Method for analyzing influence factors of voltage quality of traditional power distribution network

Abstract

The invention discloses an influence factor analysis method for voltage quality of a traditional power distribution network. The method comprises the following steps: acquiring historical voltage quality core parameter data and numerical value type data of associated influence factors related to the historical voltage quality core parameter data; establishing a grey correlation degree analysis influence factor; performing conversion on the basic data, and performing dimension elimination; calculating correlation coefficients and grey correlation degrees of the voltage quality core parameters and different correlation influence factors, and selecting main correlation influence factors as analyzed attributes; establishing an association rule base of different association influence factors by using a Hotspot association algorithm; correlation influence factor data are known, the Hotspot correlation rule base is matched through correlation influence factor states, and the occurrence probability of different correlation influence factors influencing the voltage quality can be obtained; according to the method, various correlation factors influencing the voltage quality can be analyzed, the correlation rule base is established, weak links of operation of the power distribution network can be quickly found, an operation line of the power distribution network can be perfected conveniently, and the voltage quality of the power distribution network is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of distribution network voltage quality analysis, and in particular relates to a method for analyzing factors affecting the voltage quality of a traditional distribution network. Background Art

[0002] Voltage quality is one of the important indicators for measuring power quality in power systems. It characterizes the deviation between the actual voltage and the ideal voltage in the power system. This indicator can effectively show whether the electric energy delivered by the power supply company to the power user terminal through the transmission and distribution process is qualified. The level of voltage quality is largely related to the safety, economy and efficiency of power supply. The demand of power users for high voltage quality is also increasing. Many automated and intelligent equipment require higher voltage quality to meet operating conditions.

[0003] The distribution network is the basic network responsible for distributing electric energy in the power system and is the core link in the entire power system that is directly connected to power users. This means that the planning, management and operation of the distribution network have a direct impact on the voltage quality of users.

[0004] Since distribution lines are exposed to the external environment for a long time and usually have characteristics such as many points and long lines, the distribution network is prone to failure during operation, affecting the reliability of power supply. At the same time, the load situation of the distribution network is relatively complicated, and there are problems such as unreasonable transformer nodes, excessive line losses, and uneven load distribution, which can easily cause voltage fluctuations, voltage deviations, harmonics, etc. in the distribution network. These problems will affect the stable operation of users' electrical equipment.

[0005] Therefore, in order to solve the above problems, it is necessary to develop an analysis method for the influencing factors of the voltage quality of the traditional distribution network. Summary of the invention

[0006] The purpose of the present invention is to overcome the shortcomings of the prior art and to provide a method for analyzing factors affecting the voltage quality of a traditional distribution network. The method analyzes the various related factors affecting the voltage quality and establishes an associated rule base, which can quickly discover the weak links in the operation of the distribution network, facilitate the improvement of the distribution network operation lines, and improve the voltage quality of the power grid.

[0007] The object of the present invention is achieved by: a method for analyzing factors affecting voltage quality of a traditional power distribution network, comprising the following steps:

[0008] (1) Obtaining historical voltage quality core parameter data and numerical data of related influencing factors;

[0009] (2) Establishing the grey correlation analysis influencing factors; taking the voltage quality core parameter index as the basic reference sequence, marked as x0(k), and marking the associated influencing factors as x i(k), the value range of i is 1, 2, 3, 4...n, and the value range of k is 1, 2, 3, 4...n;

[0010] (3) Transform the basic data, eliminate the dimension, obtain a new transformation sequence, and achieve data comparability;

[0011] (4) Calculate the correlation coefficient and grey correlation degree between the voltage quality core parameters and different correlation influencing factors, and select the main correlation influencing factors of the voltage quality core parameters as the analyzed attributes;

[0012] (5) Using the Hotspot association algorithm, establish an association rule base for different association influencing factors, construct an association rule tree for each association influencing factor, and form a Hotspot global association rule base;

[0013] (6) Given the data of associated influencing factors, the occurrence probabilities of the associated influencing factors that affect the voltage quality can be obtained by matching the associated influencing factor states with the above-mentioned Hotspot association rule library.

[0014] Furthermore, the voltage quality core parameter data in step (1) includes voltage deviation parameters, three-phase voltage imbalance parameters and voltage fluctuation and flicker parameters.

