CN113516295A - Space load prediction method and system for rapid power restoration after disaster - Google Patents

Space load prediction method and system for rapid power restoration after disaster Download PDF

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CN113516295A
CN113516295A CN202110574453.5A CN202110574453A CN113516295A CN 113516295 A CN113516295 A CN 113516295A CN 202110574453 A CN202110574453 A CN 202110574453A CN 113516295 A CN113516295 A CN 113516295A
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周荣生
李欣
田慧丽
凌毓畅
顾大德
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Guangzhou Power Supply Bureau of Guangdong Power Grid Co Ltd
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Abstract

The invention discloses a space load prediction method and system for rapid power restoration after disaster, which are used for determining a period T before prediction according to typhoon disaster information1And load prediction target period T2(ii) a According to the pre-prediction period T1And predicting the target period T2Acquiring load data of a power supply grid in a disaster area, wherein the load data comprises a period T before prediction1Real-time load data Xc(T1) Historical load data X of period before predictionh(T1) Historical load data X of target prediction periodh(T2) (ii) a Based on the period T before prediction1Corresponding historical load data Xh(T1) Obtaining T by fuzzy mean clustering1Historical typical load curve C of time interval power supply gridh(T1) And corresponding membership degree matrix Uh(ii) a Clustering the result C according to the fuzzy meanh(T1) And real-time load data Xc(T1) Construction of Pre-prediction time-Domain real-time load data Xc(T1) For historical typical load curve Ch(T1) Membership degree matrix Uc(ii) a Based on membership degree matrix Uh、UcAnd historical load data X of the target prediction periodh(T2) Constructing a load X for a target time periodc(T2) The load of the target period is predicted.

Description

Space load prediction method and system for rapid power restoration after disaster
Technical Field
The invention belongs to the technical field of restoring force improvement of power systems, and particularly relates to a space load prediction method and system for rapid power restoration after disaster.
Background
With the continuous promotion of the terminal electrification process, the dependence degree of various key infrastructures for human society to live on electric energy is higher and higher, the electric energy becomes the most important energy form for normal operation of the human society, and the guarantee of safe, reliable and continuous supply of electric power is of great importance. However, with the change of global climate, typhoon disasters are frequent, which has resulted in many large-scale power failure accidents, thereby causing huge economic loss and political social impact. The power distribution network is located at the tail end of a power system and a user, safe and reliable operation of the power distribution network is directly related to safe and reliable power supply of the user, and the capability of rapidly recovering power supply after the power distribution network fails in typhoon disasters is very important. However, metering equipment of a power distribution system in a typhoon may have a fault, so that the observability of the system is reduced, and key load data for determining a fault first-aid repair sequence and formulating a recovery strategy cannot be obtained.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a space load prediction method and system for fast power restoration after a disaster, aiming at the defects in the prior art, so as to quickly predict the space load after a typhoon disaster, and solve the problem that a recovery strategy cannot be formulated due to the loss of key load data in the disaster.
A space load prediction method for rapid power restoration after disaster comprises the following steps:
s1, according to the typhoon disaster informationDetermining a pre-prediction period T1And load prediction target period T2；
S2, the period T before prediction determined according to the step S11And predicting the target period T2Acquiring load data of a power supply grid in a disaster area, wherein the load data comprises a period T before prediction1Real-time load data Xc(T1) Historical load data X of period before predictionh(T1) Historical load data X of target prediction periodh(T2)；
S3, period T before prediction based on step S21Corresponding historical load data Xh(T1) Obtaining T by fuzzy mean clustering1Historical typical load curve C of time interval power supply gridh(T1) And corresponding membership degree matrix Uh(ii) a Clustering the result C according to the fuzzy meanh(T1) And real-time load data X of step S2c(T1) Construction of Pre-prediction time-Domain real-time load data Xc(T1) For historical typical load curve Ch(T1) Membership degree matrix Uc；
S4, membership degree matrix U constructed based on step S3h、UcAnd the historical load data X of the target prediction period in step S2h(T2) Constructing a load X for a target time periodc(T2) The load of the target period is predicted.
Specifically, in step S2, the pre-prediction real-time load data XcAnd historical daily load data XhWith the same sampling frequency.
