A method and system for assessing voltage sag in medium and low voltage DC distribution networks

Abstract

This invention discloses a method and system for assessing voltage sags in medium- and low-voltage DC distribution networks, comprising: determining an evaluation index system for voltage sag assessment; determining the evaluation index value corresponding to each node in the DC distribution network based on sag voltage data at the time of a voltage sag event; determining the weight of the lowest-level index in the evaluation index system relative to the first-level index based on the evaluation index value; determining the proximity degree corresponding to each node based on the weight of the lowest-level index in the evaluation index system relative to the first-level index; and identifying weak nodes in the DC distribution network based on the proximity degree. This invention considers both energy characteristics, reflecting the impact of sag events on the grid and load sides, and waveform characteristics, reflecting the impact of sag waveforms on severity. It assesses the severity of a single voltage sag in a medium- and low-voltage DC distribution network based on the proximity degree, and can evaluate the severity of different sag types on the DC distribution network system.

Description

Technical Field

[0001] This invention relates to the field of power system and automation technology, and more specifically, to a method and system for assessing voltage sag in medium and low voltage DC distribution networks. Background Technology

[0002] In recent years, environmental and energy shortages have become increasingly serious. With the large-scale integration of clean energy into urban power grids, DC distribution systems have gradually come into focus due to their advantages in enabling large-scale integration and flexible management of renewable energy, DC, and frequency conversion loads. In the foreseeable future, the widespread adoption of DC distribution systems is a foregone conclusion.

[0003] However, it is important to note that the widespread adoption of DC distribution systems inevitably involves addressing the increasingly prominent power quality issues, particularly ensuring the robustness of power conversion equipment and systems against deep voltage sags. Voltage sags are not a new problem, but in DC distribution systems, the economic losses they cause to both the grid and load sides are significant. Therefore, scientifically and rationally assessing DC voltage sags, identifying vulnerable areas in DC distribution systems, and understanding the severity of their impact are of great practical importance. This is also one of the key issues in the planning, design, and operation control of DC distribution systems. Summary of the Invention

[0004] This invention proposes a method and system for assessing voltage sags in medium- and low-voltage DC distribution networks to solve the problem of how to assess voltage sags.

[0005] To address the aforementioned problems, according to one aspect of the present invention, a method for assessing voltage sags in medium- and low-voltage DC distribution networks is provided, the method comprising:

[0006] Establish an evaluation index system for voltage sag assessment;

[0007] The evaluation index value corresponding to each node in the DC distribution network is determined based on the voltage sag data when a voltage sag event occurs.

[0008] The weight of the lowest-level indicator in the evaluation indicator system relative to the first-level indicator is determined based on the evaluation indicator value.

[0009] Based on the weight of the lowest-level indicator in the evaluation index system relative to the first-level indicator, the proximity degree corresponding to each node is determined.

[0010] Weak nodes in the DC distribution network are identified based on the proximity.

[0011] Preferably, the evaluation index system includes: a first-level index including single voltage sag time assessment; a second-level index including waveform characteristics and energy characteristics; and a third-level index including: residual voltage sag, sag duration, sag voltage drop rate and sag voltage recovery rate belonging to the waveform characteristic index, and sag energy and sensitive load failure rate belonging to the energy characteristic index.

[0012] Preferably, determining the evaluation index value corresponding to each node in the DC distribution network based on the sag voltage data at the time of the sag event includes:

[0013] The residual voltage of the voltage sag is determined by the minimum node voltage during the duration of the voltage sag event.

[0014] The duration of a voltage sag is determined based on the time between the start and end of the event duration in the voltage sag event.

[0015] The rate of decrease δ of the voltage sag is determined based on the rate of change of the node voltage from a preset percentage of the rated voltage to the residual voltage during a voltage sag event. fall ,include:

[0016]

[0017] The recovery rate δ of the voltage sag is determined based on the rate of change of the node voltage from the residual voltage to a preset percentage of the rated voltage during a voltage sag event. rise ,include:

[0018]

[0019] The transient energy E is determined using the following method. vs ,include:

[0020]

[0021] The sensitive load failure rate P is determined using the following methods:

[0022]

[0023] Among them, U 额定 U is the rated voltage of the node; res The residual voltage; T1 is the time point at which the node voltage drops from a preset percentage of the rated voltage to the residual voltage; T2 is the time point at which the node voltage equals U. res The corresponding time point is T3, which is the time point when the node voltage is about to recover from the residual voltage to the preset percentage of the rated voltage; U(t) is the expression for the change of node voltage with time; T duration For voltage sag duration events; T min and T maxThese represent the minimum and maximum voltage sag durations of the node's overall voltage withstand curve, respectively; U max and U min These represent the maximum and minimum residual voltage sags of the node's integrated voltage withstand curve; A, B, and C are the uncertain regions, and D is the equipment fault region.

[0024] Preferably, determining the weight of the lowest-level indicator relative to the first-level indicator in the evaluation indicator system based on the evaluation indicator value includes:

[0025] The subjective weight W of the third-level indicator relative to the second-level indicator is obtained using the IAHP method. 3 AHP The objective weight W of the third-level indicator relative to the second-level indicator is obtained using the CRITIC method. 3 CRITIC And based on the subjective weight W 3 AHP and objective weight W 3 CRITIC The combined weight W of the third-level indicators relative to the second-level indicators is calculated using the difference coefficient method. 3 ;

[0026] The weight W of the second-level indicator relative to the first-level indicator is calculated using the IAHP method. 2 ;

[0027] Based on the combined weight W of the third-level indicators relative to the second-level indicators 3 The weight W of the second-level indicator relative to the first-level indicator 2 Perform hierarchical multi-sorting to obtain the weight W of the third-level indicator relative to the first-level indicator. 1 .

