CN113328444A - Method for using cloud computing for power flow computing - Google Patents
Method for using cloud computing for power flow computing Download PDFInfo
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- CN113328444A CN113328444A CN202110758143.9A CN202110758143A CN113328444A CN 113328444 A CN113328444 A CN 113328444A CN 202110758143 A CN202110758143 A CN 202110758143A CN 113328444 A CN113328444 A CN 113328444A
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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
- H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
- H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
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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
- H02J3/01—Arrangements for reducing harmonics or ripples
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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
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- 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 relates to the technical field of electric power, in particular to a method for using cloud computing for power flow computing, which comprises the following steps: s1, dividing the whole wind power system into a plurality of areas; s2, measuring the output power of the nodes in a plurality of areas; and S3, generating a positive Taiyun digital characteristic value by using a reverse generator according to the data sample: the expected value, the entropy and the super entropy are input into a cloud computing system, and an admittance matrix is formed for each node through the cloud computing system; s4, setting the power of the node according to the balance point power value and the initial value of the voltage of the non-balance point power node and the expected value; and S5, calculating the injection harmonic current of the node according to the harmonic source, and obtaining the voltage of each node according to the power and the harmonic current of the node. The method has the advantages of ensuring the accuracy of obtaining the voltage of each node and avoiding the inaccurate condition of power flow calculation caused by voltage flicker.
Description
Technical Field
The invention relates to the technical field of electric power, in particular to a method for using cloud computing for power flow computing.
Background
When an electric power system is in operation, under the action of the excitation of the power supply potential, current or power flows from the power supply through the various elements of the system into the load, distributed throughout the power grid, referred to as power flow. Electricity (electricity) is generated at power stations, some of which are lost through transmission and distribution lines, substations and substations, and the vast majority of the remainder is ultimately consumed by loads. Therefore, from the generation of electricity to the consumption of loads, the voltage of each node flows through which power transmission and distribution line, and the calculation is called power flow calculation or flow calculation for short. The output power of the wind energy system is uncertain, and meanwhile, the influence of weather conditions is large, so that voltage fluctuation is easily caused, and meanwhile, when electric power is connected to the grid and disconnected, the motor can be dropped, and the voltage flicker is easily caused under the influence of wind speed change, so that the accurate condition of electric power load flow calculation is caused.
Disclosure of Invention
The invention aims to solve the problem that inaccurate power flow calculation occurs due to voltage flicker in the prior art, and provides a method for using cloud calculation for power flow calculation.
In order to achieve the purpose, the invention adopts the following technical scheme:
designing a method for using cloud computing for power flow computing, comprising the steps of:
s1, dividing the whole wind power system into a plurality of areas;
s2, measuring the output power of the nodes of the plurality of areas, and processing the data through cloud computing to obtain data samples;
and S3, generating a positive Taiyun digital characteristic value by using a reverse generator according to the data sample: the expected value, the entropy and the super entropy are input into a cloud computing system, and an admittance matrix is formed for each node through the cloud computing system;
s4, setting the power of the node according to the balance point power value and the initial value of the voltage of the non-balance point power node and the expected value;
and S5, calculating injected harmonic current of the nodes according to the harmonic source, obtaining voltage of each node according to the power and the harmonic current of the nodes, and importing data into cloud computing to complete power flow calculation.
Preferably, when the node output power is measured, the method comprises the following steps:
a1, dividing the power of each node into N groups by measuring the power at intervals of 1-2s, wherein N is an integer greater than 10, and each group of data is N-N +50, wherein N is an integer greater than 3;
and A2, removing data at two ends in each group of data, adding the data values to obtain the total number of the data values, multiplying the number of the groups of data and the number of each group of data to obtain the total number of the data, and dividing the total number of the data values by the total number of the data to obtain the power of the measuring node.
Preferably, the calculation according to the inverse generator comprises the following steps;
b1, recording the node output power measured in the step 2 as XiThe values are then processed in cloud computing, where the central moment is B1, where X is the data sample, and the characteristic value is C (expectation value E)xEntropy EnEntropy of He)；
B2, wherein
Ex＝X，
HeIs aEn，
Wherein a is a control parameter.
