CN110674784A - Power grid frequency filtering method, user equipment, storage medium and device - Google Patents
Power grid frequency filtering method, user equipment, storage medium and device Download PDFInfo
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Abstract
The invention discloses a power grid frequency filtering method, user equipment, a storage medium and a device. The power grid frequency filtering method comprises the following steps: acquiring an initial power grid frequency of a target power grid; iterating the initial power grid frequency according to a preset random model equation; obtaining a characteristic value of the intermediate power grid frequency obtained through iteration; and when the characteristic value of the intermediate power grid frequency obtained through iteration meets a preset condition, the intermediate power grid frequency is used as the filtered target power grid frequency. The power grid frequency after iterative processing of the random model equation effectively removes system noise, so that the sampled power grid frequency is more accurate.
Description
Technical Field
The present invention relates to the field of signal filtering technologies, and in particular, to a power grid frequency filtering method, a user equipment, a storage medium, and an apparatus.
Background
Theoretically, the frequencies of interconnected power grids distributed in different regions are the same, and due to the influence of factors such as signal acquisition and transmission, the power grid frequency signals have certain noise.
In the process of analyzing and calculating the power grid, the power grid frequency is an extremely important parameter, however, the current frequency signal is directly used for primary frequency modulation control of a thermal power generating unit, the precision is poor, frequent fluctuation of primary frequency modulation adjustment is easily caused, the adjustment effect of the primary frequency modulation is influenced, and hidden dangers are possibly brought to the safety of operating equipment of a power plant, particularly a fire-resistant oil system.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide a power grid frequency filtering method, user equipment, a storage medium and a device, and aims to solve the technical problem that the sampled power grid frequency is inaccurate in the prior art.
In order to achieve the above object, the present invention provides a power grid frequency filtering method, which includes the following steps:
acquiring an initial power grid frequency of a target power grid;
iterating the initial power grid frequency according to a preset random model equation;
obtaining a characteristic value of the intermediate power grid frequency obtained through iteration;
and when the characteristic value of the intermediate power grid frequency obtained through iteration meets a preset condition, the intermediate power grid frequency is used as the filtered target power grid frequency.
Preferably, the iterating the initial grid frequency according to the preset stochastic model equation includes:
setting initial conditions of a preset random model equation, wherein the initial conditions comprise model parameters, the statistical variance of system noise, the statistical variance of measurement noise, the frequency optimal estimation value at an initial moment and the mean square error of estimation value deviation;
and inputting the initial power grid frequency into a preset random model equation for iteration.
Preferably, the preset stochastic model equation is:
fx(k|k)＝[1-Kg]fx(k-1|k-1)+Kg(k)fy(k)；
wherein f isx(k | k) represents the optimum frequency estimate at time k, fx(k-1| k-1) represents an optimal frequency estimation value at the k-1 moment; fy (k) denotes the frequency measurement at time k, where
kg (k) denotes the kalman gain at time k; p (k | k) represents the mean square error of the optimal frequency estimate at time k, p (k-1| k-1) represents the mean square error of the optimal frequency estimate at time k-1,
is the statistical variance of the system noise,
The statistical variance of the noise is measured.
Preferably, the inputting the initial grid frequency into a preset stochastic model equation for iteration includes:
calculating Kalman gain at the k moment according to the initial power grid frequency and a preset random model equation;
calculating an optimal frequency estimation value at the k moment;
and calculating the optimal frequency estimation value at the k +1 moment according to the optimal frequency estimation value at the k moment and the mean square error of the optimal frequency estimation value at the k moment.
Preferably, the value interval of the kalman gain is (0, 1).
Preferably, the obtaining of the characteristic value of the intermediate grid frequency obtained iteratively includes:
and acquiring Kalman gain corresponding to the intermediate power grid frequency during iteration, and taking the Kalman gain as a characteristic value of the intermediate power grid frequency.
Preferably, when the characteristic value of the intermediate grid frequency obtained through iteration meets a preset condition, taking the intermediate grid frequency as the filtered target grid frequency includes:
and when the characteristic value of the intermediate power grid frequency obtained through iteration is in a preset interval, taking the intermediate power grid frequency as the filtered target power grid frequency.
In order to achieve the above object, the present invention further provides a user equipment, where the user equipment includes: a memory, a processor and a grid frequency filter program stored on the memory and executable on the processor, the grid frequency filter program when executed by the processor implementing the steps of the grid frequency filtering method as described above.
In order to achieve the above object, the present invention further provides a storage medium, on which a grid frequency filtering program of information is stored, and the grid frequency filtering program, when executed by a processor, implements the steps of the grid frequency filtering method as described above.