[0028] Preferably, the subjective weight W is used as the basis for... 3 AHP and objective weight W 3 CRITIC The combined weight W of the third-level indicators relative to the second-level indicators is calculated using the difference coefficient method. 3 ,include:

[0029]

[0030]

[0031]

[0032] Where T′ is the difference coefficient; the m indicators are... Arranged in ascending order, we get [p1, p2, ... p... m ]; α and β are the coefficients of the combined weights.

[0033] Preferably, the combination weight W of the third-level index relative to the second-level index is... 3 The weight W of the second-level indicator relative to the first-level indicator 2 Perform hierarchical multi-sorting to obtain the weight W of the third-level indicator relative to the first-level indicator. 1 ,include:

[0034]

[0035]

[0036]

[0037] Where i = 1, 2, 3, 4; j = 5, 6; to The weights corresponding to the residual voltage sag, sag duration, descent rate, recovery rate, sag energy, and sensitive load failure rate are as follows: and These are sets of weights for waveform characteristic indicators and energy characteristic indicators, respectively. and These are the weights of the waveform characteristic index and the energy characteristic index relative to the first-level index, respectively.

[0038] Preferably, determining the proximity of each node based on the weight of the lowest-level indicator relative to the first-level indicator in the evaluation index system includes:

[0039] Construct a set of solutions based on the number of nodes, F = {f1, f2, ..., f...} n}; where f i (i = 1, 2, ... n) represents the assessment of the severity of voltage sag at the i-th node based on various indicators;

[0040] Determine the ideal solution v + and negative ideal solution v - Positive and negative ideal solutions v + v - satisfy:

[0041]

[0042]

[0043] Calculate the distance between each solution and the positive and negative ideal solutions. and include:

[0044]

[0045] Calculate the relative approximation R of each solution to the positive ideal solution. i The proximity score corresponding to the i-th node includes:

[0046]

[0047] Where v1 to v6 correspond to sag residual voltage, sag duration, sag rate, recovery rate, sag energy, and sensitive load failure rate, respectively; n is the number of nodes; and m is the number of the lowest-level indicators. Let x be the weight of indicator j relative to the first-level indicators; ij Let j be the value of the index of the i-th node.

[0048] Preferably, the determination of weak nodes in the DC distribution network based on the proximity includes:

[0049] The node with the smallest proximity is selected as the weak node in the DC distribution network.

[0050] According to another aspect of the present invention, a voltage sag assessment system for medium- and low-voltage DC distribution networks is provided, the system comprising:

[0051] The evaluation index system determination unit is used to determine the evaluation index system for voltage sag assessment.

[0052] The evaluation index value determination unit is used to determine the evaluation index value corresponding to each node in the DC distribution network based on the voltage sag data when a voltage sag event occurs.

[0053] The weight determination unit is used to determine the weight of the lowest-level indicator in the evaluation indicator system relative to the first-level indicator based on the evaluation indicator value.

[0054] The proximity determination unit is used to determine the proximity of each node based on the weight of the lowest-level indicator in the evaluation index system relative to the first-level indicator.

[0055] A weak node identification unit is used to identify weak nodes in the DC distribution network based on the proximity.

[0056] Preferably, the evaluation index system includes: a first-level index including single voltage sag time assessment; a second-level index including waveform characteristics and energy characteristics; and a third-level index including: residual voltage sag, sag duration, sag voltage drop rate and sag voltage recovery rate belonging to the waveform characteristic index, and sag energy and sensitive load failure rate belonging to the energy characteristic index.

[0057] Preferably, the evaluation index value determination unit determines the evaluation index value corresponding to each node in the DC distribution network based on the voltage sag data when a voltage sag event occurs, including:

[0058] The residual voltage of the voltage sag is determined by the minimum node voltage during the duration of the voltage sag event.

[0059] The duration of a voltage sag is determined based on the time between the start and end of the event duration in the voltage sag event.

[0060] The rate of decrease δ of the voltage sag is determined based on the rate of change of the node voltage from a preset percentage of the rated voltage to the residual voltage during a voltage sag event. fall ,include:

[0061]

[0062] The recovery rate δ of the voltage sag is determined based on the rate of change of the node voltage from the residual voltage to a preset percentage of the rated voltage during a voltage sag event. rise ,include:

[0063]

[0064] The transient energy E is determined using the following method. vs ,include:

[0065]

[0066] The sensitive load failure rate P is determined using the following methods:

[0067]

[0068] Among them, U 额定 U is the rated voltage of the node; res The residual voltage; T1 is the time point at which the node voltage drops from a preset percentage of the rated voltage to the residual voltage; T2 is the time point at which the node voltage equals U. res The corresponding time point is T3, which is the time point when the node voltage is about to recover from the residual voltage to the preset percentage of the rated voltage; U(t) is the expression for the change of node voltage with time; T duration For voltage sag duration events; T min and T max These represent the minimum and maximum voltage sag durations of the node's overall voltage withstand curve, respectively; U max and U min These represent the maximum and minimum residual voltage sags of the node's integrated voltage withstand curve; A, B, and C are the uncertain regions, and D is the equipment fault region.

[0069] Preferably, the weight determination unit determines the weight of the lowest-level indicator in the evaluation indicator system relative to the first-level indicator based on the evaluation indicator value, including:

[0070] The subjective weight W of the third-level indicator relative to the second-level indicator is obtained using the IAHP method. 3 AHP The objective weight W of the third-level indicator relative to the second-level indicator is obtained using the CRITIC method. 3 CRITIC And based on the subjective weight W 3 AHP and objective weight W 3 CRITIC The combined weight W of the third-level indicators relative to the second-level indicators is calculated using the difference coefficient method. 3 ;

[0071] Calculate the weight W of the second-level indicator relative to the first-level indicator using the IAHP system. 2 ;

[0072] Based on the combined weight W of the third-level indicators relative to the second-level indicators 3 The weight W of the second-level indicator relative to the first-level indicator 2 Perform hierarchical multi-sorting to obtain the weight W of the third-level indicator relative to the first-level indicator. 1 .

