CN108037350B - Method and system for identifying parameters of voltage waveform - Google Patents

Method and system for identifying parameters of voltage waveform Download PDF

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CN108037350B
CN108037350B CN201711139036.8A CN201711139036A CN108037350B CN 108037350 B CN108037350 B CN 108037350B CN 201711139036 A CN201711139036 A CN 201711139036A CN 108037350 B CN108037350 B CN 108037350B
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kalman filtering
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郭晓宇
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Electric Power Research Institute of Yunnan Power System Ltd
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Abstract

The application relates to the technical field of power system parameter identification methods, in particular to a voltage waveform parameter identification method and system. The parameter identification method can flexibly select different algorithms to identify the parameters of the voltage waveform according to the switch state of the circuit breaker; in a normal state of the system, the Kalman filtering algorithm is used, so that the identification precision is ensured, and meanwhile, the CPU occupancy rate can be reduced; when the system is disturbed, the disturbance can be quickly responded by using the extended Kalman filtering algorithm, and the identification precision can be ensured; under the condition of low noise of sampling voltage, the response is quicker by using a curve fitting method than that of an extended Kalman filtering algorithm, and the CPU occupancy rate is lower. The parameter identification method provided by the application saves the CPU occupancy rate, improves the system state response speed, ensures the identification precision and has wide application prospect.

Description

Method and system for identifying parameters of voltage waveform
Technical Field
The application relates to the technical field of power system parameter identification methods, in particular to a voltage waveform parameter identification method and system.
Background
The parameter identification of voltage and current is important for the stable operation of the power system. Accurate parameter identification can truly reflect the state of the power system, can provide reference for normal operation of control and maintenance equipment of the power system, and can also provide convenience for technical personnel to analyze the power system.
At present, there are various methods for identifying parameters of voltage and current waveforms, including discrete fourier algorithm, curve fitting method, kalman filtering method, and the like. Since each method has its own advantages and disadvantages, it is impossible to accurately identify the parameter characteristics by using only one algorithm.
In the existing filtering algorithm, a Kalman filtering method can well identify parameters with white noise signals, but when the system state is suddenly changed, the response is too slow, and the identification precision is extremely low. The curve fitting method has good response when the system state suddenly changes, but the noise processing capability is weak, and the accuracy is low under the condition of large sampling noise. In addition, the calculation amount is large by using different parameter identification methods, the difficulty of data processing in the monitoring equipment is increased, the delay of the identification result is easily caused, and the rapidity of the control equipment and the protection equipment is reduced.
Disclosure of Invention
In order to solve the problem that the response speed and the identification precision are difficult to balance when the system state is suddenly changed in the existing voltage waveform parameter identification method, the application provides a voltage waveform parameter identification method and system.
A method for identifying parameters of a voltage waveform specifically comprises the following steps:
s1, collecting voltage signals of monitoring points;
s2, acquiring the switch state of a circuit breaker electrically connected with the monitoring point;
s3, if the switch of the breaker is in a closed state, selecting a Kalman filtering algorithm; otherwise, judging the noise level of the voltage signal of the monitoring point;
s4, if the noise value of the voltage signal of the monitoring point is more than 100mv, selecting an extended Kalman filtering algorithm; otherwise, selecting a curve fitting method;
s5, the switch state of the circuit breaker electrically connected with the monitoring point is obtained again, and the following cycle is executed:
s6, if the switch of the breaker is in a closed state, selecting a Kalman filtering algorithm; otherwise, judging the noise level of the voltage signal of the monitoring point;
s7, if the noise value of the voltage signal of the monitoring point is more than 100mv, selecting and resetting an extended Kalman filtering algorithm covariance matrix; otherwise, selecting a curve fitting method;
s8, returning to S5.
Further, the voltage signal of the monitoring point is acquired by a digital sampling device.
Further, the quantity of circuit breaker is a plurality of, just a plurality of circuit breakers all with the monitoring point electric connection.
Further, the parameter identification method comprises a Kalman filtering algorithm, an extended Kalman filtering algorithm and a curve fitting method.
The application also provides a voltage waveform parameter identification system, which comprises a sampling device, a circuit breaker monitoring device, a controller and an execution unit which are connected in sequence, wherein,
the sampling device is used for collecting voltage signals of monitoring points;
the circuit breaker monitoring device is used for monitoring the switch state of a circuit breaker electrically connected with the monitoring point;
the controller is used for controlling the execution unit to execute a corresponding parameter identification method according to the switch state of the circuit breaker;
and the execution unit is used for executing a corresponding parameter identification method to carry out filtering processing on the voltage waveform.