Preferably, the method for measuring the injection harmonic current of the node comprises the following steps:
c1, measuring voltages of input points, storing data, grouping the data, processing the data through cloud computing, and solving the average value and tolerance of each group of data;
c2, comparing the tolerance of each group of values, selecting the group with the smallest tolerance, and outputting the average value of the data of the group as the value of the injected harmonic current.
Preferably, the number of each group of data in C1 is 100-200, and the number of the groups of data is 50-60.
The method for using cloud computing for power flow computing has the advantages that: by processing the values of the power and the current of each node, the error between the actual values and the power and the current is reduced, so that the accuracy of obtaining the voltage of each node is ensured, the voltage flicker is avoided, and the situation that the power flow calculation is inaccurate due to the voltage flicker is avoided.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
Example 1
A method of using cloud computing for power flow computing, comprising the steps of:
s1, dividing the whole wind power system into a plurality of areas;
s2, measuring the output power of the nodes of the plurality of areas, and processing the data through cloud computing to obtain data samples;
and S3, generating a positive Taiyun digital characteristic value by using a reverse generator according to the data sample: the expected value, the entropy and the super entropy are input into a cloud computing system, and an admittance matrix is formed for each node through the cloud computing system;
s4, setting the power of the node according to the balance point power value and the initial value of the voltage of the non-balance point power node and the expected value;
s5, calculating injected harmonic current of nodes according to a harmonic source, obtaining voltage of each node according to power and harmonic current of the nodes, guiding data into cloud computing to complete power flow calculation, and reducing errors between the power and current and actual values by processing the power and current values of each node, so that accuracy of obtaining voltage of each node is guaranteed, and inaccurate power flow calculation caused by voltage flicker is avoided.
When the node output power is measured, the method comprises the following steps:
a1, measuring the power of each node every 1s, and dividing the power into N groups, wherein N is an integer greater than 10, and each group of data is N, wherein N is an integer greater than 3;
a2, removing data at two ends in each group of data, adding the data values to obtain the total number of the data values, multiplying the number of the data groups by the number of the data in each group to obtain the total number of the data, dividing the total number of the data values by the total number of the data to obtain the power of the measuring node, and processing the data by adopting the steps A1 and A2, thereby ensuring the accuracy of the data.
The calculation according to the reverse generator comprises the following steps;
b1, recording the node output power measured in the step 2 as XiThe values are then processed in cloud computing, where the central moment is B1, where X is the data sample, and the characteristic value is C (expectation value E)xEntropy EnEntropy of He)；
B2, wherein
Ex＝X，
HeIs aEn，
Wherein a is a control parameter.
When the injected harmonic current of the node is measured, the following steps are included, and by adopting the characteristic value C, the power value of the node plays a role of further optimization:
c1, measuring voltages of input points, storing data, grouping the data, processing the data through cloud computing, and solving the average value and tolerance of each group of data;
and C2, comparing the tolerance of each group of numerical values, selecting the group with the minimum tolerance, outputting the average value of the data of the group as the numerical value of the injected harmonic current, and selecting the data by adopting the small variance, thereby ensuring the stability of the data and enabling the processed data to be more accurate.
In C1, the number of data groups is 100, and the number of data groups is 50.
Example 2
A method of using cloud computing for power flow computing, comprising the steps of:
s1, dividing the whole wind power system into a plurality of areas;
s2, measuring the output power of the nodes of the plurality of areas, and processing the data through cloud computing to obtain data samples;
and S3, generating a positive Taiyun digital characteristic value by using a reverse generator according to the data sample: the expected value, the entropy and the super entropy are input into a cloud computing system, and an admittance matrix is formed for each node through the cloud computing system;
s4, setting the power of the node according to the balance point power value and the initial value of the voltage of the non-balance point power node and the expected value;
s5, calculating injected harmonic current of nodes according to a harmonic source, obtaining voltage of each node according to power and harmonic current of the nodes, guiding data into cloud computing to complete power flow calculation, and reducing errors between the power and current and actual values by processing the power and current values of each node, so that accuracy of obtaining voltage of each node is guaranteed, and inaccurate power flow calculation caused by voltage flicker is avoided.