[0073] Preferably, the weight determination unit is based on the subjective weight W. 3 AHP and objective weight W 3 CRITIC The combined weight W of the third-level indicators relative to the second-level indicators is calculated using the difference coefficient method. 3 ,include:

[0074]

[0075]

[0076]

[0077] Where T′ is the difference coefficient; the m indicators are... Arranged in ascending order, we get [p1, p2, ... p... m ]; α and β are the coefficients of the combined weights.

[0078] Preferably, the determining unit determines the weights based on the combined weights W of the third-level indicators relative to the second-level indicators. 3 The weight W of the second-level indicator relative to the first-level indicator 2 Perform hierarchical multi-sorting to obtain the weight W of the third-level indicator relative to the first-level indicator. 1 ,include:

[0079]

[0080]

[0081]

[0082] Where i = 1, 2, 3, 4; j = 5, 6; to The weights corresponding to the residual voltage sag, sag duration, descent rate, recovery rate, sag energy, and sensitive load failure rate are as follows: and These are sets of weights for waveform characteristic indicators and energy characteristic indicators, respectively. and These are the weights of the waveform characteristic index and the energy characteristic index relative to the first-level index, respectively.

[0083] Preferably, the proximity determination unit determines the proximity of each node based on the weight of the lowest-level indicator in the evaluation index system relative to the first-level indicator, including:

[0084] Construct a set of solutions based on the number of nodes, F = {f1, f2, ..., f...} n}; where f i (i = 1, 2, ... n) represents the assessment of the severity of voltage sag at the i-th node based on various indicators;

[0085] Determine the ideal solution v + and negative ideal solution v - Positive and negative ideal solutions v + v - satisfy:

[0086]

[0087]

[0088] Calculate the distance between each solution and the positive and negative ideal solutions. and include:

[0089]

[0090] Calculate the relative approximation R of each solution to the positive ideal solution. i The proximity score corresponding to the i-th node includes:

[0091]

[0092] Where v1 to v6 correspond to sag residual voltage, sag duration, sag rate, recovery rate, sag energy, and sensitive load failure rate, respectively; n is the number of nodes; and m is the number of the lowest-level indicators. Let x be the weight of indicator j relative to the first-level indicators; ij Let j be the value of the index of the i-th node.

[0093] Preferably, the weak node determination unit, based on the proximity, determines weak nodes in the DC distribution network, comprising:

[0094] The node with the smallest proximity is selected as the weak node in the DC distribution network.

[0095] According to another aspect of the present invention, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the methods for evaluating voltage sags in a medium- or low-voltage DC distribution network.

[0096] According to another aspect of the present invention, the present invention provides an electronic device, comprising:

[0097] The aforementioned computer-readable storage medium; and

[0098] One or more processors for executing a program in the computer-readable storage medium.

[0099] This invention provides a method and system for assessing voltage sags in medium- and low-voltage DC distribution networks, comprising: determining an evaluation index system for voltage sag assessment; determining the evaluation index value corresponding to each node in the DC distribution network based on sag voltage data at the time of a voltage sag event; determining the weight of the lowest-level index in the evaluation index system relative to the first-level index based on the evaluation index value; determining the proximity degree corresponding to each node based on the weight of the lowest-level index in the evaluation index system relative to the first-level index; and identifying weak nodes in the DC distribution network based on the proximity degree. This invention designs a multi-dimensional, multi-level index system considering waveform characteristics and energy characteristics. It considers both energy characteristics, reflecting the impact of sag events on the grid and load sides, and waveform characteristics, reflecting the impact of sag waveforms on severity. Based on the proximity degree, it assesses the severity of a single voltage sag in the medium- and low-voltage DC distribution network, identifies vulnerable areas, and can assess the severity of different sag types on the DC distribution network system. Furthermore, this invention comprehensively uses the IAHP and CRITIC methods for weighting, integrating subjective and objective opinions, resulting in weights that better reflect the intrinsic correlation of the indicators. Attached Figure Description

[0100] Exemplary embodiments of the present invention can be more fully understood by referring to the following figures:

[0101] Figure 1 This is a flowchart of a voltage sag assessment method 100 for medium- and low-voltage DC distribution networks according to an embodiment of the present invention;

[0102] Figure 2 This is a schematic diagram of the evaluation index system according to an embodiment of the present invention;

[0103] Figure 3 This is a node integrated voltage withstand curve according to an embodiment of the present invention;

[0104] Figure 4 This is a flowchart illustrating the calculation of index weights according to an embodiment of the present invention;

[0105] Figure 5 This is a power grid simulation topology diagram according to an embodiment of the present invention;

[0106] Figure 6 This is a schematic diagram of the fault location according to an embodiment of the present invention;

[0107] Figure 7 This is a voltage waveform diagram at a monitoring point according to an embodiment of the present invention;

[0108] Figure 8 This is a schematic diagram of the fault location according to an embodiment of the present invention;

[0109] Figure 9 This is a voltage waveform diagram at a monitoring point according to an embodiment of the present invention;

[0110] Figure 10 This is a schematic diagram of the structure of a voltage sag assessment system 1000 for a medium- and low-voltage DC distribution network according to an embodiment of the present invention. Detailed Implementation

[0111] Exemplary embodiments of the invention will now be described with reference to the accompanying drawings. However, the invention may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided to fully and completely disclose the invention and to fully convey its scope to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the drawings is not intended to limit the invention. In the drawings, the same units / elements are referred to by the same reference numerals.

[0112] Unless otherwise stated, the terms used herein (including technical terms) have their common meaning as understood by one of ordinary skill in the art. Furthermore, it is understood that terms defined in commonly used dictionaries should be understood to have a meaning consistent with the context of their relevant field, and not to be interpreted as having an idealized or overly formal meaning.

