CN112651169A - Method and system for determining stability analysis data sample of power system - Google Patents
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Abstract
The application discloses a method and a system for determining stability analysis data samples of a power system. Wherein, the method comprises the following steps: determining a plurality of key variables representing system characteristics according to a plurality of operation modes of a power grid; determining a plurality of response curves corresponding to the plurality of key variables characterizing the system characteristics in the plurality of operation modes and different AC/DC fault states through simulation calculation; classifying the response curves according to different stability requirements of the power system, and determining a characteristic variable curve corresponding to a specific stability requirement; and determining a sampling time window according to the power grid control demand, and determining a stability analysis data sample of the power system according to a characteristic variable curve corresponding to the specific stability demand and the sampling time window.
Description
Technical Field
The present application relates to the field of power system technologies, and in particular, to a method and system for determining a stability analysis data sample of a power system.
Background
New energy large-scale grid connection and large-capacity long-distance extra-high voltage direct current transmission are two main characteristics and development trends of the current operation of a power grid in China. Due to the random fluctuation of new energy and the large impact of extra-high voltage direct current faults, the operation conditions of a power grid are variable, the fault forms causing grid instability are more diversified, the complexity and the uncertainty are further enhanced, faults are more and more difficult to effectively distinguish, control strategies are more and more difficult to formulate, and the effectiveness of a traditional control method is further reduced.
The artificial intelligence learning and reasoning technology can fully utilize the real-time state information, the off-line simulation data and the dynamic simulation experiment data of the primary and secondary equipment of the power grid to extract the main characteristic quantity, establish the mapping relation between the fault form and the learning rule, and has the reasoning capability. The method provides a chance for effectively dealing with the problems of variability of the power system mode, uncertainty of the fault form and the like, and provides a technical basis for realizing intelligent identification and judgment of the complex fault of the large power grid.
However, the scale of the power grid is huge and the data dimensionality is high in China, and how to quickly extract reasonable and effective power grid fault sample data from mass data and classify and process the data according to different stability problems is the basis for effectively improving the learning efficiency of the artificial intelligence algorithm.
Aiming at the technical problems that the power grid in China is large in scale and high in data dimensionality, how to quickly extract reasonable and effective power grid fault sample data from mass data and classify and process different stability problems in the prior art, an effective solution is not provided at present.
Disclosure of Invention
The embodiment of the disclosure provides a method and a system for determining stability analysis data samples of a power system, which at least solve the technical problems that in the prior art, the scale of a power grid in China is large, the data dimension is high, how to quickly extract reasonable and effective power grid fault sample data from mass data, and how to perform classification processing aiming at different stability problems.
According to an aspect of an embodiment of the present disclosure, there is provided a method of determining stability analysis data samples of a power system, including: determining a plurality of key variables representing system characteristics according to a plurality of operation modes of a power grid; determining a plurality of response curves corresponding to a plurality of key variables representing system characteristics in a plurality of operation modes and different alternating current and direct current fault modes through simulation calculation; classifying the plurality of response curves according to different stability requirements of the power system, and determining a characteristic variable curve corresponding to a specific stability requirement; and determining a sampling time window according to the power grid control demand, and determining a stability analysis data sample of the power system according to the characteristic variable curve corresponding to the specific stability demand and the sampling time window.
According to another aspect of the embodiments of the present disclosure, there is also provided a system for determining stability analysis data samples of an electric power system, including: the key variable determining module is used for determining a plurality of key variables representing system characteristics according to a plurality of operation modes of the power grid; the device comprises a response curve determining module, a response curve determining module and a response curve determining module, wherein the response curve determining module is used for determining a plurality of response curves corresponding to a plurality of key variables representing system characteristics in a plurality of operation modes and different alternating current/direct current fault modes through simulation calculation; the characteristic variable curve determining module is used for classifying the multiple response curves according to different stability requirements of the power system and determining a characteristic variable curve corresponding to a specific stability requirement; and the data sample determining module is used for determining a sampling time window according to the power grid control requirement and determining a stability analysis data sample of the power system according to the characteristic variable curve corresponding to the specific stability requirement and the sampling time window.
The method for determining the stability analysis data sample of the power system is provided, the correlation between the data sample obtained by the method and the stability level of the system is strong, and the electrical quantity information required by stability evaluation can be effectively met; and only key variable information is extracted, so that the reduction of artificial intelligence algorithm learning and judging efficiency caused by overhigh dimension of the characteristic quantity can be effectively avoided, and a foundation is laid for intelligent identification and control of faults of a complex large power grid. The technical problems that in the prior art, the power grid in China is large in scale and high in data dimensionality, how to quickly extract reasonable and effective power grid fault sample data from mass data and how to perform classification processing aiming at different stability problems are solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the disclosure and together with the description serve to explain the disclosure and not to limit the disclosure. In the drawings:
FIG. 1 is a schematic flow diagram of a method of determining stability analysis data samples for a power system in accordance with an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of a method for determining stability analysis data samples for a power system according to an embodiment of the present disclosure; and
FIG. 3 is a schematic diagram of a system for determining stability analysis data samples for a power system according to an embodiment of the present disclosure.
