CN108197822B - Power distribution network fault line selection adaptability evaluation decision method - Google Patents

Power distribution network fault line selection adaptability evaluation decision method Download PDF

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CN108197822B
CN108197822B CN201810070383.8A CN201810070383A CN108197822B CN 108197822 B CN108197822 B CN 108197822B CN 201810070383 A CN201810070383 A CN 201810070383A CN 108197822 B CN108197822 B CN 108197822B
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樊三军
熊小伏
王波
欧阳金鑫
丁健
马格土
徐修远
龙毅
郝越峰
杨明波
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Abstract

The invention discloses a power distribution network fault line selection adaptability evaluation decision method, which comprises the following steps of 1, establishing various influence factors aiming at a power distribution network fault line selection mode; step 2, carrying out dimensionless treatment on the influence factors under different dimensions, and forming a uniform forward evaluation index aiming at different route selection modes; step 3, normalizing the evaluation indexes of each line selection mode, wherein the maximum value of the indexes forms an ideal sample, and the minimum value of the indexes forms a negative ideal sample; step 4, making an evaluation decision according to the sorting of the line selection indexes; the problem that factors such as the distribution network structure, the outgoing line type and the load condition of the distribution network have different degrees of influence on different line selection methods when the line is selected aiming at the fault of the distribution network is solved; due to the lack of research on comprehensive evaluation of a line selection method, an optimal line selection mode cannot be selected according to a specific structure of a power distribution network, accurate selection of a fault line cannot be guaranteed, and the safe and reliable operation of the power distribution network is threatened.

Description

Power distribution network fault line selection adaptability evaluation decision method
Technical Field
The invention belongs to the technical field of power system analysis, and particularly relates to a power distribution network fault line selection adaptability evaluation decision method.
Background
With the further development of domestic power distribution systems, the structure of a power distribution network is increasingly complex, and the power distribution network is characterized in that 10kV bus outgoing lines of the power distribution network are gradually increased, the distribution of fault zero-sequence currents is increasingly complex, and the types of faults are various. The line selection method capable of quickly and accurately selecting the fault line when a fault occurs is an important guarantee for safe and reliable operation of the power distribution network.
At present, a commonly used line selection method for a power distribution network comprises the following steps: zero-sequence current amplitude comparison method, zero-sequence current active component method, signal injection method, wavelet analysis method, first half wave method, etc. Theoretically, all the line selection methods can accurately select a fault line, but in actual operation, the effect of each line selection method is influenced by different factors. Line selection methods based on steady-state components of fault signals, such as zero-sequence current amplitude comparison method, zero-sequence power method, zero-sequence current active component method and other line selection methods, are greatly influenced by factors such as line length, unbalanced current, inductive current flowing through a grounding arc suppression coil and the like; the method for selecting the line based on the fault component transient characteristic quantity, such as a first half wave method, a wavelet analysis method and the like, is greatly influenced by factors such as fault occurrence time, transition resistance, power electronic equipment and the like; and fault line selection based on information fusion technology such as neural network, fuzzy theory, rough set theory and the like. The method is complex, the realization difficulty is high, and the practical effect is still to be verified. Obviously, for an actual low-current grounding system, factors such as a distribution network structure, an outgoing line type, a load condition and the like of the system have different degrees of influence on different line selection methods. Due to the lack of research on comprehensive evaluation of a line selection method, an optimal line selection mode cannot be selected according to a specific structure of a power distribution network, and accurate selection of a fault line cannot be guaranteed, so that safe and reliable operation of the power distribution network is threatened.
The invention content is as follows:
the technical problems to be solved by the invention are as follows: the method is used for solving the problem that factors such as the structure of the power distribution network, the type of outgoing lines, the load condition and the like of an actual low-current grounding system have different degrees of influence on different line selection methods when the power distribution network fault line selection is carried out in the prior art; due to the lack of research on comprehensive evaluation of a line selection method, an optimal line selection mode cannot be selected according to a specific structure of a power distribution network, accurate selection of a fault line cannot be guaranteed, and the safe and reliable operation of the power distribution network is threatened.