[0113] This invention addresses the voltage sag problem in medium- and low-voltage DC distribution networks by proposing a multi-dimensional and multi-level index system that considers waveform and energy characteristics. It comprehensively uses the IAHP and CRITIC methods for weighting and employs the TOPSIS model to assess the severity of single voltage sags in medium- and low-voltage DC distribution networks. This approach can reasonably assess DC voltage sags, identify vulnerable areas of DC distribution systems affected by voltage sags, and determine the severity of their impact.

[0114] Figure 1 This is a flowchart of a voltage sag assessment method 100 for medium- and low-voltage DC distribution networks according to an embodiment of the present invention. Figure 1 As shown, the voltage sag assessment method for medium- and low-voltage DC distribution networks provided by this invention designs a multi-dimensional and multi-level index system that considers waveform characteristics and energy characteristics. It considers both energy characteristics, reflecting the impact of sag events on the grid and load sides, and waveform characteristics, reflecting the impact of sag waveforms on severity. Based on proximity, it assesses the severity of a single voltage sag in the medium- and low-voltage DC distribution network, identifies vulnerable areas, and can assess the severity of different sag types on the DC distribution network system. Furthermore, this invention comprehensively uses the IAHP and CRITIC methods for combined weighting, integrating subjective and objective opinions, resulting in weights that better reflect the intrinsic correlation of the indicators. The voltage sag assessment method 100 for medium- and low-voltage DC distribution networks provided by this invention begins at step 101, where the evaluation index system for voltage sag assessment is determined.

[0115] Preferably, the evaluation index system includes: a first-level index including single voltage sag time assessment; a second-level index including waveform characteristics and energy characteristics; and a third-level index including: residual voltage sag, sag duration, sag voltage drop rate and sag voltage recovery rate belonging to the waveform characteristic index, and sag energy and sensitive load failure rate belonging to the energy characteristic index.

[0116] Combination Figure 2 As shown, this invention establishes a multi-dimensional, multi-level index system considering waveform characteristics and energy characteristics. Specifically, a multi-level index system is established, with the evaluation of a single voltage sag event as the first layer, i.e., the target layer; and waveform characteristic indicators and energy characteristic indicators together constitute the second layer, i.e., the criterion layer; wherein, the waveform characteristic indicator is composed of the residual voltage U of the sag. res Duration of temporary descent T duration The rate of decrease of the sag voltage δ fall With recovery rate δ rise It consists of four third-level indicators, and the energy characteristic indicators are composed of the transient energy index E. vs Together with the sensitive load failure rate P, these two third-level indicators constitute the third layer, also known as the scheme layer.

[0117] In step 102, the evaluation index value corresponding to each node in the DC distribution network is determined based on the voltage sag data when a voltage sag event occurs.

[0118] Preferably, determining the evaluation index value corresponding to each node in the DC distribution network based on the sag voltage data at the time of the sag event includes:

[0119] The residual voltage of the voltage sag is determined by the minimum node voltage during the duration of the voltage sag event.

[0120] The duration of a voltage sag is determined based on the time between the start and end of the event duration in the voltage sag event.

[0121] The rate of decrease δ of the voltage sag is determined based on the rate of change of the node voltage from a preset percentage of the rated voltage to the residual voltage during a voltage sag event. fall ,include:

[0122]

[0123] The recovery rate δ of the voltage sag is determined based on the rate of change of the node voltage from the residual voltage to a preset percentage of the rated voltage during a voltage sag event. rise ,include:

[0124]

[0125] The transient energy E is determined using the following method. vs ,include:

[0126]

[0127] The sensitive load failure rate P is determined using the following methods:

[0128]

[0129] Among them, U 额定 U is the rated voltage of the node; res The residual voltage; T1 is the time point at which the node voltage drops from a preset percentage of the rated voltage to the residual voltage; T2 is the time point at which the node voltage equals U. res The corresponding time point is T3, which is the time point when the node voltage is about to recover from the residual voltage to the preset percentage of the rated voltage; U(t) is the expression for the change of node voltage with time; T duration For voltage sag duration events; T min and T max These represent the minimum and maximum voltage sag durations of the node's overall voltage withstand curve, respectively; U max and U minThese represent the maximum and minimum residual voltage sags of the node's integrated voltage withstand curve; A, B, and C are the uncertain regions, and D is the equipment fault region.

[0130] In this invention, the residual pressure U is temporarily reduced. res Defined as the minimum node voltage during the duration of a voltage sag event. Sag duration T duration Defined as the time from the start to the end of a voltage sag event. The rate of decrease of the sag voltage, δ. fall Defined as a voltage sag event, where the node voltage drops from a preset percentage of 90% of the rated voltage to the residual voltage U. res The rate of voltage change. The recovery rate δ of the sag voltage. rise Defined as a voltage sag event, where the node voltage is changed by the residual voltage U res The rate of voltage change recovering to 90% of rated voltage. To clarify, the duration of a voltage sag event is defined as the period during which the node voltage drops from 90% of rated voltage to the residual voltage and then recovers to 90% of rated voltage. The formulas for the rate of decrease and the rate of recovery are as follows:

[0131]

[0132]

[0133] In the formula, U 额定 T1 is the node's rated voltage, T2 is the time point when the node voltage is about to drop from 90% of the rated voltage to the residual voltage, and T2 is the time point when the node voltage equals U. res The corresponding time point is T3, which is the time point when the node voltage is about to recover from the residual voltage to 90% of the rated voltage.

[0134] Among them, the transient energy index E vs For a single voltage sag event, as shown in equation (3):

[0135]

[0136] In the formula, U(t) is the expression for the node voltage over time.