[0015] Furthermore, the voltage deviation parameter is expressed as ΔU, and its calculation formula is expressed as:

[0016]

[0017] , where U X is the actual voltage, U N is the rated voltage;

[0018] The three-phase voltage unbalance parameter uses the voltage unbalance degree ε U To characterize, that is, the degree of deviation of the three-phase voltage, the calculation formula is expressed as:

[0019]

[0020] , where U megative is the amplitude of the negative sequence component after the three-phase voltage symmetrical component is decomposed, U positive is the amplitude of the positive sequence component after decomposition of the symmetrical components of the three-phase voltage;

[0021] In the voltage fluctuation and flicker parameters, the voltage fluctuation value is the two extreme values ​​of the voltage effective value U max and U min The difference between the rated voltage U N The percentage represents its relative percentage value, and its calculation formula is expressed as:

[0022]

[0023] , where U max and U min They are two adjacent extreme voltages of the power frequency voltage amplitude modulation wave; specifically, when the voltage change rate is greater than 0.2% per second, it is regarded as voltage fluctuation, otherwise it is regarded as voltage deviation.

[0024] Furthermore, the associated influencing factors in step (1) include load increase or decrease, increase or decrease in line resistance and inductance due to changes in line length and cross-sectional area, power supply capacity, and high impedance faults caused by grounding or insulation faults in the power grid.

[0025] Furthermore, in step (3), the basic data is dimensionally eliminated using the initial value method to obtain a new transformation sequence.

[0026] Furthermore, the correlation coefficient in step (4) is calculated according to the formula:

[0027]

[0028] It is defined as the grey correlation coefficient, where Δ min is |x0(k)-x i The minimum value of (k)|,Δ max is |x0(k)-x i (k)|, ρ is the resolution coefficient, ρ is 0.5, based on the above formula, by calculating the difference sequence, the grey relational coefficient sequence is further calculated;

[0029] Grey relational degree is based on the formula:

[0030]

[0031] Combined with the grey relational coefficient, the grey relational degree is obtained.

[0032] Furthermore, in the creation of the HotSpot association rule base in step (5), the association rules are measured by support and confidence, wherein the support and confidence calculation formulas are as follows:

[0033]

[0034] ; In the formula, support(A→B) is the support of type B samples in interval A, confidence(A→B) is the confidence of interval A for type B samples, lift(A→B) is the lift, count(A∩B) is the number of type B samples in interval A, count(A) is the total number of type A samples, count(B) is the total number of type B samples, and count(T) is the total number of samples.

[0035] Furthermore, the creation of the association rule base in step (5) includes the following steps:

[0036] ① Data normalization processing; ② Voltage quality core parameter type selection, and calculation of the correlation coefficient and gray correlation degree between the voltage quality core parameter type and all associated influencing factors; ③ Select the main analysis factors affecting the voltage quality core parameter type; ④ Create a root node and the child nodes under the node; ⑤ Search the data, calculate the support of the factor sequence and load it into the candidate child node list; ⑥ Search the candidate node list, calculate the lifting degree of the factor sequence and load it into the child node list; ⑦ After searching the child node list, a global association rule base for the current voltage quality core parameter type is formed; ⑧ After all voltage quality core parameter types are searched, a HotSpot global association rule base for all voltage quality core parameter types is formed.

[0037] Due to the adoption of the above technical scheme, the beneficial effects of the present invention are as follows: by adopting the grey correlation theory to select the associated influencing factors affecting the voltage quality, and by using the Hotspot correlation algorithm to establish an association rule library of the core parameter types characterizing the voltage quality and the associated influencing factors, the weak links in the operation of the distribution network can be quickly discovered, which is convenient for the subsequent coordinated control of the voltage quality of the distribution network, and the dynamic regulation and stabilization of the voltage can be achieved, the problem of excessively high or low voltage can be solved, the impact of voltage fluctuations on user equipment can be reduced, and the voltage quality of the power grid can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a flow chart of the present invention.

[0039] Figure 2 It is a flowchart of the creation process of the Hotspot association rule base in the present invention. DETAILED DESCRIPTION

[0040] The technical solution of the present invention is further specifically described below through embodiments and in conjunction with the accompanying drawings.

[0041] like Figure 1 , Figure 2 As shown, a method for analyzing factors affecting voltage quality of a traditional distribution network includes the following steps:

[0042] Step (1) obtains historical voltage quality core parameter data and numerical data of related influencing factors; specifically, the voltage quality core parameter data includes voltage deviation parameters, three-phase voltage imbalance parameters and voltage fluctuation and flicker parameters; the related influencing factors include load increase or decrease, increase or decrease of line resistance and inductance due to changes in line length and cross-sectional area, power supply capacity, and high impedance faults caused by grounding or insulation faults in the power grid.