The technical scheme of the invention is as follows:
a power distribution network fault line selection adaptability evaluation decision method comprises the following steps:
step 1, establishing various influence factors aiming at a fault line selection mode of a power distribution network;
step 2, carrying out dimensionless treatment on the influence factors under different dimensions, and forming a uniform forward evaluation index aiming at different route selection modes;
and step 3: normalizing the evaluation indexes of each line selection mode, wherein the maximum value of the indexes forms an ideal sample, and the minimum value of the indexes forms a negative ideal sample;
and 4, making an evaluation decision according to the sorting of the line selection indexes.
The influence factors in the step 1 comprise: influence factors of a zero-sequence current amplitude comparison method, influence factors of a zero-sequence current active component method, influence factors of a signal injection method and influence factors of a wavelet analysis method;
the influence factors of the zero-sequence current amplitude comparison method comprise:
a11＝σ2in the formula: a is11The first influencing factor, sigma, of the zero-sequence current amplitude comparison method2The variance of the outgoing line length of the feeder line is shown;
a12epsilon, wherein: a is12The second influence factor is zero sequence current amplitude comparison method, and epsilon is the cabling rate of the outgoing line;
a13＝Rgin the formula: a is13A third influencing factor, R, of the zero-sequence current amplitude comparison methodgA grounding transition resistor when the fault occurs;
Figure BDA0001557981740000031
in the formula: a is14A fourth influencing factor of the zero-sequence current amplitude comparison method, IdcIs the direct current component of the current transformer, and S is the capacity of the current transformer;
the influence factors of the zero-sequence current active component method comprise:
Figure BDA0001557981740000032
in the formula: a is21The first influencing factor, r, of the zero-sequence current active component method0Is a zero sequence resistance, x, of the feeder line per unit length0Zero sequence reactance in unit length of the feeder line;
Figure BDA0001557981740000033
in the formula: a is22A second influencing factor, R, for zero-sequence current active component methodLThe content of resistance in the grounding arc suppression coil is shown, omega is the power frequency rotation angular velocity, and L is the inductance of the grounding arc suppression coil;
the influence factors of the signal injection method include:
a31f, wherein: a is31Is the first influence factor of the signal injection method, and f is the injection signal frequency;
a32s, wherein: a is32The second influence factor is a signal injection method, and S is the capacity of the current transformer;
a33＝Rgin the formula: a is33Third influencing factor of signal injection method, RgA grounding transition resistor when a fault occurs;
the influence factors of the wavelet analysis method include:
a41＝Kφin the formula: a is41Is the first influencing factor of wavelet analysis, KφIs the voltage higher harmonic content.
The method for forming the uniform forward evaluation index by carrying out dimensionless treatment on the influence factors under different dimensions and aiming at different route selection modes comprises the following steps:
step 2.1, the method for calculating the zero sequence current amplitude ratio method evaluation index comprises the following steps:
and (3) de-dimensionalizing the influence factors to obtain:
Figure BDA0001557981740000041
Figure BDA0001557981740000042
Figure BDA0001557981740000043
the influence factors are normalized to obtain:
Figure BDA0001557981740000044
Figure BDA0001557981740000045
Figure BDA0001557981740000046
Figure BDA0001557981740000051
the evaluation index of the zero-sequence current amplitude comparison method is represented as:
μ1＝a″11a″12a″13a″14；
step 2.2, the method for calculating the zero sequence current active component method evaluation index comprises the following steps of removing dimension of the influence factors to obtain:
Figure BDA0001557981740000052
Figure BDA0001557981740000053
the influence factor is normalized to:
a″21＝a′21
a″22＝a′22
the evaluation index of the zero sequence current active component method is expressed as:
μ2＝a″21a″22；
step 2.3, the method for calculating the evaluation index of the signal injection method comprises the following steps:
de-dimensionalizing the influence factors to obtain:
Figure BDA0001557981740000054
Figure BDA0001557981740000061
Figure BDA0001557981740000062
the influence factor is normalized to:
a″31＝a′31
a″32＝a′32
Figure BDA0001557981740000063
the evaluation index of the zero sequence current active component method is expressed as:
μ3＝a″31a″32a″33；
step 2.4, the method for calculating the evaluation index of the wavelet analysis method comprises the following steps:
only one influence factor of the wavelet analysis method is negatively correlated with the line selection accuracy, and the influence factor is directly converted into a positive direction and then expressed as an evaluation index of the wavelet analysis method, and the evaluation index of the wavelet analysis method is expressed as:
Figure BDA0001557981740000064