[0137] Among them, the value of the sensitive load failure rate P is related to the node voltage tolerance curve and the energy loss formula. Figure 3 For a given node's comprehensive VTC (Voltage Control Tranche), A, B, and C represent uncertain regions, D represents the equipment failure region, and the remaining region represents the normal operating condition region. Sensitive load failures can be considered as energy losses caused by voltage dips, according to the energy loss formula... The formula for the sensitive load failure rate is derived as follows:

[0138]

[0139] Among them, T duration For voltage sag duration events; T min and T max These represent the minimum and maximum voltage sag durations of the node's overall voltage withstand curve, respectively; U max and U min These represent the maximum and minimum residual voltage sags of the node's integrated voltage withstand curve; A, B, and C are the uncertain regions, and D is the equipment fault region.

[0140] In step 103, the weight of the lowest-level indicator in the evaluation indicator system relative to the first-level indicator is determined based on the evaluation indicator value.

[0141] Preferably, determining the weight of the lowest-level indicator relative to the first-level indicator in the evaluation indicator system based on the evaluation indicator value includes:

[0142] The subjective weight W of the third-level indicator relative to the second-level indicator is obtained using the IAHP method. 3 AHP The objective weight W of the third-level indicator relative to the second-level indicator is obtained using the CRITIC method. 3 CRITIC And based on the subjective weight W 3 AHP and objective weight W 3 CRITIC The combined weight W of the third-level indicators relative to the second-level indicators is calculated using the difference coefficient method. 3 ;

[0143] The weight W of the second-level indicator relative to the first-level indicator is calculated using the IAHP method. 2 ;

[0144] Based on the combined weight W of the third-level indicators relative to the second-level indicators 3 The weight W of the second-level indicator relative to the first-level indicator 2 Perform hierarchical multi-sorting to obtain the weight W of the third-level indicator relative to the first-level indicator. 1 .

[0145] Preferably, the subjective weight W is used as the basis for... 3 AHP and objective weight W 3 CRITIC The combined weight W of the third-level indicators relative to the second-level indicators is calculated using the difference coefficient method. 3 ,include:

[0146]

[0147]

[0148]

[0149] Where T′ is the difference coefficient; the m indicators are... Arranged in ascending order, we get [p1, p2, ... p... m ]; α and β are the coefficients of the combined weights.

[0150] Preferably, the combination weight W of the third-level index relative to the second-level index is... 3 The weight W of the second-level indicator relative to the first-level indicator 2 Perform hierarchical multi-sorting to obtain the weight W of the third-level indicator relative to the first-level indicator. 1 ,include:

[0151]

[0152]

[0153]

[0154] Where i = 1, 2, 3, 4; j = 5, 6; to The weights corresponding to the residual voltage sag, sag duration, descent rate, recovery rate, sag energy, and sensitive load failure rate are as follows: and These are sets of weights for waveform characteristic indicators and energy characteristic indicators, respectively. and These are the weights of the waveform characteristic index and the energy characteristic index relative to the first-level index, respectively.

[0155] Combination Figure 4 As shown, in this invention, the combined weights of the indicators in the established indicator system are obtained by combining the IAHP and CRITIC methods. Specifically, the IAHP method is first used to calculate the subjective weight W of the third-level indicator relative to the second-level indicator. 3 AHP Then, the CRITIC method is used to calculate the objective weight W of the third-level indicator relative to the second-level indicator. 3 CRITIC Then, the difference coefficient method is used to calculate the combined weight W. 3 After determining the weights of the third-level indicators relative to the second-level indicators, the IAHP method is used again to calculate the weights W of the second-level indicators relative to the first-level indicators. 2 The weight W of the third-level indicator relative to the second-level indicator was determined. 3 And the weight W of the second-level indicator relative to the first-level indicator.2 Then, perform hierarchical multi-sorting to obtain the weight W of the third-level indicator relative to the first-level indicator. 1 That is, the total weight.

[0156] In this invention, the subjective weight W of the third-level index relative to the second-level index is obtained using the IAHP method. 3 AHP The specific steps are as follows:

[0157] (1) Construct the judgment matrix A using the coefficient of variation method.

[0158] The coefficient of variation (CV) is the ratio of the dispersion of a set of index data to its mean, that is, the ratio of the standard deviation to the mean.

[0159]

[0160] Where: CV j Let σ be the coefficient of variation of the j-th index. j Let μ be the standard deviation of the j-th index. j Let be the average value of the j-th indicator.

[0161] Given m indicators, the basic weight W1 of the j-th indicator is defined as follows:

[0162]

[0163] Dividing each indicator's base weight pairwise, we obtain the judgment matrix A as follows:

[0164]

[0165] In this invention, the first dimension index of the third layer is m=4 relative to the second layer index, and the second dimension index is m=2 relative to the second layer index.

[0166] (2) Hierarchical single sorting and consistency test

[0167] Find the largest eigenvalue of the judgment matrix A and the corresponding eigenvalue λ. max The eigenvectors are normalized and denoted as . The elements represent the ranking weights of elements at the same level relative to the factors at the previous level; this process is called hierarchical single ranking. Since the construction of the judgment matrix still involves subjective factors, a consistency check is required before hierarchical ranking.

[0168] ① Define the consistency index CI as:

[0169]

[0170] In the formula, n is the order of the discrimination matrix, and λ maxCI is the largest eigenvalue of the discrimination matrix A. The larger CI is, the worse the consistency of the discrimination matrix; the closer CI is to 0, the better the consistency of the discrimination matrix. When CI is 0, the discrimination matrix is ​​completely consistent.

[0171] ② To measure the magnitude of CI, the search consistency metric RI is introduced:

[0172] Table 1 shows the consistency index RI.

[0173] Matrix order 1 2 3 4 5 6 7 8 9 10 RI 0 0 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49

[0174] The value of RI depends only on the order of the judgment matrix.

