Quantification method and device for source-charge uncertainty

Abstract

A method and device for quantifying source-load uncertainty obtain a power value group and send the power value group into an information table; in the information table, the power value group is evenly divided and cut according to 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, thereby deriving a power value cluster after redundant value adjustment; based on the power value cluster after redundant value adjustment, missing values ​​in the power value cluster are registered according to the OPTICS method, and the registered missing values ​​are completed according to the Thiessen polygon method, thereby obtaining a power value cluster after missing value adjustment; the power value group is denoised through a power adjustment monitoring mode; power values ​​of various types and various main power grids are processed to ensure the integrity and accuracy of the adjustment, and power value defects can be detected and corrected in real time through real-time processing and analysis, so as to efficiently inject the rated capacity of the substation through active and reactive power settings, thereby quantifying source-load uncertainty.

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] Source-load uncertainty mainly involves the uncertainty between power output forecasts on the power source side and demand forecasts on the load side. This uncertainty comes from multiple 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 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 injected substation through the active and reactive power, so as to perform quantification of source and load ambiguity.

[0004] In practical applications, the amount of active and reactive power in the main grid is very large. When dealing with various types and structures of active and reactive power, losses or errors often occur, so adjustments must be performed. The current adjustment methods for the active and reactive power of the main grid are often not efficient enough, 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 uncertainty. 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, which obtains the active information packet and reactive information packet of the main power grid injected into the substation power value cluster transmitted by the active sensor and the reactive sensor respectively, and obtains the active and reactive power of the main power grid injected into the substation power value cluster by parsing the active information packet and the reactive information packet respectively, 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 according to the 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 each main power grid 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 setting the rated capacity of the substation through active and reactive power, thereby quantifying the source-load ambiguity.

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

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

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

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

[0010] Step 1: Obtain active power information packets and reactive power information packets from the main grid injected into the substation power value cluster, transmitted by active power sensors and reactive power sensors, respectively. Parse the active power packets and reactive power information packets to obtain a number of active and reactive power packets injected into the substation power value cluster. Condition the power value groups to obtain power value groups, which are then stored in an information table.

[0011] Step 2: Evenly split the power value groups by a set length in the information table to obtain power value clusters, perform deduplication processing on the power value clusters based on a row-by-row comparison deduplication method, and derive power value clusters after redundancy adjustment;

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

[0013] Step 4: De-noising the power value group via the power conditioning monitoring mode.

[0014] Preferably, 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.

[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 less than the set length.

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

[0017] In step 2-1, the number of power values ​​contained in each power value cluster of the divided power value group is totaled, and the power value cluster difference of each power value cluster is calculated. The power value cluster is assigned a key value by calculating the power value similarity within the power value cluster. The power value cluster key value is obtained based on the power value cluster difference and the key value. The calculation equation for the power value cluster difference of each power value cluster is:

[0018]

[0019] Here, G e The power value cluster difference of the power value cluster, 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 ​​below the set critical value within the power value cluster, and s represents the set coordination coefficient.

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

[0021] In step 2-2, the power clusters are arranged from high to low based on their key values. The corresponding extreme points are sequentially selected starting from the power cluster with the highest key value. Power clusters are formed based on the power clusters in which the extreme points are located. Different extreme values ​​are detected within the power clusters using a simulated annealing method in a round-robin manner, and the different extreme values ​​are extracted 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 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 register the redundant value.

[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 a chained queue within a queue via a mobile queue includes: starting from the first power value, moving backward one power value at a time, removing the first power value in the original queue upon reaching a new power value, and continuing until the last power value in the power value group is entered into the queue. The mobile queue includes a starting mobile queue and a maneuvering mobile queue. The starting mobile queue sets a minimum number for the mobile queue. The calculation equation for the maneuvering mobile queue is:

[0027]

[0028] Here, t x Represents the number of elements in the next moving 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 includes:

[0030] Step 3-1: Based on the power value cluster after redundancy 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 into l groups, and determining the midpoint of each group using the mean-shift method, and using 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 group, Y n represents the nth non-missing value, ν dj Improvement group 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 group, 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 group, ZG(B n ,C n ) represents the acquisition of B n ,C n The highest value in ;

[0035] In step 3-4, the group with the highest similarity is selected as the group of the power value where the missing value is located, and the power value is assigned to this group. Repeat steps 3-3 to 3-4 until the missing value group is NULL.

