Method, device and system for evaluating self-healing capability of power distribution network

Abstract

The application provides a method, device and system for evaluating self-healing capability of a power distribution network. The method obtains line overload indicators, voltage out-of-limit indicators, load loss indicators, dangerous degree indicators of reverse heavy overload of transformer substation and distribution transformer, severity indicators of low-voltage line power flow out-of-limit of transformer substation, reverse load indicators of medium-voltage feeder itself and transformer substation contained in the feeder, and performs weighted summation processing on the line overload indicators, voltage out-of-limit indicators, load loss indicators, dangerous degree indicators of reverse heavy overload of transformer substation and distribution transformer, severity indicators of low-voltage line power flow out-of-limit of transformer substation, reverse load indicators of medium-voltage feeder itself and transformer substation contained in the feeder to obtain a comprehensive performance indicator value of the power distribution network. The finally obtained comprehensive evaluation indicator is more accurate and more consistent with actual working conditions. That is, the problem that the existing scheme has poor efficiency in evaluating the self-healing capability of the power distribution network and cannot meet actual working conditions is solved.

Description

Technical Field

[0001] This application relates to the field of power system operation technology, and more specifically, to a method, apparatus and system for evaluating the self-healing capability of a distribution network. Background Technology

[0002] With the increasing complexity of distribution networks, researching the fault detection, isolation, and self-healing capabilities of power grids under various fault conditions is crucial for minimizing the impact of anomalies or faults on the grid with minimal human intervention. Self-healing capability is the most important and significant characteristic of distribution network automation. To evaluate the actual effectiveness of distribution network construction, it is necessary to conduct a comprehensive and objective assessment of its self-healing capability, thereby accurately analyzing existing problems and providing guidance for improving the grid's self-healing ability.

[0003] Regarding the evaluation of power grid self-healing, existing schemes define the power source self-healing speed and self-healing rate, but only have two evaluation indicators, which is not comprehensive. Some existing schemes use fuzzy hierarchical analysis to evaluate the comprehensive benefits of smart grid self-healing, involving the technical, economic, social and practical aspects of the power grid, but do not provide quantitative calculation methods for the indicators. Some existing schemes have evaluated the self-healing capabilities of each section of the distribution network after a fault, but have not evaluated the economic effects of self-healing, nor have they considered the uncertainties in the self-healing process.

[0004] In other words, the existing schemes are inefficient at assessing the self-healing capabilities of the distribution network and cannot meet the actual operating conditions. Summary of the Invention

[0005] The main objective of this application is to provide a method, apparatus, and system for evaluating the self-healing capability of a distribution network, so as to at least solve the problem that existing solutions for evaluating the self-healing capability of distribution networks are inefficient and cannot meet actual operating conditions.

[0006] To achieve the above objectives, according to one aspect of this application, a method for evaluating the self-healing capability of a distribution network is provided, the method comprising:

[0007] The system acquires the following indicators for the power distribution network: line overload index, voltage limit exceedance index, load loss index, transformer reverse heavy overload hazard index, low-voltage line power flow limit exceedance severity index, medium-voltage feeder self-reverse load index, and feeder-included transformer reverse load index. The line overload index is used to assess whether power lines have exceeded preset loads. The voltage limit exceedance index is used to assess whether voltage in the distribution network exceeds permissible limits. The load loss index is used to assess load loss in the distribution network due to faults or load changes. The transformer reverse heavy overload hazard index is used to assess whether transformers in the distribution area are at risk of reverse heavy overload. The low-voltage line power flow limit exceedance severity index is used to assess whether low-voltage line power flow in the distribution area exceeds preset limits. The medium-voltage feeder self-reverse load index is used to assess whether medium-voltage feeders have reverse loads. The feeder-included transformer reverse load index is used to assess the reverse load situation of transformers included in the feeder.

[0008] The following parameters of the distribution network are weighted and summed: line overload index, voltage limit exceedance index, load loss index, transformer reverse heavy overload danger index, low-voltage line power flow limit exceedance severity index, medium-voltage feeder self-reverse load index, and feeder-included transformer reverse load index. The resulting comprehensive performance index value of the distribution network is obtained. This comprehensive performance index value characterizes the self-healing capability of the distribution network.

[0009] Optionally, obtain the line overload indicators of the distribution network, including:

[0010] according to Determine the line overload index of the distribution network, wherein R L G is the overload index of the distribution network lines. r (W i Let be the probability of line disconnection when the i-th incident occurs in time period t. W represents the degree of overload risk of line l caused by the i-th accident. i For the i-th accident, Let I be the active power of line l when the i-th accident occurs, and let L be the set of accident indices and L be the set of line indices.

[0011] Optionally, voltage over-limit indicators are obtained, including:

[0012] according to Determine the voltage over-limit index, wherein R V For the voltage over-limit index, G r (V i S(V) represents the voltage exceedance rate of node i within time period t, S(V) represents the voltage exceedance risk level of each node, and N... b This refers to the set of nodes in the distribution network that are prone to voltage over-limit occurrences.

[0013] Optionally, obtain the loss-of-load indicators, including:

[0014] According to R F =G r *S Fi Determine the load shedding index, wherein R F G is the load shedding index. r To determine the probability of loss of load, S Fi The degree of risk of loss of load.

[0015] Optionally, obtain the risk level index of reverse heavy overload of the distribution transformer in the transformer area, including:

[0016] according to The degree of reverse heavy overload hazard of the transformer substation in the specified area is determined by the index, where K(r) q,i D(r) represents the degree of reverse heavy overload risk of the transformer substation in the aforementioned area. q,i ) is 0, or is r q,i Let r be the transformer load rate of the distribution area at time i, and t be the upper limit threshold of the transformer load rate of the distribution area. n t represents the current time. s The starting time for the load rate of the distribution transformer in the transformer area to exceed the limit.