[0175] ③ Calculate the consistency ratio (CR):

[0176]

[0177] Generally, when CR < 0.1, the inconsistency of the judgment matrix A is considered to be within the acceptable range, indicating satisfactory consistency, and thus passing the consistency test. Otherwise, the comparison matrix A needs to be reconstructed and adjusted.

[0178] In this invention, the CRITIC method is used to determine the objective weight W of the third-level index relative to the second-level index. 3 CRITIC The specific steps are as follows:

[0179] (1) Dimensionless treatment

[0180] Assuming there are n nodes and m evaluation indicators, the original indicator matrix is ​​formed as follows:

[0181]

[0182] The indicator is processed using either positive or negative methods. If the indicator is a price-type indicator, i.e., the larger the better, then it is processed according to formula (11):

[0183]

[0184] If the indicator is a cost-type indicator, that is, the smaller the better, then it should be handled according to formula (12):

[0185]

[0186] In this invention, the first dimension index of the third layer is m=4 relative to the second layer index, and the second dimension index is m=2 relative to the second layer index.

[0187] (2) Calculate the variability of the indicators

[0188] In the CRITIC method, standard deviation is used to represent the variation and fluctuation of each indicator. The larger the standard deviation, the greater the numerical variation of the indicator, the more information it can reflect, and the stronger the evaluation intensity of the indicator itself. Therefore, more weight should be assigned to the indicator, as shown in equation (13):

[0189]

[0190] In the formula, S is the average value of the j-th indicator. j Let be the standard deviation of the j-th indicator.

[0191] (3) Conflict in the calculation of indicators

[0192] In the CRITIC method, the correlation coefficient is used to represent the correlation between indicators. The stronger the correlation with other indicators, the more information the indicator reflects overlaps with other indicators, and the weight assigned to the indicator should be reduced, as shown in equation (14):

[0193]

[0194] In the formula, R j Let r be the conflict coefficient of the j-th index. ij Let be the correlation coefficient between the i-th indicator and the j-th indicator.

[0195] (4) Calculate the amount of information

[0196]

[0197] In the formula, C j C represents the amount of information contained in the j-th indicator. j The larger the value, the greater the weight of the j-th indicator should be.

[0198] (5) Calculate the weights

[0199] Define the CRITIC weight of the j-th index. for:

[0200]

[0201] In this invention, the difference coefficient method is used to calculate the combined weight W. 3 The specific steps are as follows:

[0202] Let the combined weight W 3 satisfy:

[0203]

[0204] In the formula, α and β are the coefficients of the combined weights, which satisfy the following conditions:

[0205]

[0206] (1) Sorting And calculate the coefficient of difference.

[0207] First of all Arranged in ascending order, we get [p1, p2, ... p... m The coefficient of difference T′ is calculated as follows:

[0208]

[0209] (2) Calculate the combined weight coefficients α and β

[0210]

[0211] Then, the combined weight W is obtained through equation (17). 3 .

[0212] In this invention, after determining the weights of the third-level indicators relative to the second-level indicators, the IAHP method is used again to obtain the weights W of the second-level indicators relative to the first-level indicators. 2 The specific steps are as follows:

[0213] (1) Construct the judgment matrix A using the three-scale method.

[0214] Since the second-level indicators only include waveform characteristic indicators and energy characteristic indicators, it is more practical to construct the judgment matrix using the three-scale method.

[0215] Let the judgment matrix A be:

[0216]

[0217] In the formula, the second-level index is m=2 relative to the first-level index, c ij The values ​​for the comparison of the importance of indicator i and indicator j are as follows:

[0218]

[0219] (2) Hierarchical single sorting and consistency test

[0220] Find the largest eigenvalue of the judgment matrix A and the corresponding eigenvalue λ. max The eigenvectors are then normalized to W based on empirical methods. A consistency check is performed on the judgment matrix A, and the eigenvectors are then normalized to W. 2 .

[0221] The weight W was determined above. 3 With W 2 Then, perform hierarchical multi-sorting and calculate the weight W of the third-level indicator relative to the first-level indicator. 1 The specific steps are as follows:

[0222] Because of the third-level indicator, the temporary decrease in residual pressure U res Duration of temporary descent T duration descent rate δ fall Recovery rate δ rise Only correlated with waveform characteristic indicators in the second-level indicators, the sag energy indicator E vs If the sensitive load failure rate P is only related to energy characteristic indicators, then:

[0223]

[0224]

[0225] In the formula, to Corresponding to the temporary decrease in residual pressure U res Duration of temporary descent T duration descent rate δ fall Recovery rate δ rise With energy index E vs The weight of the sensitive load failure rate P and These are sets of weights for waveform characteristic indicators and energy characteristic indicators, respectively. and These are the weights of the waveform characteristic index and the energy characteristic index relative to the first-level index, respectively.

[0226] Weight W 1 Satisfying equation (25):

[0227]

[0228] In the formula, i = 1, 2, 3, 4; j = 5, 6.

[0229] In step 104, based on the weight of the lowest-level indicator in the evaluation index system relative to the first-level indicator, the proximity degree corresponding to each node is determined.

[0230] Preferably, determining the proximity of each node based on the weight of the lowest-level indicator relative to the first-level indicator in the evaluation index system includes:

[0231] Construct a set of solutions based on the number of nodes, F = {f1, f2, ..., f...} n}; where f i (i = 1, 2, ... n) represents the assessment of the severity of voltage sag at the i-th node based on various indicators;

[0232] Determine the ideal solution v + and negative ideal solution v - Positive and negative ideal solutions v+ v - satisfy:

[0233]

[0234]

[0235] Calculate the distance between each solution and the positive and negative ideal solutions. and include:

[0236]

[0237] Calculate the relative approximation R of each solution to the positive ideal solution. i The proximity score corresponding to the i-th node includes:

[0238]

[0239] Where v1 to v6 correspond to sag residual voltage, sag duration, sag rate, recovery rate, sag energy, and sensitive load failure rate, respectively; n is the number of nodes; and m is the number of the lowest-level indicators. Let x be the weight of indicator j relative to the first-level indicators; ij Let j be the value of the index of the i-th node.

