A distribution network management method containing large-scale distributed energy

Abstract

The present invention relates to the field of energy management technology, and in particular to a distribution network management method containing large-scale distributed energy. The present invention calculates discrete fluctuations according to differences in corresponding monitoring information of acquired monitoring nodes to determine implicit abnormality categories, selects an analysis method for monitoring data of monitoring nodes according to the implicit abnormality categories of the monitoring nodes, adjusts the information monitoring granularity of the monitoring information acquired by the monitoring nodes, analyzes discrete fluctuations in different time domain segments to identify characteristic time domain segments, constructs voltage change curves and current change curves of the characteristic time domain segments, and fits them with sample curve segments in a sample database to determine whether the distribution network has abnormalities, or performs global analysis on the voltage change curves and the current change curves in each time domain segment within a predetermined time, and fits them with the sample curve segments in the sample database to determine whether the distribution network has abnormalities. The present invention improves the efficiency of distribution network management while ensuring the reliability of analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management, and in particular to a method for managing a distribution network containing large-scale distributed energy. Background Art

[0002] With the development of renewable energy technology and the decline in costs, the proportion of distributed energy, such as solar photovoltaic, wind power, micro hydropower, etc., in the energy system continues to increase. These distributed energy sources are usually distributed in different locations in the power grid in a small-scale manner, changing the traditional centralized energy generation model and posing new challenges and demands on the management of distribution networks.

[0003] For example, Chinese Patent Publication No.: CN115421003A discloses a distribution network fault monitoring system and monitoring method, the system includes a data acquisition unit, a data transmission unit, a fault identification unit and a fault isolation unit; the data acquisition unit is used to collect multiple analog signals; the data transmission unit is used to group multiple analog signals and transmit them to the fault identification unit; the fault identification unit performs fault identification based on the grouped data and sends the result of fault identification to the fault isolation unit; the fault isolation unit performs at least one fault isolation and equipment protection action based on the fault identification result. The method includes a data acquisition step, a data transmission step, a fault identification step and a fault isolation step. The fault monitoring scheme of the present invention conforms to the structure and fault occurrence characteristics of the three-phase four-wire distribution network equipment, and has made targeted improvements in data acquisition, transmission and identification, and has a good fault identification effect.

[0004] However, there are still the following problems in the prior art:

[0005] In the prior art, when testing a large power grid, a massive amount of data needs to be processed, and a great deal of computing power is consumed when traversing and processing the massive data, resulting in low efficiency in analyzing the operating status of the distribution network. Summary of the invention

[0006] To this end, the present invention provides a distribution network management method containing large-scale distributed energy, which is used to overcome the problems in the prior art that when detecting large power grids, massive amounts of data need to be processed, a large amount of computing power is consumed when traversing and processing massive amounts of data, and the efficiency of analyzing the operating status of the distribution network is low.

[0007] To achieve the above object, the present invention provides a distribution network management method containing large-scale distributed energy, which comprises:

[0008] Step S1, acquiring monitoring information of each monitoring node of the distribution network every predetermined monitoring period, wherein the monitoring information includes voltage value and current value;

[0009] Step S2, calculating the discrete fluctuation amount according to the difference of the corresponding monitoring information of the monitoring nodes to determine the hidden abnormality category;

[0010] Step S3, selecting an analysis method for monitoring data of the monitoring node according to the hidden abnormality category of the monitoring node, the analysis method includes:

[0011] Adjust the information monitoring granularity of the monitoring nodes to obtain monitoring information, analyze the discrete fluctuations in different time domain segments to identify the characteristic time domain segments, respectively construct the voltage change curve and current change curve of the characteristic time domain segment and fit them with the sample curve segments in the sample database to determine whether there is an abnormality in the distribution network;

[0012] Or, perform a global analysis on the voltage change curve and the current change curve in each time domain segment within a predetermined time, and fit them with the sample curve segments in the sample database to determine whether there is an abnormality in the distribution network.

[0013] Wherein, the voltage change curve and the current change curve are both time domain waveform segments constructed based on monitoring information.

[0014] Furthermore, in step S2, the discrete fluctuation amount is calculated according to formula (1):

[0015] (1);

[0016] In formula (1), E represents the discrete fluctuation amount, Vi represents the voltage value detected for the i-th time, Ve represents the average voltage value, Ii represents the current value detected for the i-th time, Ie represents the average current value, n represents the number of monitoring times, and n is a positive integer greater than 0.

