _k w _k , the subjective weights of other types of indicator data can be further calculated as: w 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.

So far, the subjective weights of the five types of indicator data are: voltage deviation 0.284, voltage total harmonic rate 0.203, three-phase unbalance 0.169, long-term voltage flicker 0.141, and power factor 0.203.

6. Calculate the comprehensive weight of various indicator data based on subjective weight and objective weight

This embodiment adopts a subjective and objective comprehensive weighting method to weight each data indicator, avoiding the use of a single weighting method that makes the weight of the power quality indicator too subjective or too objective.

The calculation formula for the comprehensive weight of the i-th category indicator data is:

_Wi = _αwi + _βwi ′ (10)

In formula (10), α is the subjective weight coefficient of the indicator, and β is the objective weight coefficient of the indicator. The values of the two coefficients can be adjusted as needed. In this embodiment, both are set to 0.5. The subjective weight, objective weight and comprehensive weight of the five types of indicator data calculated according to formula (10) are shown in Table 6 below:

Table 6

index Subjective weight Objective weight Comprehensive weight Voltage deviation 0.284 0.213 0.249 Voltage total harmonic rate 0.203 0.179 0.191 Three-phase unbalance 0.169 0.222 0.195 Long-term voltage flicker 0.141 0.197 0.169 Power Factor 0.203 0.189 0.196

7. Calculate the comprehensive power quality of each monitoring point

According to the single index quality shown in Table 3 calculated in the third part and the comprehensive weight shown in Table 6 calculated in the sixth part, the present invention can calculate the comprehensive power quality of each monitoring point, and the formula is:

表示第i个监测点的有效监测周期中第j类数据的单指标质量，n为数据种类数量，在本实施例中即为5，根据式(11)，7个监测点的综合电能质量结果参考图8。Where _Pi represents the comprehensive power quality of the ith monitoring point,

It represents the single indicator quality of the jth type of data in the effective monitoring period of the ith monitoring point, n is the number of data types, which is 5 in this embodiment. According to formula (11), the comprehensive power quality results of the 7 monitoring points refer to Figure 8.

8. Evaluate the power quality level of each monitoring point

After the comprehensive power quality of each monitoring point is calculated in the seventh part, the present embodiment can further analyze the comprehensive power quality results, such as analyzing the data of monitoring points with similar comprehensive power quality scores to infer the factors affecting the power quality. The present embodiment also evaluates the power quality level of each monitoring point based on the comprehensive power quality results.

In the grade assessment, the power quality grade is divided into 5 grades: excellent, good, medium, poor and extremely poor. _{Pi ≥} 90 indicates that the power quality is excellent, 80 ≤ _Pi < 90 indicates that the power quality is good, 70 ≤ _Pi < 80 indicates that the power quality is medium, 60 ≤ _Pi < 70 indicates that the power quality is poor, and _Pi < 60 indicates that the power quality is extremely poor.

In Figure 8, the total power quality assessment score of the 6th monitoring point is 92.97 points, which is an excellent level. The assessment scores of monitoring points 2 and 5 are 81.26 and 84.23 respectively, which are good levels. Monitoring points 3 and 7 are medium levels, monitoring point 4 is a poor level, and monitoring point 1 is a very poor level.

At the same level, the evaluation score of monitoring point 2 is higher than that of monitoring point 5, and the evaluation score of monitoring point 7 is higher than that of monitoring point 3, which means that at the same level, the power quality levels of monitoring points 2 and 7 are better. This can be used to further analyze the factors affecting the power quality difference between the two monitoring points, further refine the power quality analysis, provide a data basis for line maintenance, and ensure safe and stable operation of the line.

Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

The present application is described with reference to the flowchart and/or block diagram of the method, device (system) and computer program product according to the embodiment of the present application. It should be understood that each process and/or box in the flowchart and/or block diagram, and the combination of the process and/or box in the flowchart and/or block diagram can be realized by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for realizing the function specified in one process or multiple processes in the flowchart and/or one box or multiple boxes in the block diagram.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device that implements the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection of the present invention.

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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