CN116736043B - Active enhancement detection method for line fault characteristics of power system - Google Patents
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- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
- G01R31/085—Locating faults in cables, transmission lines, or networks according to type of conductors in power transmission or distribution lines, e.g. overhead
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- G01R19/16538—Indicating that current or voltage is either above or below a predetermined value or within or outside a predetermined range of values characterised by the application in AC or DC supplies
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Abstract
The invention relates to the field of power system line detection and relay protection, in particular to an active enhancement detection method for power system line fault characteristics. A detection method for actively enhancing the line fault characteristics of an electric power system comprises the steps of data acquisition, online data enhancement processing and detection, and judging whether faults occur or not through comparison with a threshold value. Wherein the data is obtained from a device in the power system capable of performing data measurements on the electrical quantity. The active enhancement processing of the online data is to detect the voltage difference between two ends of the line caused by the enhancement fault of the auxiliary signal by injecting the auxiliary signal in real time, and detect the probability distribution deviation of the voltage after the active enhancement, when the detection result meets the threshold judgment condition, the line fault is established, and the line is normal instead. The detection method can inject the enhanced signal in real time to improve the characteristics of the fault signal on the premise of not affecting the normal operation of the power system, and improves the detection speed and the detection accuracy of the fault signal.
Description
Technical Field
The invention relates to the field of power system line detection and relay protection, in particular to an active enhancement detection method for power system line fault characteristics.
Background
The micro faults of the power system are mainly divided into two major types of high-resistance faults and early faults of the cable, and the common characteristics are that the time-frequency domain characteristics are weak, the transition resistance is large and even larger than 20kΩ, so that the fault current is smaller than 10% of the load current. The main problems associated with this type of fault detection method in the prior art include: (1) The processing mode of the corresponding signal characteristics is enhanced after the faults are firstly, so that the fault report missing rate and the false report rate are increased. The existing fault characteristic enhancement technology generally responds in coordination with the action of the protection equipment, that is, the fault signal characteristic can be enhanced only after the mechanical equipment acts, and the fault without the action of the protection equipment cannot be distinguished and the characteristic can be enhanced. The precision of the protection equipment is limited, and when many micro faults occur, the protection equipment is not triggered to act, so that fault detection is omitted; in order to increase the sensitivity of the protection device to faults, a method of lowering the threshold of the device or algorithm is generally adopted. The method can often cause frequent starting of protection equipment due to noise and various interferences on a line, thereby increasing the maintenance cost of a power system and having high false detection rate. (2) The time/frequency of the electric quantity is simply used as the fault judgment feature, and the false alarm rate of detection is high. At present, a large number of distributed power supplies and energy storage devices are connected into a power system, and normal switching of the devices can generate a time-frequency domain transient process similar to a tiny fault, so that the reliability of detecting and identifying the fault by only using the time/frequency of the electric quantity is reduced, and the detection false alarm rate is increased. The reasons mentioned above lead to the fact that the fault is not easily detected in the early stages of a minor fault in the power system, which makes such faults exist for a long time and even cause more serious two-phase or three-phase short-circuit faults. The mode of initially judging that faults occur and finally judging the faults by means of the signal enhancement mode can influence the output voltage of the system during the signal enhancement.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides an active enhancement detection method for the line fault characteristics of a power system.
In order to solve the problems in the prior art, the invention discloses a detection method for actively enhancing the line fault characteristics of a power system. The method comprises the following steps:
acquiring data, namely acquiring reference historical data from equipment capable of measuring electrical quantity in an electric power system;
on-line data active enhancement, injecting an enhancement signal eta into a line data signal fed back by data measurement equipment in real time, and adding the enhancement signal alpha eta to a line voltage signal after adjusting to increase the voltage difference delta U at two ends of a line caused by faults b ；
On-line data detection, reconstructing data to obtain reconstructed historyData X 1 And reconstructed online data X 2 Calculating the history data X after reconstruction 1 And online data X 2 The statistical distance W of the reconstructed signal and the history data X 1 And online data X 2 Voltage probability distribution bias of (2);
setting a threshold value xi of voltage probability distribution deviation, selecting two different historical data, and reconstructing the two historical data to obtain reconstructed historical data X n And X n-1 Calculating the reconstructed historical data X n And X n-1 Is the statistical distance W of (2) 1 The statistical distance W 1 History data X after reconstruction of signal n And X n-1 For W 1 Average value mu is calculated for W 1 Solving a standard deviation sigma, and then xi=mu+epsilon sigma, wherein epsilon is a constant;
fault determination, wherein the fault determination index comprises a voltage probability distribution deviation threshold value xi and a determination time limit t w Reconstructed online data X 2 And reconstructed historical data X 1 The voltage deviation of (a) is W, and the time duration exceeding the threshold value xi is t d When the detection result simultaneously meets the following discrimination conditions,if the failure is judged to be small, the failure is judged to be small if either one or both of the two judging conditions are not satisfied.