[0017] Furthermore, in step S2, the discrete fluctuation amount is compared with a preset discrete fluctuation amount comparison threshold.

[0018] If the discrete fluctuation amount is greater than the preset discrete fluctuation amount comparison threshold, it is determined that there is a strong hidden abnormality category,

[0019] If the discrete fluctuation amount is less than or equal to the preset discrete fluctuation amount comparison threshold, it is determined that a weak latent abnormality category exists.

[0020] Furthermore, in step S3, an analysis method for monitoring data of the monitoring node is selected according to the hidden abnormality category of the monitoring node, wherein:

[0021] If it is determined that there is a strong hidden abnormality category, a full domain analysis is performed on the voltage change curve and the current change curve in each time domain segment within the predetermined time, and the sample curve segments in the sample database are fitted to determine whether there is an abnormality in the distribution network;

[0022] If it is determined that there is a weak hidden anomaly category, the information monitoring granularity of the monitoring node to obtain the monitoring information is adjusted, and the discrete fluctuations in different time domain segments are analyzed to identify the characteristic time domain segments. The voltage change curve and current change curve of the characteristic time domain segment are constructed respectively and fit with the sample curve segments in the sample database to determine whether there is an abnormality in the distribution network.

[0023] Furthermore, in step S3, the entire monitoring time domain is divided into a number of time domain segments, and the discrete fluctuation amount in each time domain segment is calculated.

[0024] Furthermore, in step S3, the discrete fluctuation amount in each time domain segment is compared with a preset time domain segment discrete fluctuation amount comparison threshold.

[0025] If the discrete fluctuation amount in the time domain segment is greater than a preset time domain segment discrete fluctuation amount comparison threshold, then the time domain segment is a characteristic time domain segment.

[0026] Furthermore, in step S3, the information monitoring granularity of the monitoring information acquired by the monitoring nodes in the time domain segment is adjusted according to the discrete fluctuation amount in the time domain segment, wherein the discrete fluctuation amount in the time domain segment is positively correlated with the information monitoring granularity of the monitoring information acquired by the monitoring nodes in the time domain segment.

[0027] Furthermore, in step S3, a current change curve over time and a voltage change curve over time in the characteristic time domain segment are respectively constructed, and the current change curve and the voltage change curve are fitted and compared with the sample curve segments in the sample database. If the fitting value of the current change curve or the voltage change curve and the sample curve segments in the sample database is less than a preset fitting comparison threshold, it is determined that there is an abnormality in the distribution network.

[0028] Furthermore, in step S3, a full monitoring time domain current change curve and a voltage change curve are constructed respectively, and the full monitoring time domain current change curve and the full monitoring time domain voltage change curve are fitted and compared with the sample curve segments in the sample database. If the fitting value of the full monitoring time domain current change curve or the full monitoring time domain voltage change curve and the sample curve segments in the sample database is less than a preset fitting comparison threshold, it is determined that there is an abnormality in the distribution network.

[0029] Furthermore, in step S3, the time lengths of the time domain segments are the same.

[0030] Compared with the prior art, the present invention calculates discrete fluctuations according to the differences in the corresponding monitoring information of the acquired monitoring nodes to determine the hidden abnormality category, selects the analysis method for the monitoring data of the monitoring nodes according to the hidden abnormality category of the monitoring nodes, adjusts the information monitoring granularity of the monitoring information acquired by the monitoring nodes, analyzes the discrete fluctuations in different time domain segments to identify the characteristic time domain segments, constructs the voltage change curve and the current change curve of the characteristic time domain segment to fit with the sample curve segments in the sample database to determine whether there is an abnormality in the distribution network, or performs a global analysis on the voltage change curve and the current change curve in each time domain segment within a predetermined time, and fits them with the sample curve segments in the sample database to determine whether there is an abnormality in the distribution network. The present invention improves the efficiency of distribution network management while ensuring the reliability of analysis.