Detailed Description
The exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, however, the present invention may be embodied in many different forms and is not limited to the embodiments described herein, which are provided for complete and complete disclosure of the present invention and to fully convey the scope of the present invention to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the accompanying drawings is not intended to be limiting of the invention. In the drawings, the same units/elements are denoted by the same reference numerals.
Unless otherwise defined, terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Further, it will be understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense.
According to a first aspect of the present embodiment, a method 100 of determining stability analysis data samples of a power system is provided. Fig. 1 shows a schematic flow diagram of the method, and referring to fig. 1, the method 100 includes:
s102: determining a plurality of key variables representing system characteristics according to a plurality of operation modes of a power grid;
s104: determining a plurality of response curves corresponding to a plurality of key variables representing system characteristics in a plurality of operation modes and different alternating current and direct current fault modes through simulation calculation;
s106: classifying the plurality of response curves according to different stability requirements of the power system, and determining a characteristic variable curve corresponding to a specific stability requirement; and
s108: and determining a sampling time window according to the power grid control requirement, and determining a stability analysis data sample of the power system according to the characteristic variable curve corresponding to the specific stability requirement and the sampling time window.
Specifically, referring to fig. 2, on the basis of mastering the stability characteristics of different typical operation modes of the power grid, key variables representing system characteristics, such as a weak section of the power grid, key bus nodes, an important generator set and the like, are determined. And acquiring key variable response curves in various operation modes and different AC/DC fault modes through simulation calculation. And classifying the response curves according to the characteristic variables required for judging the power angle stability, the voltage stability and the frequency stability of the system. And determining a sampling time window according to the actual power grid control requirements, thereby obtaining characteristic variable multi-time scale segmented sampling data samples required by different stability problems.
The method for acquiring the sampling data samples with different stability problems mainly comprises the following steps:
(1) determining the sampling data starting time T0 as 50ms before the fault starts;
(2) and determining a sampling time window delta T according to the actual power grid control requirement. The system emergency control time limit, namely the action time of the safety and stability control device is generally 300ms after the fault occurs, so that the sampling time window delta T is determined to be 200ms, and 100ms is reserved for judging the system stability by an artificial intelligence algorithm;
(3) and obtaining segmented sampling data samples with multiple time scales according to different stability problem characteristic variable curves, wherein the specific sampling time is Ti-Ti +1, and Ti is T0+ i.DELTA T (i is a natural number) and covers sample data before a fault, in the fault process and after the fault is removed.
Therefore, the invention provides a method for determining the stability analysis data sample of the power system, the correlation between the data sample obtained by the method and the stability level of the system is strong, and the electrical quantity information required by stability evaluation can be effectively met; and only key variable information is extracted, so that the reduction of artificial intelligence algorithm learning and judging efficiency caused by overhigh dimension of the characteristic quantity can be effectively avoided, and a foundation is laid for intelligent identification and control of faults of a complex large power grid. The technical problems that in the prior art, the power grid in China is large in scale and high in data dimensionality, how to quickly extract reasonable and effective power grid fault sample data from mass data and how to perform classification processing aiming at different stability problems are solved.
Optionally, determining a plurality of key variables characterizing the system according to a plurality of operation modes of the power grid, including: determining key variables of characteristics of a power grid weak section representation system according to various operation modes of a power grid; determining key variables of the characteristics of a key bus node representation system according to various operation modes of a power grid; and determining key variables of the characteristics of the important generator set characterization system according to various operation modes of the power grid.
Optionally, determining a plurality of key variables characterizing the system according to a plurality of operation modes of the power grid, including: the multiple operation modes of the power grid comprise a flat load mode, a peak load mode and a low-ebb load mode; based on multiple operation modes, fault simulation scanning is carried out, and fault simulation scanning results are determined, wherein the faults comprise three-permanent-current N-1 faults and three-permanent-current N-2 faults of an alternating current circuit, and direct current locking, phase commutation failure and restarting faults; and acquiring a weak alternating-current section with large power fluctuation, a key bus node with large voltage drop amplitude, an important generator set with large power angle swing and an important generator set with large speed deviation in the fault process according to the fault simulation scanning result.