Further, the sampling device is a digital sampling device.
The technical scheme provided by the application comprises the following beneficial effects: the application provides a parameter identification method can flexibly select different algorithms to identify the parameters of the voltage waveform according to the switch state of the circuit breaker: in a normal state of the system (the circuit breaker is in a closed state), the Kalman filtering algorithm is used, the identification precision is ensured, and meanwhile, the CPU occupancy rate can be reduced; when the system is disturbed (the breaker is in a disconnected state), the disturbance can be quickly responded by using the extended complex Kalman filtering algorithm, and the identification precision can be ensured; under the condition of low noise of sampling voltage, the response is quicker by using a curve fitting method than an extended complex Kalman filtering algorithm, and the CPU occupancy rate is lower. The parameter identification method provided by the application saves the CPU occupancy rate, improves the system state response speed, ensures the identification precision and has wide application prospect.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a schematic flow chart illustrating a method for identifying parameters of a voltage waveform according to the present application;
fig. 2 is a schematic diagram illustrating connection between a monitoring point and each circuit breaker in the power system provided by the present application.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application; rather, they are merely examples of apparatus consistent with certain aspects of the present application, as detailed in the appended claims.
To further explain the technical means and effects adopted by the present application to achieve the intended purpose, the following detailed description of the embodiments, structural features and effects of the present application will be made with reference to the accompanying drawings and examples.
Referring to fig. 1, a schematic flow chart of a method for identifying parameters of a voltage waveform provided in the present application specifically includes the following steps:
s1, collecting voltage signals of monitoring points;
s2, acquiring the switch state of a circuit breaker electrically connected with the monitoring point;
s3, if the switch of the breaker is in a closed state, selecting a Kalman filtering algorithm; otherwise, judging the noise level of the voltage signal of the monitoring point;
s4, if the noise value of the voltage signal of the monitoring point is more than 100mv, selecting an extended Kalman filtering algorithm; otherwise, selecting a curve fitting method;
s5, the switch state of the circuit breaker electrically connected with the monitoring point is obtained again, and the following cycle is executed:
s6, if the switch of the breaker is in a closed state, selecting a Kalman filtering algorithm; otherwise, judging the noise level of the voltage signal of the monitoring point;
s7, if the noise value of the voltage signal of the monitoring point is more than 100mv, selecting and resetting an extended Kalman filtering algorithm covariance matrix; otherwise, selecting a curve fitting method;
s8, returning to S5.
In step S3, Kalman filtering (Kalman filtering) is an algorithm that performs optimal estimation on the system state by inputting and outputting observation data through the system using a linear system state equation. In the early 60 s, kalman (r.e.kalman) and budge (r.s.bucy) published an important paper "new achievements in linear filtering and prediction theory", which presented a new theory of linear filtering and prediction, called kalman filtering.
Kalman filtering is used to estimate the state variable X of a discrete time processkThe calculation formula is as follows:
Figure GDA0002272027230000031
in the formula (1), the reaction mixture is,
Figure GDA0002272027230000032
linearly mapping the system state of the last moment k to a gain matrix of the current moment k + 1; w is akExciting noise for the process.
Defining an observed variable ZkThe calculation formula is as follows:
Zk＝HkXk+vk(2) in the formula (2), HkTo observe the matrix, vkTo observe noise, assume
Figure GDA0002272027230000033
And vkThe covariance of the process excitation noise is Q, which is independent and normally distributed white noisekThe covariance of the observed noise is Rk。
Specifically, the kalman filter algorithm is in the following order:
predicting the system state variable of the current time k +1, namely:
Figure GDA0002272027230000034
predicting the covariance of the system state error at the current time k +1, namely:
Figure GDA0002272027230000035
calculating the system state Kalman gain of the current time k +1, namely:
Figure GDA0002272027230000036
updating the covariance of the system state error at the current time k +1, namely:
P(k+1|k+1)＝[I-Kk+1Hk+1]p (K +1| K) (6) in equations (3) to (6), K is the kalman gain and P is the error covariance.
Decomposing the sampled signal Y (t) into Y (t) ═ Yccos(wt)+Yssin (wt), let ψ be w Δ t, where Δ t is a sampling interval, and y (t) is an instantaneous value of an alternating current voltage or a power supply.