[0031] In particular, the present invention calculates discrete fluctuations based on the differences in monitoring information corresponding to the monitoring nodes. Monitoring current values ​​can help determine the load conditions in the distribution network, including whether the load is balanced, whether the load is overloaded, etc. Monitoring voltage values ​​can help determine voltage stability and voltage fluctuations in the distribution network. Current and voltage values ​​are easy to monitor and have certain data representation. Although they cannot be used solely to determine abnormalities using current or voltage thresholds, they can be used as explicit parameters that are easy to detect and can reflect the operating conditions of the distribution network to a certain extent. Furthermore, by calculating discrete fluctuations, the operating conditions of the distribution network can be more reliably represented to a certain extent, providing data reference for subsequent analysis and improving the reliability of distribution network management.

[0032] In particular, the present invention determines the existence of hidden abnormality categories based on discrete fluctuations. Since discrete fluctuations can characterize the operating conditions of the distribution network to a certain extent, when the discrete fluctuations are large, the operating state of the distribution network is relatively unstable and has a certain abnormal tendency. When the discrete fluctuations are small, there may be relatively unstable time domain segments in the operation of the distribution network. Different analysis methods of monitoring node monitoring data are adopted according to the hidden abnormality categories, which can improve the analysis efficiency while ensuring the reliability of the analysis.

[0033] In particular, the present invention identifies characteristic time domain segments and adjusts the information monitoring granularity of the monitoring nodes to obtain monitoring information. When there is a weak hidden abnormality category, it is only necessary to analyze the characteristic time domain segment. By extracting the characteristic time domain segment with a large discrete fluctuation amount, the information monitoring granularity of the monitoring nodes in the characteristic time domain segment to obtain monitoring information is adjusted. This allows a more reliable analysis of the distribution network operation status in the characteristic time domain segment, thereby improving the reliability of distribution network management.

[0034] In particular, the present invention constructs a voltage change curve and a current change curve of a characteristic time domain segment respectively, and fits them with the sample curve segments in the sample database to determine whether there is an abnormality in the distribution network. When there is no abnormality in the operation of the distribution network, the voltage change curve and the current change curve should be highly fitted with the sample curve segments in the sample database. When the degree of fitting is low, it can be determined that there is an abnormality in the distribution network. However, compared with threshold judgment, fitting takes up more computing power, and when applied to large-scale power grids in the face of massive data, the above phenomenon will be aggravated. Only the voltage and current conditions in the characteristic time domain segment are analyzed, which improves the analysis efficiency of distribution network management while ensuring the reliability of the analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A step diagram of a method for managing a distribution network containing large-scale distributed energy according to an embodiment of the invention;

[0036] Figure 2 A diagram for selecting an analysis method for monitoring data of monitoring nodes according to an embodiment of the invention;

[0037] Figure 3 It is a time domain segment logic decision diagram of the identification feature of the embodiment of the invention;

[0038] Figure 4 This is a logic determination diagram for determining whether there is an abnormality in the distribution network according to an embodiment of the present invention. DETAILED DESCRIPTION

[0039] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0040] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0041] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the term "connection" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0042] See also Figure 1-Figure 4 As shown, Figure 1 This is a step diagram of a method for managing a distribution network containing large-scale distributed energy according to an embodiment of the invention. Figure 2The analysis method of monitoring data of monitoring nodes in the embodiment of the invention is selected as a diagram. Figure 3 This is a time domain segment logic decision diagram of the identification feature of the embodiment of the invention, Figure 4 In order to determine whether there is an abnormal logic determination diagram in the distribution network according to the embodiment of the invention, a distribution network management method containing large-scale distributed energy sources of the present invention includes:

[0043] Step S1, acquiring monitoring information of each monitoring node of the distribution network every predetermined monitoring period, wherein the monitoring information includes voltage value and current value;

[0044] Step S2, calculating the discrete fluctuation amount according to the difference of the corresponding monitoring information of the monitoring nodes to determine the hidden abnormality category;

[0045] Step S3, selecting an analysis method for monitoring data of the monitoring node according to the hidden abnormality category of the monitoring node, the analysis method includes:

[0046] Adjust the information monitoring granularity of the monitoring nodes to obtain monitoring information, analyze the discrete fluctuations in different time domain segments to identify the characteristic time domain segments, respectively construct the voltage change curve and current change curve of the characteristic time domain segment and fit them with the sample curve segments in the sample database to determine whether there is an abnormality in the distribution network;

[0047] Or, perform a global analysis on the voltage change curve and the current change curve in each time domain segment within a predetermined time, and fit them with the sample curve segments in the sample database to determine whether there is an abnormality in the distribution network.