Specifically, step S3 specifically includes:
s301, the number of power supply grids is n, the number k of clustering centers is given, and an initial membership matrix U is obtainedh (0)Weighting index m, iteration threshold epsilon, and iteration time t being 0;
s302, calculating a clustering center C according to the membership matrixh (t+1)(T1)；
S303, updating the membership degree;
s304, if | | Chi (t+1)(T1)-Chi (t)(T1) If | < epsilon, the convergence condition is satisfied, the iteration is stopped, and the clustering center C is outputh(T1) And membership matrix Uh；
S305, according to the real-time load data Xc(T1) And historical typical load curve Ch(T1) Construction of Pre-prediction time-Domain real-time load data Xc(T1) For historical typical load curve ChMembership degree matrix Uc。
Further, in step S304, if the convergence condition is not satisfied, the iteration time t is t +1, and the process returns to step S302 to continue the iteration; if the convergence condition is satisfied, the process proceeds to step S305.
Further, in step S305, the membership matrix UcComprises the following steps:
Figure BDA0003083768470000031
Figure BDA0003083768470000032
wherein i is more than or equal to 1 and less than or equal to n, j is more than or equal to 1 and less than or equal to k and mucijMembership, X, of real-time load to jth historical typical load for ith power gridci(T1) For the ith power supply grid at T1Real-time load data of time slots, Chj(T1) For the jth historical typical load, m is a weighted index in fuzzy mean clustering, m is 2, n is the number of power supply grids, and k is the number of clustering centers.
Specifically, step S4 specifically includes:
s401, load X corresponding to target time period in the historical load data in the step S2h(T2) And membership degree matrix U in step S3hConstruction of T2Historical typical load curve C of time periodh(T2)；
S402, based on T in step S4012Historical typical load curve C of time periodh(T2) And the membership degree matrix U in step S3cBuilding the load X of the target time intervalc(T2)。
Further, in step S401, T2Historical typical load curve C of time periodh(T2)：
Ch(T2)＝{Ch1(T2),Ch2(T2)...,Chk(T2)}
Figure BDA0003083768470000033
Wherein, muhijThe membership degree of the ith power supply grid historical load to the jth historical typical load is obtained in step S3; xhi(T2) For the ith power supply grid at T2Historical load of the time period; m is a weighted index in the fuzzy mean clustering, and m is 2; k is the number of cluster centers.
Further, in step S402, the load X of the target period is constructedc(T2)：
Xc(T2)＝Uc·Ch(T2)。
Specifically, after completion of step S4, the pre-prediction period T is newly determined1And load prediction target period T2And load prediction is carried out until the comprehensive power recovery.
Another technical solution of the present invention is a space load prediction system for rapid power restoration after a disaster, including:
a time module for determining a pre-prediction time period T according to the typhoon disaster information1And load prediction target period T2；
A load module for predicting the time interval T according to the time determined by the time module1And predicting the target period T2Acquiring load data of a power supply grid in a disaster area, wherein the load data comprises a period T before prediction1Real-time load data Xc(T1) Historical load number of period before predictionAccording to Xh(T1) Historical load data X of target prediction periodh(T2)；
Matrix module based on pre-prediction period T of load module1Corresponding historical load data Xh(T1) Obtaining T by fuzzy mean clustering1Historical typical load curve C of time interval power supply gridh(T1) And corresponding membership degree matrix Uh(ii) a Clustering the result C according to the fuzzy meanh(T1) And real-time load data X of load modulec(T1) Construction of Pre-prediction time-Domain real-time load data Xc(T1) For historical typical load curve Ch(T1) Membership degree matrix Uc；
Prediction module, membership degree matrix U constructed based on matrix moduleh、UcAnd historical load data X of target prediction period in load moduleh(T2) Constructing a load X for a target time periodc(T2) The load of the target period is predicted.
Compared with the prior art, the invention has at least the following beneficial effects:
the invention relates to a space load prediction method for rapid power restoration after disaster, which applies a membership matrix to space load prediction, facilitates subsequent power companies to specifically make a power distribution system emergency repair sequence and a recovery strategy after disaster, and realizes the purposes of rapid load by digging and driving new load prediction through historical data, wherein the data source is simple, the algorithm complexity is low, and the purpose of rapid load can be achieved; load prediction can be continuously carried out along with the advancement of emergency repair work through rolling prediction, and the requirement on load data when the power distribution system recovers power supply after typhoon disasters is met; missing data in the real-time load data are corrected through the prediction result, and the problem of data missing caused by metering equipment failure under typhoon disasters is solved.
