Quantification method and device for unaffirmative source load

Abstract

The invention discloses a source-load unaffirmative quantification method and device, and the method comprises the steps: obtaining a power value group, and transmitting the power value group into an information table; equally dividing and cutting the power value group through a set length in the information table to obtain a power value cluster, performing deduplication processing on the power value cluster according to a data line-by-line comparison deduplication method, and exporting the power value cluster after redundant value conditioning; registering lost values in the power value cluster through an OPTICS method according to the power value cluster subjected to redundancy value conditioning, and complementing the registered lost values according to a Thiessen polygon method, thereby obtaining a power value cluster subjected to missing value conditioning; performing de-noising on the power value group through a power conditioning detection and control mode; according to the method, the power values of various types and various main power grids are handled, the integrity and accuracy of conditioning are guaranteed, power value defects can be found out and overcome in time through immediate handling and analysis, the rated capacity injected into a transformer substation can be set efficiently through active power and reactive power, and therefore source load unaffirmative quantification is executed.

Description

Technical Field

[0001] The present invention belongs to the technical field of source charge ambiguity, and in particular relates to a method and device for quantifying source charge ambiguity. Background Art

[0002] The uncertainty of source and load mainly involves the uncertainty of output forecast on the power supply side and demand forecast on the load side. This uncertainty comes from many aspects.

[0003] The multi-objective coordinated planning of distribution network sources, grids, loads and storage is often achieved using the existing technical solution with patent publication number "CN117522054B", which includes: obtaining the active and reactive power of the main power grid injected into the substation power value cluster based on the number of substations in the distribution network, and setting the rated capacity of the substation injected through the active and reactive power, so as to quantify the ambiguity of source and load.

[0004] In practical applications, the amount of active and reactive power in the main power grid is very large. When dealing with various types and structures of active and reactive power, loss or errors often occur, so adjustment must be performed. The current adjustment methods for the active and reactive power of the main power grid are often inefficient, making the adjustment process too time-consuming and not suitable for setting the rated capacity of the substation through active and reactive power to quantify the source-load ambiguity. Summary of the invention

[0005] In order to solve the defects in the prior art, the present invention proposes a quantification method and device for source-load uncertainty, obtains active information packets and reactive information packets of the main power grid injected into the substation power value cluster respectively transmitted by the active sensor and the reactive sensor, and obtains several active and reactive powers of the main power grid injected into the substation power value cluster respectively by parsing the active information packets and the reactive information packets, performs conditioning on the power value group, obtains the power value group, and sends the power value group to the information table; in the information table, the power value group is evenly divided and cut by a set length to obtain the power value cluster, and the power value cluster is deduplicated according to the data row-by-row comparison deduplication method. The redundant value adjusted power cluster is derived; based on the redundant value adjusted power cluster, the missing values ​​in the power cluster are registered by the OPTICS method, and the registered missing values ​​are completed by the Thiessen polygon method, thereby obtaining the missing value adjusted power cluster; through the power adjustment monitoring mode, the power value group is denoised; the power values ​​of various types and various main power grids are processed to ensure the integrity and accuracy of the adjustment. Through real-time processing and analysis, the power value defects can be detected and corrected in real time, which is conducive to efficiently injecting the rated capacity of the substation through active and reactive power settings, so as to quantify the source-load ambiguity.

[0006] The present invention uses the following technical solutions.