[0048] Wherein, the voltage change curve and the current change curve are both time domain waveform segments constructed based on monitoring information.

[0049] Specifically, the present invention does not limit the method of obtaining monitoring information. The monitoring nodes are several nodes on the distribution network line. It is only necessary to set up corresponding monitoring equipment to obtain monitoring information, which will not be repeated here.

[0050] Specifically, the present invention does not limit the way of fitting the time domain curve segments. For example, the correlation coefficient can be used as the fitting degree. The value range of the correlation coefficient is between -1 and 1. The closer to 1, the higher the correlation between the two curves.

[0051] Specifically, the present invention does not limit the method for obtaining the sample curve. In this embodiment, the sample curve is obtained in advance, and a number of monitoring information in the distribution network without abnormal conditions are counted, and a voltage time domain waveform curve segment and a current time domain waveform curve segment are constructed as sample curve segments to characterize the current and voltage conditions in the distribution network under normal conditions for later fitting, which will not be repeated here.

[0052] In this embodiment, in order to ensure timely response, the predetermined monitoring period is selected within the range of [5s, 10s], and a single monitoring can be maintained for a certain period of time, which will not be described in detail.

[0053] Specifically, in step S2, the discrete fluctuation amount is calculated according to formula (1):

[0054] (1);

[0055] In formula (1), E represents the discrete fluctuation amount, Vi represents the voltage value detected for the i-th time, Ve represents the average voltage value, Ii represents the current value detected for the i-th time, Ie represents the average current value, n represents the number of monitoring times, and n is a positive integer greater than 0.

[0056] Specifically, the present invention calculates discrete fluctuations based on the differences in monitoring information corresponding to the monitoring nodes. Monitoring current values ​​can help determine the load conditions in the distribution network, including whether the load is balanced, whether the load is overloaded, etc. Monitoring voltage values ​​can help determine voltage stability and voltage fluctuations in the distribution network. Current and voltage values ​​are easy to monitor and have certain data representation. Although they cannot be used solely to determine abnormalities by current or voltage thresholds, they can be used as explicit parameters that are easy to detect, which can reflect the operating conditions of the distribution network to a certain extent. Furthermore, by calculating discrete fluctuations, the operating conditions of the distribution network can be more reliably represented to a certain extent, providing data reference for subsequent analysis and improving the reliability of distribution network management.

[0057] Specifically, in step S2, the discrete fluctuation amount is compared with a preset discrete fluctuation amount comparison threshold.

[0058] If the discrete fluctuation amount is greater than the preset discrete fluctuation amount comparison threshold, it is determined that there is a strong hidden abnormality category,

[0059] If the discrete fluctuation amount is less than or equal to the preset discrete fluctuation amount comparison threshold, it is determined that a weak latent abnormality category exists.

[0060] In this embodiment, the preset discrete fluctuation quantity comparison threshold is selected within the range of [0.1, 0.3].

[0061] Specifically, the present invention determines the existence of hidden abnormality categories based on discrete fluctuations. Since discrete fluctuations can characterize the operating conditions of the distribution network to a certain extent, when the discrete fluctuations are large, the operating state of the distribution network is relatively unstable and has certain abnormal tendencies. When the discrete fluctuations are small, there may be relatively unstable time domain segments in the operation of the distribution network. Different analysis methods of monitoring node monitoring data are adopted according to the hidden abnormality categories, which can improve the analysis efficiency while ensuring the reliability of the analysis.

[0062] Specifically, in step S3, an analysis method for monitoring data of a monitoring node is selected according to the hidden abnormality category of the monitoring node, wherein:

[0063] If it is determined that there is a strong hidden abnormality category, a full domain analysis is performed on the voltage change curve and the current change curve in each time domain segment within the predetermined time, and the sample curve segments in the sample database are fitted to determine whether there is an abnormality in the distribution network;

[0064] If it is determined that there is a weak hidden anomaly category, the information monitoring granularity of the monitoring node to obtain the monitoring information is adjusted, and the discrete fluctuations in different time domain segments are analyzed to identify the characteristic time domain segments. The voltage change curve and current change curve of the characteristic time domain segment are constructed respectively and fit with the sample curve segments in the sample database to determine whether there is an abnormality in the distribution network.

[0065] Specifically, in step S3, the entire monitoring time domain is divided into a number of time domain segments, and the discrete fluctuation amount in each time domain segment is calculated.

