A sensitivity evaluation method for factors affecting the action behavior of a stabilization and control system

Abstract

The present invention provides a sensitivity evaluation method for factors affecting the action behavior of a stabilization control system, a gray iterative analysis method of introducing a set value iteration method into a gray correlation analysis method, and evaluating the sensitivity of the action behavior of the stabilization control system to various influencing factors during a fault, including: determining a reference series reflecting the system behavior characteristics as the system cutting capacity, determining a comparison series as the data sequence of various influencing factors, normalizing the reference series and the comparison series, calculating the weight coefficient of each influencing factor by the set value iteration method, calculating the correlation of each influencing factor by the gray correlation analysis method, and obtaining the sensitivity coefficient of each influencing factor and the action behavior according to the weight coefficient and the correlation. The higher the sensitivity coefficient, the greater the influence on the action behavior of the stabilization control system. The present invention provides an effective evaluation method for the analysis of factors affecting the action behavior of the stabilization control system, and provides a reasonable basis for optimizing the operation of the power grid and improving the reliability of the stabilization control system.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid safety and stability control, and in particular to a sensitivity evaluation method for factors affecting the action behavior of a stability control system. Background Art

[0002] With the continuous expansion of the scale of power grids and the increasing complexity of their operation modes, the safe and stable operation of power systems is becoming increasingly important. As the "second line of defense" for the safety and stability of power systems, the results of the action of safety and stability control systems directly affect whether the power system can maintain safe and stable operation in the event of a fault. At present, the research on stability control systems is mostly focused on the formulation and implementation of stability control strategies, and the research on the influencing factors of the action behavior of stability control systems is mostly focused on the causes and avoidance measures, but lacks a quantifiable and reliable evaluation of the influencing factors. Summary of the invention

[0003] The technical problem to be solved by the present invention is: to propose a sensitivity evaluation method for the influencing factors of the action behavior of the stabilization control system, and to use the grey iterative analysis method to evaluate the sensitivity of the action behavior of the stabilization control system to various influencing factors when a fault occurs, so as to provide a reasonable basis for the formulation and implementation of the stabilization control strategy and improve the operational reliability of the stabilization control system.

[0004] The present invention adopts the following technical solution to solve the above technical problems: a sensitivity evaluation method for factors affecting the action behavior of a control system, comprising:

[0005] Determine the reference series reflecting the system behavior characteristics as the system cutting capacity;

[0006] Determine the comparison series as the data sequence of each influencing factor;

[0007] The reference series and the comparison series are normalized, and the weight coefficient of each influencing factor is calculated by the set value iteration method;

[0008] The correlation degree of each influencing factor is calculated by grey correlation analysis method;

[0009] The sensitivity coefficient of each influencing factor and the action behavior is obtained according to the weight coefficient and the correlation degree. The higher the sensitivity coefficient, the greater the influence of the influencing factor on the action behavior of the stability control system.

[0010] Furthermore, the influencing factors include fault type, fault clearing time, whole group action time, one switch execution time, and system voltage level.

[0011] Furthermore, the reference sequence and the comparison sequence are normalized respectively using the averaging or initialization method.

[0012] Furthermore, the weight coefficient of each influencing factor is calculated by the set value iteration method, including:

[0013] Step 1: Establish a fuzzy set of influencing factors;

[0014] Step 2: Experts randomly select subsets from the fuzzy set of influencing factors:

[0015] A q ={A 1q ,A 2q ,…,A zq}(q=1,2,…,Q)

[0016] set up:

[0017]

[0018] make Calculating element weights The weight coefficient is calculated by normalization

[0019] Step 3: Repeat Step 2 for s times;

[0020] Step 4: Calculate the weight coefficient after s iterations Furthermore, the correlation degree of each influencing factor is calculated by grey correlation analysis, including:

[0021] Calculate the reference sequence y(k) and the comparison sequence x i (k) Absolute value of the difference Δ i (k)=|y(k)-x i (k)|;

[0022] According to the correlation coefficient formula, calculate the correlation coefficient ξ of each influencing factor i (k) and the correlation r i , where the calculation formula of the correlation coefficient is:

[0023]

[0024] Among them, ρ∈(0,1) is called the resolution coefficient, k=1,2,∧,n;

[0025] Correlation r i The calculation formula is:

[0026]

[0027] Furthermore, the sensitivity coefficients of the influencing factors and the action behaviors are obtained according to the weight coefficients and the correlation degree, specifically including: multiplying the weight coefficients and the correlation degree to obtain the sensitivity coefficients of the influencing factors and the action behaviors.