in the formula: a is11Amplitude comparison method for zero sequence currentA first impact factor; a is12The second influence factor is a zero-sequence current amplitude comparison method; a is13A third influencing factor of the zero sequence current amplitude comparison method, a14The fourth influence factor is a zero-sequence current amplitude comparison method; a is1'1The first influence factor of zero sequence current amplitude comparison after the dimensionalization of the amount is removed; a is1'2A second influence factor of the zero sequence current amplitude comparison method after the dimensionalization of the amount is removed; a is1'3A third influence factor of the zero sequence current amplitude comparison method after the dimensionalization of the amount is removed; a is1'4A fourth influence factor of the zero-sequence current amplitude comparison method after the dimensionalization of the vector is removed; a ″)11The first influence factor of the zero sequence current amplitude comparison method after the positive conversion; a ″)12The second influence factor is a zero sequence current amplitude comparison method after the positive conversion; a ″)13The third influence factor is the zero sequence current amplitude comparison method after the positive conversion; a ″14The fourth influence factor is the zero sequence current amplitude comparison method after the positive conversion; a is21The first influence factor of the zero sequence current active component method is provided; a is22A second influence factor of the zero-sequence current active component method; a'21The first influence factor of zero sequence current active component method after the dimensionalization of the amount is removed; a'22A second influence factor of zero sequence current active component method after the dimensionalization of the zero sequence current; a ″)21The first influence factor of the zero sequence current after the positive conversion by the active component method; a ″)22A second influence factor of the zero sequence current after the positive direction transformation by an active component method; a is31The first influencing factor is a signal injection method; a is a32A second influencing factor for signal injection; a is33A third influencing factor of the signal injection method; a'31A first influence factor for the signal injection method after the dimensionalization of the de-dimensionalization; a'32A second influence factor for the signal injection method after the dimensionalization of the de-dimensionalization; a'33A third influence factor for the signal injection method after the dimensionalization of the de-dimensionalization; a ″)31A first influence factor of a signal injection method after the forward quantization; a ″32A second influencing factor of the signal injection method after the forward transformation; a ″)33A third influence factor of the signal injection method after the forward transformation; a is41Is the first influencing factor of wavelet analysis method; mu.s1Line selection evaluation finger for zero-sequence current amplitude comparison methodMarking; mu.s2Selecting a line evaluation index for the zero-sequence current active component method; mu.s3Selecting a line evaluation index for a signal injection method; mu.s4Selecting a line evaluation index for a wavelet analysis method; mu.smaxRepresents μ1,μ2,μ3,μ4Maximum value of (1); mu.sminRepresents μ1,μ2,μ3,μ4Minimum value of (1).
Step 3, the method for normalizing the evaluation indexes of each line selection mode, wherein the maximum value of the indexes forms an ideal sample, and the minimum value of the indexes forms a negative ideal sample comprises the following steps:
step 3.1, normalizing the line selection evaluation index according to the following formula
Figure BDA0001557981740000081
Figure BDA0001557981740000082
Figure BDA0001557981740000083
Figure BDA0001557981740000084
Step 3.2, forming ideal sample by index maximum value
Figure BDA0001557981740000085
Index minimum constitutes a negative ideal sample
Figure BDA0001557981740000086
In the formula:
Figure BDA0001557981740000087
for zero sequence current amplitude comparison method line selection evaluation after normalizationIndexes;
Figure BDA0001557981740000088
selecting line evaluation indexes for the normalized zero sequence current active component method;
Figure BDA0001557981740000089
selecting a line evaluation index for the normalized signal injection method;
Figure BDA00015579817400000810
the line selection evaluation index of the normalized wavelet analysis method is provided.
The invention has the beneficial effects that:
the invention aims at four common line selection methods, including a zero sequence current amplitude comparison method, a zero sequence current active component method, a signal injection method and a wavelet analysis method, and respectively carries out quantitative analysis on respective influence factors of the four common line selection methods, provides the influence factors of the line selection modes, and forms an evaluation index of each line selection mode through scaling-off and forward processing. And sorting the evaluation indexes according to the absolute values through normalization processing, and performing sorting evaluation on the advantages and the disadvantages of the line selection method to obtain the optimal line selection mode under the evaluation decision method.
According to the invention, the influence factor of each line selection mode is obtained by carrying out quantitative analysis on the adaptability of the power distribution network by different line selection modes, the evaluation indexes of the different line selection modes are obtained by means of normalization and forward transformation, the quality of the line selection method is subjected to sequencing evaluation, and a scheme is provided for the decision of the line selection mode in the specific power distribution network.