Order to
Figure GDA0002272027230000037
Hk＝[1 0]
Note: the sampled signals have different decomposition methods, here
Figure GDA0002272027230000038
Is only one; qkSet to 0, RkCan be set according to the site noise, and the initial value of P is [ 10; 01](applicable only to the decomposition method described above). Due to different sampling signal decomposition methods, different results will be obtained
Figure GDA0002272027230000039
The matrix, the decomposition method described above, results in the entire parameter estimation process including only the fundamental of the signal. In addition, different decomposition methods can be used as needed without affecting the integrity of the methods described hereinBody function.
If the relevant circuit breaker is disconnected, the sub-system where the circuit breaker is located is in a fault, at the moment, the voltage or current signal of the monitoring point is disturbed, and the conventional parameter identification method cannot respond quickly.
In step S4, if the noise value of the voltage of the monitoring point is more than 100mv, selecting an extended Kalman filtering algorithm; and if the noise value of the voltage of the monitoring point is not more than 100mv, the controller switches the algorithm into a curve fitting method while receiving the opening state of the circuit breaker.
Curve fitting is to experimentally obtain finite pair test data (xi, yi), and use these data to find the approximation function y ═ f (x). Wherein x is the output quantity and y is the measured physical quantity. Unlike interpolation, curve fitting does not require that the curve of y ═ f (x) pass through all the discrete points (xi, yi), but only that y ═ f (x) reflect the general trend of these discrete points, with no local fluctuations. A common method of curve fitting is generally based on a least squares method.
Specifically, the calculation order of the curve fitting method is as follows:
let ykY (k Δ t), wherein ykFor the k-th sample value, ω, of the sampled signal y (t)oThe angular speed of the power frequency of the system is shown, and delta t is a sampling interval;
decomposing the sampled signal Y (t) into Y (t) ═ Yccos(ω0t)+Yssin(ω0t), wherein y (t) is the instantaneous value of the ac voltage or power supply; assuming that 3 samples of the sampled signal y (t) are sampled at- Δ t, 0, Δ t, respectively
y-1＝y(-Δt)
y0＝y(0)
y1＝y(Δt)
Thus, there are
Figure GDA0002272027230000041
Wherein theta is a sampling interval power frequency angle;
a least squares estimate is made from the 3 sampled signals:
Figure GDA0002272027230000042
for any time k there is:
Figure GDA0002272027230000043
Figure GDA0002272027230000044
Figure GDA0002272027230000045
specifically, the order of the extended kalman filter algorithm is as follows:
suppose the sampled signal is
Figure GDA0002272027230000046
Wherein A is1In order to be the amplitude value,
Figure GDA0002272027230000047
is the phase, y (t) is the instantaneous value of the AC voltage or power supply, ykThe k-th sample value of y (t), ωoThe angular speed of the power frequency of the system is shown, and delta t is a sampling interval;
the sampled signal is described by the following form:
Figure GDA0002272027230000048
Figure GDA0002272027230000049
in the formula (12), vkFor sampling noise, α ═ exp (jw)1Δt)，
Figure GDA00022720272300000410
Figure GDA00022720272300000411
The above non-linear process can be described as:
xk+1＝f(xk) (13)
yk+1＝Hxk+vk(14)
wherein the content of the first and second substances,
Figure GDA0002272027230000051
H＝[0 0.5-0.5]；
similar to kalman filtering, the extended kalman filtering process is:
predicting the system state variables at the k +1 moment, namely:
Figure GDA0002272027230000052
the covariance of the system state error at the time of prediction k +1 is:
Figure GDA0002272027230000053
calculating the system state Kalman gain at the moment of k +1, namely:
Figure GDA0002272027230000054
updating the covariance of the system state error at the time k +1, namely:
P(k+1|k+1)＝[I-Kk+1Hk+1]P(k+1|k) (18)
in the formula (16), the compound represented by the formula,
Figure GDA0002272027230000055
xk+1(2) representation matrix xk+1The second element of (1), and so on. Matrix P in the above processkFor the error covariance matrix, the initial value is usually
Figure GDA0002272027230000056
The matrix tends to follow the filtering algorithm
Figure GDA0002272027230000057
Resetting the matrix means restoring this matrix to the initial value.
Optionally, the voltage of the monitoring point is acquired by a digital sampling device.
Optionally, the quantity of circuit breaker is a plurality of, just a plurality of circuit breakers all with the monitoring point electric connection.
Optionally, the parameter identification method includes a kalman filter algorithm, an extended kalman filter algorithm, and a curve fitting method.
