CN106786581B - Active filter optimal configuration method - Google Patents
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/40—Arrangements for reducing harmonics
Abstract
The invention provides an active filter optimal configuration method, which comprises the following steps: setting an objective function based on the influence of the active filter configuration; setting constraint conditions meeting the optimal configuration of the active filter of the intelligent power distribution network; and optimally configuring the active filter of the intelligent power distribution network by adopting a configuration algorithm combining modal analysis and a genetic algorithm. According to the active filter optimal configuration method provided by the invention, the candidate position nodes of the active filter are determined by introducing a modal analysis method, so that the calculation amount of the genetic algorithm in determining the configuration position of the active filter is reduced, the problem that the genetic algorithm is easy to fall into local optimization is solved, the calculation speed is high, the algorithm is favorable for fast convergence, a better optimal solution can be found, the optimal access position of the active filter can be effectively calculated, and the method plays a good role in improving the economy of system operation and improving the power quality.
Description
Technical Field
The invention relates to an intelligent power distribution network system, in particular to an active filter optimal configuration method.
Background
The wide application of power electronic devices makes the harmonic pollution of power grids increasingly serious, and the harmonic problem of power systems draws more and more attention.
At present, in an electric power system, harmonic treatment is mainly to reduce harmonic content by installing a filter. The passive filter is a main means for suppressing harmonics, but has the disadvantages of poor filtering effect, possibility of causing harmonic amplification, and the like. The active filter is a power electronic device for dynamically inhibiting harmonic waves, can compensate the harmonic waves with frequency and amplitude changes, can make up for the defects of a passive filter, obtains better compensation characteristics than the passive filter, and is an ideal harmonic wave compensation device. Therefore, the application of active filters in power systems is increasing.
In the smart grid, the optimal configuration method of the active filter mainly comprises the following steps: heuristic search methods, simulated annealing methods, nonlinear programming methods, and genetic algorithms. Genetic algorithms are most commonly used, but when the system is slightly complex, the iteration time is very long, and the accuracy of the optimization result is influenced.
Disclosure of Invention
The invention provides a method for optimizing and configuring an active filter, which can solve the problem that a plurality of harmonic sources are merged into an intelligent power distribution network to generate a large number of harmonics.
In order to solve the above problems, the present invention provides an active filter optimization configuration method, which includes the following steps: setting an objective function based on the influence of the active filter configuration; setting constraint conditions meeting the optimal configuration of the active filter of the intelligent power distribution network; and optimally configuring the active filter of the intelligent power distribution network by adopting a configuration algorithm combining modal analysis and a genetic algorithm.
Preferably, the configuration algorithm using the combination of modal analysis and genetic algorithm specifically includes:
s1, inputting parameters of the intelligent power distribution network, and calculating harmonic parameters of the initial state of the intelligent power distribution network through harmonic voltage content and voltage total harmonic distortion rate constraints, wherein the harmonic parameters of the initial state of the intelligent power distribution network comprise harmonic parameters of each order of elements;
s2, according to each harmonic admittance matrix formed in each harmonic parameter process of the element, obtaining a characteristic value and a corresponding characteristic vector of the harmonic admittance matrix by using a modal analysis method, and selecting a candidate position node configured by an active filter according to the characteristic value and the characteristic vector;
and S3, inputting the relevant parameters in the genetic algorithm and the candidate position nodes, and outputting an optimization result by using the genetic algorithm.
Preferably, the modal impedance of each mode is calculated according to the characteristic values in S2 to select the mode in which resonance occurs, all the participation factors of the mode in which resonance occurs are calculated according to the characteristic vectors, and the candidate position node of the active filter configuration is selected according to the magnitude of the participation factors.
Preferably, S3 specifically includes:
s3 specifically includes:
s31, randomly generating an initial population and setting the initial iteration times;
s32, performing harmonic power flow calculation to obtain the voltage content rate of each subharmonic and the total harmonic distortion rate of the voltage of the candidate position node;
s33, obtaining the rated installation capacity of each active filter by applying each harmonic current absorption coefficient of the active filter in the individual, the overload constraint of the filter and each harmonic voltage of the candidate position node;
s34, calculating the fitness of the individuals according to the objective function, and ordering the fitness values of all the individuals based on the consideration of whether the harmonic voltage constraint conditions are violated;
s35, carrying out sequencing selection, crossover and mutation genetic operations to generate a new population and increase iteration times;
and S36, judging whether the result meets a termination condition, if so, terminating the genetic iteration and outputting an optimization result, and if not, returning to S32.