[0240] In step 105, weak nodes in the DC distribution network are identified based on the proximity.

[0241] Preferably, the determination of weak nodes in the DC distribution network based on the proximity includes:

[0242] The node with the smallest proximity is selected as the weak node in the DC distribution network.

[0243] In this invention, the total weight W is obtained. 1 Then, the TOPSIS model based on Euclidean distance is applied for evaluation, calculating and sorting the bonding progress of each node to identify the weak points of the medium and low voltage DC distribution system. The specific steps are as follows:

[0244] (1) Construct a set of solutions and determine the positive and negative ideal solutions v + v -

[0245] Similarly, in equation (10), there are n nodes and m evaluation indicators.

[0246] In this invention, the third-level index is m=6 relative to the first-level index, so the constructed scheme set is:

[0247] F = {f1, f2, ..., f n} (26)

[0248] Among them, f i (i = 1, 2, ..., n) represents the assessment of the severity of voltage sag at the i-th node based on various indicators.

[0249] Positive ideal solution v + The negative ideal solution v is defined as the value taken when all indicators reach the optimal value in the set of solutions. - Defined as the value taken when all indicators reach the worst value in the solution set. In this invention, due to the temporary reduction in residual voltage U... res With recovery rate δ rise This is a price-based indicator; a higher value is better. The duration of the temporary decline is T. duration descent rate δ fall Energy index E vs The sensitive load failure rate P is a cost-related indicator; a smaller value is better. The positive and negative ideal solutions are then determined by this. + v - satisfy:

[0250]

[0251]

[0252] In the formula, v1 to v6 correspond to the temporary reduction in residual pressure U, respectively. res Duration of temporary descent T duration descent rate δ fall Recovery rate δ rise With energy index E vs , Sensitive load failure rate P.

[0253] (2) Find the distance between each solution and the positive and negative ideal solutions. include:

[0254]

[0255] (3) Calculate the relative approximation R of each scheme from the positive ideal solution. i And sort, including:

[0256]

[0257] Then, the calculated R i Sort, R i A larger value indicates that the solution is closer to the ideal solution, and the severity of voltage sag at that node is lower; conversely, a smaller value indicates a higher degree of voltage sag. Therefore, the node corresponding to the smallest proximity is selected as the weak node in the DC distribution network.

[0258] To verify the effectiveness of the method of this invention in evaluating single voltage sag events in medium- and low-voltage DC distribution networks, a medium- and low-voltage DC distribution network model was built in Matlab / Simulink. Its topology is as follows: Figure 5 As shown, this topology integrates four DC power distribution system power supply modes: DC load concentration area, industrial park, load center, and renewable energy aggregation. The medium-voltage DC voltage level of the system is selected as 20kV (±10kV), and the low-voltage DC voltage level is selected as 750V.

[0259] The source load parameters of the system are shown in Table 2.

[0260] Table 2 shows the source and load parameters of the low-voltage DC system.

[0261]

[0262] The DC lines use copper cables with a resistance of 0.3417 Ω / km and an inductance of 0.34 mH / km. The DC bus and power supply branches are numbered, and the lengths of each line are shown in Table 3.

[0263] Table 3 shows the line parameters of the low-voltage DC system.

[0264] Line number Line Description Line length 1 Converter station H1 - DC switching station K1 0.1km 2 DC switch K1 - Civil load distribution room 0.1km 3 K1 DC Switchgear - Data Center Power Distribution Room 0.1km 4 K1 DC Switchgear - Photovoltaic Power Station Distribution Room 0.1km 5 K2 DC Switchgear - Industrial Load Distribution Room 1.5km 6 K2 DC Switchgear - Municipal Load Distribution Room 0.1km 7 K2 DC Switchgear - Photovoltaic Power Station Distribution Room 0.1km 8 K2 DC Switchgear - Commercial Load Distribution Room 2km 9 Converter station H2 - DC switching station K1 6.8km 10 Converter station H1 - DC switching station K2 7.2km 11 Converter station H2 - DC switching station K2 0.1km

[0265] Example 1: The fault initiation time is t0 = 0.4s, and the fault location is shown in [reference needed]. Figure 6 A bipolar short-circuit fault occurs. The fault resistance (also known as the transition resistance) is taken as 5Ω, and the fault duration is taken as 0.100s. The transient waveform is shown below. Figure 7 .

[0266] Based on the simulation data, the calculated values ​​of each indicator are shown in Table 4:

[0267] Table 4. Index values ​​for each detection node

[0268]

[0269]

[0270] The calculation results of the weights of each indicator at each level are shown in Table 5:

[0271] Table 5. Calculation results of weights for each indicator at each level.

[0272]

[0273] The distances and proximity between the monitoring nodes and the positive and negative ideal solutions are shown in Table 6.

[0274] Table 6 shows the weighted distance and proximity between each monitoring node and the positive and negative ideal solutions.

[0275] node <![CDATA[Distance C to the positive ideal solution + > <![CDATA[Distance C from the negative ideal solution - > Proximity R 1 0.1179 0.4206 0.2189 2 0.1197 0.3510 0.2543 3 0.1197 0.3510 0.2543 4 0.2620 0.1873 0.5831 5 0.4249 0.1176 0.7831 6 0.1093 0.3814 0.2228

[0276] The evaluation results are as follows:

[0277] f5 > f4 > f3 > f2 > f6 > f1

[0278] The ranking of the monitoring nodes from best to worst is 5, 4, 3, 2, 6, 1.

