CN111553060B - Power grid inertia normalization continuous estimation method based on noise-like disturbance - Google Patents
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Abstract
The power grid inertia normalization continuous estimation method based on noise-like disturbance divides a power grid into a plurality of regional power grids through frequency characteristic distribution of each bus of the power grid and carries out inertia identification respectively; segmenting active-frequency data in a time period to be estimated by adopting a mobile data window; carrying out range-based selection identification on each section of data by adopting an OE model to obtain a group of models with high fitting degree; extracting an inertia sample from the model according to the characteristics of the power grid inertia in the power grid frequency response model; combining the inertia estimation samples obtained in the steps, removing outliers and taking a mean value as the inertia of the time period to be estimated; after the inertia of each area is obtained according to the method, the inertia of the whole power grid is estimated by combining the capacity information of each area, and further the normalized estimation of the inertia of the power grid is realized. The method can continuously and accurately estimate the power grid inertia, and has important reference value for further realizing daily and online estimation application of the power grid inertia and evaluating the power grid inertia and the frequency stability level.
Description
Technical Field
The invention belongs to the field of operation and control of power systems, and particularly relates to a power grid inertia normalized continuous estimation method based on noise-like disturbance.
Background
The weakening of the inertia of the power grid can cause the frequency drop degree under high-power shortage to be deepened, and serious frequency accidents are caused. On the other hand, the power grid inertia has real variable characteristics, but the power shortage cannot be predicted, a potential serious frequency accident can occur under any inertia level, and only by mastering the power grid inertia in real time, the frequency supporting capacity of the power grid can be pre-judged and pre-decided, so that the serious frequency accident can be effectively avoided. Therefore, the method for accurately and continuously estimating the power grid inertia in a normal state has important significance for evaluating the frequency stability margin and ensuring the safe operation of the power grid.
The traditional method carries out inertia estimation based on a frequency large disturbance transient process, depends on random frequency disturbance accidents, can only obtain discrete estimation values under certain power grid operation states, and cannot realize continuous estimation application of the power grid inertia. An electromechanical mode identification method based on noise-like wavelet decomposition reconstruction is provided, a new thought is provided for power system inertia estimation, and inertia estimation by utilizing normalized load small disturbance data becomes possible.
Disclosure of Invention
The invention provides a power grid inertia normalization continuous estimation method based on noise-like disturbance, and the power grid inertia is continuously and accurately estimated by extracting normalization noise-like information by using the method, so that the method has important reference values for further realizing daily and online estimation application of the power grid inertia and evaluating the power grid inertia and frequency stability level.
The technical scheme adopted by the invention is as follows:
the power grid inertia normalized continuous estimation method based on noise-like disturbance comprises the following steps:
step 1: dividing the power grid into a plurality of regional power grids through the frequency characteristic distribution of each bus of the power grid, and measuring the load increment of each region, the power increment of a contact section and the regional frequency deviation;
step 2: obtaining corresponding equivalent electromagnetic power through the load increment and the power increment of the contact section of each area, and segmenting active-frequency data in a time period to be estimated by adopting a mobile data window;
and step 3: for each section of active-frequency data, carrying out range-by-range order selection identification by adopting an OE model to obtain an OE model group with high fitting degree;
and 4, step 4: extracting inertia estimation samples from the OE model group in the step 3 based on characteristics of power grid inertia in the power grid frequency response model;
and 5: combining the inertia estimation samples obtained in the step 4, removing outliers, and taking a mean value as the inertia of the time period to be estimated;
step 6: and (5) after the inertia of each area is obtained according to the step (5), estimating the inertia of the whole network by combining the capacity information of each area, and further realizing the normalized estimation of the inertia of the power grid.
In the step 1, clustering and dividing a plurality of regional power grids through frequency characteristic distribution of each bus of the power grid, and taking the power grids as inertia estimation objects.
In the step 2, the area equivalent electromagnetic power is obtained according to the load increment and the connection section power increment obtained in the step 1, and the relation is as follows:
ΔP e ＝ΔP L +ΔP cut
in the formula: delta P e For regional equivalent electromagnetic power, Δ P L For disturbance of load in a zone, Δ P cut For the purpose of connecting the power increment of the section and treating the active-frequency in the estimated time periodAnd (4) segmenting the rate data by adopting a moving window.
In step 3, the OE model is represented by the following formula:
wherein B (q) = B 0 +b 1 q -1 +…+b nb q -nb ，F(q)＝1+f 1 q -1 +…+f nf q -nf Y (t), u (t), e (t) are system output, system input, zero mean white noise, respectively, n b 、n f Respectively the order of B (q) and F (q), n k For input-output delay, n k Typically 1,q is taken as the backshifting operator; the order of the OE model is the number of parameters to be identified, and thus the order of the OE model is the number of polynomial terms B (q), F (q), i.e. n = n b +1+n f (ii) a And searching various model structures with high fitting degree in an information quantity criterion AIC fractional order range to describe the dynamic process of each section so as to obtain an OE model group.