Wherein, the voltage difference delta U between two ends of the line after and before the fault occurs b The calculation method of (1) is as follows: voltage U at line end b after failure bf The voltage U at the line end b before the fault occurs b Namely, the formula (1) -formula (2) gives the formula (3).
Taking a line head end node as an a point, a line tail end node as a b point, the full length of the line as l, and generating a micro fault at a position away from the a point ml, wherein the fault point is an f point, and then the equivalent circuit diagram of the line is as follows: the voltage and the current at the point a of the line head end node are U respectively a And I a ，U b For the terminal voltage, the impedance of the whole line is Z, the fault pointThe impedance of the front line is mZ, the impedance of the line after the fault point is (1-m) Z, and the ground resistance at the point f from the point a ml is R f Y is the line earth admittance, and the earth admittance at the left side and the right side of the f point is Y/2;
the voltage at line end b before the fault occurs is:
U b ＝ABU a -DI a (1)
wherein: a=1/g+ (r+sl) (1+sc/G), b=g, d=r+sl, s is laplace operator, G is line-to-ground conductance, R is line resistance, C is line-to-ground capacitance, and L is line inductance;
the voltage at line end b after the fault occurs is:
U bf ＝A f B f U a -D f I a ， (2)
wherein: u (U) bf Is the voltage at point b at the end of the line at the time of failure,D f ＝R eq +sL eq ，G a eq for the a end to ground conduction->Is a end-to-ground capacitance, R eq Is equivalent resistance L eq Is an equivalent inductance.
Then the voltage difference before and after the failure obtained by the formulas (2) - (1) is:
ΔU b ＝A f Δ B U a +Δ A BU a -Δ D I a ， (3)
in which the coefficient difference matrix is delta A ＝A f –A,Δ B ＝B f –B，Δ D ＝D f –D。
The variables involved in formula (3) are replaced by the physical quantities involved: r is R eq 、L eq 、And C beq Wherein R is eq Is equivalent resistance L eq Is equivalent inductance, < >>And->Ground conductance at a terminal and b terminal, respectively, < >>And C beq The capacitances to ground at the a-terminal and the b-terminal, respectively. The method for obtaining these physical quantities is as follows:
converting a circuit equivalent circuit diagram after faults, wherein the equivalent circuit is as follows: the voltage and the current at the point a of the line head end node are U respectively a And I a The voltage and current at the line end node b point are U respectively b And I b The impedance at the line fault is Z, Y a ,Y b Respectively representing the admittances to ground of the a end and the b end;
the laplace transform form of the parameters in the equivalent circuit of the fault system is:
Z(s)＝R eq +sL eq
wherein: r is R eq Is equivalent resistance L eq Is the equivalent inductance of the inductor,and->Ground conductance at terminal a and terminal b, respectively, ">And Cbeq is the capacitance to ground of the a end and the b end respectively;
then R is eq 、L eq 、And C beq The calculation method of (a) is as follows:
wherein ω is the power frequency angular frequency.
Substituting each obtained variable into the formula (3) to obtain the voltage difference DeltaU before and after the fault b 。
To obtain the voltage difference DeltaU before and after the fault b Then, the method needs to be enhanced, is convenient for extracting the characteristics, and further enables the characteristics to be compared with the data probability distribution deviation in the normal historical data, so that the purposes of fault detection and identification are achieved in sequence. Adding an enhancement signal eta and an adjustment coefficient alpha in a circuit, dividing the enhancement signal eta into two parts alpha eta and alpha eta by the adjustment coefficient alpha, superposing the enhancement signal with a normal voltage signal, and enabling the voltage of a point a after superposition enhancement to beThe voltage difference before and after the fault after the boost signal is introduced in equation (3) is represented by:
ΔU b ＝A f Δ B U a +Δ A B(U a +αη)-Δ D I a ， (4)
when the characteristic is actively enhanced, the system structure circuit of the enhanced signal is as follows: the signal eta is connected in parallel at the point a at the head end of the line, the eta is regulated by a regulating coefficient alpha to obtain a reinforcing signal alpha eta, the reinforcing signal alpha eta is divided into two paths, one path is a voltage signal of alpha eta, the other path is a voltage signal of-alpha eta, and the voltage of the point a after reinforcing is superposed to beStructure with rear part [ B f B]Combined with A f The transition reaches the line end point b.