[0017] Optionally, obtain the severity of low-voltage line power flow exceeding the limit in the transformer area, including:

[0018] according to The starting time for the transformer load rate exceeding the limit in the specified distribution area is determined, where is the starting time for the transformer load rate exceeding the limit in the specified distribution area, n1 is the number of lines contained in the distribution area, and D(r l,i ) is 0, or is r l,i Let r be the line load factor at time point i. l t is the upper limit threshold for line current carrying capacity. n t represents the current time. s The starting time for the load rate of the distribution transformer in the transformer area to exceed the limit.

[0019] Optionally, obtain the reverse load index of the medium-voltage feeder itself, including:

[0020] exist In the case of, according to Determine the reverse load index of the medium-voltage feeder, where K(θ1) is the reverse load index of the medium-voltage feeder, p g,i For the input of the i-th power source, p j,i n1 represents the output of the i-th load, n2 represents the total power supply connected to the feeder, and n3 represents the number of loads on the feeder.

[0021] exist In this case, the reverse load index of the medium-voltage feeder itself is determined to be 0.

[0022] Optionally, obtain the reverse load index of the transformer substations included in the feeder, including:

[0023] exist In the case of, according to Determine the reverse load index of the transformer substations included in the feeder, where K(θ2) is the reverse load index of the transformer substations included in the feeder, p g,i For the input of the i-th power source, p j,i n4 is the output of the i-th load, n5 is the number of power supplies connected to the c-th transformer area, n6 is the number of loads connected to the c-th transformer area, and n7 is the number of transformer areas connected by the feeder.

[0024] exist In this case, the reverse load index of the transformer area contained in the feeder is determined to be 0.

[0025] According to another aspect of this application, an evaluation device for the self-healing capability of a power distribution network is provided, the device comprising:

[0026] The acquisition unit is used to acquire the following indicators for the distribution network: line overload index, voltage limit exceedance index, load loss index, transformer reverse heavy overload hazard index, low-voltage line power flow limit exceedance severity index, medium-voltage feeder self-reverse load index, and feeder-included transformer reverse load index. The line overload index is used to assess whether the power line exceeds the preset load. The voltage limit exceedance index is used to assess whether the voltage in the distribution network exceeds the allowable range. The load loss index is used to assess the load loss caused by faults or load changes in the distribution network. The transformer reverse heavy overload hazard index is used to assess whether the transformer in the distribution area has a risk of reverse heavy overload. The low-voltage line power flow limit exceedance severity index is used to assess whether the power flow in the low-voltage line in the distribution area exceeds the preset range. The medium-voltage feeder self-reverse load index is used to assess whether the medium-voltage feeder has a reverse load. The feeder-included transformer reverse load index is used to assess the reverse load situation of the transformers included in the feeder.

[0027] The processing unit is used to perform weighted summation on the following indicators of the distribution network: line overload index, voltage over-limit index, load loss index, reverse heavy overload danger index of the distribution transformer in the distribution area, power flow over-limit severity of the low-voltage line in the distribution area, reverse load index of the medium-voltage feeder itself, and reverse load index of the distribution area contained in the feeder, to obtain the comprehensive performance index value of the distribution network. The comprehensive performance index value of the distribution network characterizes the self-healing capability of the distribution network.

[0028] According to another aspect of this application, an evaluation system for the self-healing capability of a power distribution network is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including methods for performing any one of the methods described.

[0029] By applying the technical solution of this application, the following indicators of the distribution network are obtained: line overload index, voltage over-limit index, load shedding index, risk level of reverse heavy overload of distribution transformers in the distribution area, severity of power flow over-limit of low-voltage lines in the distribution area, reverse load of medium-voltage feeders themselves, and reverse load of distribution areas contained in the feeders. These indicators are then weighted and summed to obtain the comprehensive performance index value of the distribution network. This makes the final comprehensive evaluation index more accurate and more consistent with actual operating conditions. In other words, it solves the problem that existing solutions for assessing the self-healing capability of distribution networks are inefficient and fail to reflect actual operating conditions. Attached Figure Description

[0030] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0031] Figure 1 A flowchart illustrating a method for evaluating the self-healing capability of a power distribution network according to an embodiment of this application is shown.

[0032] Figure 2 A flowchart illustrating another method for evaluating the self-healing capability of a power distribution network according to an embodiment of this application is shown.

[0033] Figure 3 A structural block diagram of an evaluation device for the self-healing capability of a power distribution network according to an embodiment of this application is shown. Detailed Implementation

[0034] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0035] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0036] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0037] For ease of description, the following explains some of the nouns or terms used in the embodiments of this application:

[0038] Line overload indicators are used to measure whether lines in a power system are under overload. They are typically used to assess the load on power grid transmission lines to ensure that lines operate within safe limits. Line overload indicators can include parameters such as line current load, power load, and temperature. When the load on a line exceeds its design capacity, an overload occurs, which can lead to dangers such as line overheating, equipment damage, and even fires. By monitoring and analyzing line overload indicators, overload problems can be detected in a timely manner, allowing for appropriate adjustments or upgrades to ensure the safe and stable operation of the power grid.

[0039] Voltage limit exceedance indicators are used to monitor whether the voltage in a power system exceeds the safe range. In a power system, voltage exceeding limits can lead to equipment damage, degraded power quality, and even accidents. Therefore, voltage limit exceedance indicators are crucial for ensuring the safe and stable operation of the power system. Voltage limit exceedance indicators typically include upper and lower limits. The upper limit refers to voltage exceeding the normal range, which may cause equipment damage or other problems; the lower limit refers to voltage falling below the normal range, which may also cause equipment malfunction or other problems. Power system operators can monitor voltage limit exceedance indicators to promptly identify problems and take appropriate measures to ensure the safe and stable operation of the power system.

