CN111193267A - Extra-high voltage alternating current and direct current voltage stability control method based on cloud computing - Google Patents

Extra-high voltage alternating current and direct current voltage stability control method based on cloud computing Download PDF

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CN111193267A
CN111193267A CN201911220386.6A CN201911220386A CN111193267A CN 111193267 A CN111193267 A CN 111193267A CN 201911220386 A CN201911220386 A CN 201911220386A CN 111193267 A CN111193267 A CN 111193267A
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current
cloud computing
output
extra
direct current
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徐明忻
王俊生
韩永强
党伟
赵树野
刘宏扬
康赫然
张昭
顾大可
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State Grid Corp of China SGCC
Northeast Electric Power University
Economic and Technological Research Institute of State Grid Inner Mongolia Electric Power Co Ltd
State Grid Eastern Inner Mongolia Power Co Ltd
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State Grid Corp of China SGCC
Northeast Dianli University
Economic and Technological Research Institute of State Grid Inner Mongolia Electric Power Co Ltd
State Grid Eastern Inner Mongolia Power Co Ltd
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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
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • 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
    • H02J3/36—Arrangements for transfer of electric power between ac networks via a high-tension dc link
    • 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
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/60—Arrangements for transfer of electric power between AC networks or generators via a high voltage DC link [HVCD]

Abstract

The invention provides an extra-high voltage alternating current and direct current voltage stability control method based on cloud computing, and relates to the technical field of new energy of cloud computing analysis. According to the method, firstly, the influence factors influencing the power generation output of the new energy are extracted, the current illumination intensity, the current wind speed, the current environment temperature, the air relative humidity, the battery energy storage in the current system and the capacities Q1 and Q2 of the electric heat storage device, secondly, the current converter is controlled according to the current output electric quantity, the accurate calculation is carried out through a cloud calculation method aiming at the volatility and the intermittence of the new energy power generation, the output voltage of the current converter is controlled, and therefore the stability of the ultra-high voltage direct current transmission is improved. For an extra-high voltage direct current transmission system, reactive power requirements of a transmission line are carried out through the output of current new energy power generation and load requirements and a cloud computing method, so that reactive power loss in the process of alternating current-direct current conversion is reduced, reactive power compensation of extra-high voltage alternating current transmission is carried out, and the voltage stability of an alternating current line is guaranteed.