[0007] A method for quantifying source-charge uncertainty includes:

[0008] According to the number of substations in the distribution network, the active and reactive power of the main grid injected into the substation power value cluster is obtained for processing, and the rated capacity of the injected substation is set by the processed active and reactive power, so as to quantify the source-load uncertainty;

[0009] The method for performing treatment by obtaining active and reactive power of the main power grid injected into the substation power value cluster according to the number of substations in the distribution network comprises:

[0010] Step 1, obtaining active information packets and reactive information packets of the main power grid injected into the substation power value cluster respectively transmitted by the active sensor and the reactive sensor, respectively obtaining a number of active and reactive powers of the main power grid injected into the substation power value cluster by parsing the active information packets and the reactive information packets, performing conditioning on the power value group, obtaining the power value group, and sending the power value group into the information table;

[0011] Step 2, in the information table, the power value group is evenly divided and cut by a set length to obtain a power value cluster, and the power value cluster is deduplicated according to a row-by-row comparison deduplication method to derive a power value cluster after redundant value adjustment;

[0012] Step 3, according to the power value cluster after redundant value adjustment, the missing values ​​in the power value cluster are registered by the OPTICS method, and the registered missing values ​​are completed by the Thiessen polygon method to obtain the power value cluster after missing value adjustment;

[0013] Step 4, performing denoising on the power value group via the power conditioning monitoring mode.

[0014] Preferably, the active power or reactive power of the main power grid injected into the substation power value cluster is a power value group, and the active power or reactive power is a power value.

[0015] Preferably, the set length represents the number of power values, and the number of power values ​​in the power value cluster is the set length or lower than the set length.

[0016] Preferably, the deduplication method may further include:

[0017] Step 2-1, totaling the number of power values ​​contained in each power value cluster of the power value group after cutting, and calculating the power value cluster difference of the power value cluster, by performing a key value assignment on the power value cluster by the power value similarity in the power value cluster, and obtaining the power value cluster key value according to the power value cluster difference and the key value, the calculation equation of the power value cluster difference of the power value cluster is:

[0018]

[0019] Here, G e The power value cluster difference between power value clusters, y g represents the number of power values ​​in the power value cluster, P represents the number of power values ​​in all power value clusters, g j represents the number of power values ​​with frequency j, j and p represent the frequency of the power values ​​with similar values ​​lower than the set critical value in the power value cluster, and s represents the set coordination coefficient.

[0020] Preferably, the deduplication method may further include:

[0021] Step 2-2, perform arrangement from high to low according to the value of the power value cluster focus quantity, select the corresponding extreme value points in sequence from the power value cluster with the highest power value cluster focus quantity, form a power value cluster group according to the power value cluster where the extreme value point is located, and use the simulated annealing method according to the polling method to find different extreme values ​​in the power value cluster group, and extract different extreme values ​​as the arrangement extreme values.

[0022] Preferably, the deduplication method may further include:

[0023] In step 2-3, all power values ​​in the power value cluster containing the extreme value of the arrangement are aggregated into a chain queue, and the chain queue in the queue is detected by moving the queue to determine whether there is redundancy, and the redundant value is registered.

[0024] Preferably, the deduplication method may further include:

[0025] Step 2-4, repeatedly execute step 2-2 to step 2-3 until the extreme value extraction is completed, obtain the redundant power value group, and refresh the redundant power value group into the overall group through the Warshall algorithm.

[0026] Preferably, the method for detecting the chain queue in the queue via the mobile queue comprises: starting from the first power value, moving backward by one power value each time, and when moving to a new power value, removing the first power value in the original queue, until the last power value in the power value group is sent to the queue, the mobile queue comprises a starting mobile queue and a mobile mobile queue, the starting mobile queue sets a minimum amount for the mobile queue, and the operation equation of the mobile mobile queue is:

[0027]

[0028] Here, t x Represents the number of elements in the next move queue, x zd Represents the minimum number of elements in the mobile queue, x zg Represents the maximum number of elements in the mobile queue, t prepresents the number of elements in the current mobile queue, sy represents the sequence number of the power value to be removed from the mobile queue in the chain queue, k represents the single power value in the mobile queue, C k Indicates whether the kth power value is redundant with the power value indicated by the serial number sy. If the redundancy C k is one, otherwise C k is zero.