The invention makes up the defects of the current power distribution network line selection adaptability analysis and decision method; the method considers specific influence factors of a zero-sequence current amplitude comparison method, a zero-sequence current active component method, a signal injection method and a wavelet analysis method, provides an evaluation index capable of evaluating the adaptability of each line selection method to the net rack by combining the structural parameters of the power distribution network, and performs sequencing evaluation on the advantages and the disadvantages of the line selection methods by processing means such as normalization, forward transformation and the like on the index to obtain the optimal line selection mode under the evaluation decision method, and can make reference for decision of selecting the line selection mode in the power distribution network; the problem that factors such as the distribution network structure, the outgoing line type, the load condition and the like of an actual low-current grounding system have different degrees of influence on different line selection methods when the line selection is performed aiming at the power distribution network fault in the prior art is solved; due to the lack of research on comprehensive evaluation of a line selection method, an optimal line selection mode cannot be selected according to a specific structure of a power distribution network, accurate selection of a fault line cannot be guaranteed, and the safe and reliable operation of the power distribution network is threatened.
The specific implementation mode is as follows:
a power distribution network fault line selection adaptability evaluation decision method comprises the steps of firstly forming line selection mode influence factors according to influence factors of a line selection mode, and analyzing positive and negative correlation of the influence factors and line selection accuracy. And (3) carrying out dimension removal treatment on the influence factor elements under different dimensions, and forming a unified forward evaluation index aiming at different line selection modes, including a zero-sequence current amplitude comparison method, a zero-sequence current active component method, a wavelet analysis method and a signal injection method. And then, carrying out normalization processing on the evaluation indexes of each line selection mode, wherein the maximum value of the indexes forms an ideal sample, the minimum value of the indexes forms a negative ideal sample, and an evaluation decision is made according to the sequence of the line selection indexes. The influence factor and the evaluation index of the line selection mode are obtained by the following method:
(1) influence factors of the zero-sequence current amplitude comparison method are as follows:
when the length difference of the feeder lines is large, if a short circuit has a certain phase grounding, the zero sequence current difference of the circuit and the zero sequence current of the non-fault long circuit is not large, and the success rate of line selection is low, so that the variance of the lengths of the feeder lines is taken as one of the influence factors of the zero sequence current amplitude comparison method, and is expressed as:
a11＝σ2 (1)
the influence factor is inversely related to the line selection accuracy;
when the cabling rate of the outgoing line is higher, the capacitive current effect is more obvious in the fault period, the zero-sequence current of the fault line is more obvious compared with the non-fault line, and the line selection accuracy is higher. Therefore, the cabling ratio of the feeder line is taken as one of the influence factors of the zero-sequence current amplitude-comparison method, and is expressed as:
a12＝ε (2)
the influence factor is positively correlated with the accuracy of line selection;
when the fault point is grounded through a high resistance, the voltage deviation of the neutral point is very small, the zero sequence current is small, and the line selection success rate is low. Therefore, the transition resistance in the fault is taken as one of the influence factors of the zero-sequence current amplitude comparison method, and is expressed as:
a13＝Rg (3)
the influence factor is inversely related to the line selection accuracy.
The current transformer saturation causes unbalanced current, and zero sequence current measured values of all lines can be changed, so that line selection misjudgment is caused. Therefore, the saturation degree of the current transformer is taken as one of the influence factors of the zero sequence current amplitude comparison method, and is expressed as follows:
Figure BDA0001557981740000101
the influence factor is inversely related to the line selection accuracy.
(2) Zero sequence current active component method
Because the circuit has conductance to the ground and the arc suppression coil has a certain resistance, the zero sequence current active component can be used for selecting the line.
The ratio of the zero-sequence resistance and the zero-sequence reactance of the feeder line unit length is taken as one of the influence factors of the zero-sequence active power component method, and is expressed as follows:
Figure BDA0001557981740000111
the larger the value is, the larger the content of the active component of the zero sequence current is. The influence factor is positively correlated with the accuracy of line selection.
The resistance component in the arc suppression coil is taken as one of the influence factors of the zero sequence active component method, and is expressed as follows:
Figure BDA0001557981740000112
the larger the value is, the larger the effect of the arc suppression coil on increasing the active component of the zero sequence current is. The influence factor is positively correlated with the accuracy of line selection.