[0066] Specifically, in step S3, the discrete fluctuation amount in each time domain segment is compared with a preset time domain segment discrete fluctuation amount comparison threshold.

[0067] If the discrete fluctuation amount in the time domain segment is greater than a preset time domain segment discrete fluctuation amount comparison threshold, then the time domain segment is a characteristic time domain segment.

[0068] In this embodiment, the preset time domain discrete fluctuation quantity comparison threshold is selected within the range of [0.05, 0.1].

[0069] Specifically, in step S3, the information monitoring granularity of the monitoring information acquired by the monitoring nodes in the time domain segment is adjusted according to the discrete fluctuation amount in the time domain segment, wherein the discrete fluctuation amount in the time domain segment is positively correlated with the information monitoring granularity of the monitoring information acquired by the monitoring nodes in the time domain segment.

[0070] In this embodiment, the information monitoring granularity is the number of monitoring times per second;

[0071] Optional,

[0072] 0.1<D<0.15, the information monitoring granularity L1=[L0+△L1] of the monitoring node to obtain monitoring information in the time domain segment;

[0073] 0.15≤D, the information monitoring granularity L2=[L0+△L2] of the monitoring node to obtain monitoring information in the time domain segment;

[0074] D is the discrete fluctuation amount in the time domain segment, L0 is the information monitoring granularity at which the monitoring node obtains monitoring information in the initial time domain segment, 5 times / s<L0<10 times / s, △L1 is the first adjustment amount, △L2 is the second adjustment amount, 0.1L0<△L1<△L2<0.3L0.

[0075] Specifically, the present invention identifies characteristic time domain segments and adjusts the information monitoring granularity of monitoring nodes for acquiring monitoring information. When there are weak hidden abnormality categories, it is only necessary to analyze the characteristic time domain segments. By extracting characteristic time domain segments with large discrete fluctuations, the information monitoring granularity of monitoring nodes in the characteristic time domain segments for acquiring monitoring information is adjusted. This allows for more reliably analyzing the operating status of the distribution network in the characteristic time domain segments and improving the reliability of distribution network management.

[0076] Specifically, in step S3, a current change curve over time and a voltage change curve over time in the characteristic time domain segment are respectively constructed, and the current change curve and the voltage change curve are fitted and compared with the sample curve segments in the sample database. If the fitting value of the current change curve or the voltage change curve and the sample curve segments in the sample database is less than a preset fitting comparison threshold, it is determined that there is an abnormality in the distribution network.

[0077] In this embodiment, the preset fitting comparison threshold is selected within the range of [0.8, 0.9].

[0078] Specifically, the present invention constructs a voltage change curve and a current change curve of a characteristic time domain segment respectively, and fits them with the sample curve segments in the sample database to determine whether there is an abnormality in the distribution network. When there is no abnormality in the operation of the distribution network, the voltage change curve and the current change curve should be highly fitted with the sample curve segments in the sample database. When the degree of fitting is low, it can be determined that there is an abnormality in the distribution network. However, compared with threshold judgment, fitting takes up more computing power, and when applied to large-scale power grids, the above phenomenon will be aggravated in the face of massive data. Only the voltage and current conditions in the characteristic time domain segment are analyzed, which improves the analysis efficiency of distribution network management while ensuring the reliability of the analysis.

[0079] Specifically, in step S3, a full monitoring time domain current change curve and a voltage change curve are constructed respectively, and the full monitoring time domain current change curve and the full monitoring time domain voltage change curve are fitted and compared with the sample curve segments in the sample database. If the fitting value of the full monitoring time domain current change curve or the full monitoring time domain voltage change curve and the sample curve segments in the sample database is less than a preset fitting comparison threshold, it is determined that there is an abnormality in the distribution network.

[0080] Specifically, in step S3, the time lengths of the time domain segments are the same.