Preferably, the objective function is a single objective function of cost, harmonic network loss and voltage distortion rate.
Preferably, the objective function is a multi-objective function targeting cost, harmonic net loss and voltage total harmonic distortion.
Preferably, the multi-objective function is a weighted minimum of cost, harmonic network loss and voltage total harmonic distortion rate, and the multi-objective function is
Wherein, v1、ν2、ν3In order to be a weighting factor, the weighting factor,
respectively an objective function of the investment cost of the dimensionless active filter, an objective function of the total harmonic loss in the system and an objective function of the harmonic control.
Preferably, the constraints include at least constraints of the system power flow, node harmonic content and total voltage harmonic distortion.
Compared with the prior art, the invention has the beneficial effects that: the candidate position nodes of the active filter are determined by introducing a modal analysis method, the calculation amount of the genetic algorithm in determining the configuration position of the active filter is reduced, the problem that the genetic algorithm is easy to fall into local optimization is solved, the calculation speed is high, the algorithm is favorable for fast convergence, a better optimal solution can be found, the optimal access position of the active filter can be effectively calculated, and the method plays a good role in improving the economy of system operation and improving the power quality.
Drawings
Fig. 1 is a flowchart of an active filter optimization configuration method according to an embodiment of the present invention;
FIG. 2 is a flow chart of an active filter optimization configuration method according to another embodiment of the present invention;
fig. 3 is a detailed flowchart of step S3 in the active filter optimization configuration method according to another embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the figures and the examples, but without limiting the invention.
As shown in fig. 1, the present invention discloses an active filter optimization configuration method, which comprises the following steps: setting an objective function based on the influence of the active filter configuration; setting constraint conditions meeting the optimal configuration of the active filter of the intelligent power distribution network; and optimally configuring the active filter of the intelligent power distribution network by adopting a configuration algorithm combining modal analysis and a genetic algorithm.
The objective function may be a single objective function, such as cost, harmonic loss, or voltage distortion rate. The objective function can also be a multi-objective function based on cost, harmonic network loss and voltage total harmonic distortion, and the multi-objective function is set, so that the optimal configuration of the active filter can be more reasonable.
In this embodiment, the objective function of the active filter of the smart distribution network is established by comprehensively considering the two effects of economy and filtering effect, specifically, the weighted minimum of the cost, the harmonic network loss and the total voltage harmonic distortion rate is used as the objective function, and the weighting factor of each objective function can be adjusted according to actual conditions to adapt to different systems.
Specifically, the objective function is
Wherein minF represents the minimum value of the function after the unified unit, v1、ν2、ν3Which represents a weighting factor, is given by the weighting factor,
respectively representing the tariff of dimensionless active filtersAn objective function, an objective function of total harmonic loss in a dimensionless system, and an objective function of dimensionless harmonic suppression.
Active filter tariff
Wherein N represents the total number of nodes, Tf, of the distribution networkiRepresents the installation cost of the i-th node active filter, diIndicates whether or not the ith node is equipped with a filter (wherein di1 denotes installation, di0 indicates not mounted), QiThe installation capacity of the ith node active filter is represented; k is a radical ofcRepresenting the price per unit capacity of the active filter.
H-th harmonic loss in system
Wherein, IhRepresents the harmonic current injected into the grid by the active filter;
representing the h-th harmonic resistance between node i and node j.
Harmonic suppression
Wherein the content of the first and second substances,
respectively representing the fundamental voltage and the h-th harmonic voltage of the nth node.
For the constraint conditions which are set to meet the optimal configuration of the active filter of the intelligent power distribution network, the constraint conditions can be that the harmonic voltage and the total distortion rate of the network nodes meet the harmonic standard and the active filter operates safely and reliably, and the constraint conditions can include the constraint of system power flow, the constraint of the voltage content of each subharmonic of the nodes, the constraint of the voltage total harmonic distortion rate of the nodes and the constraint of the switching capacity and the switching number of the active filter.
Specifically, the voltage content of each harmonic of the node
Wherein, CHRUA limiting coefficient indicating a voltage content rate.