Description

Extra-high voltage alternating current and direct current voltage stability control method based on cloud computing
Technical Field
The invention relates to the technical field of new energy of cloud computing analysis, in particular to an extra-high voltage alternating current and direct current voltage stability control method based on cloud computing.
Background
With the development of renewable energy power generation technology, the power generation proportion of renewable energy in a power grid is continuously increased, and for northwest areas rich in renewable energy power generation, the electric energy generated by the renewable energy cannot be completely consumed due to small load, so that the electric energy is transmitted by adopting an extra-high voltage alternating current and direct current outgoing mode, and the loss of the electric energy is reduced. Because the power generation of the renewable energy source has intermittence and uncertainty, the output fluctuation can generate certain influence on the voltage of a power grid, in order to solve the problems, the invention collects the influence factors causing the new energy source power generation fluctuation, obtains an output model through corresponding optimization operation, and improves the stability of the extra-high voltage alternating current and direct current voltage through corresponding scheduling and control of a multi-level converter.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides an extra-high voltage alternating current and direct current voltage stability control method based on cloud computing, which comprises the following steps:
step 1: the method comprises the steps that a temperature sensor, a humidity sensor, a wind speed sensor and an illumination intensity sensor are used for collecting the ambient temperature T, the air phase relative humidity G and the air speed V and the illumination intensity S respectively, meanwhile, the capacities Q1 and Q2 of the battery energy storage and electric heat storage devices in the current system are collected, and neural network calculation is carried out on collected signals;
the input signal calculated by the neural network is x ═ x1,x2,x3,x4,x5,x6]＝[T,G,V,S,Q1,Q2]The data are stored, analyzed and calculated through the cloud computing platform, the output signals are data predicted in the future day after the data are analyzed and calculated through the cloud computing platform, and y is [ y ═ y [, y [ ]1,y2,y3,y4,y5,y6]＝[T*,G*,V*,S*,Q1 *,Q2 *]；
Wherein the mapping relation between the input signal phasor and the output signal phasor is
Figure BDA0002300668120000011
Wherein, yiRepresenting the output signal, omega, after passing through the calculationjThe weight value R of the neural network from the hidden layer to the output layer in the cloud computing processj(x) Representing a basis function, wherein m is the number of hidden layer neurons in cloud computing;
the specific basis function expression is as follows:
Figure BDA0002300668120000021
where x is the phasor of the input signal component, cjIs the center of the jth Gaussian function; sigmajIs the width of the jth base function center point, m is the number of hidden layer neurons, | x-cjI is the input vector to cjEuclidean distance of Rj(x) At cjObey a gaussian distribution.
Step 2: the control of a multi-level converter for grid connection is carried out aiming at power fields with different output forces, so that the stability of voltage is ensured;
and step 3: controlling the extra-high voltage alternating current-direct current power grid through the data obtained by calculation in the step 2, wherein the data has a certain error with actual output data in the calculation process, so that the error correction needs to be carried out on the calculation result, and the mean square error between the output signal and the expected signal is calculated as follows;
Figure BDA0002300668120000022
wherein, yiRepresenting the output signal after passing through the calculation, piThe value of the output signal to be expected to ensure voltage balance of the system, n being the number of power stations connected to the grid, ei＝yi-piRepresenting an output error of the ith power plant;
and 4, step 4: controlling the multi-level converter according to the calculation result, controlling the output voltage, determining the trigger angle, adjusting according to the new energy power generation condition, judging whether the trigger angle needs to be set or not by outputting the output signal calculated above, and determining the trigger time for triggering the IGBT when E is greater than 0.1;
Figure BDA0002300668120000023
Figure BDA0002300668120000024
Figure BDA0002300668120000025
Figure BDA0002300668120000026
Figure BDA0002300668120000027
Figure BDA0002300668120000028
wherein, UgIs the rated voltage of the direct current transmission line,
Figure BDA0002300668120000029
will u1＝Ug，u3＝0，u5＝0，u7＝0，u9＝0，u11The firing angle α can be obtained by calculating the above formula as 01、α2、α3、α4、α5The triggering angles are conduction angles of the IGBT in one period respectively;
and 5: the trigger angle obtained by the calculation of the formula is used for calculating the trigger time, so that the voltage stability of the extra-high voltage alternating current and direct current system is ensured;
Figure BDA0002300668120000031
adopt the produced beneficial effect of above-mentioned technical scheme to lie in:
according to the cloud computing extra-high voltage alternating current and direct current voltage stability control method, the fluctuation condition of the system is obtained through real-time computing through the output fluctuation of renewable energy in the system, and the voltage stability of extra-high voltage direct current transmission is controlled by changing the trigger angle of a power electronic device in a multi-level converter.
Drawings
FIG. 1 is a schematic diagram of a neural network architecture;
fig. 2 is a schematic diagram of an inverter structure.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
The invention provides an extra-high voltage alternating current and direct current voltage stability control method based on cloud computing, which comprises the following steps of:
step 1: the method comprises the steps that a temperature sensor, a humidity sensor, a wind speed sensor and an illumination intensity sensor are used for collecting the ambient temperature T, the air phase relative humidity G and the air speed V and the illumination intensity S respectively, meanwhile, the capacities Q1 and Q2 of the battery energy storage and electric heat storage devices in the current system are collected, and neural network calculation is carried out on collected signals;
in this embodiment, it is assumed that an ambient temperature T in a certain day is 20oC, a relative humidity G of air is 2%, a wind speed V is 6m/S, a solar radiation intensity S is 2kW/h, and capacities Q1 and Q2 of the battery energy storage and electric heat storage device in the current system are 300MW and 150MW, and neural network calculation is performed on an acquired signal, where the input signal is x ═ x1,x2,x3,x4,x5,x6]＝[T,G,V,S,Q1,Q2]＝[20,2,6,2,300,150]；
Wherein the mapping relation between the input signal phasor and the output signal phasor is
Figure BDA0002300668120000033
Wherein, yiRepresenting the output signal, omega, after passing through the calculationjThe weight value R of the neural network from the hidden layer to the output layer in the cloud computing processj(x) Representing a basis function, wherein m is the number of hidden layer neurons in cloud computing;
the specific basis function expression is as follows:
Figure BDA0002300668120000041
where x is the phasor of the input signal component, cjIs the center of the jth Gaussian function; sigmajIs the width of the jth base function center point, m is the number of hidden layer neurons, | x-cjI is the input vector to cjEuclidean distance of Rj(x) At cjObey a gaussian distribution.
Step 2: the method comprises the following steps that power generated by new energy power stations under different weather conditions is different, a multi-level converter which is connected with a grid is controlled aiming at power fields with different power, and the stability of voltage is guaranteed, wherein n represents the number of the new energy power stations as shown in figure 1;
and step 3: the data obtained by calculation in the step 2 is used for controlling the extra-high voltage alternating current and direct current power grid, and because certain errors exist between the data and actual output data in the calculation process, the error correction needs to be carried out on the calculation results, the data are arranged in the whole extra-high voltage alternating current and direct current system, the number n of grid-connected power stations is 15, and the mean square error between output signals and expected signals is calculated as follows;
Figure BDA0002300668120000042
wherein, yiRepresenting the output signal after passing through the calculation, piThe value of the output signal to be expected to ensure voltage balance of the system, n being the number of power stations connected to the grid, ei＝yi-piRepresenting an output error of the ith power plant;
calculating the data to obtain a mean square error E which is 7.9;
and 4, step 4: controlling the multi-level converter through the calculation result to control the output voltage, as shown in fig. 2, controlling the output voltage by the conduction of each IGBT, controlling the conduction of the IGBT by a corresponding trigger pulse generator, firstly determining a trigger angle, adjusting according to the new energy power generation condition, determining the trigger time for triggering the IGBT by outputting the output signal of the calculation result, and setting the trigger angle when E is more than 0.1 to determine the trigger time for triggering the IGBT;
Figure BDA0002300668120000043
Figure BDA0002300668120000044
Figure BDA0002300668120000045
Figure BDA0002300668120000046
Figure BDA0002300668120000051
Figure BDA0002300668120000052
wherein, UgIs the rated voltage of the direct current transmission line,
Figure BDA0002300668120000053
will u1＝Ug，u3＝0，u5＝0，u7＝0，u9＝0， u11The firing angle can be obtained by calculating the above formula as 0
Figure BDA0002300668120000054
The triggering angles are conduction angles of the IGBT in one period respectively;
and 5: the trigger angle obtained by the calculation of the formula is used for calculating the trigger time, so that the voltage stability of the extra-high voltage alternating current and direct current system is ensured;
Figure BDA0002300668120000055
Figure BDA0002300668120000056
finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art; the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions as defined in the appended claims.