[0040] Load shedding indices refer to the degree to which a power system cannot provide its expected power supply capacity when a fault or failure occurs. Load shedding indices are typically measured as the amount or percentage of load the power system cannot provide and are used to assess the reliability and stability of the power system. A lower load shedding index indicates a more reliable power system. Common load shedding indices include load unreliability and mean time between load failures (MTBF).

[0041] The reverse heavy overload hazard index for distribution transformers is used to assess the degree of danger posed by distribution transformer equipment under reverse heavy overload conditions. This index primarily considers factors such as the potential damage to the equipment, the scope of impact, and the degree of influence on power grid operation under reverse heavy overload conditions. The reverse heavy overload hazard index helps operators and equipment managers take timely measures to ensure the safe and reliable operation of equipment.

[0042] The reverse load capacity index of a medium-voltage feeder refers to the amount of reverse load that a medium-voltage feeder experiences during operation in a medium-voltage power grid system. Reverse load refers to the load in a power system where the current direction is opposite to the normal direction, usually caused by various reasons leading to abnormal current direction. In a medium-voltage power grid system, the reverse load capacity index of a medium-voltage feeder is a crucial parameter, reflecting the potential load conditions the feeder may encounter during operation. It is essential for ensuring the reliability and stability of the medium-voltage power grid system. By monitoring and evaluating the reverse load capacity index of medium-voltage feeders, potential problems can be identified and resolved promptly, ensuring the normal operation of the medium-voltage power grid system.

[0043] The reverse load index of the transformer substations connected to a feeder refers to the proportion of reverse load in the transformer substations connected to the feeder within the power grid. Reverse load refers to a situation in the power grid where the load direction is opposite to the normal load direction, usually caused by the integration of distributed energy sources (such as solar photovoltaic, wind power, etc.) or other reasons. The reverse load index of the transformer substations connected to a feeder can be expressed as the ratio of the peak load of the reverse load to the peak load of the forward load, reflecting the degree of reverse load in the power grid. Monitoring and controlling this index is of great significance for ensuring the safe and stable operation of the power grid.

[0044] Entropy weighting is a multi-indicator comprehensive evaluation method that determines the weight of each indicator in the overall evaluation by calculating the entropy value of each indicator. In practical applications, entropy weighting can help decision-makers more accurately assess the importance of different indicators, thereby making more scientific decisions. This method is mainly used in fields such as multi-indicator decision-making, comprehensive evaluation, and risk assessment.

[0045] As described in the background section, existing technologies define power supply self-healing speed and self-healing rate, but only provide two evaluation indicators, which is incomplete. Some existing solutions use fuzzy hierarchical analysis to evaluate the comprehensive benefits of smart grid self-healing, involving technical, economic, social, and practical aspects of the grid, but do not provide quantitative calculation methods for the indicators. Other existing solutions assess the self-healing capabilities of different segments of the distribution network after a fault, but do not evaluate the economic effects of self-healing or consider the uncertainties in the self-healing process. In other words, existing solutions are inefficient in evaluating the self-healing capabilities of distribution networks and cannot reflect actual operating conditions. To address the problem of inefficient evaluation of distribution network self-healing capabilities in existing solutions and their inability to reflect actual operating conditions, embodiments of this application provide a method, apparatus, and system for evaluating the self-healing capabilities of distribution networks.

[0046] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0047] This embodiment provides a method for evaluating the self-healing capability of a power distribution network. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Also, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0048] Figure 1 This is a flowchart illustrating a method for evaluating the self-healing capability of a power distribution network according to an embodiment of this application. Figure 1 As shown, the method includes the following steps:

[0049] Step S101: Obtain the following indicators for the distribution network: line overload index, voltage limit exceedance index, load loss index, transformer reverse heavy overload hazard index, low-voltage line power flow limit exceedance severity, medium-voltage feeder self-reverse load index, and feeder-included transformer reverse load index. The line overload index is used to assess whether the power line exceeds the preset load. The voltage limit exceedance index is used to assess whether the voltage in the distribution network exceeds the allowable range. The load loss index is used to assess the load loss caused by faults or load changes in the distribution network. The transformer reverse heavy overload hazard index is used to assess whether the transformer in the distribution area has the risk of reverse heavy overload. The low-voltage line power flow limit exceedance severity is used to assess whether the power flow in the low-voltage line in the distribution area exceeds the preset range. The medium-voltage feeder self-reverse load index is used to assess whether the medium-voltage feeder has reverse load. The feeder-included transformer reverse load index is used to assess the reverse load situation of the transformers included in the feeder.

[0050] Specifically, a multi-level post-event self-healing evaluation index system for distribution networks is constructed using the following indicators: line overload index, voltage over-limit index, load loss index, risk level of reverse heavy overload of distribution transformers in the distribution area, severity of power flow over-limit of low-voltage lines in the distribution area, reverse load of medium-voltage feeders themselves, and reverse load of distribution areas contained in the feeders.

[0051] In one embodiment of this application, obtaining the line overload index of the distribution network includes:

[0052] according to Determine the line overload index of the above distribution network, where R L G represents the overload index of the aforementioned power distribution network lines. r (W i Let be the probability of line disconnection when the i-th incident occurs in time period t. W represents the degree of overload risk of line l caused by the i-th accident. i For the i-th accident, Let I be the active power of line l when the i-th accident occurs, and let L be the set of accident indices and L be the set of line indices.

[0053] Specifically, the probability of line interruption within a preset time period (e.g., 1 hour) can be expressed as:

[0054]

[0055] Among them, G r (W i Let W be the probability of line disconnection when the i-th incident occurs in time period t. i For the i-th accident, α i Let be the number of times the i-th accident occurs.

[0056] At this point, the degree of danger of line overload is as follows: This indicates the degree of overload risk of line l caused by the occurrence of the i-th accident; P represents the active power of line l at the time of the i-th accident; max This represents the maximum active power of line l.