In order to achieve the above object, the present invention further provides an intelligent information input device, including:
the frequency sampling module is used for acquiring the initial power grid frequency of the target power grid;
the iteration module is used for iterating the initial power grid frequency according to a preset random model equation;
the characteristic value sampling module is used for acquiring a characteristic value of the intermediate power grid frequency obtained by iteration;
and the output module is used for taking the intermediate power grid frequency as the filtered target power grid frequency when the characteristic value of the intermediate power grid frequency obtained through iteration meets a preset condition.
According to the technical scheme, iteration is carried out on the initial power grid frequency through a preset random model equation, and when the characteristic value of the intermediate power grid frequency obtained through iteration meets a preset condition, the intermediate power grid frequency is used as the filtered target power grid frequency. The power grid frequency after iterative processing of the random model equation effectively removes system noise, so that the sampled power grid frequency is more accurate.
Drawings
FIG. 1 is a schematic diagram of a user equipment architecture of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating a method for filtering a grid frequency according to an embodiment of the present invention;
FIG. 3 is a detailed flowchart of step S20 in FIG. 2;
FIG. 4 is a detailed flow of step S22 in FIG. 3;
fig. 5 is a functional block diagram of an embodiment of a grid frequency filtering apparatus according to the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a user equipment in a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the user equipment may include: a processor 1001, such as a CPU, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may comprise a Display screen (Display), an input unit such as keys, and the optional user interface 1003 may also comprise a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory (e.g., a magnetic disk memory). The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the user equipment configuration shown in fig. 1 does not constitute a limitation of the user equipment and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a storage medium, may include therein an operating system, a network communication module, a user interface module, and a grid frequency filtering program.
In the user equipment shown in fig. 1, the network interface 1004 is mainly used for connecting an external network and performing data communication with other network equipment; the user interface 1003 is mainly used for connecting a user terminal and performing data communication with the terminal; the user equipment of the present invention calls the power grid frequency filtering program stored in the memory 1005 through the processor 1001, and executes the implementation method of the power grid frequency filtering provided by the embodiment of the present invention.
The user equipment can be a personal computer or intelligent power equipment.
Based on the hardware structure, the embodiment of the power grid frequency filtering method is provided.
Referring to fig. 2, fig. 2 is a schematic flow chart of a power grid frequency filtering method according to a first embodiment of the present invention.
In a first embodiment, the grid frequency filtering method comprises the following steps:
step S10: and acquiring the initial grid frequency of the target grid. And adopting the frequency of a certain power grid after extra-high voltage tripping as the initial power grid frequency. In the process of analyzing and calculating the power grid, the power grid frequency is an extremely important parameter. The initial power grid frequency can more accurately reflect the power grid operation condition only through certain filtering processing, and particularly under the accident condition, only a more accurate signal is beneficial to more finely developing reason analysis and formulating processing measures.
Step S20: and iterating the initial power grid frequency according to a preset random model equation. Because the object to be processed is the power grid frequency, the kalman filtering method can adopt a scalar form, and then the following one-dimensional linear random model equation can be obtained:
w (k) and v (k) represent the noise interferences of the process itself and of the measurement system, respectively, which are assumed to be white gaussian noise, and whose statistical variances are
And
the mean value is 0.
Step S30: and obtaining the characteristic value of the intermediate power grid frequency obtained by iteration. In this embodiment, the kalman gain is used as the characteristic value of the intermediate grid frequency. And the value interval of the Kalman gain is (0, 1).
Step S40: and when the characteristic value of the intermediate power grid frequency obtained through iteration meets a preset condition, the intermediate power grid frequency is used as the filtered target power grid frequency. In this embodiment, an algorithm of scalar kalman filtering is used for iterative computation, and when kalman gain tends to be stable in an iterative process, an intermediate power grid frequency obtained through iteration is used as a target power grid frequency when the kalman gain is stable.
According to the technical scheme, iteration is carried out on the initial power grid frequency through a preset random model equation, and when the characteristic value of the intermediate power grid frequency obtained through iteration meets a preset condition, the intermediate power grid frequency is used as the filtered target power grid frequency. The power grid frequency after iterative processing of the random model equation effectively removes system noise, so that the sampled power grid frequency is more accurate.
Referring to fig. 3, the iterating the initial grid frequency according to the preset stochastic model equation includes:
step S21: setting initial conditions of a preset random model equation, wherein the initial conditions comprise model parameters, the statistical variance of system noise, the statistical variance of measurement noise, the frequency optimal estimation value at the initial moment and the mean square error of estimation value deviation.
