CN110445149B - Unequal capacity grouping method for parallel compensation capacitor bank of transformer substation - Google Patents
Unequal capacity grouping method for parallel compensation capacitor bank of transformer substation Download PDFInfo
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- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/18—Arrangements for adjusting, eliminating or compensating reactive power in networks
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
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Abstract
The invention discloses a transformer substation parallel compensation capacitor bank unequal capacity grouping method which includes the steps of collecting reactive demand of a transformer substation at n moments, taking the reactive demand of the transformer substation at the n moments as samples, dividing the n samples in a reactive demand time sequence curve to obtain an optimal S-section reactive demand time sequence, confirming the samples in each section of reactive demand time sequence based on a target function F (n, S), and finally determining the number of capacitor banks and the capacity of each group of capacitors based on the S-section reactive demand time sequence; the invention carefully considers the dynamic change characteristic of the load through the method, obtains more reasonable capacity of the capacitor, and reduces the times of putting in and cutting off the capacitor bank.
Description
Technical Field
The invention belongs to the technical field of reactive compensation of transformer substations, and particularly relates to a method for grouping unequal capacity of parallel compensation capacitor banks of a transformer substation.
Background
The reactive compensation of the transformer substation mainly has the functions of compensating the reactive shortage of a network and the reactive loss of a main transformer, improving the electric energy quality, reducing the network loss and ensuring the safe and economic operation of the system. At present, a plurality of transformer substation reactive compensation modes such as a synchronous adjustment camera, a parallel capacitor, a shunt reactor, a static reactive compensation device and the like exist, wherein the parallel capacitor is largely used with the advantages of simple operation and maintenance, good economy and the like. However, due to the lifetime and maintenance of the capacitor banks, the switching times are strictly limited, and therefore the grouping of the capacitor banks is very important. The reasonable capacitor optimization grouping can effectively improve the electric energy quality of the system, particularly the voltage quality, can also reduce the network loss, improves the economical efficiency of the system operation, and can prolong the service life of equipment by reducing the switching times of the capacitors. On the contrary, the input rate of the capacitor bank may be low, and the reactive compensation effect may be affected.
At present, two capacity grouping modes of a transformer substation capacitor bank are mainly adopted, wherein one mode is equal capacity grouping, and the other mode is unequal capacity grouping. The equal-capacity grouping has the characteristics that the capacities of all groups are the same, different capacitor groups can be replaced mutually, the operation time of all groups can be ensured to be close to the maximum extent, and the maintenance period can be conveniently set. But is not flexible enough to operate and has fewer offset capacity schemes that can be combined. The unequal capacity grouping mainly comprises two forms of equal difference grouping and equal ratio grouping, and is characterized in that a plurality of compensation schemes can be realized by combining capacitor banks with different capacities, so that the unequal capacity grouping is suitable for various load levels, but the running time difference of the capacitor banks is large, the running and maintenance cost is increased, and the control scheme is complex. The capacitor bank capacity unequal capacity grouping is usually an optimal coverage method, the method is based on a probability distribution curve of reactive power demand, an input curve of the capacitor bank is used for fitting the probability distribution curve, however, the time sequence change of the reactive power demand is not considered in the existing method, if the fluctuation of the actual reactive power demand curve is large, the capacitor bank is frequently input and cut off, and adverse effects are caused on equipment.
Disclosure of Invention
The invention aims to provide a method for grouping unequal capacity of parallel compensation capacitor banks of a transformer substation, which considers the time sequence change of reactive demand, groups the parallel compensation capacitor banks based on a reactive demand time sequence curve of the transformer substation by using an objective function, reduces the difference of respective sample reactive demand in each section of sequence, further obtains more reasonable capacity of the capacitor, and reduces the times of investing and cutting the capacitor banks.
