CN106295862B - Reactive power optimization data processing method for power distribution network - Google Patents
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Abstract
The invention provides a power distribution network reactive power optimization data processing method which comprises the steps of determining sampling data of power distribution network reactive power optimization, initializing power distribution network reactive power optimization mixed component parameters, optimizing the power distribution network reactive power optimization mixed component parameters, judging the convergence of a data processing process, and if the convergence is not achieved, adding 1 to the iteration times to further optimize the power distribution network reactive power optimization mixed component parameters; after the data processing process converges, a probability density function is calculated. The technical scheme provided by the invention realizes reasonable allocation of reactive resources, provides more systematic and comprehensive data support for reactive power optimization of the power distribution network, and greatly improves the economy, safety and stability of operation of the power system.
Description
Technical Field
The invention relates to a data processing method in the field of power distribution networks, in particular to a power distribution network reactive power optimization data processing method.
Background
The reactive power flow distribution of the power system not only directly affects the safety and stability of the power system, but also is closely related to economic benefits. The voltage of the system is reduced due to insufficient reactive power flow, and a voltage collapse accident is caused when the system is serious; the excessive reactive power flow causes the deterioration of the system voltage quality and the damage to the safety of the system and equipment, and the excessive reactive power reserve wastes unnecessary investment. The reasonable configuration of the input and the exit of the reactive power supply can effectively reduce the network loss, ensure the voltage quality, prevent the occurrence of accidents and prevent the expansion of the accidents, thereby improving the economical efficiency, the safety and the stability of the operation of the power system.
The reactive operation optimization is dynamic optimization which takes the minimum electric energy loss of a system, the highest voltage qualified rate of each node and the minimum switching times of a transformer tap, a capacitor and a reactor as objective functions on the basis of the fixed and unchangeable grid structure of a power grid, and the nature of the reactive operation optimization is a large-scale nonlinear mixed integer optimization problem.
With the continuous advance of the intelligent process of the power grid, the construction of various power networks and the rapid increase of the number of users, the information data borne by the IT system of the power unit is more and more huge in scale. The 'big data' resources owned by the power industry include massive user information, service information, basic resources and platform resources, and can be roughly classified into three types: the method comprises the following steps that firstly, production data of a power enterprise, such as data in the aspects of power generation amount, voltage stability and the like, are obtained; second, the operation data of the electric power enterprise, such as the data in the aspects of the price of the trade electricity, the quantity of selling electricity, the customer of using electricity, etc.; and thirdly, managing data of the power enterprise, such as ERP, integrated platform, cooperative work and the like.
Along with the development of an intelligent active power distribution network, a distributed power supply is connected into the power distribution network, and various novel services derived from the power distribution network in an intelligent mode enable the resource release of 'big data' to be increased, and the data storage, maintenance and processing of the power distribution network are subjected to huge tests. The method has the advantages that the mass data in the power industry are stored and managed, the supporting effect of information resources on service operation is fully exerted, meanwhile, the information resources are prevented from being threatened by safety, and the method becomes one of the problems which need to be solved urgently for power enterprises.
The traditional reactive power optimization method has strict data limitation and is difficult to fully exert the advantage of big data, and the application of the big data technology in the field of power distribution networks is used for the global optimization of power grids in a theoretical analysis mode based on data modeling, so that the conventional assumption and simplification of random factors are broken through.
The random matrix is a matrix formed by taking random variables as elements under a certain given probability space; and large-dimension data refers to data in which the sample dimension and the sample size tend to be infinite with the same order. The random matrix theory is used as a method for effectively processing large-dimensional data, the large-dimensional random matrix theory is introduced into the analysis of the large data of the power distribution network, the correlation theory of the random matrix, such as characteristic root spectrum analysis, central limit theorem and the like, is reasonably utilized to analyze and process the large data of reactive power optimization of the power distribution network, and a new path for solving the problem of reactive power optimization is searched under the framework of the large data of the power distribution network.
The invention provides a power distribution network reactive power optimization data processing method in order to meet the processing requirements of power enterprises on power distribution network reactive power optimization and big data.