[0029] Preferably, step 3 comprises:

[0030] Step 3-1, according to the power value cluster after redundancy value adjustment, the power value cluster is divided into a complete power value group and a lost value group, as follows:

[0031] Step 3-2, grouping the complete power value group according to the OPTICS method, dividing the complete power value group into l groups, and identifying the midpoint of each group through the mean shift method, and taking the midpoint as the improved grouping center of the group;

[0032] Step 3-3, calculate the Pearson coefficient of the power value of the missing value in the missing value group to the improved grouping center of each group, and calculate the similarity between the power value of the missing value and each group based on the Pearson coefficient. The calculation equation of the similarity is:

[0033]

[0034] Here, D n represents the power value of the missing value and the similarity of the group, N represents the total number of non-missing values ​​in the power value of the missing value, e n represents the Pearson coefficient of the nth non-missing value to the center of the improved grouping, Y n represents the nth non-missing value, ν dj Improved grouping center representing the group, B n represents the mean of the Pearson coefficients between the nth non-missing value and the complete power value in the group outside the center of the improved grouping, C n represents the mean of the Pearson coefficients of the nth non-missing value and the complete power values ​​of other groups outside the center of the improved grouping, ZG(B n ,C n ) represents the acquisition of B n ,C n The highest value in ;

[0035] Step 3-4, select the group with the highest similarity as the group of the power value where the missing value is located, and put the power value into this group, and repeat steps 3-3 to 3-4 until the missing value group is NULL;

[0036] Step 3-5, based on the previously divided groups, use the Thiessen polygon method to complete the missing value group.

[0037] Preferably, the power conditioning monitoring mode uses a median filtering method to perform denoising on the power value group.

[0038] A device for quantifying source charge uncertainty, comprising:

[0039] A conditioning module, which is used to obtain active information packets and reactive information packets of the main power grid injected into the substation power value cluster respectively transmitted by the active sensor and the reactive sensor, respectively obtain a number of active and reactive powers of the main power grid injected into the substation power value cluster by parsing the active information packets and the reactive information packets, perform conditioning on the power value group, obtain the power value group, and send the power value group into the information table;

[0040] A deduplication module is used to perform equal division and cutting of the power value group by a set length in the information table to obtain a power value cluster, perform deduplication processing on the power value cluster according to a data row-by-row comparison deduplication method, and derive a power value cluster after redundant value conditioning;

[0041] A registration module is used to register missing values ​​in the power value cluster according to the power value cluster after redundant value adjustment by using the OPTICS method, and to complete the registered missing values ​​according to the Thiessen polygon method to obtain the power value cluster after missing value adjustment;

[0042] The denoising module is used to perform denoising on the power value group through the power conditioning monitoring mode.

[0043] The beneficial effects of the present invention are that, compared with the prior art, the technical effects of the present invention include:

[0044] It handles the power values ​​of various types and main power grids to ensure the integrity and accuracy of the regulation. Through real-time processing and analysis, it can detect and correct power value defects in real time, and is convenient for efficiently setting the rated capacity of the substation through active and reactive power, so as to quantify the source-load ambiguity. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 is a flow chart of the method for quantifying source charge uncertainty in the present invention;

[0046] Figure 2 It is a schematic diagram of the architecture of the device for quantifying the source-charge uncertainty of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely expressed in combination with the drawings in the embodiments of the present invention. The embodiments expressed in this application are only part of the embodiments of the present invention, not all of them. According to the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the protection scope of the present invention.

[0048] like Figure 1 As shown, a method for quantifying source-charge uncertainty of the present invention includes:

[0049] The processing is performed by obtaining the active and reactive power of the main power grid injected into the substation power value cluster according to the number of substations in the distribution network, and setting the rated capacity of the injected substation based on the processed active and reactive power, so as to quantify the source-load ambiguity; the method for setting the rated capacity of the injected substation can be achieved by the corresponding method in the prior art solution with patent publication number "CN117522054B".