Further, in step S2, the pre-prediction real-time load data XcAnd historical daily load data XhThe advantages of having the same sampling frequency setting are that no data dimension normalization is needed when fuzzy mean clustering is carried out, and the data loss is reducedAnd (4) error.
Further, step S3 is to obtain a mapping relationship between the historical load and the real-time load by fuzzy mean clustering and using the membership degree to simply and effectively characterize the characteristics of the historical load and the real-time load.
Further, if the convergence condition is not satisfied, the iteration time t is t +1, and the process returns to step S302 to continue the iteration to determine whether the fuzzy mean clustering converges.
Further, a membership matrix U is setcThe real-time load is characterized by the membership degree of the real-time load to the historical typical load.
Further, the load of the target time period is predicted according to the mapping relationship between the historical load and the real-time load obtained in step S3.
Further, T in step S4012Historical typical load curve C of time periodh(T2) The purpose of the setting is to obtain T from S31The relation between the historical load and the historical typical load of the time interval is constructed2The historical typical load of the period.
Further, the load X of the target period is constructed in step S402c(T2) Is through T2Obtaining the load X of the target time interval according to the historical typical load of the time interval and the relation between the trial load and the historical loadc(T2) I.e. the load that needs to be predicted.
Further, after completion of step S4, the pre-prediction period T is newly determined1And load prediction target period T2And load prediction is carried out until comprehensive power restoration refers to that the load in the next time period is dynamically predicted along with the development of time, and data basis is provided for power restoration decisions after typhoon disasters until the power supply of all power supply grids is recovered to be normal.
In conclusion, the method is based on fuzzy mean clustering, the fuzziness and the complexity of the load are fully considered, an accurate prediction result can be obtained, the time complexity is low, the calculation is simple and convenient, the program is clear, the requirement of quick power restoration after typhoon is met, and the load prediction result can be used for optimizing the element fault first-aid repair sequence and making a power supply recovery strategy.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a graph of load prediction results according to an embodiment of the present invention;
FIG. 3 is a graph of a load prediction result for a power grid;
fig. 4 is a graph of the rolling prediction results.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
Various structural schematics according to the disclosed embodiments of the invention are shown in the drawings. The figures are not drawn to scale, wherein certain details are exaggerated and possibly omitted for clarity of presentation. The shapes of various regions, layers and their relative sizes and positional relationships shown in the drawings are merely exemplary, and deviations may occur in practice due to manufacturing tolerances or technical limitations, and a person skilled in the art may additionally design regions/layers having different shapes, sizes, relative positions, according to actual needs.
The invention provides a space load prediction method for fast power restoration after disaster, which relates to historical data and real-time data of a power supply grid under a power distribution system, and comprises the steps of firstly obtaining power supply grid load data and typhoon influence time intervals, then establishing a mapping relation between load and historical load in the time intervals before prediction through fuzzy mean clustering, obtaining a membership matrix of the historical data and the real-time data relative to typical power consumption behaviors through fuzzy mean clustering calculation, predicting the load of a target time interval according to the membership matrix, and finally predicting the load in the target time interval based on the historical load data; a spatial load distribution over a target time period is obtained.
Referring to fig. 1, the method for predicting space load for fast power restoration after disaster according to the present invention includes the following steps:
s1, determining the period T before prediction according to the typhoon disaster information1And load prediction target period T2；
In the first prediction, T in step S11To predict the pre-period:
T1＝{t0,t1,...,ta}
load prediction target period T2Satisfies the following conditions:
T2＝{ta+1,ta+2,...,ta+b}
b＜a
wherein, is.
S2, obtaining load data of the power supply grid in the disaster area, including real-time load data X before predictioncAnd historical daily load data Xh；
Pre-prediction real-time load data XcAnd historical daily load data XhThe same sampling frequency, i.e. XcAnd XhHaving the same dimension s.
