CN116243042A - Voltage sag detection method for power distribution network - Google Patents

Voltage sag detection method for power distribution network Download PDF

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CN116243042A
CN116243042A CN202211532286.9A CN202211532286A CN116243042A CN 116243042 A CN116243042 A CN 116243042A CN 202211532286 A CN202211532286 A CN 202211532286A CN 116243042 A CN116243042 A CN 116243042A
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voltage signal
voltage
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何勇
张冲标
陈金威
杨柳
赵彦旻
钱辰雯
冯晓真
陆阳
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Jiashan County Power Supply Co Of State Grid Zhejiang Electric Power Co ltd
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    • G01R19/0084—Arrangements for measuring currents or voltages or for indicating presence or sign thereof measuring voltage only
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    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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    • G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
    • G01R31/086—Locating faults in cables, transmission lines, or networks according to type of conductors in power transmission or distribution networks, i.e. with interconnected conductors
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
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Abstract

The invention discloses a voltage sag detection method of a power distribution network, which comprises the following steps: acquiring a voltage signal, and constructing a strong tracking filter model according to the voltage signal; bringing a filter model of the voltage signal into a strong tracking filtering algorithm of a covariance matrix in a real-time adjusting process to calculate a suboptimal fading factor; and determining the sag time and the recovery time of the voltage signal according to the obtained abrupt change time of the suboptimal fading factor. The invention introduces suboptimal fading factors in the monitoring process, increases the influence of a new observation value on the whole system so as to inhibit the filtering divergence, adjusts the estimated covariance matrix, the process covariance matrix and the Kalman gain, improves the filtering precision on the basis of solving the problem of easy dispersion, and enhances the detection capability of the abrupt change state.