[0050] The method for performing treatment by obtaining active and reactive power of the main power grid injected into the substation power value cluster according to the number of substations in the distribution network comprises:

[0051] Step 1, obtaining active information packets and reactive information packets of the main power grid injected into the substation power value cluster respectively transmitted by the active sensor and the reactive sensor, respectively obtaining a number of active and reactive powers of the main power grid injected into the substation power value cluster by parsing the active information packets and the reactive information packets, performing conditioning on the power value group, obtaining the power value group, and sending the power value group into the information table;

[0052] In a preferred but non-limiting embodiment of the present invention, the active power or reactive power of the main power grid injected into the substation power value cluster is a power value group, and the active power or reactive power is a power value.

[0053] Step 2, in the information table, the power value group is evenly divided and cut by a set length to obtain a power value cluster, and the power value cluster is deduplicated according to a row-by-row comparison deduplication method to derive a power value cluster after redundant value adjustment;

[0054] In a preferred but non-limiting embodiment of the present invention, the set length represents the number of power values, and the number of power values ​​in the power value cluster is the set length or lower than the set length.

[0055] Step 3, according to the power value cluster after redundant value adjustment, the missing values ​​in the power value cluster are registered by the OPTICS method, and the registered missing values ​​are completed by the Thiessen polygon method to obtain the power value cluster after missing value adjustment;

[0056] Step 4, performing denoising on the power value group via the power conditioning monitoring mode.

[0057] In a preferred but non-limiting embodiment of the present invention, the deduplication method may further include:

[0058] Step 2-1, totaling the number of power values ​​contained in each power value cluster of the power value group after cutting, and calculating the power value cluster difference of the power value cluster, by performing a key value assignment on the power value cluster by the power value similarity in the power value cluster, and obtaining the power value cluster key value according to the power value cluster difference and the key value, the calculation equation of the power value cluster difference of the power value cluster is:

[0059]

[0060] Here, G e The power value cluster difference between power value clusters, y g represents the number of power values ​​in the power value cluster, P represents the number of power values ​​in all power value clusters, g j represents the number of power values ​​with frequency j, j and p represent the frequency of the power values ​​with similar values ​​lower than the set critical value in the power value cluster, and s represents the set coordination coefficient.

[0061] In a preferred but non-limiting embodiment of the present invention, the deduplication method may further include:

[0062] Step 2-2, perform arrangement from high to low according to the value of the power value cluster focus quantity, select the corresponding extreme value points in sequence from the power value cluster with the highest power value cluster focus quantity, form a power value cluster group according to the power value cluster where the extreme value point is located, and use the simulated annealing method according to the polling method to find different extreme values ​​in the power value cluster group, and extract different extreme values ​​as the arrangement extreme values.

[0063] In a preferred but non-limiting embodiment of the present invention, the deduplication method may further include:

[0064] In step 2-3, all power values ​​in the power value cluster containing the extreme value of the arrangement are aggregated into a chain queue, and the chain queue in the queue is detected by moving the queue to determine whether there is redundancy, and the redundant value is registered.

[0065] In a preferred but non-limiting embodiment of the present invention, the deduplication method may further include:

[0066] Step 2-4, repeatedly execute step 2-2 to step 2-3 until the extreme value extraction is completed, obtain the redundant power value group, and refresh the redundant power value group into the overall group through the Warshall algorithm.

[0067] In a preferred but non-limiting embodiment of the present invention, a method for detecting a chain queue in a queue via a mobile queue includes: starting from the first power value, moving backward by one power value each time, and when moving to a new power value, removing the first power value in the original queue, until the last power value in the power value group is sent to the queue, the mobile queue includes a starting mobile queue and a mobile mobile queue, the starting mobile queue sets a minimum amount for the mobile queue, and the operation equation of the mobile mobile queue is:

[0068]

[0069] Here, t x Represents the number of elements in the next move queue, x zd Represents the minimum number of elements in the mobile queue, x zg Represents the maximum number of elements in the mobile queue, t p represents the number of elements in the current mobile queue, sy represents the sequence number of the power value to be removed from the mobile queue in the chain queue, k represents the single power value in the mobile queue, C k Indicates whether the kth power value is redundant with the power value indicated by the serial number sy. If the redundancy C k is one, otherwise C k is zero.