[0057] when hour,

[0058]

[0059] when hour,

[0060]

[0061] In one embodiment of this application, obtaining the voltage over-limit index includes:

[0062] according to The above voltage over-limit indicators are determined, where R V For the above voltage over-limit indicators, G r (V i S(V) represents the voltage exceedance rate of node i within time period t, S(V) represents the voltage exceedance risk level of each node, and N... b This refers to the set of nodes in the aforementioned distribution network that are prone to voltage over-limit occurrences.

[0063] Specifically, the voltage over-limit rate of node i within time period t is:

[0064]

[0065] G r (V i f(V) represents the voltage over-limit rate; t,i Let V be the voltage distribution function of the i-th node during time period t. t,i V represents the voltage at the i-th node during time period t; the per-unit voltage fluctuation range is ±10%; u,t,i,max and V d,t,i,max Let represent the maximum upper and lower limit voltages of the i-th node during time period t, respectively.

[0066] The degree of danger of voltage exceeding the limit is as follows, S(V i () indicates the degree of voltage over-limit danger at node i, V i Let be the voltage at node i;

[0067] When V i When <0.9,

[0068]

[0069] When 0.9≤V i When ≤1.1,

[0070] S(V i ) = 0;

[0071] When V i When >1.1,

[0072]

[0073] In one embodiment of this application, obtaining the load shedding index includes:

[0074] According to R F =G r *S Fi Determine the above-mentioned load shedding indicators, where R F For the above-mentioned load shedding index, G r To determine the probability of loss of load, S Fi The degree of risk of loss of load.

[0075] Specifically, the probability of a load shedding in the distribution network is:

[0076]

[0077] G r (F i ) represents the probability of load failure; i represents the expected fault number; N represents the probability of load failure. I F(i) represents the expected number of faults; if fault i is expected to cause a load loss, then F(i) is 1, otherwise it is 0.

[0078] Level of risk of loss of load:

[0079]

[0080] P k For distribution network capacity; H(F) i S represents the load loss caused by fault i; Fi This indicates the degree of risk of loss of load.

[0081] In one embodiment of this application, obtaining the reverse heavy overload hazard index of the distribution transformer area includes:

[0082] according to The above-mentioned transformer substation reverse heavy overload hazard level index was determined, among which K(r) q,i D(r) represents the degree of reverse heavy overload risk of the above-mentioned transformer substation. q,i ) is 0, or is r q,i Let r be the transformer load rate of the distribution area at time i, and t be the upper limit threshold of the transformer load rate of the distribution area. n t represents the current time. s The starting time for the load rate of the distribution transformer in the transformer area to exceed the limit.

[0083] When r q,i When >r,

[0084]

[0085] When r q,i When ≤r,

[0086] D(r q,i ) = 0.

[0087] Specifically, the danger level of reverse heavy overload of the distribution transformer in the transformer area is as follows, i s i is the start time of the transformer load rate exceeding the limit in the distribution area. n is the current time, is the transformer load rate of the distribution area at time i, and r is the upper limit threshold of the transformer load rate of the distribution area, which is 0.8.

[0088] In one embodiment of this application, obtaining the severity of power flow exceeding the limit in the low-voltage line of the distribution area includes:

[0089] according to The starting time for the above-mentioned transformer substation load rate exceeding the limit is determined, where is the starting time for the above-mentioned transformer substation load rate exceeding the limit, n1 is the number of lines contained in the substation, and D(r l,i ) is 0, or is r l,i Let r be the line load factor at time point i. l t is the upper limit threshold for line current carrying capacity. n t represents the current time. s The starting time for the load rate of the distribution transformer in the transformer area to exceed the limit.

[0090] Specifically, when r l,i >r l hour,

[0091]

[0092] When r l,i ≤r l hour,

[0093] D(r l,i ) = 0.

[0094] In one embodiment of this application, obtaining the reverse load index of the medium-voltage feeder itself includes:

[0095] exist In the case of, according to The reverse load index of the above-mentioned medium-voltage feeder is determined, where K(θ1) is the reverse load index of the above-mentioned medium-voltage feeder, and p g,i For the input of the i-th power source, p j,i n1 represents the output of the i-th load, n2 represents the total power supply connected to the feeder, and n3 represents the number of loads on the feeder.

[0096] exist Under these circumstances, the reverse load index of the aforementioned medium-voltage feeder is determined to be 0.

[0097] Specifically, the reverse load index of a medium-voltage feeder refers to the amount of reverse load that a medium-voltage feeder experiences during operation in a medium-voltage power grid system. Reverse load refers to the load in a power system where the current direction is opposite to the normal direction, usually due to various reasons causing abnormal current direction. In a medium-voltage power grid system, the reverse load index of a medium-voltage feeder is a crucial parameter, reflecting the load conditions that the feeder may encounter during operation, and is of great significance for ensuring the reliability and stability of the medium-voltage power grid system. By monitoring and evaluating the reverse load index of medium-voltage feeders, potential problems can be identified and resolved in a timely manner, ensuring the normal operation of the medium-voltage power grid system.

[0098] In one embodiment of this application, obtaining the reverse load index of the transformer substations contained in the feeder includes:

[0099] exist In the case of, according to Determine the reverse load index of the transformer substations included in the above feeder, where K(θ2) is the reverse load index of the transformer substations included in the above feeder, p g,i For the input of the i-th power source, p j,i n4 is the output of the i-th load, n5 is the number of power supplies connected to the c-th transformer area, n6 is the number of loads connected to the c-th transformer area, and n7 is the number of transformer areas connected by the feeder.

[0100] exist In this case, the reverse load index of the transformer area included in the above feeder is determined to be 0.