Description

Voltage sag detection method for power distribution network
Technical Field
The invention relates to the technical field of electric energy quality, in particular to a voltage sag detection method of a power distribution network.
Background
In recent years, with the rapid development of modern industry and high-tech industry, devices with computers and microprocessors as cores are widely used, and the devices are more sensitive to system disturbance and have more severe requirements on electric energy quality. Among the various power quality problems, the voltage sag has the most serious influence on sensitive equipment, is the main cause of damage of a fault machine, and can cause huge economic loss, so that the voltage sag is necessary to be monitored. At present, aiming at the problem of voltage sag, a large number of research results are developed in recent years, and the research results can be mainly summarized into 3 types of time domain, frequency domain and transform domain, wherein the Kalman filter based on the characteristics of strong dynamic instantaneity and high detection precision can be used for detecting harmonic waves and voltage sag so as to obtain better detection effect. However, voltage sag detection based on Kalman filtering causes the problems that noise statistical characteristics are estimated inaccurately, and the self-adaptive capacity to filtering parameters cannot adjust the filtering parameters along with the change of the noise statistical characteristics, so that the filtering divergence probability is high, and the detection of the voltage sag is inaccurate.
The patent document discloses a Kalman filtering-based dynamic voltage drop detection method for a cross power supply system, which has the publication number of CN110579638B and the publication date of 2020-11-06, and comprises the following steps: and detecting a voltage disturbance signal by adopting a Kalman filtering algorithm, using the effective voltage value as a state variable, determining effective voltage value information by utilizing real-time update of the Kalman state variable, determining the moment of voltage disturbance, and acquiring the amplitude information of voltage rise or fall so as to detect the voltage disturbance condition of the cross power supply system in real time. According to the method, the fact that the power grid is often distorted greatly and the harmonic content is considered when the voltage drops is considered, so that the harmonic component is added when a composite voltage model is built; in consideration of deviation of angular frequency tracking generated in voltage sag detection, multiple fading factors are introduced into a detection algorithm, so that each dimension state of a voltage signal is fading through different fading factors, and the accuracy of the detection method is improved. However, the technology still cannot solve the problem of inaccurate voltage sag detection caused by high filtering divergence probability of the Kalman filter.
Disclosure of Invention
The invention aims to solve the problem of inaccurate voltage sag detection caused by high filter divergence probability in the method for detecting the voltage sag through Kalman filtering in the prior art, and provides a voltage sag detection method of a power distribution network.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a voltage sag detection method of a power distribution network comprises the following steps:
acquiring a voltage signal, and constructing a strong tracking filter model according to the voltage signal;
bringing a filter model of the voltage signal into a strong tracking filtering algorithm of a covariance matrix in a real-time adjusting process to calculate a suboptimal fading factor;
and determining the sag time and the recovery time of the voltage signal according to the obtained abrupt change time of the suboptimal fading factor.
The strong tracking filtering algorithm is a parameter estimation method for nonlinear system fault diagnosis, is based on an extended Kalman filter, introduces a time-varying suboptimal fading factor, and can solve the problem of easy dispersion of the Kalman filter due to the fact that an estimation covariance matrix, a process covariance matrix and a Kalman gain matrix can be adjusted on line; in the detection method, the abrupt change state has stronger detection capability, and the abrupt change moment of the optimal fading factor can be used for indicating the start-stop moment of the occurrence and recovery of the voltage sag, so that the start-stop time and the duration of the voltage sag can be accurately detected, and then the sag depth and the phase jump of the voltage sag are determined according to the amplitude and the intersection of the fundamental wave signals in the voltage signals, so that the detection of the voltage sag is completed.
Preferably, the strong tracking model of the voltage signal includes:
discrete form of voltage signal:
Figure BDA0003974797300000021
wherein U is 0 (k) As the DC offset, U n (k) The amplitude of the n-th wave, the fundamental wave when n=1,
Figure BDA0003974797300000022
is the initial phase angle of n-time wave, T is the sampling time interval omega 0 For fundamental wave frequency, N is the highest harmonic frequency, e (k) is observation noise;
observation equation: z (k) =hx (k) +e (k), where H is the observation matrix and x (k) is the state variable.
The ideal voltage signal is a 50Hz sine wave, but in actual production and life, due to the use of nonlinear power electronic devices and the zero drift influence caused by an actual measurement circuit, the voltage signal often contains harmonic waves, direct current offset and other interferences, so that the voltage signal can be expressed in a discrete form to facilitate subsequent sag detection, and the constructed strong tracking model of the voltage signal can be used for detecting abrupt changes and measuring sag depth and phase jump, and the real-time estimated value and amplitude are obtained through alpha beta/dq conversion of the state variable of the nth-order harmonic wave.