[0028] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects: there are many factors affecting the action behavior of the stabilization control system, and they are of different dimensions and units. In actual engineering applications, it is difficult to compare the importance of each influencing factor, but to avoid its influence as much as possible. This method is unsustainable. The present invention discloses a sensitivity evaluation method for factors affecting the action behavior of the stabilization control system, which quantifies the influence of the factors on the action behavior of the stabilization control system, and ranks their influence in a standardized way, which can significantly improve the effectiveness of the action behavior of the stabilization control system in actual engineering applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic diagram of factors affecting the action behavior of the stability control system;

[0030] Figure 2 The present invention is a flowchart of a method for evaluating the sensitivity of factors affecting the action behavior of a stabilization control system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] like Figure 1 As shown, in the stabilization control system, the fault type, fault clearing time, whole group action time, one switch execution time, system voltage level, etc. will affect the action behavior result of the stabilization control system. Therefore, the embodiment of the present invention determines the factors affecting the action behavior result of the stabilization control system to include: fault type, fault clearing time, whole group action time, one switch execution time, system voltage level. For different stabilization control systems, their grid structures, fixed value settings, action delays, transmission rates, and transmission delays are all different. Therefore, the influence of each influencing factor on the action behavior of the stabilization control system is different.

[0033] Grey Relation Analysis (GRA) is a multi-factor statistical analysis method that seeks the correlation between the influencing factors in the system and can be used to analyze the relative magnitude of the influence of related factors on the behavior of a system. Grey relational analysis requires the establishment of parent sequences and child sequences.

[0034] The parent sequence is a reference sequence that reflects the behavior characteristics of the system. In the stable control system, it is the action behavior result of the stable control system, that is, the system switching capacity, which is expressed as follows:

[0035] Y={Y(k)}

[0036] Among them, k=1,2,∧,n.

[0037] Subsequence is a data sequence composed of factors that affect system behavior, called comparison series. In the stabilization control system, it is the influencing factors of the stabilization control action behavior, including fault type, fault clearing time, whole group action time, one-time switch execution time, system voltage level, etc., which are expressed as follows:

[0038] X i ={X i (k)}

[0039] Among them, k=1,2,∧,n,i=1,2,∧,m.

[0040] The calculation formula of correlation coefficient is as follows:

[0041]

[0042] Among them, ρ∈(0,1) is called the resolution coefficient. When ρ<0.5463, the resolution is best, and ρ=0.5 is usually taken.

[0043] Note Δ i (k)=|y(k)-x i (k)|, the calculation formula of the correlation coefficient can be simplified as:

[0044]

[0045] The correlation coefficient is too scattered to be easy to compare as a whole, so the correlation coefficient r is used. i express:

[0046]

[0047] The set-valued iteration method is a statistical iteration method. After multiple statistical iterations, it can effectively remove the influence of subjective judgment on the evaluation results. First, the set of influencing factors is determined to be a fuzzy set. Q experts randomly select z elements that are considered important in the set. The factor subset selected by the qth expert is recorded as A. q ={A 1q ,A 2q ,…,A zq}(q=1,2,…,Q), let:

[0048]

[0049] make The weight of each element is: After normalization, we have: For greater accuracy, each expert is asked to perform iterative calculations s times without memory and calculate the arithmetic mean, which can weaken the influence of subjective judgment and obtain the weight coefficient of each influencing factor.

[0050] The weight coefficient ω obtained by set-valued iteration method i And the correlation degree r obtained by grey correlation analysis i Comprehensive calculation can get the sensitivity coefficient η of each influencing factor i =ω i ·r i The higher the sensitivity coefficient, the greater the influence of the influencing factor on the action behavior of the stability control system, and the more obvious the influence of the factor on the action result of the stability control system.

[0051] like Figure 2 As shown, the present invention implements a method for evaluating the sensitivity of factors affecting the action behavior of a control system, and the specific steps are as follows:

[0052] Step 1: Determine the action behavior results of the stability control system (reflecting the system behavior characteristics), that is, the system cutting capacity is the reference sequence Y = {Y(k)}, and each influencing factor is the comparison sequence X i ={X i (k)}, where k = 1, 2, ∧, n, i = 1, 2, ∧, m.

[0053] Step 2: Use averaging or initialization methods to normalize the reference series and comparison series respectively.

[0054] The factors that affect the action behavior of the stability control system are of different nature and dimensions and cannot be directly compared. In order to reduce the difference in data types, the grey correlation analysis normalizes the data of all reference series and comparison series, unifies the influencing factors into an approximate range, and focuses on their changes and trends. They are expressed as follows:

[0055]

[0056] Among them, Y N and X iN is the normalized reference value.

[0057] There are two methods for normalization: initialization and averaging. The reference value used in initialization is the first value of each set of comparison series, while the reference value used in averaging is the average value of each set of comparison series. By dividing by the reference value, both methods can organize the data to near "1", which is convenient for subsequent correlation analysis.