Thus, after the fault occurs, the system for enhancing the signal is as follows:
then when operating normally, the system parameter matrix A f ，B f ，D f The transition to a, B, D, i.e. the signal injected at this time has no effect on the line-end voltage, whereas when a fault occurs, the voltage difference generated due to the fault is enhanced by equation (4). In particular due to B in case of failure f And B is unequal, so that two parts of voltage signals distributed by the auxiliary signal eta cannot be mutually offset by the adjusting parameter alpha, thereby realizing the enhancement of the terminal voltage of the injected auxiliary signal; r when the line is operating normally f Can be regarded as infinity, i.e. B f Equal to B, then the two parts of the auxiliary signal η divided by the adjustment parameter α can cancel each other out, thus eliminating the effect on the auxiliary signal. This is the basis for the possibility of introducing an enhanced signal in the detection method.
In equation (4), we obtain the voltage difference DeltaU before and after the fault after the enhancement b The following detection method is needed: firstly, we need to reconstruct the history data and the online data to obtain the reconstructed history data X 1 And reconstructed online data X 2 Calculating the history data X after reconstruction 1 And online data X 2 The statistical distance W of the reconstructed signal and the history data X 1 And online data X 2 Is used for the voltage probability distribution deviation of the voltage (V).
The method for reconstructing the data comprises the following steps: the detection signals are decomposed through signal decomposition algorithms such as Empirical Mode Decomposition (EMD), ensemble Empirical Mode Decomposition (EEMD), variational Mode Decomposition (VMD) or Wavelet Transform (WT) to obtain k components, and then the energy of each component is calculated as follows:
wherein: u (u) i (n) nth sample data for the ith component, E i (n) is the energy value of the obtained nth sample point of the ith component;
then, the window length is set to be psi, and the ith component energy entropy H of each window i The method comprises the following steps:
wherein: p is p n The specific gravity of the energy of the ith component in the total energy for the window;
thereby obtaining the corresponding window component weight w i Is that
The reconstructed signal X is:
importing (11) the history data and the on-line data stored in the substation, the master station and the wave recording device connected with the data measuring equipment to obtain the reconstructed history data X 1 And reconstructed online data X 2 Calculating reconstructed historical data X 1 And reconstructed online data X 2 Statistical distance W:
the statistical distance W is the voltage probability distribution deviation of the history data and the online data after reconstructing the signal, wherein X 1 And X 2 Respectively reconstructing historical data and online data of the signal,which respectively satisfy the probability distribution P 1 And P 2 I.e. X 1 ～P 1 And X 2 ～P 2 ，Π(P 1 ,P 2 ) Representing the edge as P 1 And P 2 Is a set of joint probability distributions; d (X) 1 ,X 2 ) Is the distance of the sample.
Now we find the voltage probability distribution deviation W of the reconstructed historical data and the online data, then it is only necessary to compare it with the threshold value.
Setting a threshold value xi of voltage probability distribution, selecting two different historical data, and reconstructing the two historical data to obtain reconstructed historical data X n And X n-1 Calculating the reconstructed historical data X n And X n-1 Is the statistical distance W of (2) 1 The statistical distance W 1 History data X after reconstruction of signal n And X n-1 For W 1 Average value mu is calculated for W 1 The standard deviation σ is found, then ζ=μ+εσ, (13), where ε is a constant, and ε is adjusted according to the actual situation. In practical applications, if higher sensitivity and accuracy are required, a smaller constant can be selected, and the upper limit is narrower, so that abnormal conditions are easier to find.
Setting of threshold value in addition to threshold value ζ of voltage probability distribution deviation, it is necessary to set determination time period t w The determination time limit t w Is the longest threshold value of continuous out-of-limit time in the historical data, in practice, t w The value range is as follows: 160ms-200ms. Obviously, the historical data involved are normal, fault-free historical data.
On the premise that the deviation of the threshold value and the real-time data is known, the judgment conditions are as follows:only when the voltage probability distribution deviation of the online data is larger than a threshold value xi and the continuous out-of-limit time limit t of the online data d Greater than the determination time limit t w Judging that the micro fault occurs under the condition that both conditions are met; arbitraryNeither condition is satisfied nor is it determined that a minute occurrence.
To this end, the detection is completed.