S3, predicting the previous period T based on the historical load data of the step S11Corresponding load data Xh(T1) Obtaining T by fuzzy mean clustering1Historical typical load curve C of time interval power supply gridh(T1) And corresponding membership degree matrix Uh(ii) a Construction of Pre-prediction time-Domain real-time load data Xc(T1) For historical typical load curve Ch(T1) Membership degree matrix Uc；
S301, the number of power supply grids is n, the number k of clustering centers is given, and an initial membership matrix U is obtainedh (0)Weighting index m, iteration threshold epsilon, and iteration time t being 0, wherein the membership matrix:
Figure BDA0003083768470000081
satisfies the following conditions:
Figure BDA0003083768470000082
if t is 0, randomly selecting an initial membership matrix; the initial membership matrix satisfies the following conditions:
Figure BDA0003083768470000083
satisfies the following conditions:
Figure BDA0003083768470000084
s302, calculating a clustering center C according to the membership matrixh (t+1)(T1) The calculation formula is as follows:
Figure BDA0003083768470000091
wherein the content of the first and second substances,
Figure BDA0003083768470000096
s303, updating the membership degree, wherein the calculation formula is as follows:
Figure BDA0003083768470000092
wherein the content of the first and second substances,
Figure BDA0003083768470000093
s304, if
Figure BDA0003083768470000094
If the convergence condition is satisfied, stopping iteration, otherwise, if the iteration time t is t +1, and returning to step S302 to continue iteration;
s305, constructing real-time load data X of period before predictionc(T1) For historical typical load curve ChMembership degree matrix Uc：
Figure BDA0003083768470000095
Wherein, mucijMembership, X, of real-time load to jth historical typical load for ith power gridci(T1) For the ith power supply grid at T1Real-time load data of time slots, Chj(T1) For the jth historical typical load, m is a weighted index in fuzzy mean clustering, m is 2, n is the number of power supply grids, and k is the number of clustering centers.
S4, membership degree matrix U constructed based on step S3cAnd the load X corresponding to the target time interval in the historical daily load datah(T2) Building the load X of the target time intervalc(T2) When it is against the targetPredicting the load of the section;
s401, based on membership matrix UhLoad X corresponding to target time interval in historical daily load datah(T2) Construction of T2Historical typical load curve C of time periodh(T2)：
Figure BDA0003083768470000101
Wherein, muhijThe membership degree of the ith power supply grid historical load to the jth historical typical load is obtained in step S3; xhi(T2) For the ith power supply grid at T2And (3) historical load of a period, wherein m is a weighted index in the fuzzy mean clustering, m is 2, and k is the number of clustering centers.
S402, based on T2Historical typical load curve C of time periodh(T2) And membership matrix UcBuilding the load X of the target time intervalc(T2)：
Xc(T2)＝Uc·Ch(T2)
And S5, continuously recovering the load along with the advancement of the emergency repair work, re-determining the time period before prediction and the target time period, and predicting the load until the power is fully recovered.
With increasing time, T1And T2The dimension of (2) is unchanged, newly added time nodes are added, and initial time nodes are deleted:
T1'＝{t1,t2,...,ta+1}
T2'＝{ta+2,ta+3,...,ta+b+1}
adding new node data x if the real-time load of the period before predictionc(T1',ta+1) The node data x in the last prediction result can be used when the node data x cannot be acquired due to the failure of the metering equipmentc(T2,ta+1) And (4) replacing.
In another embodiment of the present invention, a space load prediction system for fast power restoration after a disaster is provided, where the system can be used to implement the space load prediction method for fast power restoration after a disaster, and specifically, the space load prediction system for fast power restoration after a disaster includes a time module, a load module, a matrix module, and a prediction module.