The invention provides a method for grouping unequal capacity of parallel compensation capacitor banks of a transformer substation, which comprises the following steps:
s1: acquiring reactive demand of a transformer substation at n moments, and taking the reactive demand at the n moments as samples, wherein n is a positive integer;
s2: dividing n samples in the reactive demand time sequence curve to obtain an optimal S-section reactive demand time sequence, wherein the number of the samples in the ith section of reactive demand time sequence is Li，
Confirming samples in each reactive demand time sequence based on an objective function F (n, S), wherein the objective function F (n, S) is as follows:
in the formula, DiA sample difference characteristic value representing the ith segment of the reactive demand time series, the sample difference characteristic value being calculated based on the samples in the reactive demand time series and being used for representing the difference degree of the samples in the segment;
s3: determining the number of capacitor groups and the capacity of each group of capacitors based on the S-section reactive demand time sequence obtained in the step S2, wherein the number of the capacitor groups is equal to S;
the method comprises the steps of calculating and sequencing sample average values in the S-section reactive demand time sequence, calculating the difference value of the sample average values between two adjacent sections, taking the minimum sample average value as the capacity of one group of capacitors, and calculating the difference value of the sample average values as the capacity of the rest other groups of capacitors. Further preferably, in step S2, based on a Fisher optimization segmentation method and a recursive method, n samples in the reactive power demand time sequence curve are divided to obtain an optimal S-segment reactive power demand time sequence;
firstly, dividing the first S samples in the n samples in the reactive demand time sequence curve to obtain S sections of reactive demand time sequences, wherein each initial section of reactive demand time sequence comprises one sample;
secondly, sequentially adding the residual samples by adopting a recursion method based on a Fisher optimization segmentation method to obtain an optimal S-segment reactive power demand time sequence corresponding to the n samples, wherein the recursion formula is as follows:
wherein,
in the formula, F (S, S) respectively represents an objective function of S-segment reactive power demand time sequence corresponding to S samples, and S-segment results of each recursive segmentation are represented as Di，i＝1,2,...,S；DiSample difference characteristic value representing the ith segment of time series of reactive demand, Di+1,i+2Representing the value of a sample difference characteristic of a segment consisting of the i +1 th segment and the i +2 th segment, DS+1And the sample difference characteristic value of taking the currently added sample as a segment of sequence in each recursion process is represented.
When the n samples are divided into S sections to construct the reactive demand time sequence, each section at least comprises one sample, and the method exists
Different combination methods are adopted, and if the combination method is adopted, the calculation amount is large;
the invention can reduce the operation amount by using a recursive method. As shown in fig. 1, if n is larger, the recursion of the present invention is more obvious.
TABLE 1
Further preferably, the sample difference characteristic value is a class diameter, and a class diameter calculation formula of the sample in the ith section of reactive demand time series is as follows:
wherein D isiRepresenting the class diameter, x, of the samples in the ith segment of the reactive demand time seriesijRepresents the j sample in the normalized i segment reactive demand time sequence,
and the average value of the samples in the normalized ith section of reactive demand time sequence is shown.
Further preferably, a min-max normalization method is adopted to perform normalization processing on the reactive demand time series, and the formula is as follows:
in the formula, Xmax，XminRespectively, the maximum and minimum values of the sample, XijThe j sample in the ith reactive demand time sequence.
More preferably, the reactive demand at each time in step S1 is composed of a reactive load and a reactive loss of the transformer, as follows:
Q＝QLoad+QT
wherein Q is the reactive demand, PLoad、QLoadRespectively of an electricity distribution networkActive load, reactive load, QT、XT、I0％、SNRespectively the reactive loss, short circuit reactance, no-load current, rated capacity, V, of the transformerNIs the rated voltage of the distribution network.
Advantageous effects
According to the unequal capacity grouping method for the parallel compensation capacitor bank of the transformer substation, provided by the invention, the dynamic change characteristic of the reactive power demand of the transformer substation is considered on one hand, and the target function based on the sample difference characteristic value is set on the other hand, so that the obtained difference of the reactive power demand of the sample in each section of sequence is reduced no matter the reactive power demand curve with larger or smaller volatility is segmented, the more reasonable capacity of the capacitor is obtained, the times of inputting and removing the capacitor bank are reduced, and particularly the problem of inputting and removing the capacitor bank frequently when the volatility is larger is solved.
Drawings
FIG. 1 is a schematic flow chart of an unequal capacity grouping method for a parallel compensation capacitor bank of a substation according to the present invention;
fig. 2 is a schematic diagram of recursive computation provided by an embodiment of the present invention.
Detailed Description
The present invention will be further described with reference to the following examples.
The invention provides a method for grouping unequal capacity of parallel compensation capacitor banks of a transformer substation in consideration of time-sequence change of reactive power requirements aiming at capacity grouping of the capacitor banks of the transformer substation, which comprises the following steps:
1. and determining the reactive demand of the transformer substation.
Load active power and reactive power of n moments in a day are uniformly sampled, reactive demand of a transformer substation at n sampling moments is calculated, and the transformer substation mainly comprises reactive load and reactive loss of a transformer, namely:
Q＝QLoad+QT (1)
the latter mainly consists of the reactive component of the transformer winding loss and the reactive component of the excitation winding, namely:
in the formulas (1) and (2), Q is the reactive power requirement of the transformer substation, PLoad、QLoadRespectively the active load, the reactive load, Q of the distribution networkT、XT、I0％、SNRespectively the reactive loss, short circuit reactance, no-load current, rated capacity, V, of the transformerNIs the rated voltage of the distribution network. The method includes the steps that n samples are obtained from a reactive demand curve of the transformer substation through formulas (1) and (2), the segmentation is carried out on the basis of the n samples, and in other feasible embodiments, if the reactive demand time sequence curve can be obtained first, the n samples can be selected from the curve to be segmented.