The invention has the beneficial effects that: 1. the detection accuracy is higher. Whether faults occur or not is judged without realizing, and possible micro faults can be enhanced in real time; the method has the advantages that the method does not need to cooperate with the operation of protection equipment, can directly carry out integral enhancement on online data, avoids error response of the protection equipment, increases errors of fault signals and normal signals, and ensures detection accuracy. Through field detection, the micro faults with the ground impedance below 55kΩ can be reliably detected and identified, and the detection result is not affected by the normal operation of the system. 2. The application range is wide and the existing line equipment is not required to be replaced. In the aspect of equipment use, the enhanced signal is directly overlapped with the normal signal to be output, and external equipment is not needed; the fault characteristic enhancement technology can be used on the primary side, the secondary side and the equipment inside of the system, and the application range is wide; in terms of a detection signal reconstruction algorithm, the method can be directly deployed at a master station or an intelligent terminal, and large-scale updating of extra equipment or software is not needed. The invention is suitable for the complex fault environment of the actual circuit of the power system, does not need to know the actual fault point environment, can realize detection and identification by only upgrading the existing device and software, and is cheap and efficient.
Drawings
FIG. 1 is a circuit diagram of a system upon line failure;
FIG. 2 is a diagram of an equivalent circuit of the system after Laplace transform;
FIG. 3 is a block diagram of a feature active enhancement system;
fig. 4 is a schematic flow chart of the invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for the purpose of more clearly illustrating the structure of the present invention.
The detection method comprises the steps of obtaining reference historical data, enhancing the online data, detecting the enhanced online data, comparing the enhanced online data with a threshold value, and determining whether the line is faulty or not according to a comparison result.
The reference history data may be obtained from devices in the power system capable of measuring electrical quantities, such as RTU (remote terminal unit), DTU (data transmission terminal), FTU (feeder terminal device), TTU (distribution transformer monitoring terminal), PMU (phasor measurement device), micro-PMU (micro phasor measurement device), and other IEDs (intelligent electronic devices) with data measurement function. The data may be obtained from a substation or master station to which the device is connected, or directly from a device having a wave recording function.
The active enhancement processing of the on-line data is to superimpose enhancement signals on the basis of normal voltage signals by enhancing the voltage difference between two ends of a line caused by faults, and the specific enhancement algorithm is as follows:
taking a line head end node as an a point, a line tail end node as a b point, the full length of the line as l, and generating a micro fault at a position away from the a point ml, wherein the fault point is an f point, and then the equivalent circuit diagram of the line is as follows: the voltage and the current at the point a of the line head end node are U respectively a And I a ，U b For terminal voltage, the impedance of the whole line is Z, and the ground resistance of the point f at the distance of a-point ml is R f Y is the line earth admittance, and the earth admittance at the left side and the right side of the f point is Y/2; the equivalent circuit of the circuit is shown in fig. 1.
The voltage at line end b before the fault occurs is: u (U) b ＝ABU a -DI a (1)
Wherein: u (U) a And I a The voltage and the current of the line head end are respectively A=1/G+ (R+sL) (1+sC/G), B=G, D=R+sL, U b For the terminal voltage, s is the Laplacian.
The voltage at line end b after the fault occurs is: u (U) bf ＝A f B f U a -D f I a (2)
Wherein: u (U) bf Is the voltage at point b at the end of the line at the time of failure,D f ＝R eq +sL eq ，G a eq for the a end to ground conduction->Is a end-to-ground capacitance, R eq Is equivalent resistance L eq Is an equivalent inductance.
The equivalent circuit in FIG. 1 is converted into the equivalent circuit of the fault system shown in FIG. 2, and the voltage and the current at the point a of the line head end node are U respectively a And I a The voltage and current at the line end node b point are U respectively b And I b The impedance at the line fault is Z, Y a ,Y b The admittances to ground at the a-and b-ends, respectively. To obtain A f 、B f 、D f The line impedance, the a-end to ground admittance and the b-end to ground admittance in the equivalent circuit of the fault system are subjected to Laplacian transformation to obtain a formula (13), and R in the formula (14) eq 、L eq 、And C beq Is a calculation method of (a).
The laplace transform form of the parameters in the equivalent circuit of the fault system is:
Z(s)＝R eq +sL eq
wherein: r is R eq Is equivalent resistance L eq Is the equivalent inductance of the inductor,and->Ground conductance at terminal a and terminal b, respectively, ">And the capacitances to ground at the a-terminal and the b-terminal, respectively. And R is eq 、L eq 、/>And C beq The calculation method of (a) is as follows:
wherein ω is the power frequency angular frequency.