Wherein, the time module determines the period T before prediction according to the typhoon disaster information1And load prediction target period T2；
A load module for predicting the time interval T according to the time determined by the time module1And predicting the target period T2Acquiring load data of a power supply grid in a disaster area, wherein the load data comprises a period T before prediction1Real-time load data Xc(T1) Historical load data X of period before predictionh(T1) Historical load data X of target prediction periodh(T2)；
Matrix module based on pre-prediction period T of load module1Corresponding historical load data Xh(T1) Obtaining T by fuzzy mean clustering1Historical typical load curve C of time interval power supply gridh(T1) And corresponding membership degree matrix Uh(ii) a Clustering the result C according to the fuzzy meanh(T1) And real-time load data X of load modulec(T1) Construction of Pre-prediction time-Domain real-time load data Xc(T1) For historical typical load curve Ch(T1) Membership degree matrix Uc；
Prediction module, membership degree matrix U constructed based on matrix moduleh、UcAnd historical load data X of target prediction period in load moduleh(T2) Constructing a load X for a target time periodc(T2) The load of the target period is predicted.
In yet another embodiment of the present invention, a terminal device is provided that includes a processor and a memory for storing a computer program comprising program instructions, the processor being configured to execute the program instructions stored by the computer storage medium. The Processor may be a Central Processing Unit (CPU), or may be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable gate array (FPGA) or other Programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, etc., which is a computing core and a control core of the terminal, and is adapted to implement one or more instructions, and is specifically adapted to load and execute one or more instructions to implement a corresponding method flow or a corresponding function; the processor provided by the embodiment of the invention can be used for the operation of the space load prediction method for the post-disaster quick power restoration, and comprises the following steps:
determining a period T before prediction according to typhoon disaster information1And load prediction target period T2(ii) a According to the pre-prediction period T1And predicting the target period T2Acquiring load data of a power supply grid in a disaster area, wherein the load data comprises a period T before prediction1Real-time load data Xc(T1) Historical load data X of period before predictionh(T1) Historical load data X of target prediction periodh(T2) (ii) a Based on the period T before prediction1Corresponding historical load data Xh(T1) Obtaining T by fuzzy mean clustering1Historical typical load curve C of time interval power supply gridh(T1) And corresponding membership degree matrix Uh(ii) a Clustering the result C according to the fuzzy meanh(T1) And real-time load data Xc(T1) Construction of Pre-prediction time-Domain real-time load data Xc(T1) For historical typical load curve Ch(T1) Membership degree matrix Uc(ii) a Based on membership degree matrix Uh、UcAnd historical load data X of the target prediction periodh(T2) Constructing a load X for a target time periodc(T2) The load of the target period is predicted.
In still another embodiment of the present invention, the present invention further provides a storage medium, specifically a computer-readable storage medium (Memory), which is a Memory device in a terminal device and is used for storing programs and data. It is understood that the computer readable storage medium herein may include a built-in storage medium in the terminal device, and may also include an extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also, one or more instructions, which may be one or more computer programs (including program code), are stored in the memory space and are adapted to be loaded and executed by the processor. It should be noted that the computer-readable storage medium may be a high-speed RAM memory, or may be a non-volatile memory (non-volatile memory), such as at least one disk memory.
The processor can load and execute one or more instructions stored in the computer-readable storage medium to realize the corresponding steps of the space load prediction method for the post-disaster quick power restoration in the embodiment; one or more instructions in the computer-readable storage medium are loaded by the processor and perform the steps of:
determining a period T before prediction according to typhoon disaster information1And load prediction target period T2(ii) a According to the pre-prediction period T1And predicting the target period T2Acquiring load data of a power supply grid in a disaster area, wherein the load data comprises a period T before prediction1Real-time load data Xc(T1) Historical load data X of period before predictionh(T1) Historical load data X of target prediction periodh(T2) (ii) a Based on the period T before prediction1Corresponding historical load data Xh(T1) Obtaining T by fuzzy mean clustering1Historical typical load curve C of time interval power supply gridh(T1) And corresponding membership degree matrix Uh(ii) a Clustering the result C according to the fuzzy meanh(T1) And real-time load data Xc(T1) Construction of Pre-prediction time-Domain real-time load data Xc(T1) For historical typical load curve Ch(T1) Membership degree matrix Uc(ii) a Based on membership degree matrix Uh、UcAnd historical load data X of the target prediction periodh(T2) Constructing a load X for a target time periodc(T2) The load of the target period is predicted.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
And performing load prediction on the power supply grid, wherein the load time sequence data of the power supply grid is 48-point daily load time sequence data obtained by sampling every half hour from 00:00 of the day to 23:30 of the day. All power grids have historical load data for the current year of typhoons. Selecting a pre-prediction time period T1Is 00: 00-05: 00, and the target prediction time interval T2Is 05: 00-10: 00, and records the historical daily load data as XhReal-time load data X before predictioncWherein the historical data is 48-dimensional, and the real-time load data before prediction is the period T before prediction1Load data of when T110D when the ratio is 00: 00-05: 00.