Disclosure of Invention
In order to meet the development requirements of the prior art, the invention provides a power distribution network reactive power optimization data processing method, which is used for processing the distributed power supply and load data of a power distribution network, converting the data into a data format expressed by a probability density function and providing data support for power distribution network reactive power optimization.
The invention provides a power distribution network reactive power optimization data processing method, which is improved in that the data processing method comprises the following steps:
(1) determining sampling data;
(2) initializing parameters of the hybrid component;
(3) optimizing the parameters of the mixing component;
(4) judging the convergence of the data processing process;
(5) a probability density function is calculated.
Further, the reactive power optimization sampling data of the power distribution network obtained from the distributed power supply of the power distribution network in the step (1) is shown as a matrix z in the following formula (1):
Z＝[Z1，Z2，…，Zi，…，ZN] (1)
wherein Z isi: sampling values i of the reactive power optimization data of the power distribution network; n: the number of the reactive power optimization data samples of the power distribution network;
further, the initialization of the distribution network reactive power optimization hybrid component in the step (2) comprises: weights of hybrid components
Mean value of the Mixed Components mujIs a random number; variance of mixing component
Is 1; and,
parameter set gamma of power distribution network reactive power optimization mixing component obeying Gaussian distributionjAs shown in the following formula (2):
in the formula, j: the reactive power optimization hybrid component number of the power distribution network, j belongs to [1, 2., M ], M: the number of the reactive power optimization mixing assemblies of the power distribution network.
Further, in the step (3), the optimization method for the reactive power optimization hybrid component parameters of the power distribution network comprises the following steps;
weights of blending components in the s +1 th iteration
As shown in the following formula (3):
mean of the mixing Components in the s +1 th iteration
As shown in the following formula (4):
variance of mixing component in s +1 th iteration
As shown in the following formula (5):
wherein,
k: an iteration factor; m: the number of the reactive power optimization mixing components of the power distribution network; gamma rays: the value of the s iteration of the parameter set; zi: sampling values i of the reactive power optimization sampling data of the power distribution network; j: and numbering the reactive power optimization hybrid components of the power distribution network.
Further, in the step (4), the convergence condition for judging the convergence of the reactive power optimization data processing process of the power distribution network is shown as the following formula (6):
wherein,
correcting the reactive power optimization mixing component parameter set of the power distribution network in the s-th iteration process; epsilon: the convergence standard of the reactive power optimization data processing process of the power distribution network is obtained; slim: and limiting the maximum iteration times of the reactive power optimization data processing of the power distribution network.
Further, in the step (5), parameters of the reactive power optimization mixing component of the power distribution network are fused, and a probability density function f (x | γ) of the data sample is calculated according to the following formula (7):
wherein, x: the probability density function independent variable of the reactive power optimization data sample of the power distribution network; omegaj: the weight of the reactive power optimization mixing component j of the power distribution network; mu.sj: the average value of the reactive power optimization mixing component j of the power distribution network;
and the variance of the power distribution network reactive power optimization mixing component j.
Compared with the closest prior art, the technical scheme provided by the invention has the following excellent effects:
1. according to the technical scheme provided by the invention, under the condition of uncertain data statistical characteristics, aiming at the randomness and uncertainty of the distributed power output and load data in the reactive power optimization of the power distribution network, a Gaussian equivalent model is used for obtaining the probability density function of sample data, so that more systematic and comprehensive data support is provided for the reactive power optimization of the power distribution network.
2. The technical scheme provided by the invention optimizes the configuration of the reactive power flow, reasonably distributes the input and the exit of the reactive power supply, can effectively ensure the voltage quality, prevent the occurrence of accidents and the enlargement of the accidents, and greatly improves the economical efficiency, the safety and the stability of the operation of the power system.
3. The technical scheme provided by the invention introduces a big data technology, realizes the management of mass data, and fully exerts the supporting function of information resources on service operation.
Drawings
Fig. 1 is a flow chart of a power distribution network reactive power optimization data processing method provided by the invention.
Detailed Description
The technical scheme provided by the invention is further described in detail in the following description in combination with the accompanying drawings.