And the voltage difference before and after the fault obtained by the formula (2) -formula (1) is as follows:
ΔU b ＝A f Δ B U a +Δ A BU a -Δ D I a (3)
in which the coefficient difference matrix is delta A ＝A f –A,Δ B ＝B f –B，Δ D ＝D f –D。
Substituting the data obtained in the formula (7) into the formula (3) can obtain Δu b 。
To strengthen the voltage difference caused by the fault, a strengthening signal eta is added in the line, the strengthening signal is overlapped with the normal voltage signal, and the regulating coefficient alpha is increased, so that the added strengthening signal is: αη, the voltage difference before and after the fault after the enhancement signal η is introduced in the formula (3) is:
ΔU b ＝A f Δ B U a +Δ A B(U a +αη)-Δ D I a (4)
thus, after the fault occurs, the system for enhancing the signal is as follows:
further, the system structure after feature active enhancement is as shown in fig. 3, and the system structure circuit for enhancing the signal is as follows: the signal eta is connected in parallel at the point a of the head end of the line, the eta is injected into the line after being regulated by the regulating coefficient alpha, namely the enhancement signal is alpha eta, and the voltage at the point a isAnd through parameter matrix [ B ] f B]A is a f The transition reaches the line end point b.
Then when operating normally, the system parameter matrix A f ，B f ，D f The transition to a, B, D, that is to say the signal injected at this time has no effect on the line-end voltage. When a fault occurs, the voltage difference generated due to the fault can be enhanced by the formula (4). Thus, the active enhancement step of the online data is completed. In case of line failure, due to B in case of failure f And B is unequal, so that two parts of voltage signals distributed by the auxiliary signal eta cannot be mutually offset by the adjusting parameter alpha, thereby realizing the enhancement of the terminal voltage of the injected auxiliary signal; r when the line is operating normally f Can be regarded as infinity, i.e. B f Equal to B, then the two parts of the auxiliary signal η divided by the adjustment parameter α can cancel each other out, thus eliminating the effect on the auxiliary signal.
After the actively enhanced voltage difference signal is obtained, we need to perform fault detection on it. Firstly, the reconstruction is needed, and the reconstruction method comprises the following steps: the detection signals are decomposed through signal decomposition algorithms such as Empirical Mode Decomposition (EMD), ensemble Empirical Mode Decomposition (EEMD), variational Mode Decomposition (VMD) or Wavelet Transform (WT) to obtain k components, and then the energy of each component is calculated as follows:
wherein: u (u) i (n) nth sample data for the ith component, E i (n) is the energy value of the obtained nth sample point of the ith component;
then, the window length is set to be psi, and the ith component energy entropy H of each window i The method comprises the following steps:
wherein: p is p n The specific gravity of the energy of the ith component in the total energy for the window;
thereby obtaining the corresponding window component weight w i Is that
The reconstructed signal X is:
according to the formulas (6) to (9), the history data and the online data are respectively substituted into the formulas to obtain the history data X after the signals are reconstructed 1 And online data X 2 X is then 1 And X 2 Is a statistical distance W:
wherein X is 1 And X 2 Respectively reconstructing historical data and online data of the signal, P 1 And P 2 Respectively X 1 And X 2 Probability distribution of pi (P) 1 ,P 2 ) Representing the edge as P 1 And P 2 Is a set of joint probability distributions; d (X) 1 ,X 2 ) Is the distance of the sample. The statistical distance W is the reconstructed signalThe voltage probability distribution deviation of the post-history data and the on-line data can be determined whether it is a fault signal by comparing whether the deviation is within a threshold range.
Now we find the voltage probability distribution deviation W of the reconstructed historical data and the online data, then it is only necessary to compare it with the threshold value.
Setting a threshold value xi of voltage probability distribution deviation, selecting two different historical data, and reconstructing the two historical data to obtain reconstructed historical data X n And X n-1 Calculating the reconstructed historical data X n And X n-1 Is the statistical distance W of (2) 1 The statistical distance W 1 History data X after reconstruction of signal n And X n-1 For W 1 Average value mu is calculated for W 1 When standard deviation σ is obtained, ζ=μ+εσ, (13) where: mu is W 1 Mean value, sigma is W 1 Standard deviation, epsilon, is a constant. Epsilon can be adjusted according to the actual situation. In practical applications, if higher sensitivity and accuracy are required, a smaller constant can be selected, and the upper limit is narrower, so that abnormal conditions are easier to find. Setting of threshold value in addition to threshold value ζ of voltage probability distribution deviation, it is necessary to set determination time period t w The determination time limit t w Is the longest threshold value of continuous out-of-limit time in the historical data, in practice, t w The value range is as follows: 160ms-200ms.