[0101] Specifically, the reverse load index of the transformer substations connected to the feeder refers to the proportion of reverse load in the transformer substations connected to the feeder within the power grid. Reverse load refers to a situation in the power grid where the load direction is opposite to the normal load direction, usually caused by the integration of distributed energy sources (such as solar photovoltaic, wind power, etc.) or other reasons. The reverse load index of the transformer substations connected to the feeder can be expressed as the ratio of the peak load of the reverse load to the peak load of the forward load, reflecting the degree of reverse load in the power grid.

[0102] Step S102: The above-mentioned line overload index, voltage over-limit index, load loss index, transformer reverse heavy overload danger index, low-voltage line power flow over-limit severity index, medium-voltage feeder self-reverse load index, and feeder-included transformer reverse load index of the above-mentioned distribution network are weighted and summed to obtain the distribution network comprehensive performance index value. The above-mentioned distribution network comprehensive performance index value characterizes the self-healing capability of the above-mentioned distribution network.

[0103] Specifically, the indicators were normalized:

[0104]

[0105] Where m is the total number of indicators, z ij Represents a standardized indicator; y ij This represents the attribute value corresponding to evaluation index j in evaluation scheme i;

[0106] Calculate the information entropy E of the j-th indicator. j :

[0107]

[0108] Among them, z ij This represents the probability that the j-th indicator falls within the ith risk level range.

[0109] Calculate the weight coefficient Q of the j-th indicator. j :

[0110]

[0111] Among them, E k The information entropy of the k-th indicator;

[0112] Obtain the comprehensive performance index value R of the distribution network S :

[0113] R S =Q1R L +Q2R V +Q3R F +Q4K(r q,i )+Q5K(r l,i )+Q6K(θ1)+Q7K(θ2);

[0114] Q1 is the weighting coefficient for the line overload index, Q2 is the weighting coefficient for the voltage limit exceedance index, Q3 is the weighting coefficient for the load shedding index, Q4 is the weighting coefficient for the risk level of reverse heavy overload of the distribution transformer in the distribution area, Q5 is the weighting coefficient for the severity of the power flow limit exceedance of the low-voltage line in the distribution area, Q6 is the weighting coefficient for the reverse load of the medium-voltage feeder itself, and Q7 is the weighting coefficient for the reverse load of the distribution area contained in the feeder.

[0115] The entropy weight method is used to assign weights to each index to obtain the comprehensive performance index value of the distribution network, so as to characterize the self-healing capability of the distribution network.

[0116] In the above steps, by acquiring the distribution network's line overload index, voltage limit exceedance index, load shedding index, transformer reverse heavy overload hazard index, low-voltage line power flow limit exceedance severity index, medium-voltage feeder self-reverse load index, and feeder-included transformer reverse load index, a weighted summation is performed on these indicators to obtain the distribution network's comprehensive performance index value. This makes the final comprehensive evaluation index more accurate and more consistent with actual operating conditions. In other words, it solves the problem that existing schemes for assessing the distribution network's self-healing capability are inefficient and fail to reflect actual operating conditions.

[0117] To enable those skilled in the art to better understand the technical solution of this application, the implementation process of the self-healing capability evaluation method of the power distribution network of this application will be described in detail below with reference to specific embodiments.

[0118] This embodiment relates to a specific method for evaluating the self-healing capability of a power distribution network, such as... Figure 2 As shown, it includes:

[0119] Input initial data;

[0120] The pre-emptive self-healing evaluation index system includes line overload index, voltage over-limit index, and load loss index.

[0121] The post-event self-healing evaluation index system includes the risk level of reverse heavy overload of the transformer substation, the severity of power flow exceeding the limit of the low-voltage line in the substation, the reverse load of the medium-voltage feeder itself, and the reverse load of the substation contained in the feeder.

[0122] The distribution network's line overload index, voltage limit exceedance index, load loss index, transformer reverse heavy overload danger index, low-voltage line power flow limit exceedance severity index, medium-voltage feeder's own reverse load index, and feeder's contained transformer reverse load index are weighted and summed to obtain the distribution network's comprehensive performance index value. The distribution network's comprehensive performance index value characterizes the distribution network's self-healing capability.

[0123] By acquiring the following indicators from the distribution network: line overload, voltage limit exceedance, load shedding, risk level of reverse heavy overload of transformer substations, severity of power flow exceedance of low-voltage lines in the distribution area, reverse load of medium-voltage feeders themselves, and reverse load of substations contained in the feeders, a weighted summation is performed on these indicators to obtain the comprehensive performance index value of the distribution network. This makes the final comprehensive evaluation index more accurate and more in line with actual operating conditions. In other words, it solves the problem that existing methods for assessing the self-healing capability of distribution networks are inefficient and fail to reflect actual operating conditions.

[0124] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0125] This application also provides an apparatus for evaluating the self-healing capability of a distribution network. It should be noted that this apparatus can be used to execute the method for evaluating the self-healing capability of a distribution network provided in this application. This apparatus is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0126] The following describes the self-healing capability evaluation device for power distribution networks provided in the embodiments of this application.

[0127] Figure 3 This is a structural block diagram of a device for evaluating the self-healing capability of a power distribution network, provided according to an embodiment of this application. Figure 3 As shown, the device includes:

[0128] The acquisition unit 31 is used to acquire the following indicators of the distribution network: line overload index, voltage limit exceedance index, load loss index, transformer reverse heavy overload hazard index, low-voltage line power flow limit exceedance severity index, medium-voltage feeder self-reverse load index, and feeder-included transformer reverse load index. The line overload index is used to assess whether the power line exceeds the preset load. The voltage limit exceedance index is used to assess whether the voltage in the distribution network exceeds the allowable range. The load loss index is used to assess the load loss caused by faults or load changes in the distribution network. The transformer reverse heavy overload hazard index is used to assess whether the transformer in the distribution area has the risk of reverse heavy overload. The low-voltage line power flow limit exceedance severity index is used to assess whether the power flow in the low-voltage line in the distribution area exceeds the preset range. The medium-voltage feeder self-reverse load index is used to assess whether the medium-voltage feeder has reverse load. The feeder-included transformer reverse load index is used to assess the reverse load situation of the feeder-included transformer areas.