Preferably, the calculation process of the strong tracking filtering algorithm includes:
prediction of state:
Figure BDA0003974797300000023
P(k+1|k)＝λ(k+1)P(k|k)+Q(k)
update of state:
Figure BDA0003974797300000024
Figure BDA0003974797300000036
P(k+1|k+1)＝[E-K(k+1)H]P(k+1|k)
wherein the method comprises the steps of
Figure BDA0003974797300000037
For the estimated value of the k time state versus the k+1 time state,/for the k time state>
Figure BDA0003974797300000038
For the estimated value of the state at the moment K, F and H are the state transition matrix and the observation matrix, respectively, P is the estimated covariance matrix, Q is the process covariance matrix, E is the identity matrix, K (k+1) is the kalman gain at the moment k+1, and λ (k+1) is the suboptimal fading factor at the moment k+1.
In the invention, gamma (k+1) is a residual sequence, and the strong tracking filtering algorithm is to extract effective information in the residual sequence by introducing a suboptimal fading factor on the basis of Kalman filtering and adjusting Kalman gain in real time so that the residual sequence is always orthogonal.
Preferably, the calculation of the suboptimal fading factor is:
Figure BDA0003974797300000031
N(k+1)＝V 0 (k+1)-β ′ (HQ(k)H T +R(k+1))
M(k+1)＝β ′ HFP(k|k)F T H T
Figure BDA0003974797300000032
wherein beta is ′ As a weakening factor, R (k+1) is an observed noise covariance matrix at time k+1, ρ is a forgetting factor, and tra (·) represents a trace of the matrix.
The invention introduces attenuation factor beta in the calculation of suboptimal fading factor ′ The estimated value of the state can be smoother, and the weakening factor beta is fixed ′ Compared with the existing strong tracking algorithm, the method has the advantages that beta is selected more conveniently according to experience, and the threshold setting of the filter divergence is increased, so that the probability of the filter divergence is reduced; further, ρ is a forgetting factor, which is greater than zero and equal to or less than 1, and is preferably 0.95 in the present invention.
Preferably, for a process covariance matrix
Figure BDA0003974797300000033
Figure BDA0003974797300000034
Figure BDA0003974797300000035
Where W is the time window length when the mean square error is calculated.
According to the invention, not only can the estimated covariance matrix and the Kalman gain be adjusted, but also the process covariance matrix can be adjusted, and compared with an algorithm with a constant process covariance matrix, the method has higher filtering precision and stronger tracking capacity; in the present invention, W is the time window length when the mean square error is calculated, and the expression means that the number of sampling time intervals included corresponds to the number k of sampling points.
Preferably, the strong tracking model further comprises the equation of state:
x(k)＝Fx(k-1)+v(k-1)
Figure BDA0003974797300000041
Figure BDA0003974797300000042
where i is any one of numbers 1 to N, the observation matrix h= [1,0, …,1, 0], and v (k-1) is the state noise at the time of k-1.
The strong tracking model of the voltage signal in the invention comprises a discrete expression form, a state equation and an observation equation of voltage, F is a state transition matrix, a i Is a diagonal parameter of the state transition matrix, which has a total of N numbers corresponding to the order of the highest order harmonic in the discrete representation of the voltage signal.
The invention has the following beneficial effects: the suboptimal fading factor is introduced in the monitoring process, the influence of a new observation value on the whole system is increased so as to inhibit filtering divergence, and meanwhile, the estimated covariance matrix, the process covariance matrix and the Kalman gain are all adjusted, so that the filtering precision is improved on the basis of solving the problem of easy dispersion, and the detection capability of the abrupt change state is enhanced.
Drawings
Fig. 1 is a flow chart of a voltage sag detection method of the present invention.
Detailed Description
The invention is further described below with reference to the drawings and detailed description.
As shown in fig. 1, a method for detecting a voltage sag of a power distribution network includes:
acquiring a voltage signal, and constructing a strong tracking filter model according to the voltage signal;
bringing a filter model of the voltage signal into a strong tracking filtering algorithm of a covariance matrix in a real-time adjusting process to calculate a suboptimal fading factor;
and determining the sag time and the recovery time of the voltage signal according to the obtained abrupt change time of the suboptimal fading factor.
Preferably, the strong tracking model of the voltage signal includes:
discrete form of voltage signal:
Figure BDA0003974797300000043
wherein U is 0 (k) As the DC offset, U n (k) The amplitude of the n-th wave, the fundamental wave when n=1,
Figure BDA0003974797300000044
is the initial phase angle of n-time wave, T is the sampling time interval omega 0 For fundamental wave frequency, N is the highest harmonic frequency, e (k) is observation noise;
the observation equation is: z (k) =hx (k) +e (k), where H is the observation matrix and x (k) is the state variable.
The state equation is:
x(k)＝Fx(k-1)+v(k-1)
Figure BDA0003974797300000051
Figure BDA0003974797300000052
where i is any one of numbers 1 to N, the observation matrix h= [1,0, …,1,0]V (k-1) is the state noise at time k-1. Equation of state of voltage signal
Figure BDA0003974797300000053
Figure BDA0003974797300000054
The calculation process of the strong tracking filtering algorithm comprises the following steps:
prediction of state:
Figure BDA0003974797300000055
P(k+1|k)＝λ(k+1)P(k|k)+Q(k)
update of state:
Figure BDA0003974797300000056
/>
Figure BDA0003974797300000057
P(k+1|k+1)＝[E-K(k+1)H]P(k+1|k)
Figure BDA0003974797300000058
wherein the method comprises the steps of
Figure BDA0003974797300000059
For the estimated value of the k time state versus the k+1 time state,/for the k time state>
Figure BDA00039747973000000510