The two pieces of history data used in the threshold value ζ for determining the voltage probability distribution deviation are different from the history data used in the online data detection step. The two pieces of history data when determining the threshold value xi of the voltage probability distribution deviation can be selected from two pieces of normal history data, and the history data can be stable data between days and even tens of days; whereas the historical data used in the online data detection step may be only the most recently sampled normal historical data. Obviously, the two pieces of history data used in the threshold value xi for determining the deviation of the voltage probability distribution and the history data used in the online data detection step are both normal and fault-free history data.
On the premise that the threshold value and the probability distribution deviation of the real-time data are known, the judgment conditions are as follows:only when the voltage probability distribution deviation of the online data is larger than the threshold value xi and the voltage probability distribution deviation of the online data continuously exceeds the limit time t d Greater than the determination time limit t w Judging that the micro fault occurs under the condition that both conditions are met; neither or both conditions are satisfied and no minute occurrence is determined.
To this end, the detection is completed.
The principle basis of the detection of the invention is as follows: with the large-scale installation of the real-time measurement device, the measurement data of the power system generally meets the requirement of the central limit theorem, which means that the power measurement data from the measurement equipment meets the gaussian distribution, and has great redundancy, namely sparse measurement data. And when a high-resistance fault occurs, although the physical characteristics of the electric quantity are tiny, probability distributions of the electric quantity before and during the fault can show obvious differences, so that the high-resistance fault and the fault phase can be detected and identified through the divergence distance (probability distribution deviation) of the data before the fault (normal history) and the data during the fault (on-line measurement).
The invention has the beneficial effects that: 1. the detection accuracy is higher. Whether faults occur or not is judged without realizing, and possible micro faults can be enhanced in real time; the method has the advantages that the method does not need to cooperate with the operation of protection equipment, can directly carry out integral enhancement on online data, avoids error response of the protection equipment, increases errors of fault signals and normal signals, and ensures detection accuracy. Through field detection, the micro faults with the ground impedance below 55kΩ can be reliably detected and identified, and the detection result is not affected by the normal operation of the system. 2. The application range is wide and the existing line equipment is not required to be replaced. In the aspect of equipment use, the enhanced signal is directly overlapped with the normal signal to be output, and external equipment is not needed; the fault characteristic enhancement technology can be used on the primary side, the secondary side and the equipment inside of the system, and the application range is wide; in terms of a detection signal reconstruction algorithm, the method can be directly deployed at a master station or an intelligent terminal, and large-scale updating of extra equipment or software is not needed. The invention is suitable for the complex fault environment of the actual circuit of the power system, does not need to know the actual fault point environment, can realize detection and identification by only upgrading the existing device and software, and is cheap and efficient.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that modifications and variations could be made by those skilled in the art without departing from the technical principles of the present invention, and such modifications and variations should also be regarded as being within the scope of the invention.
Claims (8)
1. The active enhancement detection method for the line fault characteristics of the power system is characterized by comprising the following steps of:
(a) Acquiring data, wherein related data are acquired from equipment which can perform data measurement on electric quantity in an electric power system;
(b) On-line data active enhancement, injecting an enhancement signal eta into a line data signal fed back by data measurement equipment in real time, and adding the enhancement signal eta to a line voltage signal after adjusting, so as to increase a voltage difference delta U at two ends of a line caused by faults b ；
(c) On-line data detection, reconstructing data to obtain reconstructed historical data X 1 And reconstructed online data X 2 Calculating the history data X after reconstruction 1 And online data X 2 The statistical distance W of the reconstructed signal and the history data X 1 And online data X 2 Voltage probability distribution bias of (2);
(d) Setting a voltage divergence threshold value xi, selecting two different historical data, and reconstructing the two historical data to obtain reconstructed historical data X n And X n-1 Calculating the reconstructed historical data X n And X n-1 Is the statistical distance W of (2) 1 The statistical distance W 1 History data X after reconstruction of signal n And X n-1 For W 1 AveragingValue mu, for W 1 Solving a standard deviation sigma, and then xi=mu+epsilon sigma, wherein epsilon is a constant;
(e) Fault determination, wherein the fault determination index comprises a voltage divergence threshold value xi and a determination time limit t w Reconstructed online data X 2 And reconstructed historical data X 1 The voltage probability distribution deviation of (2) is W, and the time duration exceeding the threshold value xi is t d When the detection result simultaneously meets the following discrimination conditions,if the failure is judged to be small, the failure is judged to be small if either one or both of the two judging conditions are not satisfied.