(3) Signal injection method
After the system has a ground fault, a signal with a specific frequency can be injected into the neutral point of the system. The signal flows back to the neutral point of the system along the fault line through the grounding point, and the fault line can be selected by detecting whether the current of each line contains the special frequency signal or not.
Since the influence of the arc suppression coil compensation on the faulty line selection can be eliminated as the impedance of the arc suppression coil to the high-frequency signal increases as the frequency of the injection signal increases, the frequency of the injection signal is expressed as one of the influence factors of the line selection method:
a31＝f (7)
the influence factor is positively correlated with the accuracy of line selection.
When the capacity of the current transformer is larger, the strength of the signal which can be injected is stronger, so that the capacity of the current transformer is taken as one of the influence factors of the line selection method and is expressed as follows:
a32＝S (8)
the influence factor is positively correlated with the accuracy of line selection.
When the fault point is grounded through high resistance, the zero sequence impedance of the fault line is increased, and the injected signal may leak into the non-fault line to cause line selection failure. Therefore, the transition resistance is taken as one of the influence factors of the line selection method, and is expressed as:
a33＝Rg (9)
the influence factor is inversely related to the line selection accuracy.
(4) Wavelet analysis method
The method comprises the steps of firstly collecting fault signals in a system, then carrying out wavelet transformation on the collected signals according to a wavelet singularity detection theory, obtaining a modulus maximum value point, and determining a fault line by taking the size and the direction of the obtained modulus maximum value of the zero sequence current of each line as criteria.
When the higher the harmonic content in the distribution network, the greater the influence on the accuracy of the wavelet transform. Therefore, the harmonic content is taken as one of the influence factors of the line selection method, and is expressed as:
a41＝Kφ (10)
the influence factor is inversely related to the line selection accuracy.
The meaning of each parameter in the above formula is:
σ2: variance of outgoing line length of the feeder line;
epsilon: the outgoing cable rate;
Rg: a ground transition resistance in case of a fault;
Idc: a current transformer direct current component;
s: current transformer capacity;
r0: zero-sequence resistance of a feeder line in unit length;
x0: zero sequence reactance of a feeder line unit length;
RL: resistance content in the grounding arc suppression coil;
ω: power frequency rotation angular velocity;
l: a ground arc suppression coil inductance;
f: injecting a signal frequency;
Kφ: voltage higher harmonic content;
aij: selecting a line influence factor;
1. inputting specific structure and operation parameters of power distribution network
The operating parameters of the power distribution network include: the system comprises a feeder line length, a wire outgoing cabling rate, a current transformer direct current component, a current transformer capacity, an injection signal frequency, a feeder line unit length zero sequence resistance, a feeder line unit length zero sequence reactance, a resistance content in a grounding arc suppression coil, a power frequency rotation angular velocity, a grounding arc suppression coil inductance and an injection signal frequency.
2. Inputting the fault position of the power distribution system: the fault position can be any position of the network
3. Inputting fault parameters of a power distribution system: including fault-to-ground transition resistance, and voltage higher harmonic content at fault.
4. And obtaining each influence factor of a zero-sequence current amplitude comparison method, a zero-sequence current active component method, an injection signal method and a wavelet analysis method according to the operation parameters and the fault characteristics of the system.
5. And carrying out the derogation and normalization treatment on the influence factors of each line selection mode, and carrying out the product to obtain the evaluation index of each line selection mode.