[0129] The processing unit 32 is used to perform weighted summation processing on the above-mentioned line overload index, voltage over-limit index, load loss index, reverse heavy overload danger index of the above-mentioned transformer substation, power flow over-limit severity of the above-mentioned low-voltage line in the above-mentioned transformer substation, reverse load index of the above-mentioned medium-voltage feeder itself, and reverse load index of the transformer substation contained in the above-mentioned feeder, to obtain the comprehensive performance index value of the distribution network. The comprehensive performance index value of the distribution network characterizes the self-healing capability of the above-mentioned distribution network.

[0130] The aforementioned device acquires the following indicators from the distribution network: line overload, voltage limit exceedance, load shedding, risk level of reverse heavy overload of transformer substations, severity of power flow exceedance of low-voltage lines in the distribution area, reverse load of medium-voltage feeders themselves, and reverse load of substations contained in the feeders. These indicators are then weighted and summed to obtain a comprehensive performance index value for the distribution network. This makes the final comprehensive evaluation index more accurate and better reflects actual operating conditions. In other words, it solves the problem that existing schemes for assessing the self-healing capability of distribution networks are inefficient and fail to reflect actual operating conditions.

[0131] In one embodiment of this application, the acquisition unit includes a first determining module.

[0132] The first determining module is used to determine based on Determine the line overload index of the above distribution network, where R L G represents the overload index of the aforementioned power distribution network lines. r (W i Let be the probability of line disconnection when the i-th incident occurs in time period t. W represents the degree of overload risk of line l caused by the i-th accident. i For the i-th accident, Let I be the active power of line l when the i-th accident occurs, and let L be the set of accident indices and L be the set of line indices.

[0133] In one embodiment of this application, the acquisition unit includes a second determining module.

[0134] The second determining module is used to determine based on The above voltage over-limit indicators are determined, where R V For the above voltage over-limit indicators, G r (V i S(V) represents the voltage exceedance rate of node i within time period t, S(V) represents the voltage exceedance risk level of each node, and N... b This refers to the set of nodes in the aforementioned distribution network that are prone to voltage over-limit occurrences.

[0135] In one embodiment of this application, the acquisition unit includes a third determining module.

[0136] The third determining module is used to determine R. F =G r *S Fi Determine the above-mentioned load shedding indicators, where R F For the above-mentioned load shedding index, G r To determine the probability of loss of load, S Fi The degree of risk of loss of load.

[0137] In one embodiment of this application, the acquisition unit includes a fourth determining module.

[0138] The fourth determining module is used to determine based on The above-mentioned transformer substation reverse heavy overload hazard level index was determined, among which K(r) q,i D(r) represents the degree of reverse heavy overload risk of the above-mentioned transformer substation. q,i ) is 0, or is r q,i Let r be the transformer load rate of the distribution area at time i, and t be the upper limit threshold of the transformer load rate of the distribution area. n t represents the current time. s The starting time for the load rate of the distribution transformer in the transformer area to exceed the limit.

[0139] In one embodiment of this application, the acquisition unit includes a fifth determining module.

[0140] The fifth determining module is used to determine based on The starting time for the above-mentioned transformer substation load rate exceeding the limit is determined, where is the starting time for the above-mentioned transformer substation load rate exceeding the limit, n1 is the number of lines contained in the substation, and D(rl,i ) is 0, or is r l,i Let r be the line load factor at time point i. l t is the upper limit threshold for line current carrying capacity. n t represents the current time. s The starting time for the load rate of the distribution transformer in the transformer area to exceed the limit.

[0141] In one embodiment of this application, the acquisition unit includes a sixth determining module and a seventh determining module.

[0142] The sixth determining module is used in In the case of, according to The reverse load index of the above-mentioned medium-voltage feeder is determined, where K(θ1) is the reverse load index of the above-mentioned medium-voltage feeder, and p g,i For the input of the i-th power source, p j,i n1 represents the output of the i-th load, n2 represents the total power supply connected to the feeder, and n3 represents the number of loads on the feeder.

[0143] The seventh determination module is used to determine in Under these circumstances, the reverse load index of the aforementioned medium-voltage feeder is determined to be 0.

[0144] In one embodiment of this application, the acquisition unit includes an eighth determining module and a ninth determining module.

[0145] The eighth determining module is used in In the case of, according to Determine the reverse load index of the transformer substations included in the above feeder, where K(θ2) is the reverse load index of the transformer substations included in the above feeder, p g,i For the input of the i-th power source, p j,i n4 is the output of the i-th load, n5 is the number of power supplies connected to the c-th transformer area, n6 is the number of loads connected to the c-th transformer area, and n7 is the number of transformer areas connected by the feeder.

[0146] The ninth determining module is used in In this case, the reverse load index of the transformer area included in the above feeder is determined to be 0.

[0147] The aforementioned self-healing capability evaluation device for the power distribution network includes a processor and a memory. The acquisition and processing units are all stored as program units in the memory, and the processor executes these program units to achieve the corresponding functions. All of the above modules are located in the same processor; alternatively, the modules may be located in different processors in any combination.

[0148] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and adjusting kernel parameters can address the problem that existing methods for assessing the self-healing capabilities of power distribution networks are inefficient and fail to reflect actual operating conditions.

[0149] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0150] This invention provides a computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform an evaluation method for the self-healing capability of the power distribution network.

[0151] This invention provides a processor for running a program, wherein the program executes a method for evaluating the self-healing capability of the power distribution network.