[0070] In a preferred but non-limiting embodiment of the present invention, step 3 comprises:

[0071] Step 3-1, according to the power value cluster after redundancy value adjustment, the power value cluster is divided into a complete power value group and a lost value group, as follows:

[0072] Step 3-2, grouping the complete power value group according to the OPTICS method, dividing the complete power value group into l groups, and identifying the midpoint of each group through the mean shift method, and taking the midpoint as the improved grouping center of the group;

[0073] Step 3-3, calculate the Pearson coefficient of the power value of the missing value in the missing value group to the improved grouping center of each group, and calculate the similarity between the power value of the missing value and each group based on the Pearson coefficient. The calculation equation of the similarity is:

[0074]

[0075] Here, D n represents the power value of the missing value and the similarity of the group, N represents the total number of non-missing values ​​in the power value of the missing value, e n represents the Pearson coefficient of the nth non-missing value to the center of the improved grouping, Y n represents the nth non-missing value, ν djImproved grouping center representing the group, B n represents the mean of the Pearson coefficients between the nth non-missing value and the complete power value in the group outside the center of the improved grouping, C n represents the mean of the Pearson coefficients of the nth non-missing value and the complete power values ​​of other groups outside the center of the improved grouping, ZG(B n ,C n ) represents the acquisition of B n ,C n The highest value in ;

[0076] Step 3-4, select the group with the highest similarity as the group of the power value where the missing value is located, and put the power value into this group, and repeat steps 3-3 to 3-4 until the missing value group is NULL;

[0077] Step 3-5, based on the previously divided groups, use the Thiessen polygon method to complete the missing value group.

[0078] Therefore, a two-type grouping method combining the mean shift method and the OPTICS method is used to perform group analysis on the complete power value group. By repeatedly improving the grouping center, a more accurate number of grouping groups and grouping center are obtained, so as to better grasp the arrangement of power values, which is conducive to improving the adjustment efficiency and accuracy.

[0079] In a preferred but non-limiting embodiment of the present invention, the power conditioning monitoring mode uses a median filtering method to perform denoising on the power value group.

[0080] like Figure 2 As shown, a source-charge uncertainty quantification device according to the present invention comprises:

[0081] A conditioning module, which is used to obtain active information packets and reactive information packets of the main power grid injected into the substation power value cluster respectively transmitted by the active sensor and the reactive sensor, respectively obtain a number of active and reactive powers of the main power grid injected into the substation power value cluster by parsing the active information packets and the reactive information packets, perform conditioning on the power value group, obtain the power value group, and send the power value group into the information table;

[0082] A deduplication module is used to perform equal division and cutting of the power value group by a set length in the information table to obtain a power value cluster, perform deduplication processing on the power value cluster according to a data row-by-row comparison deduplication method, and derive a power value cluster after redundant value conditioning;

[0083] A registration module is used to register missing values ​​in the power value cluster according to the power value cluster after redundant value adjustment by using the OPTICS method, and to complete the registered missing values ​​according to the Thiessen polygon method to obtain the power value cluster after missing value adjustment;

[0084] The denoising module is used to perform denoising on the power value group through the power conditioning monitoring mode.

[0085] The beneficial effects of the present invention are that, compared with the prior art, the technical effects of the present invention include:

[0086] It handles the power values ​​of various types and main power grids to ensure the integrity and accuracy of the regulation. Through real-time processing and analysis, it can detect and correct power value defects in real time, and is convenient for efficiently setting the rated capacity of the substation through active and reactive power, so as to quantify the source-load ambiguity.