When the node output power is measured, the method comprises the following steps:
a1, measuring the power of each node every 2s, and dividing the power into N groups, wherein N is an integer greater than 10, and each group of data is N +50, and N is an integer greater than 3;
a2, removing data at two ends in each group of data, adding the data values to obtain the total number of the data values, multiplying the number of the data groups by the number of the data in each group to obtain the total number of the data, dividing the total number of the data values by the total number of the data to obtain the power of the measuring node, and processing the data by adopting the steps A1 and A2, thereby ensuring the accuracy of the data.
The calculation according to the reverse generator comprises the following steps;
b1, recording the node output power measured in the step 2 as XiThe values are then processed in cloud computing, where the central moment is B1, where X is the data sample, and the characteristic value is C (expectation value E)xEntropy EnEntropy of He)；
B2, wherein
Ex＝X，
HeIs aEn，
Wherein a is a control parameter.
When the injected harmonic current of the node is measured, the following steps are included, and by adopting the characteristic value C, the power value of the node plays a role of further optimization:
c1, measuring voltages of input points, storing data, grouping the data, processing the data through cloud computing, and solving the average value and tolerance of each group of data;
and C2, comparing the tolerance of each group of numerical values, selecting the group with the minimum tolerance, outputting the average value of the data of the group as the numerical value of the injected harmonic current, and selecting the data by adopting the small variance, thereby ensuring the stability of the data and enabling the processed data to be more accurate.
The number of data groups in each group is 200 in C1, and the number of data groups is 60.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (5)
1. A method of using cloud computing for power flow computing, comprising the steps of:
s1, dividing the whole wind power system into a plurality of areas;
s2, measuring the output power of the nodes of the plurality of areas, and processing the data through cloud computing to obtain data samples;
and S3, generating a positive Taiyun digital characteristic value by using a reverse generator according to the data sample: the expected value, the entropy and the super entropy are input into a cloud computing system, and an admittance matrix is formed for each node through the cloud computing system;
s4, setting the power of the node according to the balance point power value and the initial value of the voltage of the non-balance point power node and the expected value;
and S5, calculating injected harmonic current of the nodes according to the harmonic source, obtaining voltage of each node according to the power and the harmonic current of the nodes, and importing data into cloud computing to complete power flow calculation.
2. A method of using cloud computing for power flow computing according to claim 1, comprising the following steps in the measurement of node output power:
a1, dividing the power of each node into N groups by measuring the power at intervals of 1-2s, wherein N is an integer greater than 10, and each group of data is N-N +50, wherein N is an integer greater than 3;
and A2, removing data at two ends in each group of data, adding the data values to obtain the total number of the data values, multiplying the number of the groups of data and the number of each group of data to obtain the total number of the data, and dividing the total number of the data values by the total number of the data to obtain the power of the measuring node.
3. A method of using cloud computing for power flow computing according to claim 1, wherein the computing according to a reverse generator comprises the steps of;
b1, recording the node output power measured in the step 2 as XiThe values are then processed in cloud computing, with a central moment of B1, where X is the data sample, and a characteristic value of C (expected)Value ExEntropy EnEntropy of He)；
B2, wherein
Ex＝X，
HeIs aEnWherein a is a control parameter.
4. A method of using cloud computing for power flow calculation according to claim 1, comprising the following steps in making measurements of injected harmonic currents of nodes:
c1, measuring voltages of input points, storing data, grouping the data, processing the data through cloud computing, and solving the average value and tolerance of each group of data;
c2, comparing the tolerance of each group of values, selecting the group with the smallest tolerance, and outputting the average value of the data of the group as the value of the injected harmonic current.
5. The method as claimed in claim 4, wherein the number of each group of data in C1 is 100 and 200, and the number of the groups of data is 50-60.
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