[0152] This invention provides a device including a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs at least the following steps: acquiring line overload indicators, voltage limit exceedance indicators, load shedding indicators, transformer reverse heavy overload hazard indicators, low-voltage line power flow limit exceedance severity in the distribution network, reverse load indicators of the medium-voltage feeder itself, and reverse load indicators of the transformers contained in the feeder. The line overload indicators are used to assess whether the power lines have exceeded preset loads; the voltage limit exceedance indicators are used to assess whether the voltage in the distribution network has exceeded the allowable range; the load shedding indicators are used to assess the load loss caused by faults or load changes in the distribution network; and the transformer reverse heavy overload hazard indicators are used to assess the transformer... The existence of reverse overload risk is assessed by considering the severity of low-voltage line power flow exceeding limits in the aforementioned distribution area, whether the power flow exceeds the preset range, the reverse load index of the aforementioned medium-voltage feeder itself, and the reverse load index of the distribution areas contained in the aforementioned feeder. A weighted summation is performed on the aforementioned line overload index, voltage limit exceeding index, load shedding index, risk level of reverse heavy overload of the aforementioned distribution transformer, severity of low-voltage line power flow exceeding limits in the aforementioned distribution area, reverse load index of the aforementioned medium-voltage feeder itself, and reverse load index of the aforementioned feeder-containing distribution areas to obtain the comprehensive performance index value of the distribution network. This comprehensive performance index value characterizes the self-healing capability of the aforementioned distribution network. The devices mentioned in this article can be servers, PCs, tablets, mobile phones, etc.

[0153] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program having at least the following method steps: acquiring the following indicators for the distribution network: line overload index, voltage limit exceedance index, load shedding index, transformer reverse heavy overload hazard index, low-voltage line power flow limit exceedance severity, medium-voltage feeder self-reverse load index, and transformer reverse load index contained in the feeder. The line overload index is used to assess whether the power line exceeds the preset load; the voltage limit exceedance index is used to assess whether the voltage in the distribution network exceeds the allowable range; the load shedding index is used to assess the load loss situation in the distribution network due to faults or load changes; and the transformer reverse heavy overload hazard index is used to assess whether the transformer in the distribution network has reverse heavy load. The risk of overload is assessed by the following indicators: the severity of low-voltage line power flow exceeding the limit in the aforementioned distribution area is used to evaluate whether the power flow of the low-voltage line in the aforementioned distribution area exceeds the preset range; the reverse load index of the aforementioned medium-voltage feeder itself is used to evaluate whether there is a reverse load on the medium-voltage feeder; and the reverse load index of the distribution area contained in the aforementioned feeder is used to evaluate the reverse load situation of the distribution area contained in the feeder. The aforementioned line overload index, voltage limit exceeding index, load loss index, risk degree of reverse heavy overload of the aforementioned distribution transformer in the aforementioned distribution network, the severity of low-voltage line power flow exceeding the limit in the aforementioned distribution area, the reverse load index of the aforementioned medium-voltage feeder itself, and the reverse load index of the distribution area contained in the aforementioned feeder are weighted and summed to obtain the comprehensive performance index value of the distribution network. The comprehensive performance index value of the aforementioned distribution network characterizes the self-healing capability of the aforementioned distribution network.

[0154] This application also provides an evaluation system for the self-healing capability of a power distribution network, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include methods for performing any of the above-described methods.

[0155] By acquiring the following indicators from the distribution network: line overload, voltage limit exceedance, load shedding, risk level of reverse heavy overload of transformer substations, severity of power flow exceedance of low-voltage lines in the distribution area, reverse load of medium-voltage feeders themselves, and reverse load of substations contained in the feeders, a weighted summation is performed on these indicators to obtain the comprehensive performance index value of the distribution network. This makes the final comprehensive evaluation index more accurate and more in line with actual operating conditions. In other words, it solves the problem that existing methods for assessing the self-healing capability of distribution networks are inefficient and fail to reflect actual operating conditions.

[0156] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0157] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can 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, etc.) containing computer-usable program code.

[0158] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can 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, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0159] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0160] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0161] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0162] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0163] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0164] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0165] As can be seen from the above description, the embodiments of this application achieve the following technical effects:

[0166] 1) The self-healing capability evaluation method of this application for the distribution network obtains the following indicators: line overload, voltage limit exceedance, load shedding, risk level of reverse heavy overload of distribution transformers in the distribution area, severity of power flow exceedance of low-voltage lines in the distribution area, reverse load of medium-voltage feeders themselves, and reverse load of distribution areas contained in the feeders. These indicators are then weighted and summed to obtain a comprehensive performance index value for the distribution network. This makes the final comprehensive evaluation index more accurate and more consistent with actual operating conditions. In other words, it solves the problem that existing methods for evaluating the self-healing capability of distribution networks are inefficient and fail to reflect actual operating conditions.

[0167] 2) The self-healing capability evaluation device for the distribution network of this application obtains the following indicators of the distribution network: line overload, voltage over-limit, load loss, risk level of reverse heavy overload of distribution transformers in the distribution area, severity of power flow over-limit of low-voltage lines in the distribution area, reverse load of medium-voltage feeders themselves, and reverse load of distribution areas contained in the feeders. It then performs a weighted summation of these indicators to obtain a comprehensive performance index value for the distribution network. This makes the final comprehensive evaluation index more accurate and more consistent with actual operating conditions. In other words, it solves the problem that existing schemes for evaluating the self-healing capability of distribution networks are inefficient and fail to reflect actual operating conditions.