In the step 4, the parameter identification method based on the power grid inertia characteristics in the power grid frequency response model comprises the following steps:
firstly, all the OE model sets obtained in step 3 are converted into continuous transfer functions, which are of the form:
wherein k is 0 、…、k n-1 、l 0 、…、l n-1 Are all transfer function model coefficients, and s is a complex variable of the transfer function.
Meanwhile, the equivalent frequency response model of the regional power grid under small disturbance is as follows:
wherein H eq ，Δf，ΔP m ，△P e ，D eq The equivalent inertia, the frequency deviation, the mechanical power increment, the electromagnetic power increment and the equivalent damping of the equivalent machine are respectively areas.
Due to the presence of primary frequency modulation dead zone, Δ P m And zero when small perturbations occur. The equivalent frequency response model at this time is:
according to the initial theorem, the following relations are obtained:
therefore, according to the OE model group obtained in the step 3, inertia estimation samples in the time period to be estimated can be obtained through identification, and after outliers in the inertia estimation samples are removed, the inertia can be obtained through averaging.
In the step 5, an absolute deviation median MAD algorithm is used for eliminating outliers in the multi-segment inertia estimation samples, then the average value is taken as a final result in the estimation period, the MAD is a robust measurement of sample deviation of univariate data and corresponds to a power grid inertia univariate data set H 1 ，H 2 ，…，H n MAD is defined as the median of the absolute deviation of the data points to the median:
MAD＝median(|H i -median(H)|) (7)
where mean (H) represents the median of the H data sample set and MAD represents the median of the residual between the data and the median. When the following formula is satisfied, H i Samples are outliers:
wherein k is c Constant for the scale factor, depending on the type of sample distribution, assuming that the H value follows a normal distribution, k c =1.4826; c is a proportionality constant, taking c =2,corresponding to a normal distribution, a 95% confidence interval was taken.
In step 6, the method for calculating the inertia of the whole network comprises the following steps:
according to the inertia estimation method, the inertia of each area is estimated, and the continuous estimation of the inertia of the whole network is obtained by combining the following formula:
wherein, i represents each time period in 1 day,
evaluating for full net equivalent inertia>
Equivalent inertia estimates for the 1,2 … m zones, respectively>
The on-line capacity of the 1,2 … m zones respectively.
The invention relates to a normalized continuous estimation method of power grid inertia based on noise-like disturbance, which has the following technical effects:
(1): according to the method, the power grid inertia can be continuously and accurately estimated according to the noise disturbance of the power system;
(2): the method adopts moving window data segmentation and selects an OE model in a range for identification so as to form an inertia estimation sample, can effectively reduce the influence of random disturbance on the identification effect, and has higher robustness.
(3): the method can continuously and accurately estimate the power grid inertia, and has important reference value for further realizing daily and online estimation application of the power grid inertia and evaluating the power grid inertia and frequency stability level.
Drawings
Fig. 1 is a schematic diagram of grid partitioning.
FIG. 2 is a schematic diagram of an inertia identification implementation process.
Fig. 3 is a graph of the results of continuous estimation of the inertia of the entire network.
Detailed Description
The power grid inertia normalized continuous estimation method based on noise-like disturbance comprises the following steps:
step 1: by calculating the two norms of frequency data in the same time period of each bus of the power grid and combining a power grid topological graph for comparison, the power grid is divided into a plurality of regional power grids, and the division is as shown in fig. 1, so that inertia estimation errors caused by frequency differences at various positions can be remarkably reduced. And measuring the load increment, the power increment of the connection section and the frequency deviation of each area.
The regional frequency deviation is used for the calculation of equation (4) in step 4, as already represented in equation (4). The purpose of measuring the regional frequency deviation is as follows: inertia estimation errors due to frequency differences are reduced.
Step 2: according to the load increment obtained in the step 1 and the power increment of the contact section, obtaining the equivalent electromagnetic power of the region, wherein the relation is as follows:
ΔP e ＝ΔP L +ΔP cut (1)
in the formula: delta P e For regional equivalent electromagnetic power, Δ P L For disturbance of load in a zone, Δ P cut Is the tie profile power increment. Using a moving window of 30s, in steps N L Active-frequency data within the time period to be estimated is segmented as shown in fig. 2.