(1) And (3) calculating a zero-sequence current amplitude comparison method evaluation index:
the impact factors are de-dimensionalized to obtain:
Figure BDA0001557981740000131
Figure BDA0001557981740000132
Figure BDA0001557981740000133
Figure BDA0001557981740000134
the impact factor is normalized to obtain:
Figure BDA0001557981740000141
a″12＝a′12 (16)
Figure BDA0001557981740000142
Figure BDA0001557981740000143
the evaluation index of the zero-sequence current amplitude-to-amplitude method can be expressed as:
μ1＝a″11a″12a″13a″14 (19)
(2) calculating the evaluation index of the zero-sequence current active component method:
the impact factors are de-dimensionalized to obtain:
Figure BDA0001557981740000144
Figure BDA0001557981740000145
the impact factor is normalized to obtain:
a″21＝a′21 (22)
a″22＝a′22 (23)
the evaluation index of the zero sequence current active component method can be expressed as:
μ2＝a″21a″22 (24)
(3) and (3) calculating an evaluation index by a signal injection method:
the impact factors are de-dimensionalized to obtain:
Figure BDA0001557981740000151
Figure BDA0001557981740000152
Figure BDA0001557981740000153
the impact factor is normalized to obtain:
a″31＝a′31 (28)
a″32＝a′32 (29)
Figure BDA0001557981740000154
the evaluation index of the zero sequence current active component method can be expressed as:
μ3＝a″31a″32a″33 (31)
(4) calculating evaluation indexes by a wavelet analysis method:
because only one influence factor of the wavelet analysis method is negatively correlated with the line selection accuracy, the influence factor can be directly converted into a positive direction and then expressed as an evaluation index of the wavelet analysis method. The evaluation index of the wavelet analysis method can be expressed as:
Figure BDA0001557981740000161
6. and (4) normalizing evaluation indexes of each line selection method, and sequencing the evaluation indexes according to the absolute values of the evaluation indexes, wherein the maximum value of the indexes forms an ideal sample, the minimum value of the indexes forms a negative ideal sample, and an evaluation decision of the line selection mode of the power distribution network is obtained.
Firstly, the normalization processing of the line selection evaluation indexes comprises the following steps:
Figure BDA0001557981740000162
Figure BDA0001557981740000163
Figure BDA0001557981740000164
Figure BDA0001557981740000165
the optimal line selection method under the evaluation decision method is
Figure BDA0001557981740000166
The best line selection mode is
Figure BDA0001557981740000167
The corresponding line selection mode.
The meaning of each parameter in the above formula is:
σ2: variance of outgoing line length of the feeder line;
epsilon: the outgoing cable rate;
Rg: a ground transition resistance in case of a fault;
Idc: a current transformer direct current component;
s: current transformer capacity;
r0: zero-sequence resistance of a feeder line in unit length;
x0: zero sequence reactance of a feeder line unit length;
RL: resistance content in the grounding arc suppression coil;
ω: power frequency rotation angular velocity;
l: a ground arc suppression coil inductance;
f: injecting a signal frequency;
Kφ: voltage higher harmonic content;
a11: a first influence factor of a zero-sequence current amplitude comparison method;
a12: a second influence factor of the zero-sequence current amplitude comparison method;
a13: a third influence factor of the zero-sequence current amplitude comparison method;
a14: a fourth influence factor of the zero-sequence current amplitude comparison method;
a′11: removing the first influence factor of zero sequence current amplitude comparison after the dimensionalization;
a′12: removing a second influence factor of the zero-sequence current amplitude comparison method after the dimensionalization;
a′13: removing a third influence factor of the zero-sequence current amplitude comparison method after the dimensionalization;
a′14: after the dimensioning of the amount is removedA fourth influence factor of the zero-sequence current amplitude comparison method;
a″11: a first influence factor of the positive zero sequence current amplitude comparison method;
a″12: a second influence factor of the positive zero sequence current amplitude comparison method;
a″13: a third influence factor of the zero sequence current amplitude comparison method after the positive conversion;
a″14: a fourth influence factor of the zero sequence current amplitude comparison method after the positive conversion;
a21: a first influence factor of a zero-sequence current active component method;
a22: a second influence factor of the zero-sequence current active component method;
a'21: removing a first influence factor of zero sequence current active component method after dimensionalization;
a'22: removing a second influence factor of the zero-sequence current active component method after the dimensionalization;
a″21: a first influence factor of the zero sequence current after the positive conversion by a power component method;
a″22: a second influence factor of the zero sequence current after positive quantization by a power component method;
a31: a first influencing factor of a signal injection method;
a32: a second influencing factor of the signal injection method;
a33: a third influencing factor of the signal injection method;
a'31: removing a first influence factor of a signal injection method after dimensionalization;
a'32: removing a second influence factor of the signal injection method after the dimensionalization;
a'33: removing a third influence factor of the signal injection method after the dimensionalization;
a”31: a first influence factor of a signal injection method after forward quantization;
a″32: a second influence factor of the signal injection method after the forward transformation;
a″33: a third influence factor of a signal injection method after forward localization;
a41: a first influence factor of a wavelet analysis method;
μ1: line selection evaluation indexes by a zero-sequence current amplitude comparison method;
μ2: line selection evaluation indexes of a zero-sequence current active component method;
μ3: selecting a line for evaluating an index by a signal injection method;
μ4: line selection evaluation indexes by a wavelet analysis method;
μmax：μ1,μ2,μ3,μ4maximum value of (1);
μmin：μ1,μ2,μ3,μ4minimum value of (1);
Figure BDA0001557981740000181
line selection evaluation indexes of the normalized zero sequence current amplitude comparison method;
Figure BDA0001557981740000182
line selection evaluation indexes of the normalized zero sequence current by a power component method;
Figure BDA0001557981740000183
line selection evaluation indexes of the normalized signal injection method;
Figure BDA0001557981740000184
and (5) line selection evaluation indexes of the normalized wavelet analysis method.