Total harmonic distortion of voltage
Wherein, CTHDUA limiting factor representing the total harmonic distortion rate of the voltage.
Switching capacity Q of active filter connected to node i in systemiNeed to satisfy Qimin≤Qi≤QimaxAnd Qi＝KQ0Wherein Q isimin、QimaxExpressing the minimum and maximum values of the capacity of the active filter which can be accessed by the node i, wherein the K value is a natural number, and Q is0Indicating the capacity that a unit of active filter can provide.
The switching number d of the active filter needs to satisfy
Where D denotes the maximum number of active filter installations.
The invention discloses an active filter optimal configuration method, which sets an objective function and a constraint condition through the above and adopts a configuration algorithm combining modal analysis and a genetic algorithm, and comprises specific optimal configuration steps as shown in figure 2.
And S1, inputting parameters of the intelligent power distribution network, and calculating harmonic parameters of the initial state of the intelligent power distribution network through harmonic voltage content and voltage total harmonic distortion rate constraints, wherein the harmonic parameters of the initial state of the intelligent power distribution network comprise harmonic parameters of each element.
The parameters of the intelligent power distribution network comprise all element parameters of the intelligent power distribution network and all subharmonic source parameters, the element parameters of the intelligent power distribution network specifically comprise total node number, generator parameters, transformer parameters, line parameters and load parameters, and the parameters of all subharmonic sources specifically comprise active power and reactive power injected by all harmonic sources and active power and reactive power of all nonlinear loads.
The harmonic parameters of the initial state of the intelligent power distribution network further comprise the amplitude and the phase of the harmonic voltage of each node, the harmonic distortion rate of each sub-voltage of each node and the total harmonic distortion rate of the voltage.
And S2, according to each harmonic admittance matrix formed in each harmonic parameter process of the element, obtaining the characteristic value and the corresponding characteristic vector of the harmonic admittance matrix by using a modal analysis method, and selecting the candidate position node configured by the active filter according to the characteristic value and the characteristic vector.
Wherein, the modal impedance of each mode is calculated according to the eigenvalue in S2 to select the mode in which resonance occurs, all the participation factors of the mode in which resonance occurs are calculated according to the eigenvector, the size of the participation factor corresponds to the size influenced by resonance, the size influenced by resonance of each node is judged according to the size of the participation factor, and the candidate position node configured by the active filter is selected as the node corresponding to the larger participation factor, that is, the position in which harmonic parallel resonance occurs.
And S3, inputting relevant parameters in the genetic algorithm and the obtained candidate position nodes, and outputting an optimization result by using the genetic algorithm.
The related parameters in the genetic algorithm comprise population size, iteration times, selection parameters, cross rate and variation rate.
As shown in fig. 3, S3 specifically includes: s31, randomly generating an initial population by the system, and setting the initial iteration times; s32, performing harmonic power flow calculation to obtain the voltage content rate of each subharmonic and the total harmonic distortion rate of the voltage of the candidate position node; s33, obtaining the rated installation capacity of each active filter by applying each harmonic current absorption coefficient of the active filter in the individual, the overload constraint of the filter and each harmonic voltage of the candidate position node; s34, calculating the fitness of the individuals according to the objective function, and ordering the fitness values of all the individuals based on the consideration of whether the harmonic voltage constraint conditions are violated; s35, carrying out sequencing selection, crossover and mutation genetic operations to generate a new population and increase iteration times; and S36, judging whether the result meets the termination condition, if so, terminating the genetic iteration and outputting an optimization result, and if not, returning to S32 to continue the iteration of the genetic algorithm.
The active filter optimal configuration method of the invention combines modal analysis and genetic algorithm, determines the candidate position node of the active filter by introducing the modal analysis method, reduces the calculation amount when the genetic algorithm determines the configuration position of the active filter, solves the problem that the genetic algorithm is easy to fall into local optimization, has high operation speed, is beneficial to the rapid convergence of the algorithm, can find better optimal solution, can effectively calculate the optimal access position of the active filter, and plays a good role in improving the economy of system operation and improving the power quality.
The above embodiments are only exemplary embodiments of the present invention, and are not intended to limit the present invention, and the scope of the present invention is defined by the claims. Various modifications and equivalents may be made by those skilled in the art within the spirit and scope of the present invention, and such modifications and equivalents should also be considered as falling within the scope of the present invention.