For the estimated value of the state at the moment K, F and H are the state transition matrix and the observation matrix, respectively, P is the estimated covariance matrix, Q is the process covariance matrix, E is the identity matrix, K (k+1) is the kalman gain at the moment k+1, and λ (k+1) is the suboptimal fading factor at the moment k+1.
The calculation of the suboptimal fading factor is:
Figure BDA00039747973000000511
N(k+1)＝V 0 (k+1)-β ′ (HQ(k)H T +R(k+1))
M(k+1)＝β ′ HFP(k|k)F T H T
Figure BDA0003974797300000061
wherein β' is a weakening factor, in the present invention, the range is [2,3], 3 is taken when the order n is 5 or less, 2.5 is taken when more than 5 and 10 or less, and 2 is taken when more than 10; r (k+1) is an observation noise covariance matrix at k+1, ρ is a forgetting factor, and tra (·) represents a trace of the matrix.
For process covariance matrix
Figure BDA0003974797300000062
Figure BDA0003974797300000063
Figure BDA0003974797300000064
Where W is the time window length when the mean square error is calculated.
For the amplitude and phase calculation of the state variable in the voltage signal, because the higher order harmonic occupies smaller space in practical application, only the fundamental wave, the third harmonic, the fifth harmonic and the seventh harmonic are modeled and calculated, and the amplitude of the nth harmonic is as follows:
Figure BDA0003974797300000065
the state variable is obtained by alpha beta/dq conversion
Figure BDA0003974797300000066
Thus the real-time phase estimate of the nth harmonic is
Figure BDA0003974797300000067
Fundamental frequency of
Figure BDA0003974797300000068
The strong tracking filtering algorithm is a parameter estimation method for nonlinear system fault diagnosis, is based on an extended Kalman filter, introduces a time-varying suboptimal fading factor, and can solve the problem of easy dispersion of the Kalman filter due to the fact that an estimation covariance matrix, a process covariance matrix and a Kalman gain matrix can be adjusted on line; in the detection method, the abrupt change state has stronger detection capability, and the abrupt change moment of the optimal fading factor can be used for indicating the start-stop moment of the occurrence and recovery of the voltage sag, so that the start-stop time and the duration of the voltage sag can be accurately detected, and then the sag depth and the phase jump of the voltage sag are determined according to the amplitude and the intersection of the fundamental wave signals in the voltage signals, so that the detection of the voltage sag is completed.
The ideal voltage signal is a 50Hz sine wave, but in actual production and life, due to the use of nonlinear power electronic devices and the zero drift influence caused by an actual measurement circuit, the voltage signal often contains harmonic waves, direct current offset and other interferences, so that the voltage signal can be expressed in a discrete form to facilitate subsequent sag detection, and the constructed strong tracking model of the voltage signal can be used for detecting abrupt changes and measuring sag depth and phase jump, and the real-time estimated value and amplitude are obtained through alpha beta/dq conversion of the state variable of the nth-order harmonic wave.
In the invention, gamma (k+1) is a residual sequence, and the strong tracking filtering algorithm is to extract effective information in the residual sequence by introducing a suboptimal fading factor on the basis of Kalman filtering and adjusting Kalman gain in real time so that the residual sequence is always orthogonal.
According to the method, the weakening factor beta 'is introduced into the calculation of the suboptimal fading factor, so that the estimated value of the state is smoother, and in addition, compared with the existing strong tracking algorithm, the weakening factor beta' is more convenient to select beta according to experience, and the threshold setting of the filtering divergence is increased, so that the probability of the filtering divergence is reduced; further, ρ is a forgetting factor, which is greater than zero and equal to or less than 1, and is preferably 0.95 in the present invention.
According to the invention, not only can the estimated covariance matrix and the Kalman gain be adjusted, but also the process covariance matrix can be adjusted, and compared with an algorithm with a constant process covariance matrix, the method has higher filtering precision and stronger tracking capacity; in the present invention, W is the time window length when the mean square error is calculated, and the expression means that the number of sampling time intervals included corresponds to the number k of sampling points.
The strong tracking model of the voltage signal in the invention comprises a discrete expression form, a state equation and an observation equation of voltage, F is a state transition matrix, a i Is a diagonal parameter of the state transition matrix, which has a total of N numbers corresponding to the order of the highest order harmonic in the discrete representation of the voltage signal.
The index for voltage sag detection in the present invention is defined as:
time of action t 1 : the time required for the detected fundamental wave amplitude (or phase angle) curve to pass through 0.9 times per unit value (or phase angle change value) from the occurrence or end of the voltage sag;
adjusting time t 2 : the time required for the detected fundamental wave amplitude (or phase angle) curve to pass and remain within 3% error range of the actual fundamental wave amplitude (or phase angle) from the occurrence or end of the voltage dip;
overshoot μ: the percentage between the instantaneous maximum deviation of the fundamental voltage amplitude and the steady state value during voltage sag recovery (or the maximum phase angle deviation during voltage sag recovery).
The foregoing embodiments are further illustrative and explanatory of the invention, as is not restrictive of the invention, and any modifications, equivalents, and improvements made within the spirit and principles of the invention are intended to be included within the scope of the invention.