[0081] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A method for managing a distribution network containing large-scale distributed energy, characterized in that: include: Step S1, acquiring monitoring information of each monitoring node of the distribution network every predetermined monitoring period, wherein the monitoring information includes voltage value and current value; Step S2, calculating the discrete fluctuation amount according to the difference of the corresponding monitoring information of the monitoring nodes to determine the hidden abnormality category; Step S3, selecting an analysis method for monitoring data of the monitoring node according to the hidden abnormality category of the monitoring node, the analysis method includes: Adjust the information monitoring granularity of the monitoring nodes to obtain monitoring information, analyze the discrete fluctuations in different time domain segments to identify the characteristic time domain segments, respectively construct the voltage change curve and current change curve of the characteristic time domain segment and fit them with the sample curve segments in the sample database to determine whether there is an abnormality in the distribution network; Or, perform a global analysis on the voltage change curve and the current change curve in each time domain segment within a predetermined time, and fit them with the sample curve segments in the sample database to determine whether there is an abnormality in the distribution network. Wherein, the voltage change curve and the current change curve are both time domain waveform segments constructed based on monitoring information; In step S2, the discrete fluctuation amount is calculated according to formula (1): （1） In formula (1), E represents the discrete fluctuation amount, Vi represents the voltage value detected at the i-th time, Ve represents the average voltage value, Ii represents the current value detected at the i-th time, Ie represents the average current value, n represents the number of monitoring times, and n is a positive integer greater than 0; In step S2, the discrete fluctuation amount is compared with a preset discrete fluctuation amount comparison threshold. If the discrete fluctuation amount is greater than the preset discrete fluctuation amount comparison threshold, it is determined that there is a strong hidden abnormality category, If the discrete fluctuation amount is less than or equal to the preset discrete fluctuation amount comparison threshold, it is determined that there is a weak hidden abnormality category; In step S3, an analysis method for monitoring data of the monitoring node is selected according to the hidden abnormality category of the monitoring node, wherein: If it is determined that there is a strong hidden abnormality category, a full domain analysis is performed on the voltage change curve and the current change curve in each time domain segment within the predetermined time, and the sample curve segments in the sample database are fitted to determine whether there is an abnormality in the distribution network; If it is determined that there is a weak hidden anomaly category, the information monitoring granularity of the monitoring node to obtain the monitoring information is adjusted, and the discrete fluctuations in different time domain segments are analyzed to identify the characteristic time domain segments. The voltage change curve and current change curve of the characteristic time domain segment are constructed respectively and fit with the sample curve segments in the sample database to determine whether there is an abnormality in the distribution network.

2. The method for managing a distribution network containing large-scale distributed energy according to claim 1, characterized in that: In step S3, the entire monitoring time domain is divided into a number of time domain segments, and the discrete fluctuation amount in each time domain segment is calculated.

3. The method for managing a distribution network containing large-scale distributed energy according to claim 2, characterized in that: In step S3, the discrete fluctuation amount in each time domain segment is compared with a preset time domain segment discrete fluctuation amount comparison threshold. If the discrete fluctuation amount in the time domain segment is greater than a preset time domain segment discrete fluctuation amount comparison threshold, then the time domain segment is a characteristic time domain segment.

4. The method for managing a distribution network containing large-scale distributed energy according to claim 1, characterized in that: In step S3, the information monitoring granularity of the monitoring information acquired by the monitoring nodes in the time domain segment is adjusted according to the discrete fluctuation amount in the time domain segment, wherein the discrete fluctuation amount in the time domain segment is positively correlated with the information monitoring granularity of the monitoring information acquired by the monitoring nodes in the time domain segment.

5. The method for managing a distribution network containing large-scale distributed energy according to claim 1, characterized in that: In the step S3, a current change curve over time and a voltage change curve over time in the characteristic time domain segment are respectively constructed, and the current change curve and the voltage change curve are fitted and compared with the sample curve segments in the sample database. If the fitting value of the current change curve or the voltage change curve and the sample curve segments in the sample database is less than a preset fitting comparison threshold, it is determined that there is an abnormality in the distribution network.

6. The method for managing a distribution network containing large-scale distributed energy according to claim 1, characterized in that: In the step S3, a full monitoring time domain current change curve with time and a voltage change curve with time are respectively constructed, and the full monitoring time domain current change curve and the full monitoring time domain voltage change curve are fitted and compared with the sample curve segments in the sample database. If the fitting value of the full monitoring time domain current change curve or the full monitoring time domain voltage change curve and the sample curve segments in the sample database is less than a preset fitting comparison threshold, it is determined that there is an abnormality in the distribution network.

7. The method for managing a distribution network containing large-scale distributed energy according to claim 1, characterized in that: In step S3, the time lengths of the time domain segments are the same.

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