2. And dividing n samples in the reactive demand time sequence curve to obtain an optimal S-section reactive demand time sequence. As follows:
X＝{X11,X12...X1L1；X21,X12...X2L2；...；Xi1,Xi2...XiLi；...；XS1,XS2...XSLS}
in the formula, X represents a sequence formed by S-segment reactive demand time sequence, and XijFor the j sample in the ith reactive demand time sequence, LiFor the number of samples in the ith section of the reactive power demand time sequence, an
In the present embodiment, the S value is preset and set according to the actual requirement and the set maximum daily switching frequency, which is not specifically limited in the present invention. Since S is a known quantity, the above steps are performed to obtain how each segment of samples is divided, and the number of samples in each segment of sequence.It should be understood that there are many combinations of dividing n samples into S segments, each segment having at least one sample, and in order to reduce the number of times the capacitor bank is switched in and out, the present invention sets an objective function F (n, S), where the objective function is the smallest sum of class diameters of the segments, and in other possible embodiments, other parameters may be selected as the sample difference characteristic values, and the objective function is the smallest sum of sample difference characteristic values of the segments. The objective function F (n, S) in this embodiment is expressed as:
in this example, DiAnd (3) representing the class diameter of the ith section of reactive demand time sequence, and acquiring the class diameter as follows:
a: and (6) normalizing the data.
In this embodiment, a min-max normalization method is adopted to perform normalization processing on the reactive demand time sequence, and the formula is as follows:
in the formula, xijRepresents the j sample in the normalized i section reactive demand time sequence, Xmax，XminRespectively, the maximum and minimum values of the sample, XijThe j sample in the ith reactive demand time sequence. For example, the normalized time series x of reactive demand is as follows:
b: and calculating the class diameter.
In the formula, DiRepresents the class diameter of the sample in the ith segment of the reactive demand time series,
and the average value of the samples in the normalized ith section of reactive demand time sequence is shown.
It is mentioned above that the n samples are divided into S segments, each segment having at least one sample, and that there is a
In other feasible embodiments, the calculation may be performed based on each combination mode, and then the optimal S-segment reactive power demand time sequence is obtained based on an objective function, in this embodiment, in order to reduce the calculation amount, the calculation is performed in a recursive manner, and the implementation process is as follows:
firstly, dividing the first S samples in the n samples in the reactive demand time sequence curve to obtain S sections of reactive demand time sequences, wherein each initial section of reactive demand time sequence comprises one sample;
and secondly, sequentially adding the residual samples by adopting a recursion method based on a Fisher optimization segmentation method to obtain an optimal S-section reactive power demand time sequence corresponding to the n samples.
The derivation process is as follows:
first S samples in the n samples are divided to obtain S sections of reactive demand time sequences, each initial section of reactive demand time sequence comprises one sample, and the samples exist
F (S, S) respectively represents an objective function of the S-section reactive power demand time sequence corresponding to the S samples.
Then, considering that the S +1 th sample is added to the sequence, where the sample should be at the end based on the time sequence order, but there must be two samples in the S segment, so there are S cases, and then select the best segmentation method based on the objective function, which is expressed mathematically as:
wherein F (S +1, S) represents an objective function of the S-segment reactive demand time series corresponding to S +1 samples, wherein,
the samples of the 1 st segment and the 2 nd segment are combined into a new segment, and the other samples are respectively taken as the sum of the similar diameters of the segments when a segment sequence is formed (the 1 st segment is two samples, and the other segments are 1 sample);
it means that the i +1 th segment and the i +2 th segment form a new segment, and the sum of the diameters of the segments when other samples are respectively used as a segment sequence (the i +1 th segment is two samples, and the other segments are 1 sample). As shown in FIG. 2, assuming that S is 5, the previous 3 recursions are shown, and the result of each recursion is denoted as D i1, 2. Based on this idea, a recursion formula is obtained as follows:
adding samples based on the result of the previous recursion until the optimal S-section reactive power demand time sequence corresponding to the n samples is obtained.