Claims (3)

1. A power distribution network fault line selection adaptability evaluation decision method comprises the following steps:
step 1, establishing various influence factors aiming at a fault line selection mode of a power distribution network;
step 2, carrying out dimensionless treatment on the influence factors under different dimensions, and forming a uniform forward evaluation index aiming at different route selection modes; the implementation method comprises the following steps:
step 2.1, the method for calculating the zero sequence current amplitude ratio method evaluation index comprises the following steps:
and (3) de-dimensionalizing the influence factors to obtain:
Figure FDA0003503094710000011
Figure FDA0003503094710000012
Figure FDA0003503094710000013
the influence factor is normalized to obtain:
Figure FDA0003503094710000014
a″12＝a′12
Figure FDA0003503094710000015
Figure FDA0003503094710000016
the evaluation index of the zero-sequence current amplitude comparison method is represented as:
μ1＝a″11a″12a″13a″14；
step 2.2, the method for evaluating the index by the zero sequence current active component method comprises
De-dimensionalizing the influence factors to obtain:
Figure FDA0003503094710000021
Figure FDA0003503094710000022
the influence factor is positively converted into:
a″21＝a′21
a″22＝a′22
the evaluation index of the zero sequence current active component method is expressed as:
μ2＝a″21a″22；
step 2.3, the method for calculating the evaluation index of the signal injection method comprises the following steps:
de-dimensionalizing the influence factors to obtain:
Figure FDA0003503094710000023
Figure FDA0003503094710000024
Figure FDA0003503094710000025
the influence factor is normalized to:
a″31＝a′31
a″32＝a′32
Figure FDA0003503094710000031
the evaluation index of the zero sequence current active component method is expressed as:
μ3＝a″31a″32a″33；
step 2.4, the method for calculating the evaluation index of the wavelet analysis method comprises the following steps:
only one influence factor of the wavelet analysis method is negatively correlated with the line selection accuracy, and the influence factor is directly converted into a positive direction and then expressed as an evaluation index of the wavelet analysis method, and the evaluation index of the wavelet analysis method is expressed as:
Figure FDA0003503094710000032
in the formula: a is11The first influence factor of the zero sequence current amplitude comparison method is provided; a is12A second influence factor of the zero sequence current amplitude comparison method; a is13A third influencing factor of the zero-sequence current amplitude comparison method, a14The fourth influence factor is a zero-sequence current amplitude comparison method; a'11The first influence factor of zero sequence current amplitude comparison after the dimensionalization of the amount is removed; a'12A second influence factor of the zero sequence current amplitude comparison method after the dimensionalization of the amount is removed; a'13A third influence factor of the zero sequence current amplitude comparison method after the dimensionalization of the amount is removed; a'14A fourth influence factor of the zero-sequence current amplitude comparison method after the dimensionalization of the amount is removed; a ″)11The first influence factor of the zero sequence current amplitude comparison method after the positive conversion; a ″)12The second influence factor of the zero sequence current amplitude comparison method after the positive conversion; a ″13The third influence factor is the zero sequence current amplitude comparison method after the positive conversion; a ″)14The fourth influence factor is the zero sequence current amplitude comparison method after the positive conversion; a is21The first influence factor of the zero sequence current active component method is provided; a is a22A second influence factor of the zero sequence current active component method; a'21The first influence factor of zero sequence current active component method after the dimensionalization of the amount is removed; a'22A second influence factor of zero sequence current active component method after the dimensionalization of the zero sequence current; a ″)21The first influence factor of the zero sequence current after the positive conversion by the active component method; a ″)22Second influencing factor for positive zero-sequence current after forward quantization；a31The first influencing factor is a signal injection method; a is32A second influencing factor for signal injection; a is33A third influencing factor of the signal injection method; a'31A first influence factor for the signal injection method after the dimensionalization of the de-dimensionalization; a'32A second influencing factor for signal injection after the dimensionalization of the de-measure; a'33A third influence factor for the signal injection method after the dimensionalization of the de-dimensionalization; a ″)31The first influence factor of the signal injection method after the forward quantization; a ″)32A second influencing factor of the signal injection method after the forward transformation; a ″33A third influence factor of the signal injection method after the forward transformation; a is41Is the first influencing factor of wavelet analysis method; mu.s1A zero sequence current amplitude comparison method line selection evaluation index is adopted; mu.s2Selecting a line evaluation index for the zero-sequence current active component method; mu.s3Selecting a line evaluation index for a signal injection method; mu.s4Selecting a line evaluation index for a wavelet analysis method; mu.smaxRepresents μ1,μ2,μ3,μ4Maximum value of (1); mu.sminRepresents μ1,μ2,μ3,μ4Minimum value of (1);
and step 3: normalizing the evaluation indexes of each line selection mode, wherein the maximum index value forms an ideal sample, and the minimum index value forms a negative ideal sample;
and 4, making an evaluation decision according to the sorting of the line selection indexes.