[0087] The present disclosure can be a system, a method and / or a computer program product. The computer program product can include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0088] A computer-readable storage medium can be a tangible device that can hold and store instructions for use by an instruction execution device. A computer-readable storage medium can be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the above. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the above. The computer-readable storage medium used herein is not to be interpreted as a transient signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (such as a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0089] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing / processing device.

[0090] The computer program instructions for performing the operations of the present disclosure can be assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as "C" language or similar programming languages. The computer-readable program instructions can be executed entirely on the controller computer, partially on the controller computer, as a separate software package, partially on the controller computer and partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the controller computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (such as connecting through the Internet using an Internet service provider). In some embodiments, various aspects of the present disclosure are implemented by utilizing state information of computer-readable program instructions to personalize an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), which can execute computer-readable program instructions.

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for quantifying source-charge uncertainty, characterized in that: include: According to the number of substations in the distribution network, the active and reactive power of the main grid injected into the substation power value cluster is obtained for processing, and the rated capacity of the injected substation is set by the processed active and reactive power, so as to quantify the source-load uncertainty; The method for performing treatment by obtaining active and reactive power of the main power grid injected into the substation power value cluster according to the number of substations in the distribution network comprises: Step 1, obtaining active information packets and reactive information packets of the main power grid injected into the substation power value cluster respectively transmitted by the active sensor and the reactive sensor, respectively obtaining a number of active and reactive powers of the main power grid injected into the substation power value cluster by parsing the active information packets and the reactive information packets, performing conditioning on the power value group, obtaining the power value group, and sending the power value group into the information table; Step 2, in the information table, the power value group is evenly divided and cut by a set length to obtain a power value cluster, and the power value cluster is deduplicated according to a row-by-row comparison deduplication method to derive a power value cluster after redundant value adjustment; Step 3, according to the power value cluster after redundant value adjustment, the missing values ​​in the power value cluster are registered by the OPTICS method, and the registered missing values ​​are completed by the Thiessen polygon method to obtain the power value cluster after missing value adjustment; Step 4, performing denoising on the power value group via the power conditioning monitoring mode.

2. The quantification method of source charge uncertainty according to claim 1, characterized in that: The active power or reactive power of the main grid injected into the substation power value cluster is a power value group, and the active power or reactive power is a power value; The set length represents the number of power values, and the number of power values ​​in the power value cluster is the set length or less than the set length.

3. The method for quantifying source charge uncertainty according to claim 2, characterized in that: The deduplication method may also include: Step 2-1, totaling the number of power values ​​contained in each power value cluster of the power value group after cutting, and calculating the power value cluster difference of the power value cluster, by performing a key value assignment on the power value cluster by the power value similarity in the power value cluster, and obtaining the power value cluster key value according to the power value cluster difference and the key value, the calculation equation of the power value cluster difference of the power value cluster is: Here, G e The power value cluster difference between power value clusters, y g represents the number of power values ​​in the power value cluster, P represents the number of power values ​​in all power value clusters, g j represents the number of power values ​​with frequency j, j and p represent the frequency of the power values ​​with similar values ​​lower than the set critical value in the power value cluster, and s represents the set coordination coefficient.

4. The method for quantifying source charge uncertainty according to claim 3, characterized in that: The deduplication method may also include: Step 2-2, perform arrangement from high to low according to the value of the power value cluster focus quantity, select the corresponding extreme value points in sequence from the power value cluster with the highest power value cluster focus quantity, form a power value cluster group according to the power value cluster where the extreme value point is located, and use the simulated annealing method according to the polling method to find different extreme values ​​in the power value cluster group, and extract different extreme values ​​as the arrangement extreme values.

5. The method for quantifying source charge uncertainty according to claim 4, characterized in that: The deduplication method may also include: In step 2-3, all power values ​​in the power value cluster containing the extreme value of the arrangement are aggregated into a chain queue, and the chain queue in the queue is detected by moving the queue to determine whether there is redundancy, and the redundant value is registered.