Xh＝{xh1,xh2,...,xh48}
Xc＝{xc1,xc2,...xcT1}
Obtaining T by fuzzy mean clustering1Of time-interval power supply gridsHistorical typical load curve ChAnd corresponding membership degree matrix Uh。
Referring to FIG. 2, a typical load pattern based on historical data is shown, along with the degree of membership of the daily load curve to all load patterns.
Construction of Pre-prediction time-Domain real-time load data Xc(T1) For historical typical load curve Ch(T1) Membership degree matrix UcBased on the membership matrix and the load X corresponding to the target time interval in the historical daily load datah(T2) Building the load X of the target time intervalc(T2)。
Figure BDA0003083768470000141
Figure BDA0003083768470000142
Xc(T2)＝Uc·Ch(T2)
Fig. 3 shows the load prediction results of the power grid.
The prediction time length is selected to be 4h, and rolling prediction can be carried out on the load all day. Fig. 4 shows the results of the rolling prediction.
In summary, the space load prediction method for rapid power restoration after disaster is based on fuzzy mean clustering, and fully considers the fuzziness and complexity of the load, so that an accurate prediction result can be obtained. Meanwhile, the time complexity of the method is low, and the method meets the load spatial distribution prediction requirement after typhoon. The load prediction result can be used for optimizing the post-disaster fault first-aid repair sequence and making a power supply recovery strategy.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical idea of the present invention falls within the protection scope of the claims of the present invention.

Claims (10)

1. A space load prediction method for rapid power restoration after disaster is characterized by comprising the following steps:
s1, determining the period T before prediction according to the typhoon disaster information1And load prediction target period T2；
S2, the period T before prediction determined according to the step S11And predicting the target period T2Acquiring load data of a power supply grid in a disaster area, wherein the load data comprises a period T before prediction1Real-time load data Xc(T1) Historical load data X of period before predictionh(T1) Historical load data X of target prediction periodh(T2)；
S3, period T before prediction based on step S21Corresponding historical load data Xh(T1) Obtaining T by fuzzy mean clustering1Historical typical load curve C of time interval power supply gridh(T1) And corresponding membership degree matrix Uh(ii) a Clustering the result C according to the fuzzy meanh(T1) And real-time load data X of step S2c(T1) Construction of Pre-prediction time-Domain real-time load data Xc(T1) For historical typical load curve Ch(T1) Membership degree matrix Uc；
S4, membership degree matrix U constructed based on step S3h、UcAnd the historical load data X of the target prediction period in step S2h(T2) Constructing a load X for a target time periodc(T2) The load of the target period is predicted.
2. The method according to claim 1, wherein in step S2, the pre-prediction real-time load data XcAnd historical daily load data XhWith the same sampling frequency.
3. The method according to claim 1, wherein step S3 is specifically:
s301, the number of power supply grids is n, the number k of clustering centers is given, and an initial membership matrix U is obtainedh (0)Weighting index m, iteration threshold epsilon, and iteration time t being 0;
s302, calculating a clustering center according to the membership matrix
Figure FDA0003083768460000011
S303, updating the membership degree;
s304, if
Figure FDA0003083768460000012
Satisfying the convergence condition, stopping iteration and outputting a clustering center Ch(T1) And membership matrix Uh；
S305, according to the real-time load data Xc(T1) And historical typical load curve Ch(T1) Construction of Pre-prediction time-Domain real-time load data Xc(T1) For historical typical load curve ChMembership degree matrix Uc。
4. The method according to claim 3, wherein in step S304, if the convergence condition is not satisfied, the iteration time t is t +1, and the method returns to step S302 to continue the iteration; if the convergence condition is satisfied, the process proceeds to step S305.