Claims (2)

1. An extra-high voltage alternating current and direct current voltage stability control method based on cloud computing is characterized by comprising the following steps: the method comprises the following steps:
step 1: the method comprises the steps that a temperature sensor, a humidity sensor, a wind speed sensor and an illumination intensity sensor are used for collecting the ambient temperature T, the air relative humidity G and the illumination intensity S respectively, meanwhile, the capacities Q1 and Q2 of battery energy storage and electric heat storage devices in the current system are collected, and neural network calculation is carried out on collected signals;
step 2: the control of a multi-level converter for grid connection is carried out aiming at power fields with different output forces, so that the stability of voltage is ensured;
and step 3: controlling the extra-high voltage alternating current and direct current power grid through the data obtained by calculation in the step 2, wherein the data has a certain error with actual output data in the calculation process, so that the error correction needs to be carried out on the calculation result, and the mean square error between the output signal and the expected signal is calculated as follows;
Figure FDA0002300668110000011
wherein, yiRepresenting the output signal after passing through the calculation, piN is the number of power plants connected to the grid in order to ensure the desired output signal value for the voltage balance of the system，ei＝yi-piRepresenting an output error of the ith power plant;
and 4, step 4: controlling the multi-level converter according to the calculation result, controlling the output voltage, determining the trigger angle, adjusting according to the new energy power generation condition, judging whether the trigger angle needs to be set or not by outputting the output signal calculated above, and determining the trigger time for triggering the IGBT when E is greater than 0.1;
Figure FDA0002300668110000012
Figure FDA0002300668110000013
Figure FDA0002300668110000014
Figure FDA0002300668110000015
Figure FDA0002300668110000016
Figure FDA0002300668110000017
wherein, UgIs the rated voltage of the direct current transmission line,
Figure FDA0002300668110000018
will u1＝Ug，u3＝0，u5＝0，u7＝0，u9＝0，u11The firing angle α can be obtained by calculating the above formula as 01、α2、α3、α4、α5The trigger angles are respectively the IGBTs within one periodA conduction angle;
and 5: the trigger angle obtained by the calculation of the formula is used for calculating the trigger time, so that the voltage stability of the extra-high voltage alternating current and direct current system is ensured;
Figure FDA0002300668110000021
2. the cloud computing-based battery energy storage system dynamic control method of claim 1, wherein: the input signal calculated by the neural network in the step 1 is x ═ x1,x2,x3,x4,x5,x6]＝[T,G,V,S,Q1,Q2]The data are stored, analyzed and calculated through the cloud computing platform, the output signals are data predicted in the future day after the data are analyzed and calculated through the cloud computing platform, and y is [ y ═ y [ [ y ] of data1,y2,y3,y4,y5,y6]＝[T*,G*,V*,S*,Q1 *,Q2 *]；
Wherein the mapping relation between the input signal phasor and the output signal phasor is
Figure FDA0002300668110000022
Wherein, yiRepresenting the output signal, omega, after passing through the calculationjThe weight value R of the neural network from the hidden layer to the output layer in the cloud computing processj(x) Representing a basis function, wherein m is the number of hidden layer neurons in cloud computing;
the specific basis function expression is as follows:
Figure FDA0002300668110000023
where x is the phasor of the input signal component, cjIs the center of the jth Gaussian function; sigmajIs the width of the jth base function center point, m is the number of hidden layer neurons, | x-cjI is the input vector to cjEuclidean distance of Rj(x) At cjObey a gaussian distribution.
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Application publication date: 20200522