2. The power distribution network fault line selection adaptability evaluation decision method according to claim 1, characterized in that: the influence factors in the step 1 comprise: influence factors of a zero sequence current amplitude comparison method, influence factors of a zero sequence current active component method, influence factors of a signal injection method and influence factors of a wavelet analysis method;
the influence factors of the zero-sequence current amplitude comparison method comprise:
a11＝σ2in the formula: a is11The first influencing factor, sigma, of the zero-sequence current amplitude comparison method2The variance of the outgoing line length of the feeder line is shown;
a12epsilon, wherein: a is12The second influence factor is zero sequence current amplitude comparison method, and epsilon is the cabling rate of the outgoing line;
a13＝Rgin the formula: a is13A third influencing factor, R, of the zero-sequence current amplitude comparison methodgA grounding transition resistor when the fault occurs;
Figure FDA0003503094710000051
in the formula: a is14A fourth influencing factor of the zero-sequence current amplitude comparison method, IdcIs the direct current component of the current transformer, and S is the capacity of the current transformer;
the influence factors of the zero-sequence current active component method comprise:
Figure FDA0003503094710000052
in the formula: a is21The first influencing factor, r, of the zero-sequence current active component method0Is a zero sequence resistance, x, of the feeder line per unit length0Zero sequence reactance in unit length of the feeder line;
Figure FDA0003503094710000053
in the formula: a is22A second influencing factor, R, for zero-sequence current active component methodLThe content of resistance in the grounding arc suppression coil is omega, the power frequency rotation angular velocity is omega, and the inductance of the grounding arc suppression coil is L;
the influence factors of the signal injection method include:
a31f, wherein: a is31Is the first influence factor of the signal injection method, and f is the injection signal frequency;
a32(ii) S, wherein: a is32The second influence factor is a signal injection method, and S is the capacity of the current transformer;
a33＝Rgin the formula: a is33Third influencing factor of signal injection method, RgA grounding transition resistor when the fault occurs;
the influence factors of the wavelet analysis method include:
a41＝Kφin the formula: a is41Is the first influencing factor of wavelet analysis, KφIs the voltage higher harmonic content.
3. The power distribution network fault line selection adaptability evaluation decision method according to claim 1, characterized in that: step 3, the method for normalizing the evaluation indexes of each line selection mode, wherein the maximum value of the indexes forms an ideal sample, and the minimum value of the indexes forms a negative ideal sample, comprises the following steps:
step 3.1, normalizing the line selection evaluation index according to the following formula
Figure FDA0003503094710000061
Figure FDA0003503094710000062
Figure FDA0003503094710000063
Figure FDA0003503094710000064
Step 3.2, forming ideal sample by index maximum value
Figure FDA0003503094710000065
Index minimum constitutes a negative ideal sample
Figure FDA0003503094710000066
In the formula:
Figure FDA0003503094710000067
the zero sequence current amplitude comparison method line selection evaluation index after normalization;
Figure FDA0003503094710000068
selecting line evaluation indexes for the normalized zero sequence current active component method;
Figure FDA0003503094710000069
selecting a line evaluation index for the normalized signal injection method;
Figure FDA00035030947100000610
the method is a line selection evaluation index of a normalized wavelet analysis method.
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