Step S22: and inputting the initial power grid frequency into a preset random model equation for iteration.
It should be noted that the iterative calculation formula of the scalar kalman filter is as follows:
for the frequency system, the sampling period of the PMU (vector measurement and acquisition) device is 20ms, and in such a short time interval period, the estimated value of the frequency at the next moment can be considered to be substantially unchanged, and the measurement result can also reflect the actual value of the frequency, that is, the frequency system signal sequence and the measurement result signal sequence are both a constant amplitude sequence superimposed gaussian white noise signal sequence, and the system state equation and the measurement equation result are as follows:
the system signal model is shown in equation 2.
It can be seen that the model parameter a ═ c ═ 1, and the statistical variance of the system noise and the measurement noise, which have a large influence on the system filtering effect, are
And
must also be determined.
The optimal frequency estimation value f (0|0) at the initial moment and the mean square error p (0|0) of the estimation value deviation are also required to be given as the basis of iterative computation, and the two initial parameters have little influence on the result of Kalman filtering, and the deviation mean square error can automatically converge along with continuous prediction of a system value and continuous correction of a measured value.
Further, substituting a ═ c ═ 1 into the calculation formula, the following preset stochastic model equation can be obtained:
wherein f isx(k | k) represents the optimum frequency estimate at time k, fx(k-1| k-1) represents an optimal frequency estimation value at the k-1 moment; fy (k) denotes the frequency measurement at time k, wherekg (k) denotes the kalman gain at time k; p (k | k) represents the mean square error of the optimal frequency estimate at time k, p (k-1| k-1) represents the mean square error of the optimal frequency estimate at time k-1,
is the statistical variance of the system noise,
The statistical variance of the noise is measured.
Referring to fig. 4, inputting the initial grid frequency to a preset stochastic model equation for iteration includes:
step S201: calculating Kalman gain at the k moment according to the initial power grid frequency and a preset random model equation;
step S202: calculating an optimal frequency estimation value at the k moment;
step S203: and calculating the optimal frequency estimation value at the k +1 moment according to the optimal frequency estimation value at the k moment and the mean square error of the optimal frequency estimation value at the k moment. And repeating the loop iteration of the steps S201 to S203.
A one-step recursive model with the previous-time optimal frequency estimation value, the previous-time optimal frequency estimation value mean square error and the current-time frequency measurement value as inputs and the current-time optimal frequency estimation value mean square error as outputs is shown in formula 3.
Further, the obtaining of the characteristic value of the intermediate grid frequency obtained iteratively includes:
and acquiring Kalman gain corresponding to the intermediate power grid frequency during iteration, and taking the Kalman gain as a characteristic value of the intermediate power grid frequency.
It should be noted that, from the calculation formula of the optimal frequency estimation value, the optimal frequency estimation value at the current time is a weighted sum of the current measurement value and the optimal frequency estimation value at the previous time, and the weighting coefficients thereof are kalman gains Kg and 1-Kg, respectively. When the Kg tends to 1, the optimal frequency estimation value tends to adopt the measurement value; the more Kg goes to 0, the more the optimum frequency estimation tends to adopt the estimation.
When system is noisy
And measuring noise
Invariably, the larger the error variance of the optimal frequency estimation value at the previous moment, the more the Kalman gain tends to be 1. When system is noisy
Contrast measurement noise
The larger the kalman gain tends to be 1 at each time. In both cases, it is reasonable that the optimum frequency estimate is more likely to be used with the current measurement because the error in estimating the frequency value is large. Conversely, when the system is noisy
Contrast measurement noise
The smaller the kalman gain at each time tends to be 0, in which case it is reasonable to make the estimation value the more inclined the optimum frequency estimation value is to adopt because the error of the measurement value is small.
Substituting sub-formula 1 in the simplified calculation iteration formula set (4) into sub-formula 3 can obtain:
this results in a recursive expression of the series { P (k | k) }, which can prove to converge, i.e., that as the iterative computation proceeds, the variance of the estimated value deviation tends to converge to a fixed value, such that:
the following equation can be obtained:
the limit value of the solution sequence { P (k | k) }:
simultaneously, the following can be obtained:
substituting m into, and defining the system and measuring the variance ratio of noise:
the limit values of the kalman gain sequence { kg (k) } can be obtained:
it can be seen that, while the variance { P (k | k) } of the estimated value deviation tends to converge to a fixed value, the kalman gain also tends to converge to a fixed value under the condition that the system noise variance and the measurement noise variance are determined, and the limit value of the kalman gain is a one-valued function of the ratio n of the system noise variance and the measurement noise variance, and the function is a monotonically increasing function under the condition that n ≧ 0. Order:
then n is obtained as 1/2, and the critical point after kalman gain steady state is obtained. Namely when n is less than 1/2, the steady-state Kalman gain Kg is less than 1/2, and the whole filtering system increases the calculation weight of the estimated value; when n >1/2, the steady state Kalman gain Kg >1/2, the entire filtering system adds the calculated weight of the measurement.