DG output and load data in the reactive power optimization of the power distribution network have the characteristics of randomness and uncertainty, and different from a traditional single numerical value representation method, the big data is more prone to be described in a mode of using a region or probability density function, so that more systematic and comprehensive data support is provided. The power distribution network distributed power supply and load prediction has particularity, the statistical characteristics of data are complex, and the probability density function is difficult to obtain by direct fitting. The invention aims to process DG output and load data of a power distribution network, convert the DG output and load data into a data format expressed by a probability density function and establish data support for reactive power optimization of the power distribution network.
The invention provides a power distribution network reactive power optimization data processing method based on a Gaussian equivalent model, which comprises the following steps:
firstly, loading reactive power optimization data of a power distribution network;
the reactive power optimization sampling data of the power distribution network are derived from power distribution network distributed power supplies or loads with randomness and uncertainty, and are active power or reactive power of the power distribution network distributed power supplies or loads. The sampling data aiming at a certain distribution network distributed power source or load is represented by a distribution network reactive power optimization sampling data matrix shown in the following formula (1):
Z＝[Z1，Z2，…，Zi，…，ZN] (1)
wherein Z isi: the method comprises the following steps of 1, sampling data i for reactive power optimization of a power distribution network; n: the number of the reactive power optimization data samples of the power distribution network.
Secondly, initializing parameters of a reactive power optimization mixing component of the power distribution network;
each power distribution network reactive power optimization mixing component obeys Gaussian distribution, and the parameter set gamma of each power distribution network reactive power optimization mixing componentjAs shown in the following formula (2):
wherein j: the serial number of the reactive power optimization hybrid component of the power distribution network, j belongs to [1,2, …, M ], M: the number of the reactive power optimization mixing components of the power distribution network is larger, the final calculation result is higher in precision, and the calculation amount is increased along with the value;
ωj: the weight of the reactive power optimization hybrid component j of the power distribution network,
μj: the average value of the reactive power optimization mixing component j of the power distribution network;
and the variance of the power distribution network reactive power optimization mixing component j. The initialization is that,
μjis a random number, and is a random number,
thirdly, iterative operation is carried out, and the parameters of the reactive power optimization mixing component of the power distribution network in the next step are solved;
generating the power distribution network reactive power optimization parameter mixing component parameters in the S +1 th iteration based on the power distribution network reactive power optimization mixing component parameters of the S th iteration, and generating the weight of the power distribution network reactive power optimization mixing component j in the S +1 th iteration
Mean value
Sum variance
The calculation methods of (2) are shown in the following formulae (3), (4) and (5), respectively:
wherein,
fourthly, judging whether the reactive power optimization data processing process of the power distribution network is converged;
the convergence condition of the reactive power optimization data processing process of the power distribution network is judged as shown in the following formula (6):
in the formula,
-correction of the distribution network reactive power optimization hybrid component parameter set in the s-th iteration process;
epsilon-convergence standard of the reactive power optimization data processing process of the power distribution network;
slim-a maximum iteration limit for reactive power optimization data processing of the distribution network.
Judging whether the reactive power optimization data processing process of the power distribution network converges or not:
(1) if the reactive power optimization data processing process of the power distribution network is converged, performing the following step (5);
(2) and (4) if the power distribution network reactive power optimization data processing process is not converged, automatically increasing the iteration times by 1, performing iteration in the step (3), further solving the parameters of the power distribution network reactive power optimization hybrid components, and judging whether the data processing process is converged.
Fifthly, fusing parameters of all the components, and calculating a probability density function of the reactive power optimization data sample of the power distribution network;
after the reactive power optimization data processing process of the power distribution network converges, the probability density function f (x | γ) of the reactive power optimization data sample of the power distribution network is shown as the following formula (7):
in the formula, x: the probability density function independent variable of the reactive power optimization data sample of the power distribution network; omegaj: the weight of the reactive power optimization mixing component j of the power distribution network; mu.sj: the average value of the reactive power optimization mixing component j of the power distribution network;
and the variance of the power distribution network reactive power optimization mixing component j.