Optionally, classifying the plurality of response curves according to different stability requirements of the power system, and determining a characteristic variable curve corresponding to a specific stability requirement includes: determining a weak alternating current section power curve and an important generator set power angle curve according to the requirement of system power angle stability; determining a key bus node voltage curve according to the system voltage stability requirement; and determining an important generator set speed deviation curve according to the system frequency stability requirement.
Optionally, determining a stability analysis data sample of the power system according to a characteristic variable curve corresponding to a specific stability requirement and a sampling time window, including: determining segmented sampling data samples of multiple time scales according to a characteristic variable curve corresponding to a specific stable demand; and determining the stability analysis data sample of the power system according to the segmented sampling data sample of the multi-time scale and the sampling time window.
Therefore, the invention provides a method for determining the stability analysis data sample of the power system, the correlation between the data sample obtained by the method and the stability level of the system is strong, and the electrical quantity information required by stability evaluation can be effectively met; and only key variable information is extracted, so that the reduction of artificial intelligence algorithm learning and judging efficiency caused by overhigh dimension of the characteristic quantity can be effectively avoided, and a foundation is laid for intelligent identification and control of faults of a complex large power grid. The technical problems that in the prior art, the power grid in China is large in scale and high in data dimensionality, how to quickly extract reasonable and effective power grid fault sample data from mass data and how to perform classification processing aiming at different stability problems are solved.
According to another aspect of the present embodiment, a system 300 for determining stability analysis data samples for an electrical power system is provided. Referring to fig. 3, the system 300 includes: a key variable determining module 310, configured to determine a plurality of key variables characterizing system characteristics according to a plurality of operation modes of the power grid; a response curve determining module 320, configured to determine, through simulation calculation, a plurality of response curves corresponding to a plurality of key variables characterizing system characteristics in a plurality of operating modes and different ac/dc fault states; a characteristic variable curve determining module 330, configured to classify the multiple response curves according to different stability requirements of the power system, and determine a characteristic variable curve corresponding to a specific stability requirement; and a data sample determining module 340, configured to determine a sampling time window according to the power grid control requirement, and determine a stability analysis data sample of the power system according to a characteristic variable curve corresponding to the specific stability requirement and the sampling time window.
Optionally, the determine key variables module 310 includes: the weak section determining submodule is used for determining key variables of characteristics of the power grid weak section representation system according to various operation modes of a power grid; the bus node determining submodule is used for determining key variables of the key bus node representing system characteristics according to various operation modes of the power grid; and determining a generator set submodule, which is used for determining key variables of the characteristics of the characterization system of the important generator set according to various operation modes of the power grid.
Optionally, the determine key variables module 310 includes: the multiple operation modes of the power grid comprise a flat load mode, a peak load mode and a low-ebb load mode; the scanning result determining submodule is used for carrying out fault simulation scanning based on a plurality of operation modes and determining a fault simulation scanning result, wherein the faults comprise three-permanent-N-1 faults and three-permanent-N-2 faults of an alternating current circuit, and direct current locking, commutation failure and restarting faults; and the fault obtaining submodule is used for obtaining a weak alternating current section with large power fluctuation, a key bus node with large voltage drop amplitude, an important generator set with large power angle swing and an important generator set with large speed deviation in the fault process according to the fault simulation scanning result.
Optionally, the determining a characteristic variable curve module 330 includes: the power angle curve submodule of the generator set is used for determining a weak alternating current section power curve and an important generator set power angle curve according to the requirement of the stability of the system power angle; the bus node voltage curve determining submodule is used for determining a key bus node voltage curve according to the system voltage stability requirement; and a speed deviation curve determining submodule for determining an important generator set speed deviation curve according to the system frequency stability requirement.
Optionally, the determine data sample module 340 includes: the sub-module for determining the segmented sampling data samples is used for determining the segmented sampling data samples with multiple time scales according to the characteristic variable curve corresponding to the specific stability requirement; and the stability analysis data sample determining submodule is used for determining the stability analysis data sample of the power system according to the segmented sampling data sample with multiple time scales and the sampling time window.
The system 300 for determining a stability analysis data sample of an electric power system according to an embodiment of the present invention corresponds to the method 100 for determining a stability analysis data sample of an electric power system according to another embodiment of the present invention, and is not described herein again.
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 scheme in the embodiment of the application can be implemented by adopting various computer languages, such as object-oriented programming language Java and transliterated scripting language JavaScript.
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.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (10)
1. A method of determining stability analysis data samples for a power system, comprising:
determining a plurality of key variables representing system characteristics according to a plurality of operation modes of a power grid;
determining a plurality of response curves corresponding to the plurality of key variables characterizing the system characteristics in the plurality of operation modes and different AC/DC fault states through simulation calculation;
classifying the response curves according to different stability requirements of the power system, and determining a characteristic variable curve corresponding to a specific stability requirement; and
and determining a sampling time window according to the power grid control requirement, and determining a stability analysis data sample of the power system according to a characteristic variable curve corresponding to a specific stability requirement and the sampling time window.