[0036] Steps 3-5: Based on the previously divided groups, use the Thiessen polygon method to complete the missing value groups.

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

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

[0039] a conditioning module for obtaining active power information packets and reactive power information packets of the main power grid injected into the substation power value cluster, transmitted by active power sensors and reactive power sensors, respectively, parsing the active power information packets and reactive power information packets to obtain a number of active and reactive powers of the main power grid injected into the substation power value cluster, performing conditioning on the power value groups to obtain power value groups, and sending the power value groups to an information table;

[0040] a deduplication module for performing equal division and cutting of power value groups by a set length in the information table to obtain power value clusters, performing deduplication processing on the power value clusters according to a row-by-row comparison deduplication method, and deriving power value clusters after redundancy adjustment;

[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 processes 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 immediately detect and correct power value defects, and is suitable for efficiently injecting the rated capacity of the substation through active and reactive power settings, thereby quantifying the source-load uncertainty. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0046] Figure 2 Schematic diagram of the system architecture of the source-charge uncertainty quantification device of the present invention. DETAILED DESCRIPTION

[0047] To make the purpose, technical solutions and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely expressed below in conjunction 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. In accordance with the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without making creative work are all within the scope of protection of the present invention.

[0048] like Figure 1 As shown, the 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 the rated capacity of the injected substation is set by 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 existing technical solution with patent publication number "CN117522054B".

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

[0051] Step 1: Obtain active power information packets and reactive power information packets from the main grid injected into the substation power value cluster, transmitted by active power sensors and reactive power sensors, respectively. Parse the active power packets and reactive power information packets to obtain a number of active and reactive power packets injected into the substation power value cluster. Condition the power value groups to obtain power value groups, which are then stored in an information table.

[0052] In a preferred but non-limiting embodiment of the present invention, 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.

[0053] Step 2: Evenly split the power value groups by a set length in the information table to obtain power value clusters, perform deduplication processing on the power value clusters based on a row-by-row comparison deduplication method, and derive power value clusters after redundancy 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 less than the set length.

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

[0056] Step 4: De-noising 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] In step 2-1, the number of power values ​​contained in each power value cluster of the divided power value group is totaled, and the power value cluster difference of each power value cluster is calculated. The power value cluster is assigned a key value by calculating the power value similarity within the power value cluster. The power value cluster key value is obtained based on the power value cluster difference and the key value. The calculation equation for the power value cluster difference of each power value cluster is:

[0059]

[0060] Here, G e The power value cluster difference of the power value cluster, 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 ​​below the set critical value within 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] In step 2-2, the power clusters are arranged from high to low based on their key values. The corresponding extreme points are sequentially selected starting from the power cluster with the highest key value. Power clusters are formed based on the power clusters in which the extreme points are located. Different extreme values ​​are detected within the power clusters using a simulated annealing method in a round-robin manner, and the different extreme values ​​are extracted 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 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 register the redundant value.

[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 chained queue within a queue via a mobile queue includes: starting from the first power value, moving backward one power value at a time, removing the first power value in the original queue upon reaching a new power value, and continuing until the last power value in the power value group is entered into the queue. The mobile queue includes a starting mobile queue and a maneuvering mobile queue. The starting mobile queue sets a minimum number for the mobile queue. The calculation equation for the maneuvering mobile queue is:

[0068]

[0069] Here, t x Represents the number of elements in the next moving 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: Based on the power value cluster after redundancy 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 into l groups, and determining the midpoint of each group using the mean-shift method, and using 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 group, Y n represents the nth non-missing value, ν djImprovement group 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 group, 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 group, ZG(B n ,C n ) represents the acquisition of B n ,C n The highest value in ;

[0076] In step 3-4, the group with the highest similarity is selected as the group of the power value where the missing value is located, and the power value is assigned to this group. Repeat steps 3-3 to 3-4 until the missing value group is NULL.

[0077] Steps 3-5: Based on the previously divided groups, use the Thiessen polygon method to complete the missing value groups.

[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 group groups and grouping center are obtained, so as to better understand the distribution of power values ​​and improve the adjustment efficiency and accuracy.