5. The method of claim 3, wherein in step S305, the membership matrix UcComprises the following steps:
Figure FDA0003083768460000021
Figure FDA0003083768460000022
wherein i is more than or equal to 1 and less than or equal to n, j is more than or equal to 1 and less than or equal to k and mucijIs the ithMembership degree, X, of real-time load of power supply grid to jth historical typical loadci(T1) For the ith power supply grid at T1Real-time load data of time slots, Chj(T1) For the jth historical typical load, m is a weighted index in fuzzy mean clustering, m is 2, n is the number of power supply grids, and k is the number of clustering centers.
6. The method according to claim 1, wherein step S4 is specifically:
s401, load X corresponding to target time period in the historical load data in the step S2h(T2) And membership degree matrix U in step S3hConstruction of T2Historical typical load curve C of time periodh(T2)；
S402, based on T in step S4012Historical typical load curve C of time periodh(T2) And the membership degree matrix U in step S3cBuilding the load X of the target time intervalc(T2)。
7. The method of claim 6, wherein in step S401, T is2Historical typical load curve C of time periodh(T2)：
Ch(T2)＝{Ch1(T2),Ch2(T2)...,Chk(T2)}
Figure FDA0003083768460000023
Wherein, muhijThe membership degree of the ith power supply grid historical load to the jth historical typical load is obtained in step S3; xhi(T2) For the ith power supply grid at T2Historical load of the time period; m is a weighted index in the fuzzy mean clustering, and m is 2; k is the number of cluster centers.
8. The method of claim 6Method, characterized in that in step S402, a load X of a target period is constructedc(T2)：
Xc(T2)＝Uc·Ch(T2)。
9. The method of claim 1, wherein the period T before prediction is re-determined after step S4 is completed1And load prediction target period T2And load prediction is carried out until the comprehensive power recovery.
10. A space load prediction system for rapid post-disaster recovery is characterized by comprising:
a time module for determining a pre-prediction time period T according to the typhoon disaster information1And load prediction target period T2；
A load module for predicting the time interval T according to the time determined by the time module1And predicting the target period T2Acquiring load data of a power supply grid in a disaster area, wherein the load data comprises a period T before prediction1Real-time load data Xc(T1) Historical load data X of period before predictionh(T1) Historical load data X of target prediction periodh(T2)；
Matrix module based on pre-prediction period T of load module1Corresponding historical load data Xh(T1) Obtaining T by fuzzy mean clustering1Historical typical load curve C of time interval power supply gridh(T1) And corresponding membership degree matrix Uh(ii) a Clustering the result C according to the fuzzy meanh(T1) And real-time load data X of load modulec(T1) Construction of Pre-prediction time-Domain real-time load data Xc(T1) For historical typical load curve Ch(T1) Membership degree matrix Uc；
Prediction module, membership degree matrix U constructed based on matrix moduleh、UcAnd historical load data X of target prediction period in load moduleh(T2) Constructing a load X for a target time periodc(T2) The load of the target period is predicted.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117408394A (en) * 2023-12-14 2024-01-16 国网天津市电力公司电力科学研究院 Carbon emission factor prediction method and device for electric power system and electronic equipment
CN117408394B (en) * 2023-12-14 2024-05-31 国网天津市电力公司电力科学研究院 Carbon emission factor prediction method and device for electric power system and electronic equipment

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111738773A (en) * 2020-07-01 2020-10-02 国网宁夏电力有限公司 New energy and load-based net load peak-valley time interval dividing method and system
CN112734135A (en) * 2021-01-26 2021-04-30 吉林大学 Power load prediction method, intelligent terminal and computer readable storage medium

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111738773A (en) * 2020-07-01 2020-10-02 国网宁夏电力有限公司 New energy and load-based net load peak-valley time interval dividing method and system
CN112734135A (en) * 2021-01-26 2021-04-30 吉林大学 Power load prediction method, intelligent terminal and computer readable storage medium

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
张艳杰等: "基于模糊聚类识别及统计相关的短期负荷预测", 《电网技术》 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117408394A (en) * 2023-12-14 2024-01-16 国网天津市电力公司电力科学研究院 Carbon emission factor prediction method and device for electric power system and electronic equipment
CN117408394B (en) * 2023-12-14 2024-05-31 国网天津市电力公司电力科学研究院 Carbon emission factor prediction method and device for electric power system and electronic equipment

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