[0168] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for evaluating the self-healing capability of a power distribution network, characterized in that, include: The system acquires the following indicators for the power distribution network: line overload index, voltage limit exceedance index, load loss index, transformer reverse heavy overload hazard index, low-voltage line power flow limit exceedance severity index, medium-voltage feeder self-reverse load index, and feeder-included transformer reverse load index. The line overload index is used to assess whether power lines have exceeded preset loads. The voltage limit exceedance index is used to assess whether voltage in the distribution network exceeds permissible limits. The load loss index is used to assess load loss in the distribution network due to faults or load changes. The transformer reverse heavy overload hazard index is used to assess whether transformers in the distribution area are at risk of reverse heavy overload. The low-voltage line power flow limit exceedance severity index is used to assess whether low-voltage line power flow in the distribution area exceeds preset limits. The medium-voltage feeder self-reverse load index is used to assess whether medium-voltage feeders have reverse loads. The feeder-included transformer reverse load index is used to assess the reverse load situation of transformers included in the feeder. The following parameters of the distribution network are weighted and summed: line overload index, voltage limit exceedance index, load loss index, transformer reverse heavy overload risk index, low-voltage line power flow limit exceedance severity index, medium-voltage feeder self-reverse load index, and feeder-included transformer reverse load index. This yields a comprehensive performance index value for the distribution network, which characterizes the self-healing capability of the distribution network. Obtaining the line overload indicators of the distribution network includes: based on Determine the line overload index of the distribution network, wherein R L G is the overload index of the distribution network lines. r (W i Let be the probability of line disconnection when the i-th incident occurs in time period t. W represents the degree of overload risk of line l caused by the i-th accident. i For the i-th accident, Let L be the active power of line l at the time of the i-th accident, I be the set of accident indices, and L be the set of line indices. Obtain voltage over-limit indicators, including: according to Determine the voltage over-limit index, wherein R V For the voltage over-limit index, G r (V i S(V) represents the voltage exceedance rate of node i within time period t, S(V) represents the voltage exceedance risk level of each node, and N... b This refers to the set of nodes in the distribution network prone to voltage over-limit occurrences. Obtaining load loss indicators, including: based on R F =G r *S Fi Determine the load shedding index, wherein R F G is the load shedding index. r To determine the probability of loss of load, S Fi The degree of risk of loss of load, Obtain the risk level index of reverse heavy overload of the distribution transformer in the transformer area, including: according to The degree of reverse heavy overload hazard of the transformer substation in the specified area is determined by the index, where K(r) q，i D(r) represents the degree of reverse heavy overload risk of the transformer substation in the aforementioned area. q，i ) is 0, or is r q，i Let r be the transformer load rate of the distribution area at time i, and t be the upper limit threshold of the transformer load rate of the distribution area. n t represents the current time. s The starting time for the distribution transformer load rate to exceed the limit in the transformer area. Obtain the severity of power flow exceeding limits on low-voltage lines in the transformer area, including: based on Determine the severity of the power flow exceeding the limit in the low-voltage line of the aforementioned distribution area, where K(r) l，i ) represents the severity of low-voltage line power flow exceeding the limit in the aforementioned transformer area, n1 represents the number of lines contained in the transformer area, and D(r l，i ) is 0, or is r l，i Let r be the line load factor at time point i. l t is the upper limit threshold for line current carrying capacity. n t represents the current time. s The starting time for the distribution transformer load rate to exceed the limit in the transformer area. Obtain the reverse load capacity of the medium-voltage feeder itself, including: In the case of, according to Determine the reverse load index of the medium-voltage feeder, where K(θ1) is the reverse load index of the medium-voltage feeder, p g，i For the input of the i-th power source, p j，i For the output of the i-th load, n2 is the total power supply connected to the feeder, and n3 is the number of loads on the feeder; In this case, the reverse load index of the medium-voltage feeder itself is determined to be 0. Obtain the reverse load indicators of the transformer substations included in the feeder, including: In the case of, according to Determine the reverse load index of the transformer substations included in the feeder, where K(θ2) is the reverse load index of the transformer substations included in the feeder, p g，i For the input of the i-th power source, p j，i For the output of the i-th load, n4 is the number of power supplies connected to the c-th transformer area, n5 is the number of loads connected to the c-th transformer area, and n6 is the number of transformer areas connected by the feeder; in In this case, the reverse load index of the transformer area contained in the feeder is determined to be 0.

2. An evaluation device for the self-healing capability of a distribution network based on the evaluation method for the self-healing capability of a distribution network according to claim 1, characterized in that, include: The acquisition unit is used to acquire the following indicators for the distribution network: line overload index, voltage limit exceedance index, load loss index, transformer reverse heavy overload hazard index, low-voltage line power flow limit exceedance severity index, medium-voltage feeder self-reverse load index, and feeder-included transformer reverse load index. The line overload index is used to assess whether the power line exceeds the preset load. The voltage limit exceedance index is used to assess whether the voltage in the distribution network exceeds the allowable range. The load loss index is used to assess the load loss caused by faults or load changes in the distribution network. The transformer reverse heavy overload hazard index is used to assess whether the transformer in the distribution area has a risk of reverse heavy overload. The low-voltage line power flow limit exceedance severity index is used to assess whether the power flow in the low-voltage line in the distribution area exceeds the preset range. The medium-voltage feeder self-reverse load index is used to assess whether the medium-voltage feeder has a reverse load. The feeder-included transformer reverse load index is used to assess the reverse load situation of the transformers included in the feeder. The processing unit is used to perform weighted summation on the following indicators of the distribution network: line overload index, voltage over-limit index, load loss index, reverse heavy overload danger index of the distribution transformer in the distribution area, power flow over-limit severity of the low-voltage line in the distribution area, reverse load index of the medium-voltage feeder itself, and reverse load index of the distribution area contained in the feeder, to obtain the comprehensive performance index value of the distribution network. The comprehensive performance index value of the distribution network characterizes the self-healing capability of the distribution network.

3. A system for evaluating the self-healing capability of a power distribution network, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including methods for performing the method of claim 1.

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