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

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

[0081] a conditioning module for obtaining active power information packets and reactive power information packets of the main power grid injected into the substation power value cluster, transmitted by active power sensors and reactive power sensors, respectively, parsing the active power information packets and reactive power information packets to obtain a number of active and reactive powers of the main power grid injected into the substation power value cluster, performing conditioning on the power value groups to obtain power value groups, and sending the power value groups to an information table;

[0082] a deduplication module for performing equal division and cutting of power value groups by a set length in the information table to obtain power value clusters, performing deduplication processing on the power value clusters according to a row-by-row comparison deduplication method, and deriving power value clusters after redundancy adjustment;

[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 processes 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 immediately detect and correct power value defects, and is suitable for efficiently injecting the rated capacity of the substation through active and reactive power settings, thereby quantifying the source-load uncertainty.

[0087] The present disclosure can be a system, method, and / or 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 thereof. 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 disc read-only memory (CD-ROM), a digital versatile disc (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 thereof. As used herein, a computer-readable storage medium is not to be construed as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through an electrical 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, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A 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 to the computer-readable storage medium in the computing / processing device for storage.

[0090] The computer program instructions for performing the operations of the present disclosure can be assembly 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" 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 entirely 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 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 and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for quantifying source-charge uncertainty, characterized in that: include: The active and reactive power of the main grid injected into the substation power value cluster is obtained according to the number of distribution network substations to perform processing, and the rated capacity of the injected substation is set by the processed active and reactive power to quantify the source-load uncertainty; The method for performing processing by obtaining active and reactive power of a main grid injected into a substation power value cluster according to the number of substations in the distribution network comprises: Step 1: Obtain active power information packets and reactive power information packets from the main grid injected into the substation power value cluster, transmitted by active power sensors and reactive power sensors, respectively. Parse the active power packets and reactive power information packets to obtain a number of active and reactive power packets injected into the substation power value cluster. Condition the power value groups to obtain power value groups, which are then stored in an information table. Step 2: Evenly split the power value groups by a set length in the information table to obtain power value clusters, perform deduplication processing on the power value clusters based on a row-by-row comparison deduplication method, and derive power value clusters after redundancy adjustment; Step 3: Based on the power value cluster after redundant value adjustment, the missing values ​​in the power value cluster are registered using the OPTICS method, and the registered missing values ​​are completed using 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; Step 3 includes: Step 3-1: Based on the power value cluster after redundancy 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 into l groups, and determining the midpoint of each group using the mean-shift method, and using 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 group, Y n represents the nth non-missing value, ν dj Improvement group 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 group, 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 group, ZG(B n ,C n ) represents the acquisition of B n ,C n The highest value in ; In step 3-4, the group with the highest similarity is selected as the group of the power value where the missing value is located, and the power value is assigned to this group. Repeat steps 3-3 to 3-4 until the missing value group is NULL. Steps 3-5: Based on the previously divided groups, use the Thiessen polygon method to complete the missing value groups. The power conditioning monitoring mode uses the median filtering method to perform denoising on the power value group.

2. The method for quantifying 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 equal to 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: In step 2-1, the number of power values ​​contained in each power value cluster of the divided power value group is totaled, and the power value cluster difference of each power value cluster is calculated. The power value cluster is assigned a key value by calculating the power value similarity within the power value cluster. The power value cluster key value is obtained based on the power value cluster difference and the key value. The calculation equation for the power value cluster difference of each power value cluster is: Here, G e The power value cluster difference of the power value cluster, 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 ​​below the set critical value within 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: In step 2-2, the power clusters are arranged from high to low based on their key values. The corresponding extreme points are sequentially selected starting from the power cluster with the highest key value. Power clusters are formed based on the power clusters in which the extreme points are located. Different extreme values ​​are detected within the power clusters using a simulated annealing method in a round-robin manner, and the different extreme values ​​are extracted 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 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 register the redundant value.

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: A method for detecting a chained queue within a queue using a mobile queue includes: starting from the first power value, moving backward one power value at a time, removing the first power value in the original queue upon reaching a new power value, and continuing until the last power value in the power value group is entered into the queue. The mobile queue includes a starting mobile queue and a maneuvering mobile queue. The starting mobile queue sets a minimum number for the mobile queue. The calculation equation for the maneuvering mobile queue is: Here, t x Represents the number of elements in the next moving 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.

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