The application also provides a voltage waveform parameter identification system, which comprises a sampling device, a circuit breaker monitoring device, a controller and an execution unit which are connected in sequence, wherein,
the sampling device is used for collecting voltage signals of monitoring points;
the circuit breaker monitoring device is used for monitoring the switch state of a circuit breaker electrically connected with the monitoring point;
the controller is used for controlling the execution unit to execute a corresponding parameter identification method according to the switch state of the circuit breaker;
and the execution unit is used for executing a corresponding parameter identification method to carry out filtering processing on the voltage waveform.
Optionally, the sampling device is a digital sampling device.
According to the scheme, algorithms related in the parameter identification method provided by the application can be realized by a CPU (central processing unit) of the monitoring equipment, wherein the extended complex Kalman filtering algorithm is a transformation of the Kalman filtering algorithm. Because the transformation involves a more complex calculation process, the CPU occupancy rate can be reduced while the identification precision can be ensured by using the Kalman filtering algorithm in the normal state of the system. When the system is disturbed (the breaker is in a disconnected state), the disturbance can be quickly responded by using the extended complex Kalman filtering algorithm, and the identification precision is ensured. Under the condition of low sampling noise, the response is quicker by using a curve fitting method than an extended complex Kalman filtering algorithm, and the CPU occupancy rate is lower.
In addition, if a new circuit breaker is disconnected under the high noise condition, the voltage of the monitoring point is disturbed again, and the parameter of the covariance matrix in the extended complex Kalman filtering algorithm needs to be reset so as to activate the algorithm to respond to the disturbance. The circuit breaker switch state is directly reset, so that the process of calculating the reset condition is omitted, and the algorithm is quicker to respond to further disturbance.
The application provides a parameter identification method can flexibly select different algorithms to identify the parameters of the voltage waveform according to the switch state of the circuit breaker: in a normal state of the system (the circuit breaker is in a closed state), the Kalman filtering algorithm is used, the identification precision is ensured, and meanwhile, the CPU occupancy rate can be reduced; when the system is disturbed (the breaker is in a disconnected state), the disturbance can be quickly responded by using the extended complex Kalman filtering algorithm, and the identification precision can be ensured; under the condition of low noise of sampling voltage, the response is quicker by using a curve fitting method than an extended complex Kalman filtering algorithm, and the CPU occupancy rate is lower. The parameter identification method provided by the application saves the CPU occupancy rate, improves the system state response speed, ensures the identification precision and has wide application prospect.
It should be noted that the term "comprises/comprising" is intended to cover a non-exclusive inclusion, such that a term "comprising … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the same element, without further limitation.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It is to be understood that the present application is not limited to the parameter identification method that has been described above, and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims (6)

1. A method for identifying parameters of a voltage waveform specifically comprises the following steps:
s1, collecting voltage signals of monitoring points;
s2, acquiring the switch state of a circuit breaker electrically connected with the monitoring point;
s3, if the switch of the breaker is in a closed state, selecting a Kalman filtering algorithm; otherwise, judging the noise level of the voltage signal of the monitoring point;
s4, if the noise value of the voltage signal of the monitoring point is more than 100mv, selecting an extended Kalman filtering algorithm; otherwise, selecting a curve fitting method;
s5, the switch state of the circuit breaker electrically connected with the monitoring point is obtained again, and the following cycle is executed:
s6, if the switch of the breaker is in a closed state, selecting a Kalman filtering algorithm; otherwise, judging the noise level of the voltage signal of the monitoring point;
s7, if the noise value of the voltage signal of the monitoring point is more than 100mv, selecting and resetting an extended Kalman filtering algorithm covariance matrix; otherwise, selecting a curve fitting method;
s8, returning to S5.
2. The parameter identification method according to claim 1, wherein the voltage signal of the monitoring point is acquired by a digital sampling device.
3. The parameter identification method according to claim 1 or 2, wherein the number of the circuit breakers is multiple, and the multiple circuit breakers are electrically connected with the monitoring points.
4. The method according to claim 1, wherein the parameter identification method comprises a Kalman filtering algorithm, an extended Kalman filtering algorithm, and a curve fitting method.
5. A voltage waveform parameter identification system is characterized by comprising a sampling device, a circuit breaker monitoring device, a controller and an execution unit which are connected in sequence, wherein,
the sampling device is used for collecting voltage signals of monitoring points;
the circuit breaker monitoring device is used for monitoring the switch state of a circuit breaker electrically connected with the monitoring point;
a controller for controlling an execution unit to execute the parameter identification method according to any one of claims 1 to 4 according to a switch state of the circuit breaker;
an execution unit, configured to perform the method according to any one of claims 1 to 4 to filter the voltage waveform.
6. The parameter identification system of claim 5, wherein the sampling device is a digital sampling device.
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