And 3, step 3: for each section of data, identifying by selecting stages in a range mode by adopting an OE model, wherein the OE model is shown as the following formula:
wherein B (q) = B 0 +b 1 q -1 +…+b nb q -nb ，F(q)＝1+f 1 q -1 +…+f nf q -nf Y (t), u (t), e (t) are respectively system output, system input, zero mean white noise, n b 、n f Respectively the order of B (q) and F (q), n k In order to be an input-output delay,n k typically 1,q is taken as the backshifting operator. The order of the model is the number of parameters to be identified, so the order of the OE model is the number of B (q), F (q) polynomial terms, i.e. n = n b +1+n f . And (3) searching various model structures with high fitting degree by using an Akaike Information Criterion (AIC) fractional range to describe the dynamic process of each section, thereby obtaining an OE model group as shown in figure 2.
And 4, step 4: and (4) extracting to obtain an inertia sample according to the characteristics of the power grid inertia in the power grid frequency response model and the OE model group obtained in the step (3). Firstly, all the OE model sets obtained in step 3 are converted into continuous transfer functions, which are of the form:
wherein k is 0 、…、k n-1 、l 0 、…、l n-1 Are all transfer function model coefficients. Meanwhile, the equivalent frequency response model of the regional power grid under small disturbance is as follows:
wherein H eq ，Δf，ΔP m ，D eq The equivalent inertia, the frequency deviation, the mechanical power increment and the equivalent damping of the equivalent machine are respectively regions. Due to the presence of primary frequency modulation dead zone, Δ P m And zero when small perturbations occur. The equivalent frequency response model at this time is:
the following relationships are obtained by combining equations (10) and (12) according to the initial theorem:
therefore, according to the OE model set obtained in step 3, inertia estimation samples within the time period to be estimated can be identified.
And 5: and (4) according to the inertia estimation samples obtained in the step (4), after outliers in the samples are removed, taking a mean value as final inertia estimation in a time period to be estimated, and accordingly, inertia estimation based on noise-like disturbance is achieved.
And removing outliers in the multi-segment inertia estimation samples by using a Median Absolute Deviation (MAD) algorithm, and taking the mean value as a final result in the estimation period. The MAD is a robust measurement of sample deviation of univariate data, and corresponds to a power grid inertia univariate data set H in the invention 1 ，H 2 ，…，H n MAD is defined as the median of the absolute deviations of the data points from the median:
MAD＝median(|H i -median(H)|) (7)
where mean (H) represents the median of the H data sample set and MAD represents the median of the residual between the data and the median. When the following formula is satisfied, H i Samples are outliers:
wherein k is c Constant for the scale factor, depending on the type of sample distribution, assuming that the H value follows a normal distribution, k c =1.4826; c is a proportionality constant, c =2, corresponding to a normal distribution with a 95% confidence interval.
Step 6: according to the inertia estimation method, the inertia of each area is estimated, and the continuous estimation of the inertia of the whole network is obtained by combining the following formula:
wherein i represents each time period of 1 day,
is equivalent to a whole networkInertia estimate >>
Equivalent inertia estimates for the 1,2 … m zones, respectively>
The on-line capacity of the 1,2 … m zones respectively.
The embodiment is as follows:
under the Matlab/simulink environment, the simulation system of FIG. 1 is established, the system is a 39-node standard test system of a New England 10 machine, and parameters of a generator, a line and a transformer are shown in tables 1 and 2. Wherein, small-amplitude random variation load is added to the bus 20, the bus 21, the bus 23, the bus 25, the bus 29 and the bus 31 respectively to reflect the load disturbance in the system.
TABLE 1 Generator simulation parameter Table
TABLE 2 simulation parameter Table for circuit and transformer
In order to verify the accuracy and effectiveness of the method for continuously estimating the inertia of the whole network, the following settings are made: in the time period of 8. As can be seen from fig. 3, at 8:00-11: in the 00 time period, the power grid inertia has the time-varying characteristic of a dotted line part along with the load change. And in each power generation period, the estimated value of the total network inertia is close to the actual value. Among them, the estimation errors of 8. Therefore, by extracting the normalized noise-like information, the method is completely enough to continuously and accurately estimate the power grid inertia, and has important reference values for further realizing daily and online estimation of the power grid inertia and evaluating the power grid inertia and the frequency stability level.
Claims (7)
1. The power grid inertia normalized continuous estimation method based on noise-like disturbance is characterized by comprising the following steps of:
step 1: dividing the power grid into a plurality of regional power grids through frequency characteristic distribution of each bus of the power grid, and measuring load increment, power increment of a connection section and regional frequency deviation of each region;
step 2: obtaining corresponding equivalent electromagnetic power through the load increment and the power increment of the contact section of each area, and segmenting active-frequency data in a time period to be estimated by adopting a mobile data window;
and 3, step 3: for each section of active-frequency data, carrying out range-by-range order selection identification by adopting an OE model to obtain an OE model group with high fitting degree;
and 4, step 4: extracting an inertia estimation sample from the OE model group in the step 3 based on the characteristics of the power grid inertia in the power grid frequency response model;
and 5: combining the inertia estimation samples obtained in the step 4, removing outliers, and taking a mean value as the inertia of the time period to be estimated;
step 6: and (5) after the inertia of each area is obtained according to the step (5), estimating the inertia of the whole network by combining the capacity information of each area, and further realizing the normalized estimation of the inertia of the power grid.