Measuring noise
The sampling accuracy of a reference frequency signal sampling device (e.g., a PMU device) can be considered as the standard deviation of the measurement noise because the sampling accuracy itself has some statistical significance.
System noiseDepending on the comprehensive consideration of the system filtering effect, through the comparison of the power grid frequencies in different regions, for the frequency signals with more typical burrs, the interference of the system and the measurement noise is reduced as much as possible, the signal edge burrs existing in the measured value are eliminated, the frequency curve is smoother, and the estimated value, namely the variance ratio n of the system and the measurement noise signals, is more inclined to be adopted in the filtering process<1/2 selects the system noise. But n cannot be too small, which causes the distortion of the power grid frequency signal to be too obvious, and the filtering effect of the power grid signals of other areas is ensured by adjusting the value of n on the basis of the frequency signal with the least noise signal.
Specifically, when the characteristic value of the intermediate grid frequency obtained through iteration meets a preset condition, taking the intermediate grid frequency as the filtered target grid frequency includes:
and when the characteristic value of the intermediate power grid frequency obtained through iteration is in a preset interval, taking the intermediate power grid frequency as the filtered target power grid frequency. Given aInitial value fsystem (0|0) ═ 50Hz, p (0|0) ═ 0.033Hz2,
the calculated sequence obtained by changing n to 1/25 is shown in table 1.
Table 1 kalman filtering process calculation sequence (n as 1/25)
As can be seen from table 1, when the iterative computation proceeds to 57 th time, the kalman gain has entered the steady state, and the system filtering starts to perform the integrated computation of the estimated value and the measured value with fixed weights.
With reference to fig. 5, to achieve the above object, the present invention further provides an intelligent information input device, including:
and the frequency sampling module 100 is used for acquiring the initial power grid frequency of the target power grid. In the process of analyzing and calculating the power grid, the power grid frequency is an extremely important parameter. The initial power grid frequency can more accurately reflect the power grid operation condition only through certain filtering processing, and particularly under the accident condition, only a more accurate signal is beneficial to more finely developing reason analysis and formulating processing measures.
And the iteration module 200 is configured to iterate the initial power grid frequency according to a preset random model equation. Because the object to be processed is the power grid frequency, the kalman filtering method can adopt a scalar form, and then the following one-dimensional linear random model equation can be obtained:
w (k) and v (k) represent the noise interferences of the process itself and of the measurement system, respectively, which are assumed to be white gaussian noise, and whose statistical variances are
And
the mean value is 0.
And the eigenvalue sampling module 300 is configured to obtain an iteratively obtained eigenvalue of the intermediate grid frequency. In this embodiment, the kalman gain is used as the characteristic value of the intermediate grid frequency. And the value interval of the Kalman gain is (0, 1).
And the output module 400 is configured to, when the characteristic value of the intermediate power grid frequency obtained through iteration meets a preset condition, take the intermediate power grid frequency as the filtered target power grid frequency. In this embodiment, an algorithm of scalar kalman filtering is used for iterative computation, and when kalman gain tends to be stable in an iterative process, an intermediate power grid frequency obtained through iteration is used as a target power grid frequency when the kalman gain is stable.
Specifically, the iteration module 200 is further configured to set initial conditions of a preset random model equation, where the initial conditions include model parameters, a statistical variance of system noise, a statistical variance of measurement noise, a frequency optimal estimation value at an initial time, and a mean square error of an estimation value deviation;
and inputting the initial power grid frequency into a preset random model equation for iteration.
Specifically, the preset stochastic model equation is:
fx(k|k)＝[1-Kg]fx(k-1|k-1)+Kg(k)fy(k)；
wherein f isx(k | k) represents the optimum frequency estimate at time k, fx(k-1| k-1) represents an optimal frequency estimation value at the k-1 moment; fy (k) denotes the frequency measurement at time k, wherekg (k) denotes the kalman gain at time k; p (k | k) represents the mean square error of the optimal frequency estimate at time k, p (k-1| k-1) represents the mean square error of the optimal frequency estimate at time k-1,is the statistical variance of the system noise,
The statistical variance of the noise is measured.