2. The method for actively enhancing and detecting line fault characteristics of a power system according to claim 1, wherein the method comprises the following steps: in the step (b), the voltage difference delta U between two ends of the fault line after the signal enhancement b The calculation method of (1) is as follows:
taking a line head end node as an a point, a line tail end node as a b point, the full length of the line as l, and generating a micro fault at a position away from the a point ml, wherein the fault point is an f point, and then the equivalent circuit diagram of the line is as follows: the voltage and the current at the point a of the line head end node are U respectively a And I a ，U b For the terminal voltage, the impedance of the whole line is Z, the impedance of the line before the fault point is mZ, the impedance of the line after the fault point is (1-m) Z, and the ground resistance at the point f from the point a ml is R f Y is the line earth admittance, and the earth admittance at the left side and the right side of the f point is Y/2;
the voltage at line end b before the fault occurs is:
U b ＝ABU a -DI a ， (1)
wherein: a=1/g+ (r+sl) (1+sc/G), b=g, d=r+sl, s is laplace operator, G is line-to-ground conductance, R is line resistance, C is line-to-ground capacitance, and L is line inductance;
the voltage at line end b after the fault occurs is:
U bf ＝A f B f U a -D f I a ， (2)
wherein: u (U) bf Is the voltage at point b at the end of the line at the time of failure,D f ＝R eq +sL eq ，G a eq for the a end to ground conduction->Is a end-to-ground capacitance, R eq Is equivalent resistance L eq Is an equivalent inductance;
then the voltage difference before and after the failure obtained by the formulas (2) - (1) is:
ΔU b ＝A f Δ B U a +Δ A BU a -Δ D I a ， (3)
in which the coefficient difference matrix is delta A ＝A f –A,Δ B ＝B f –B，Δ D ＝D f –D；
In order to strengthen the voltage difference caused by faults, an enhancement signal eta and a regulating coefficient alpha are added in a circuit, the enhancement signal eta is divided into two parts alpha eta and-alpha eta, the two parts of enhancement signals are overlapped with a normal voltage signal, and the voltage at a point a after the superposition enhancement isThe voltage difference before and after the fault after the boost signal is introduced in equation (3) is:
ΔU b ＝A f Δ B U a +Δ A B(U a +αη)-Δ D I a ， (4)
and after the fault occurs, the system for enhancing the signal is as follows:
then when operating normally, the system parameter matrix A f ，B f ，D f The transition to a, B, D, i.e. the signal injected at this time has no effect on the line-end voltage, whereas when a fault occurs, the deviation of the probability distribution of the voltage due to the fault is enhanced by equation (4).
3. The method for actively enhancing and detecting line fault characteristics of a power system according to claim 2, wherein the method comprises the following steps:
a is described as formula (2) f ，B f And D f R is referred to in eq 、L eq 、And C beq The calculation method of (a) is as follows:
converting a circuit equivalent circuit diagram after faults, wherein the equivalent circuit is as follows: the voltage and the current at the point a of the line head end node are U respectively a And I a The voltage and current at the line end node b point are U respectively b And I b The impedance at the line fault is Z, Y a ,Y b Respectively representing the admittances to ground of the a end and the b end;
the laplace transform form of the parameters in the equivalent circuit of the fault system is:
Z(s)＝R eq +sL eq
wherein: r is R eq Is equivalent resistance L eq Is the equivalent inductance of the inductor,and->Ground conductance at terminal a and terminal b, respectively, ">And-> The capacitors are respectively a terminal and b terminal;
then R is eq 、L eq 、And C beq The calculation method of (a) is as follows:
wherein ω is the power frequency angular frequency.