2. The method of claim 1, wherein determining a plurality of key variables characterizing the system based on a plurality of operating modes of the power grid comprises:
determining key variables of characteristics of a power grid weak section representation system according to various operation modes of a power grid;
determining key variables of the characteristics of a key bus node representation system according to various operation modes of a power grid; and
and determining key variables of the characteristics of the characterization system of the important generator set according to various operation modes of the power grid.
3. The method of claim 1, wherein determining a plurality of key variables characterizing the system based on a plurality of operating modes of the power grid comprises:
the multiple operation modes of the power grid comprise a flat load mode, a peak load mode and a low-ebb load mode;
based on the multiple operation modes, fault simulation scanning is carried out, and a fault simulation scanning result is determined, wherein the faults comprise three-permanent-current N-1 faults and three-permanent-current N-2 faults of an alternating current circuit, and direct current locking, phase commutation failure and restarting faults; and
and acquiring a weak alternating-current section with large power fluctuation, a key bus node with large voltage drop amplitude, an important generator set with large power angle swing and an important generator set with large speed deviation in the fault process according to the fault simulation scanning result.
4. The method of claim 1, wherein classifying the plurality of response curves according to different stability requirements of the power system and determining a characteristic variable curve corresponding to a particular stability requirement comprises:
determining a weak alternating current section power curve and an important generator set power angle curve according to the requirement of system power angle stability;
determining a key bus node voltage curve according to the system voltage stability requirement; and
and determining an important generator set speed deviation curve according to the frequency stability requirement of the system.
5. The method of claim 1, wherein determining a stability analysis data sample for the power system based on the characteristic variable curve corresponding to a particular stability requirement and the sampling time window comprises:
determining segmented sampling data samples of multiple time scales according to a characteristic variable curve corresponding to a specific stable demand; and
and determining the stability analysis data sample of the power system according to the segmented sampling data samples of the multiple time scales and the sampling time window.
6. A system for determining stability analysis data samples for an electrical power system, comprising:
the key variable determining module is used for determining a plurality of key variables representing system characteristics according to a plurality of operation modes of the power grid;
the response curve determining module is used for determining a plurality of response curves corresponding to the plurality of key variables representing the system characteristics in the plurality of operation modes and different alternating current/direct current fault states through simulation calculation;
the characteristic variable curve determining module is used for classifying the response curves according to different stable requirements of the power system and determining a characteristic variable curve corresponding to a specific stable requirement; and
and the data sample determining module is used for determining a sampling time window according to the power grid control requirement and determining a stability analysis data sample of the power system according to a characteristic variable curve corresponding to a specific stability requirement and the sampling time window.
7. The system of claim 6, wherein determining a key variable module comprises:
the weak section determining submodule is used for determining key variables of characteristics of the power grid weak section representation system according to various operation modes of a power grid;
the bus node determining submodule is used for determining key variables of the key bus node representing system characteristics according to various operation modes of the power grid; and
and determining a generator set submodule for determining key variables of the characteristics of the representation system of the important generator set according to various operation modes of the power grid.
8. The system of claim 6, wherein determining a key variable module comprises:
the multiple operation modes of the power grid comprise a flat load mode, a peak load mode and a low-ebb load mode;
a scanning result determining submodule for performing fault simulation scanning based on the multiple operation modes and determining a fault simulation scanning result, wherein the faults include a three-permanent-current N-1 fault and a three-permanent-current N-2 fault of an alternating current circuit, and a direct current locking, phase commutation failure and restarting fault; and
and the fault submodule is used for acquiring a weak alternating current section with large power fluctuation, a key bus node with large voltage drop amplitude, an important generator set with large power angle swing and an important generator set with large speed deviation in the fault process according to the fault simulation scanning result.
9. The system of claim 6, wherein determining a characteristic variable curve module comprises:
the power angle curve submodule of the generator set is used for determining a weak alternating current section power curve and an important generator set power angle curve according to the requirement of the stability of the system power angle;
the bus node voltage curve determining submodule is used for determining a key bus node voltage curve according to the system voltage stability requirement; and
and the speed deviation curve determining submodule is used for determining an important generator set speed deviation curve according to the system frequency stability requirement.
10. The system of claim 6, wherein determining a data sample module comprises:
the sub-module for determining the segmented sampling data samples is used for determining the segmented sampling data samples with multiple time scales according to the characteristic variable curve corresponding to the specific stability requirement; and
and the stability analysis data sample determining submodule is used for determining the stability analysis data sample of the power system according to the segmented sampling data samples of the multiple time scales and the sampling time window.
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