Claims (6)

1. The voltage sag detection method of the power distribution network is characterized by comprising the following steps of:
acquiring a voltage signal, and constructing a strong tracking filter model according to the voltage signal;
bringing a filter model of the voltage signal into a strong tracking filtering algorithm of a covariance matrix in a real-time adjusting process to calculate a suboptimal fading factor;
and determining the sag time and the recovery time of the voltage signal according to the obtained abrupt change time of the suboptimal fading factor.
2. The method for detecting voltage sag of a power distribution network according to claim 1, wherein the strong tracking model of the voltage signal comprises:
discrete form of voltage signal:
Figure FDA0003974797290000011
wherein U is 0 (k) As the DC offset, U n (k) The amplitude of the n-th wave, the fundamental wave when n=1,
Figure FDA0003974797290000012
is the initial phase angle of n-time wave, T is the sampling time interval omega 0 For fundamental wave frequency, N is the highest harmonic frequency, e (k) is observation noise;
observation equation: z (k) =hx (k) +e (k), where H is the observation matrix and x (k) is the state variable.
3. A method for detecting a voltage dip in a power distribution network according to claim 1 or 2, wherein the calculation process of the strong tracking filtering algorithm comprises:
prediction of state:
Figure FDA0003974797290000013
p(k+l|k)＝λ(k+1)P(k|k)+Q(k)
update of state:
Figure FDA0003974797290000014
Figure FDA0003974797290000015
P(k+1|k+1)＝[E-K(k+1)H]P(k+1|k)
wherein the method comprises the steps of
Figure FDA0003974797290000016
For the estimated value of the k time state versus the k+1 time state,/for the k time state>
Figure FDA0003974797290000017
For the estimated value of the state at the moment K, F and H are the state transition matrix and the observation matrix, respectively, P is the estimated covariance matrix, Q is the process covariance matrix, E is the identity matrix, K (k+1) is the kalman gain at the moment k+1, and λ (k+1) is the suboptimal fading factor at the moment k+1.
4. A method of detecting a voltage dip in a power distribution network according to claim 3, wherein the calculation of the suboptimal fading factor is:
Figure FDA0003974797290000018
W(k+1)＝V 0 (k+1)-β′(HQ(k)H T +R(k+1))
M(k+1)＝β′HFP(k|k)F T H T
Figure FDA0003974797290000021
where β' is a weakening factor, R (k+1) is an observed noise covariance matrix at time k+1, ρ is a forgetting factor, and tra (·) represents the trace of the matrix.
5. A method of detecting voltage sags in a power distribution network according to claim 3, characterized by a process covariance matrix
Figure FDA0003974797290000022
Figure FDA0003974797290000023
Figure FDA0003974797290000024
Where W is the time window length when the mean square error is calculated.
6. A method of detecting a voltage dip in a power distribution network according to claim 2, 4 or 5, wherein the strong tracking model further comprises the equation of state:
x(k)＝Fx(k-1)+v(k-1)
Figure FDA0003974797290000025
Figure FDA0003974797290000026
where i is any one of numbers 1 to N, the observation matrix h= [1,0, …,1, 0], and v (k-1) is the state noise at the time of k-1.
CN202211532286.9A 2022-12-01 2022-12-01 Voltage sag detection method for power distribution network Pending CN116243042A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117741450A (en) * 2024-02-21 2024-03-22 新风光电子科技股份有限公司 Energy storage battery detection method for electrical parameter analysis

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117741450A (en) * 2024-02-21 2024-03-22 新风光电子科技股份有限公司 Energy storage battery detection method for electrical parameter analysis
CN117741450B (en) * 2024-02-21 2024-05-14 新风光电子科技股份有限公司 Energy storage battery detection method for electrical parameter analysis

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