4. The method for actively enhancing and detecting line fault characteristics of a power system according to claim 1, wherein the method comprises the following steps: the method for reconstructing the data in the steps (c) and (d) comprises the following steps:
the detection signals are decomposed through signal decomposition algorithms such as Empirical Mode Decomposition (EMD), ensemble Empirical Mode Decomposition (EEMD), variational Mode Decomposition (VMD) or Wavelet Transform (WT) to obtain k components, and then the energy of each component is calculated as follows:
E i (n)＝u 2 i (n)-u i (n+1)u i (n-1)， (8)
wherein: u (u) i (n) nth sample data for the ith component, E i (n) is the energy value of the obtained nth sample point of the ith component;
then, the window length is set to be psi, and the ith component energy entropy H of each window i The method comprises the following steps:
wherein: p is p n The specific gravity of the energy of the ith component in the total energy for the window;
thereby obtaining the corresponding window component weight w i Is that
The reconstructed signal X is:
calculating reconstructed historical data X 1 And reconstructed online data X 2 Statistical distance W:
the statistical distance W is the history data X after reconstructing the signal 1 And online data X 2 Wherein X is the voltage probability distribution deviation of 1 And X 2 Respectively, the history data and the on-line data of the reconstructed signal, which respectively satisfy the probability distribution P 1 And P 2 I.e. X 1 ～P 1 And X 2 ～P 2 ，Π(P 1 ,P 2 ) Representing the edge as P 1 And P 2 Is a set of joint probability distributions; d (X) 1 ,X 2 ) Is the distance of the sample.
5. The method for actively enhancing and detecting line fault characteristics of a power system according to claim 4, wherein the method comprises the following steps: in the method for calculating the threshold value xi of the voltage probability distribution deviation, the history data X after reconstruction n And X n-1 X respectively substituted into formula (12) 1 And X 2 Position, obtain history data X after reconstruction n And X n-1 Is the statistical distance W of (2) 1 For W 1 Average value mu is calculated for W 1 Find standard deviation sigmaThen ζ=μ+εσ, where ε is a constant.
6. The method for actively enhancing and detecting line fault characteristics of a power system according to claim 5, wherein the method comprises the following steps: the history data and the online data come from a substation and a main station connected with the data measuring equipment and data stored in the device with the wave recording function.
7. The method for actively enhancing and detecting line fault characteristics of a power system according to claim 1, wherein the method comprises the following steps: when feature active enhancement is performed in the step (b), the system structure circuit of the enhancement signal is as follows: the signal eta is connected in parallel at the point a at the head end of the line, the eta is regulated by a regulating coefficient alpha to obtain a reinforcing signal alpha eta, the reinforcing signal alpha eta is divided into two paths, one path is a voltage signal of alpha eta, the other path is a voltage signal of-alpha eta, and the voltage of the point a after reinforcing is superposed to beStructure with rear part [ B f B]Combined with A f The transition reaches the line end point b.
8. The method for actively enhancing and detecting line fault characteristics of a power system according to claim 1, wherein the method comprises the following steps: the historical data involved in steps (c) and (d) are both normal, fault-free data.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110687450A (en) * | 2019-08-28 | 2020-01-14 | 武汉科技大学 | Lithium battery residual life prediction method based on phase space reconstruction and particle filtering |
| CN110703033A (en) * | 2019-09-17 | 2020-01-17 | 国电南瑞科技股份有限公司 | Weak fault traveling wave signal enhancement method |
| CN113534199A (en) * | 2021-06-17 | 2021-10-22 | 长沙理工大学 | Self-adaptive generalized accumulation and GPS spoofing attack detection method |
| KR102464719B1 (en) * | 2021-11-30 | 2022-11-10 | 팩트얼라이언스 주식회사 | heavy electric equipment life prediction system based on model-data and method therefor |
| CN116068339A (en) * | 2023-02-22 | 2023-05-05 | 山东理工大学 | Power system line fault detection and discrimination method |
-
2023
- 2023-08-14 CN CN202311018909.5A patent/CN116736043B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110687450A (en) * | 2019-08-28 | 2020-01-14 | 武汉科技大学 | Lithium battery residual life prediction method based on phase space reconstruction and particle filtering |
| CN110703033A (en) * | 2019-09-17 | 2020-01-17 | 国电南瑞科技股份有限公司 | Weak fault traveling wave signal enhancement method |
| CN113534199A (en) * | 2021-06-17 | 2021-10-22 | 长沙理工大学 | Self-adaptive generalized accumulation and GPS spoofing attack detection method |
| KR102464719B1 (en) * | 2021-11-30 | 2022-11-10 | 팩트얼라이언스 주식회사 | heavy electric equipment life prediction system based on model-data and method therefor |
| CN116068339A (en) * | 2023-02-22 | 2023-05-05 | 山东理工大学 | Power system line fault detection and discrimination method |
Non-Patent Citations (1)
| Title |
|---|
| 刘鸣，等.基于轨迹簇和MBLDA的受端电网暂态电压稳定评估.《电力系统保护与控制》.2021,第49卷(第19期),27-37. * |
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