Specifically, the iteration module 200 is further configured to calculate a kalman gain at the time k according to the initial power grid frequency and a preset random model equation; calculating an optimal frequency estimation value at the k moment; and calculating the optimal frequency estimation value at the k +1 moment according to the optimal frequency estimation value at the k moment and the mean square error of the optimal frequency estimation value at the k moment.
Specifically, the value interval of the kalman gain is (0, 1).
Specifically, the eigenvalue sampling module 300 is further configured to obtain a kalman gain corresponding to the intermediate power grid frequency during iteration, and use the kalman gain as the eigenvalue of the intermediate power grid frequency.
Specifically, the output module 400 is further configured to, when the characteristic value of the intermediate power grid frequency obtained through iteration is in a preset interval, take the intermediate power grid frequency as the filtered target power grid frequency.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments. The use of the words first, second, third, etc. do not denote any order, but rather the words first, second, etc. are to be interpreted as names.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A power grid frequency filtering method is characterized by comprising the following steps:
acquiring an initial power grid frequency of a target power grid;
iterating the initial power grid frequency according to a preset random model equation;
obtaining a characteristic value of the intermediate power grid frequency obtained through iteration;
and when the characteristic value of the intermediate power grid frequency obtained through iteration meets a preset condition, the intermediate power grid frequency is used as the filtered target power grid frequency.
2. The grid frequency filtering method according to claim 1, wherein the iterating the initial grid frequency according to the preset stochastic model equation comprises:
setting initial conditions of a preset random model equation, wherein the initial conditions comprise model parameters, the statistical variance of system noise, the statistical variance of measurement noise, the frequency optimal estimation value at an initial moment and the mean square error of estimation value deviation;
and inputting the initial power grid frequency into a preset random model equation for iteration.
3. A grid frequency filtering method according to claim 2, characterized in that said preset stochastic model equation is:
fx(k|k)＝[1-Kg]fx(k-1|k-1)+Kg(k)fy(k)；
wherein f isx(k | k) represents the optimum frequency estimate at time k, fx(k-1| k-1) represents an optimal frequency estimation value at the k-1 moment; fy (k) denotes the frequency measurement at time k, where
kg (k) denotes the kalman gain at time k; p (k | k) represents the mean square error of the optimal frequency estimate at time k, p (k-1| k-1) represents the mean square error of the optimal frequency estimate at time k-1,
is the statistical variance of the system noise,
To measure the statistical variance of the noise.
4. A grid frequency filtering method according to claim 3, wherein said inputting the initial grid frequency to a predetermined stochastic model equation for iteration comprises:
calculating Kalman gain at the k moment according to the initial power grid frequency and a preset random model equation;
calculating an optimal frequency estimation value at the k moment;
and calculating the optimal frequency estimation value at the k +1 moment according to the optimal frequency estimation value at the k moment and the mean square error of the optimal frequency estimation value at the k moment.
5. The grid frequency filtering method according to claim 1, wherein the kalman gain interval is (0, 1).
6. Grid frequency filtering method according to claim 5, characterized in that said obtaining of the characteristic values of the intermediate grid frequency obtained iteratively comprises:
and acquiring Kalman gain corresponding to the intermediate power grid frequency during iteration, and taking the Kalman gain as a characteristic value of the intermediate power grid frequency.
7. Grid frequency filtering method according to any one of claims 1 to 6, wherein the root regarding the intermediate grid frequency as a filtered target grid frequency when the iteratively obtained eigenvalue of the intermediate grid frequency satisfies a preset condition comprises:
and when the characteristic value of the intermediate power grid frequency obtained through iteration is in a preset interval, taking the intermediate power grid frequency as the filtered target power grid frequency.
8. A user equipment, the user equipment comprising: memory, a processor and a grid frequency filter program stored on the memory and executable on the processor, the grid frequency filter program when executed by the processor implementing the steps of the grid frequency filtering method according to any one of claims 1 to 7.
9. Storage medium, characterized by a grid frequency filter program having information stored thereon, which when executed by a processor implements the steps of the grid frequency filtering method according to any one of claims 1 to 7.
10. An intelligent input device of information, characterized in that the intelligent input device of information comprises:
the frequency sampling module is used for acquiring the initial power grid frequency of the target power grid;
the iteration module is used for iterating the initial power grid frequency according to a preset random model equation;
the characteristic value sampling module is used for acquiring a characteristic value of the intermediate power grid frequency obtained by iteration;
and the output module is used for taking the intermediate power grid frequency as the filtered target power grid frequency when the characteristic value of the intermediate power grid frequency obtained through iteration meets a preset condition.
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