[0058] Step 3: Calculate the absolute value of the difference between the reference sequence and the comparison sequence Δ i (k)=|y(k)-x i (k)|.

[0059] Step 4: Calculate the correlation coefficient ξ of each influencing factor according to the correlation coefficient formula i (k) and the correlation r i .

[0060] Step 5: Establish a fuzzy set of influencing factors.

[0061] Step 6: Experts randomly select subset A from the fuzzy set of influencing factors q ={A 1q ,A 2q ,…,A zq}(q=1,2,…,Q), let:

[0062]

[0063] make Calculating element weights The weight coefficient is calculated by normalization

[0064] Step 7: Repeat Step 6 for s times;

[0065] Step 8: Calculate the weight coefficient after s iterations

[0066] Step 9: Calculate the sensitivity coefficient η of each influencing factor i =ω i ·r i , sort the sensitivity coefficients of each influencing factor to determine its influence. The larger the sensitivity coefficient, the greater its influence on the system action behavior results.

[0067] The following is a specific description through a set of test data. The maximum capacity of the system unit is 5820MW. Under the condition of keeping other conditions unchanged, the fault type, fault clearing time, whole group action time, primary switch execution time and system voltage level are simulated. Detailed data are shown in Table 1:

[0068] Table 1

[0069]

[0070] Since the fault type is not a specific numerical value, a standardized value is set in advance.

[0071] Determine the corresponding relationship between the reference sequence and the comparison sequence as shown in Table 2:

[0072] Table 2

[0073] System action behavior (cut-off capacity) Y(k) Fault type <![CDATA[X1(k)]]> Fault clearing time <![CDATA[X2(k)]]> Whole set action time <![CDATA[X3(k)]]> System voltage level <![CDATA[X4(k)]]> Switch execution time <![CDATA[X5(k)]]>

[0074] Determine the fuzzy set A = {X1,X2,X3,X4,X5}.

[0075] According to the above steps, the data are processed by using grey correlation analysis method and set value iteration method to calculate the correlation degree r of each influencing factor. i , weight coefficient ω i And sensitivity coefficients, as shown in Table 3:

[0076] Table 3

[0077]

[0078] By sorting the above sensitivity coefficients in order of size η3>η5>η2>η1>η4, we can obtain the sensitivity ranking of the action behavior results of the stability control system to the above five influencing factors. It can be seen that the whole group action time has the greatest impact on the action behavior results of the stability control system, followed by the switch execution time and the fault clearing time, and the system voltage level has the least impact on the stability control system.

[0079] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A sensitivity evaluation method for factors affecting the action behavior of a stabilization control system, characterized in that: Includes steps: Determine the reference series reflecting the system behavior characteristics as the system cutting capacity; Determine the comparison series as the data sequence of each influencing factor; The reference series and the comparison series are normalized, and the weight coefficient of each influencing factor is calculated by the set value iteration method; The correlation degree of each influencing factor is calculated by grey correlation analysis method; The sensitivity coefficients of each influencing factor and the action behavior are obtained according to the weight coefficient and the correlation degree. The higher the sensitivity coefficient, the greater the influence of the influencing factor on the action behavior of the stability control system. The influencing factors include fault type, fault clearing time, whole group action time, one-time switch execution time, and system voltage level; Use averaging or initialization methods to normalize the reference series and comparison series respectively; The weight coefficient of each influencing factor is calculated by the set value iteration method, including: Step 1: Establish a fuzzy set of influencing factors; Step 2: Q experts randomly select a subset from the fuzzy set of influencing factors: A q ={A 1q ,A 2q ,…,A zq }Where q=1,2,…,Q, z is the number of elements in the subset; set up: make Where i = 1, 2, ..., m, calculate the element weight The weight coefficient is calculated by normalization Step 3: Repeat Step 2 for s times; Step 4: Calculate the weight coefficient after s iterations The correlation degree of each influencing factor is calculated by grey correlation analysis, including: Calculate the reference sequence y(k) and the comparison sequence x i (k) Absolute value of the difference Δ i (k)=|y(k)-x i (k)|; Calculate the correlation coefficient ξ of each influencing factor according to the correlation coefficient formula i (k) and the correlation r i , where the calculation formula of the correlation coefficient is: Among them, ρ∈(0,1) is called the resolution coefficient, k=1,2,…,n; Correlation r i The calculation formula is:

2. The sensitivity evaluation method of the factors affecting the action behavior of the stabilization control system according to claim 1 is characterized by: Obtaining the sensitivity coefficient of each influencing factor and the action behavior according to the weight coefficient and the correlation degree specifically includes: multiplying the weight coefficient by the correlation degree to obtain the sensitivity coefficient of each influencing factor and the action behavior.

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