[0043] Specifically, the voltage deviation parameter is expressed as ΔU, and its calculation formula is expressed as:

[0044]

[0045] , where U X is the actual voltage, U N is the rated voltage.

[0046] Specifically, the three-phase voltage unbalance parameter uses the voltage unbalance degree ε U To characterize, that is, the degree of deviation of the three-phase voltage, the calculation formula is expressed as:

[0047]

[0048] , where U megative is the amplitude of the negative sequence component after the three-phase voltage symmetrical component is decomposed, U positive It is the amplitude of the positive sequence component after the symmetrical components of the three-phase voltage are decomposed.

[0049] Specifically, among the voltage fluctuation and flicker parameters, the voltage fluctuation value is the two extreme values ​​of the voltage effective value U max and U min The difference between the rated voltage U N The percentage represents its relative percentage value, and its calculation formula is expressed as:

[0050]

[0051] , where U max and U min They are two adjacent extreme voltages of the power frequency voltage amplitude modulation wave; specifically, when the voltage change rate is greater than 0.2% per second, it is regarded as voltage fluctuation, otherwise it is regarded as voltage deviation.

[0052] Step (2): Establish the grey correlation analysis influencing factors; take the voltage quality core parameter index as the basic reference sequence, marked as x0(k), and mark the associated influencing factors as x i (k), the value range of i is 1, 2, 3, 4…n, and the value range of k is 1, 2, 3, 4…n.

[0053] Step (3): transform the basic data, eliminate the dimension and obtain a new transformation sequence to achieve data comparability; specifically, the basic data can be eliminated by using the initial value method to obtain a new transformation sequence.

[0054] Step (4): Calculate the correlation coefficient and grey correlation degree between the voltage quality core parameter and different correlation influencing factors, and select the main correlation influencing factors of the voltage quality core parameter as the analyzed attribute; specifically, the correlation coefficient is calculated according to the formula:

[0055]

[0056] It is defined as the grey correlation coefficient, where Δ min is |x0(k)-x i The minimum value of (k)|,Δ max is |x0(k)-x i (k)|, ρ is the resolution coefficient, ρ is 0.5, based on the above formula, by calculating the difference sequence, the grey relational coefficient sequence is further calculated;

[0057] Grey relational degree is based on the formula:

[0058]

[0059] Combined with the grey relational coefficient, the grey relational degree is obtained.

[0060] Step (5): using the Hotspot association algorithm to establish an association rule base for different association influencing factors, construct an association rule tree for each association influencing factor, and form a Hotspot global association rule base; specifically, in the creation of the Hotspot association rule base, its association rules are measured by support and confidence, wherein the support and confidence calculation formulas are as follows:

[0061]

[0062] ; In the formula, support(A→B) is the support of type B samples in interval A, confidence(A→B) is the confidence of interval A for type B samples, lift(A→B) is the lift, count(A∩B) is the number of type B samples in interval A, count(A) is the total number of type A samples, count(B) is the total number of type B samples, and count(T) is the total number of samples.

[0063] The creation of the association rule base includes the following steps:

[0064] ① Data normalization processing; ② Voltage quality core parameter type selection, and calculation of the correlation coefficient and gray correlation degree between the voltage quality core parameter type and all associated influencing factors; ③ Select the main analysis factors affecting the voltage quality core parameter type; ④ Create a root node and the child nodes under the node; ⑤ Search the data, calculate the support of the factor sequence and load it into the candidate child node list; ⑥ Search the candidate node list, calculate the lifting degree of the factor sequence and load it into the child node list; ⑦ After searching the child node list, a global association rule base for the current voltage quality core parameter type is formed; ⑧ After all voltage quality core parameter types are searched, a HotSpot global association rule base for all voltage quality core parameter types is formed.

[0065] Step (6): Given the associated influencing factor data, the occurrence probability of the associated influencing factors that affect the voltage quality can be obtained by matching the associated influencing factor states with the above-mentioned Hotspot association rule library.