Claims (4)
1. An active filter optimization configuration method, comprising the following steps:
setting an objective function based on the influence of the active filter configuration;
setting constraint conditions meeting the optimal configuration of an active filter of the intelligent power distribution network, wherein the constraint conditions at least comprise the constraint of system power flow, the constraint of each subharmonic voltage content of a node, the constraint of total harmonic distortion rate of the voltage of the node, and the constraint of the switching capacity and the switching number of the active filter;
performing optimal configuration on the active filter of the intelligent power distribution network by adopting a configuration algorithm combining modal analysis and a genetic algorithm; wherein the content of the first and second substances,
the objective function is a multi-objective function taking cost, harmonic network loss and voltage total harmonic distortion rate as targets;
the multi-objective function is cost, harmonic network loss and voltage total harmonic distortionHaving a minimum weight value of, the multi-objective function
Wherein, v1、ν2、ν3In order to be a weighting factor, the weighting factor,
respectively an objective function of the investment cost of the dimensionless active filter, an objective function of the total harmonic loss in the system and an objective function of the harmonic control.
2. The method according to claim 1, wherein the configuration algorithm using a combination of modal analysis and genetic algorithm specifically comprises:
s1, inputting parameters of the intelligent power distribution network, and calculating harmonic parameters of the initial state of the intelligent power distribution network through harmonic voltage content and voltage total harmonic distortion rate constraints, wherein the harmonic parameters of the initial state of the intelligent power distribution network comprise harmonic parameters of each order of elements;
s2, according to each harmonic admittance matrix formed in each harmonic parameter process of the element, obtaining a characteristic value and a corresponding characteristic vector of the harmonic admittance matrix by using a modal analysis method, and selecting a candidate position node configured by an active filter according to the characteristic value and the characteristic vector;
s3, inputting relevant parameters and the candidate position nodes into a genetic algorithm, and outputting an optimization result by using the genetic algorithm;
the relevant parameters comprise population size, iteration number, selection parameters, cross rate and variation rate.
3. The method of claim 2, wherein the step of configuring the active filter further comprises the step of configuring the active filter,
and calculating the modal impedance of each mode according to the characteristic value in the S2 to select the mode which generates resonance, calculating all participation factors of the mode which generates resonance according to the characteristic vector, and selecting the candidate position node configured by the active filter according to the sizes of the participation factors.
4. The active filter optimal configuration method according to any one of claims 2 to 3, wherein S3 specifically includes:
s31, randomly generating an initial population and setting the initial iteration times;
s32, performing harmonic power flow calculation to obtain the voltage content rate of each subharmonic and the total harmonic distortion rate of the voltage of the candidate position node;
s33, obtaining the rated installation capacity of each active filter by applying each harmonic current absorption coefficient of the active filter in the individual, the overload constraint of the filter and each harmonic voltage of the candidate position node;
s34, calculating the fitness of the individuals according to the objective function, and ordering the fitness values of all the individuals based on the consideration of whether the harmonic voltage constraint conditions are violated;
s35, carrying out sequencing selection, crossover and mutation genetic operations to generate a new population and increase iteration times;
and S36, judging whether the result meets a termination condition, if so, terminating the genetic iteration and outputting an optimization result, and if not, returning to S32.
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| CN109309385A (en) * | 2018-12-05 | 2019-02-05 | 中南大学 | Hybrid active filter Optimal Configuration Method in a kind of active power distribution network |
| CN109818499A (en) * | 2019-01-31 | 2019-05-28 | 张欣 | A kind of buck converter second order filter design method based on multiple-objection optimization |
| CN110323777A (en) * | 2019-02-27 | 2019-10-11 | 华北电力大学 | It is a kind of inhibit wind power plant medium-frequency oscillator filter location determine method |
| CN110165682A (en) * | 2019-05-24 | 2019-08-23 | 国网河北省电力有限公司沧州供电分公司 | Distribution network active filtering device Optimal Configuration Method, device and storage medium |
| CN111555299B (en) * | 2020-04-28 | 2022-11-22 | 国网河北省电力有限公司电力科学研究院 | SVG configuration optimization method in power distribution network containing distributed power supply |
| CN112448393B (en) * | 2020-11-09 | 2023-03-17 | 广西电网有限责任公司电力科学研究院 | Power distribution network active power filter configuration method based on adaptive algorithm |
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