The technical scheme provided by the invention aims at the randomness and uncertainty of DG output and load data in the reactive power optimization of the power distribution network, and under the condition of not defining the statistical characteristics of data in advance, a Gaussian equivalent model is used for obtaining the probability density function of sample data, so that more systematic and comprehensive data support is provided for the reactive power optimization of the power distribution network.
Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art can make modifications and equivalents to the embodiments of the present invention without departing from the spirit and scope of the present invention, which is set forth in the claims of the present application.
Claims (1)
1. A processing method for reactive power optimization data of a power distribution network is characterized by comprising the following steps:
(1) determining sampling data;
(2) initializing parameters of the hybrid component;
(3) optimizing the parameters of the mixing component;
(4) judging the convergence of the data processing process;
(5) calculating a probability density function;
the power distribution network reactive power optimization sampling data obtained from the power distribution network distributed power supply in the step (1) is shown as a matrix z in the following formula (1):
Z＝[Z1，Z2，…，Zi，…，ZN] (1)
wherein Z isi：Sampling values i of the reactive power optimization data of the power distribution network; n: the number of the reactive power optimization data samples of the power distribution network; the initialization of the reactive power optimization hybrid component of the power distribution network in the step (2) comprises the following steps: weights of hybrid components
Mean value of the Mixed Components mujIs a random number; variance of mixing component
Is 1; and,
parameter set gamma of power distribution network reactive power optimization mixing component obeying Gaussian distributionjAs shown in the following formula (2):
in the formula, j: the reactive power optimization hybrid component number of the power distribution network, j belongs to [1,2, …, M ], M: the number of the reactive power optimization mixing components of the power distribution network;
in the step (3), the optimization method of the reactive power optimization hybrid component parameters of the power distribution network comprises the following steps of;
weights of blending components in the s +1 th iteration
As shown in the following formula (3):
mean of the mixing Components in the s +1 th iteration
As shown in the following formula (4):
variance of mixing component in s +1 th iteration
As shown in the following formula (5):
wherein,
k: an iteration factor; m: the number of the reactive power optimization mixing components of the power distribution network; gamma rays: the value of the s iteration of the parameter set; zi：Sampling values i of the reactive power optimization sampling data of the power distribution network; j: numbering the reactive power optimization hybrid components of the power distribution network; in the step (4), the convergence condition of the convergence of the reactive power optimization data processing process of the power distribution network is judged as shown in the following formula (6):
Wherein,
correcting the reactive power optimization mixing component parameter set of the power distribution network in the s-th iteration process; epsilon: the convergence standard of the reactive power optimization data processing process of the power distribution network is obtained; slim: limiting the maximum iteration times of reactive power optimization data processing of the power distribution network;
in the step (5), parameters of the reactive power optimization mixing component of the power distribution network are fused, and a probability density function f (x | gamma) of the data sample is calculated according to the following formula (7):
wherein, x: the probability density function independent variable of the reactive power optimization data sample of the power distribution network; omegaj: the weight of the reactive power optimization mixing component j of the power distribution network; mu.sj: the average value of the reactive power optimization mixing component j of the power distribution network;
and the variance of the power distribution network reactive power optimization mixing component j.
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| CN105305490A (en) * | 2015-10-26 | 2016-02-03 | 国网天津市电力公司 | Active distribution network planning method considering optimal economical efficiency of power quality |
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| CN104079003A (en) * | 2014-07-21 | 2014-10-01 | 国家电网公司 | Probability load flow calculation method for photovoltaic power contained distribution network |
| CN104376378A (en) * | 2014-11-14 | 2015-02-25 | 浙江工商大学 | Distributed-power-source-contained power distribution network reactive power optimization method based on mixed integer cone optimization |
| CN104463374A (en) * | 2014-12-23 | 2015-03-25 | 国家电网公司 | Method and system for optimal configuration of distributed power source |
| CN105305490A (en) * | 2015-10-26 | 2016-02-03 | 国网天津市电力公司 | Active distribution network planning method considering optimal economical efficiency of power quality |
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