[0066] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the specific implementation modes of the present invention can still be modified or replaced by equivalents, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A method for analyzing factors affecting voltage quality of a traditional distribution network, characterized by: The following steps are involved: (1) Obtaining historical voltage quality core parameter data and numerical data of related influencing factors; (2) Establishing the grey correlation analysis influencing factors; taking the voltage quality core parameter index as the basic reference sequence, marked as x0(k), and marking the associated influencing factors as x i (k), the value range of i is 1, 2, 3, 4...n, and the value range of k is 1, 2, 3, 4...n; (3) Transform the basic data, eliminate the dimension, obtain a new transformation sequence, and achieve data comparability; (4) Calculate the correlation coefficient and grey correlation degree between the voltage quality core parameters and different correlation influencing factors, and select the main correlation influencing factors of the voltage quality core parameters as the analyzed attributes; (5) Using the Hotspot association algorithm, establish an association rule base for different association influencing factors, construct an association rule tree for each association influencing factor, and form a Hotspot global association rule base; (6) Given the data of associated influencing factors, the occurrence probabilities of the associated influencing factors that affect the voltage quality can be obtained by matching the associated influencing factor states with the above-mentioned Hotspot association rule library.

2. The method for analyzing factors affecting voltage quality of a traditional power distribution network according to claim 1, characterized in that: The voltage quality core parameter data in step (1) includes voltage deviation parameters, three-phase voltage imbalance parameters, and voltage fluctuation and flicker parameters.

3. The method for analyzing factors affecting voltage quality of a traditional power distribution network according to claim 2, characterized in that: The voltage deviation parameter is expressed as ΔU, and its calculation formula is expressed as: Where U X is the actual voltage, U N is the rated voltage; The three-phase voltage unbalance parameter uses the voltage unbalance degree ε U To characterize, that is, the degree of deviation of the three-phase voltage, the calculation formula is expressed as: Where U megative is the amplitude of the negative sequence component after the three-phase voltage symmetrical component is decomposed, U positive is the amplitude of the positive sequence component after decomposition of the symmetrical components of the three-phase voltage; In the voltage fluctuation and flicker parameters, the voltage fluctuation value is the two extreme values ​​of the voltage effective value U max and U min The difference between the rated voltage U N The percentage represents its relative percentage value, and its calculation formula is expressed as: Where U max and U min They are two adjacent extreme voltages of the power frequency voltage amplitude modulation wave; specifically, when the voltage change rate is greater than 0.2% per second, it is regarded as voltage fluctuation, otherwise it is regarded as voltage deviation.

4. The method for analyzing factors affecting voltage quality of a traditional power distribution network according to claim 1, characterized in that: The associated influencing factors in step (1) include load increase or decrease, increase or decrease in line resistance and inductance due to changes in line length and cross-sectional area, power supply capacity, and high impedance faults caused by grounding or insulation faults in the power grid.

5. The method for analyzing factors affecting voltage quality of a traditional power distribution network according to claim 1, characterized in that: In the step (3), the basic data is dimensionally eliminated using the initial value method to obtain a new transformation sequence.

6. The method for analyzing factors affecting voltage quality of a traditional power distribution network according to claim 1, characterized in that: The correlation coefficient in step (4) is calculated according to the formula: It is defined as the grey correlation coefficient, where Δ min is |x0(k)-x i The minimum value of (k)|,Δ max is |x0(k)-x i (k)|, ρ is the resolution coefficient, ρ is 0.5, based on the above formula, by calculating the difference sequence, the grey relational coefficient sequence is further calculated; Grey relational degree is based on the formula: Combined with the grey relational coefficient, the grey relational degree is obtained.

7. The method for analyzing factors affecting voltage quality of a traditional power distribution network according to claim 1, characterized in that: In the creation of the HotSpot association rule library in step (5), the association rules are measured by support and confidence, wherein the support and confidence calculation formulas are as follows: Where support(A→B) is the support of type B samples in interval A, confidence(A→B) is the confidence of interval A for type B samples, lift(A→B) is the lift, count(A∩B) is the number of type B samples in interval A, count(A) is the total number of type A samples, count(B) is the total number of type B samples, and count(T) is the total number of samples.

8. The method for analyzing factors affecting voltage quality of a traditional power distribution network according to claim 1, characterized in that: The creation of the association rule base in step (5) includes the following steps: ① Data normalization processing; ② Voltage quality core parameter type selection, and calculation of the correlation coefficient and gray correlation degree between the voltage quality core parameter type and all associated influencing factors; ③ Select the main analysis factors affecting the voltage quality core parameter type; ④ Create a root node and the child nodes under the node; ⑤ Search the data, calculate the support of the factor sequence and load it into the candidate child node list; ⑥ Search the candidate node list, calculate the lifting degree of the factor sequence and load it into the child node list; ⑦ After searching the child node list, a global association rule base for the current voltage quality core parameter type is formed; ⑧ After all voltage quality core parameter types are searched, a HotSpot global association rule base for all voltage quality core parameter types is formed.

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