3. Determining the number of capacitor groups and the capacity of each group of capacitors based on the S-section reactive demand time sequence obtained in the step S2;
wherein the number of capacitor banks is equal to S; and calculating and sequencing the average values of the samples in the reactive demand time sequence of the S section, calculating the difference value of the average values of the samples between two adjacent sections, taking the minimum average value of the samples as the capacity of one group of capacitors, and calculating the difference value of the average values of the samples as the capacity of the rest other groups of capacitors. For example, in this embodiment, the minimum value of the average values of the samples in the S section is used as the capacity of the 1 st group of capacitor banks, and the sequentially obtained differences are used as the capacities of the 2 nd group and the 3 rd group … of the S group of capacitor banks in sequence. With this arrangement, the capacitor bank can be switched in or out as required, whether the actual reactive demand is increased or decreased. Through the mode, more reasonable capacitor capacity is obtained, the times of putting in and cutting off the capacitor bank are reduced, particularly the problem that the capacitor bank is frequently put in and cut off when the fluctuation is large is solved, various compensation schemes are realized by combining the capacitor banks with different capacities, various load levels can be adapted, and the method and the device have important significance for improving the electric energy quality of a system, reducing the network loss and improving the economical efficiency of system operation.
It should be emphasized that the examples described herein are illustrative and not restrictive, and thus the invention is not to be limited to the examples described herein, but rather to other embodiments that may be devised by those skilled in the art based on the teachings herein, and that various modifications, alterations, and substitutions are possible without departing from the spirit and scope of the present invention.
Claims (4)
1. A method for grouping unequal capacity of parallel compensation capacitor banks of a transformer substation is characterized by comprising the following steps: the method comprises the following steps:
s1: acquiring reactive demand of a transformer substation at n moments, and taking the reactive demand at the n moments as samples, wherein n is a positive integer;
s2: dividing n samples in the reactive demand time sequence curve to obtain an optimal S-section reactive demand time sequence, wherein the number of the samples in the ith section of reactive demand time sequence is Li，
Confirming samples in each reactive demand time sequence based on an objective function F (n, S), wherein the objective function F (n, S) is as follows:
in the formula, DiA sample difference characteristic value representing the ith segment of the reactive demand time series, the sample difference characteristic value being calculated based on the samples in the reactive demand time series and being used for representing the difference degree of the samples in the segment;
s3: determining the number of capacitor groups and the capacity of each group of capacitors based on the S-section reactive demand time sequence obtained in the step S2, wherein the number of the capacitor groups is equal to S;
calculating and sequencing sample average values in the S-section reactive demand time sequence, calculating the difference value of the sample average values between two adjacent sections, setting the minimum sample average value as the capacity of one group of capacitors, and setting the difference value of the sample average values obtained through calculation as the capacity of the rest other groups of capacitors;
in the step S2, based on a Fisher optimization segmentation method and a recursive method, n samples in the reactive power demand time sequence curve are divided to obtain an optimal S-segment reactive power demand time sequence;
firstly, dividing the first S samples in the n samples in the reactive demand time sequence curve to obtain S sections of reactive demand time sequences, wherein each initial section of reactive demand time sequence comprises one sample;
secondly, sequentially adding the residual samples by adopting a recursion method based on a Fisher optimization segmentation method to obtain an optimal S-segment reactive power demand time sequence corresponding to the n samples, wherein the recursion formula is as follows:
wherein,
in the formula, F (S, S) respectively represents an objective function of S-segment reactive power demand time sequence corresponding to S samples, and S-segment results of each recursive segmentation are represented as Di，i＝1,2,...,S；DiSample difference characteristic value representing the ith segment of time series of reactive demand, Di+1,i+2Representing the value of a sample difference characteristic of a segment consisting of the i +1 th segment and the i +2 th segment, DS+1And the sample difference characteristic value of taking the currently added sample as a segment of sequence in each recursion process is represented.
2. The method of claim 1, wherein: the sample difference characteristic value is a class diameter, and a class diameter calculation formula of the sample in the ith section of reactive demand time sequence is as follows:
wherein D isiRepresenting the class diameter, x, of the samples in the ith segment of the reactive demand time seriesijRepresents the j sample in the normalized i segment reactive demand time sequence,
and the average value of the samples in the normalized ith section of reactive demand time sequence is shown.
3. The method of claim 2, wherein: the min-max normalization method is adopted to perform normalization processing on the reactive power demand time sequence, and the formula is as follows:
in the formula, Xmax，XminRespectively, the maximum and minimum values of the sample, XijThe j sample in the ith reactive demand time sequence.
4. The method of claim 1, wherein: the reactive demand at each time in step S1 is composed of the reactive load and the reactive loss of the transformer, as follows:
Q＝QLoad+QT
wherein Q is the reactive demand, PLoad、QLoadRespectively the active load, the reactive load, Q of the distribution networkT、XT、I0％、SNRespectively the reactive loss, short circuit reactance, no-load current, rated capacity, V, of the transformerNIs the rated voltage of the distribution network.
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