2. The power grid inertia normalization continuous estimation method based on noise-like disturbance according to claim 1, characterized in that: in the step 1, clustering and dividing a plurality of regional power grids through frequency characteristic distribution of each bus of the power grid, and taking the power grids as inertia estimation objects.
3. The power grid inertia normalization continuous estimation method based on noise-like disturbance according to claim 1, characterized in that: in the step 2, the area equivalent electromagnetic power is obtained according to the load increment and the connection section power increment obtained in the step 1, and the relation is as follows:
ΔP e ＝ΔP L +ΔP cut
in the formula: delta P e For regional equivalent electromagnetic power, Δ P L For disturbance of load in a zone, Δ P cut And segmenting active-frequency data in a time period to be estimated by adopting a moving window for connecting section power increment.
4. The power grid inertia normalized continuous estimation method based on noise-like disturbance according to claim 1, characterized in that: in step 3, the OE model is represented by the following formula:
wherein B (q) = B 0 +b 1 q -1 +…+b nb q -nb ，F(q)＝1+f 1 q -1 +…+f nf q -nf Y (t), u (t), e (t) are respectively system output, system input, zero mean white noise, n b 、n f Respectively the order of B (q) and F (q), n k For input-output delay, n k Typically 1,q is taken as the backshifting operator; the order of the OE model is the number of parameters to be identified, and thus the order of the OE model is the number of polynomial terms B (q), F (q), i.e. n = n b +1+n f (ii) a And searching various model structures with high fitting degree in an information quantity criterion AIC fractional order range to describe the dynamic process of each section so as to obtain an OE model group.
5. The power grid inertia normalization continuous estimation method based on noise-like disturbance according to claim 1, characterized in that: in the step 4, the parameter identification method based on the power grid inertia characteristics in the power grid frequency response model comprises the following steps:
firstly, all the OE model sets obtained in step 3 are converted into continuous transfer functions, which are of the form:
wherein k is 0 、…、k n-1 、l 0 、…、l n-1 All are transfer function model coefficients, and s is a complex variable of a transfer function;
meanwhile, the equivalent frequency response model of the regional power grid under small disturbance is as follows:
wherein H eq ，Δf，ΔP m ，△P e ，D eq Respectively a region equivalent inertia, a region frequency deviation, an equivalent machine mechanical power increment, an equivalent machine electromagnetic power increment and an equivalent damping;
due to the presence of primary frequency modulation dead zone, Δ P m Zero in the presence of small perturbations; the equivalent frequency response model at this time is:
according to the initial theorem, the following relations are obtained:
therefore, according to the OE model group obtained in the step 3, inertia estimation samples in a time period to be estimated can be obtained through identification, outliers in the inertia estimation samples are removed, and then the inertia can be obtained through averaging.
6. The power grid inertia normalization continuous estimation method based on noise-like disturbance according to claim 1, characterized in that: in said step 5, use is made ofThe method comprises the steps of removing outliers in multi-segment inertia estimation samples by using an absolute deviation Median (MAD) algorithm, taking the average value as a final result in an estimation period, wherein the MAD is a robust measurement of sample deviation of univariate data and corresponds to a power grid inertia univariate data set H 1 ，H 2 ，…，H n MAD is defined as the median of the absolute deviations of the data points from the median:
MAD＝median(|H i -median(H)|) (7)
wherein, mean (H) represents the median of the H data sample set, and MAD represents the median of the residual error between the data and the median; when the following formula is satisfied, H i Samples are outliers:
wherein k is c Constant for the scale factor, depending on the type of sample distribution, assuming that the H value follows a normal distribution, k c =1.4826; c is a proportionality constant, c =2, corresponding to a normal distribution with a 95% confidence interval.
7. The power grid inertia normalization continuous estimation method based on noise-like disturbance according to claim 1, characterized in that: in step 6, the method for calculating the inertia of the whole network comprises the following steps:
according to an inertia estimation method, estimating the inertia of each area, and combining the following formula to obtain the continuous estimation of the inertia of the whole network:
wherein i represents each time period of 1 day,
evaluating for full net equivalent inertia>
Equivalent inertia estimates for the 1,2 … m zones, respectively>
The on-line capacity of the 1,2 … m zones respectively. />
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