6. The method for quantifying source charge uncertainty according to claim 5, characterized in that: The deduplication method may also include: Step 2-4, repeatedly execute step 2-2 to step 2-3 until the extreme value extraction is completed, obtain the redundant power value group, and refresh the redundant power value group into the overall group through the Warshall algorithm.

7. The method for quantifying source charge uncertainty according to claim 6, characterized in that: The method for detecting a chain queue in a queue through a mobile queue includes: starting from the first power value, moving backward by one power value each time, removing the first power value in the original queue when moving to a new power value, until the last power value in the power value group is sent to the queue, the mobile queue includes a starting mobile queue and a mobile mobile queue, the starting mobile queue sets a minimum amount for the mobile queue, and the operation equation of the mobile mobile queue is: Here, t x Represents the number of elements in the next move queue, x zd Represents the minimum number of elements in the mobile queue, x zg Represents the maximum number of elements in the mobile queue, t p represents the number of elements in the current mobile queue, sy represents the sequence number of the power value to be removed from the mobile queue in the chain queue, k represents the single power value in the mobile queue, C k Indicates whether the kth power value is redundant with the power value indicated by the serial number sy. If the redundancy C k is one, otherwise C k is zero.

8. The method for quantifying source charge uncertainty according to claim 7, characterized in that: Step 3 includes: Step 3-1, according to the power value cluster after redundancy value adjustment, the power value cluster is divided into a complete power value group and a lost value group, as follows: Step 3-2, grouping the complete power value group according to the OPTICS method, dividing the complete power value group into l groups, and identifying the midpoint of each group through the mean shift method, and taking the midpoint as the improved grouping center of the group; Step 3-3, calculate the Pearson coefficient of the power value of the missing value in the missing value group to the improved grouping center of each group, and calculate the similarity between the power value of the missing value and each group based on the Pearson coefficient. The calculation equation of the similarity is: Here, D n represents the power value of the missing value and the similarity of the group, N represents the total number of non-missing values ​​in the power value of the missing value, e n represents the Pearson coefficient of the nth non-missing value to the center of the improved grouping, Y n represents the nth non-missing value, ν dj Improved grouping center representing the group, B n represents the mean of the Pearson coefficients between the nth non-missing value and the complete power value in the group outside the center of the improved grouping, C n represents the mean of the Pearson coefficients of the nth non-missing value and the complete power values ​​of other groups outside the center of the improved grouping, ZG(B n ,C n ) represents the acquisition of B n ,C n The highest value in ; Step 3-4, select the group with the highest similarity as the group of the power value where the missing value is located, and put the power value into this group, and repeat steps 3-3 to 3-4 until the missing value group is NULL; Step 3-5, based on the previously divided groups, use the Thiessen polygon method to complete the missing value group.

9. The method for quantifying source charge uncertainty according to claim 8, characterized in that: The power conditioning monitoring mode uses the median filtering method to perform denoising on the power value group.

10. A device for quantifying source charge uncertainty, characterized in that: include: A conditioning module, which is used to obtain active information packets and reactive information packets of the main power grid injected into the substation power value cluster respectively transmitted by the active sensor and the reactive sensor, respectively obtain a number of active and reactive powers of the main power grid injected into the substation power value cluster by parsing the active information packets and the reactive information packets, perform conditioning on the power value group, obtain the power value group, and send the power value group into the information table; A deduplication module is used to perform equal division and cutting of the power value group by a set length in the information table to obtain a power value cluster, perform deduplication processing on the power value cluster according to a data row-by-row comparison deduplication method, and derive a power value cluster after redundant value conditioning; A registration module is used to register missing values ​​in the power value cluster according to the power value cluster after redundant value adjustment by using the OPTICS method, and to complete the registered missing values ​​according to the Thiessen polygon method to obtain the power value cluster after missing value adjustment; The denoising module is used to perform denoising on the power value group through the power conditioning monitoring mode.

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