[0279] Example 2: The fault initiation time is t0 = 0.4s, and the fault location is shown in [reference needed]. Figure 8 A bipolar short-circuit fault occurs. The fault resistance (also known as the transition resistance) is taken as 5Ω, and the fault duration is taken as 0.080s. The transient waveform is shown below. Figure 9 .

[0280] Based on the simulation data, the calculated values ​​of each indicator are shown in Table 8:

[0281] Table 8. Indicator values ​​for each monitoring node

[0282]

[0283]

[0284] The weights of each indicator at each level are shown in Table 9:

[0285] Table 9 shows the calculation results of the weights of each indicator at each level.

[0286]

[0287] The distances and proximity between the monitoring nodes and the positive and negative ideal solutions are shown in Table 10:

[0288] Table 10 shows the weighted distance and proximity between each monitoring node and the positive and negative ideal solutions.

[0289]

[0290] The evaluation results are as follows:

[0291] f5 > f1 > f6 > f2 > f4 > f3

[0292] The ranking of the monitoring nodes from best to worst is 5, 1, 6, 2, 4, 3.

[0293] The calculation results of the two embodiments above show that the single voltage sag assessment method for medium and low voltage DC distribution networks based on a multi-dimensional index system proposed in this invention effectively combines waveform characteristics and energy characteristics, comprehensively considers the grid side and the load side, and can effectively avoid the misassessment caused by using a single index. It can more comprehensively and accurately reflect the voltage sag situation of each node.

[0294] Figure 10This is a schematic diagram of the structure of a voltage sag assessment system 1000 for a medium- and low-voltage DC distribution network according to an embodiment of the present invention. Figure 10 As shown, the voltage sag assessment system 1000 for medium and low voltage DC distribution networks provided in this embodiment of the invention includes: an evaluation index system determination unit 1001, an evaluation index value determination unit 1002, a weight determination unit 1003, a proximity determination unit 1004, and a weak node determination unit 1005.

[0295] Preferably, the evaluation index system determination unit 1001 is used to determine the evaluation index system for voltage sag assessment.

[0296] Preferably, the evaluation index system includes: a first-level index including single voltage sag time assessment; a second-level index including waveform characteristics and energy characteristics; and a third-level index including: residual voltage sag, sag duration, sag voltage drop rate and sag voltage recovery rate belonging to the waveform characteristic index, and sag energy and sensitive load failure rate belonging to the energy characteristic index.

[0297] Preferably, the evaluation index value determination unit 1002 is used to determine the evaluation index value corresponding to each node in the DC distribution network based on the voltage sag data when a voltage sag event occurs.

[0298] Preferably, the evaluation index value determination unit 1002 determines the evaluation index value corresponding to each node in the DC distribution network based on the voltage sag data when a voltage sag event occurs, including:

[0299] The residual voltage of the voltage sag is determined by the minimum node voltage during the duration of the voltage sag event.

[0300] The duration of a voltage sag is determined based on the time between the start and end of the event duration in the voltage sag event.

[0301] The rate of decrease δ of the voltage sag is determined based on the rate of change of the node voltage from a preset percentage of the rated voltage to the residual voltage during a voltage sag event. fall ,include:

[0302]

[0303] The recovery rate δ of the voltage sag is determined based on the rate of change of the node voltage from the residual voltage to a preset percentage of the rated voltage during a voltage sag event. rise ,include:

[0304]

[0305] The transient energy E is determined using the following method. vs ,include:

[0306]

[0307] The sensitive load failure rate P is determined using the following methods:

[0308]

[0309] Among them, U 额定 U is the rated voltage of the node; res The residual voltage; T1 is the time point at which the node voltage drops from a preset percentage of the rated voltage to the residual voltage; T2 is the time point at which the node voltage equals U. res The corresponding time point is T3, which is the time point when the node voltage is about to recover from the residual voltage to the preset percentage of the rated voltage; U(t) is the expression for the change of node voltage with time; T duration For voltage sag duration events; T min and T max These represent the minimum and maximum voltage sag durations of the node's overall voltage withstand curve, respectively; U max and U min These represent the maximum and minimum residual voltage sags of the node's integrated voltage withstand curve; A, B, and C are the uncertain regions, and D is the equipment fault region.

[0310] Preferably, the weight determination unit 1003 is used to determine the weight of the lowest level indicator in the evaluation indicator system relative to the first level indicator based on the evaluation indicator value.

[0311] Preferably, the weight determination unit 1003 determines the weight of the lowest-level indicator in the evaluation indicator system relative to the first-level indicator based on the evaluation indicator value, including:

[0312] The subjective weight W of the third-level indicator relative to the second-level indicator is obtained using the IAHP method. 3 AHP The objective weight W of the third-level indicator relative to the second-level indicator is obtained using the CRITIC method. 3 CRITIC And based on the subjective weight W 3 AHP and objective weight W 3 CRITIC The combined weight W of the third-level indicators relative to the second-level indicators is calculated using the difference coefficient method. 3 ;

[0313] Calculate the weight W of the second-level indicator relative to the first-level indicator using the IAHP system. 2 ;

[0314] Based on the combined weight W of the third-level indicators relative to the second-level indicators3 The weight W of the second-level indicator relative to the first-level indicator 2 Perform hierarchical multi-sorting to obtain the weight W of the third-level indicator relative to the first-level indicator. 1 .

[0315] Preferably, the weight determination unit 1003 determines the weight based on the subjective weight W. 3 AHP and objective weight W 3 CRITIC The combined weight W of the third-level indicators relative to the second-level indicators is calculated using the difference coefficient method. 3 ,include:

[0316]

[0317]

[0318]

[0319] Where T′ is the difference coefficient; the m indicators are... Arranged in ascending order, we get [p1, p2, ... p... m ]; α and β are the coefficients of the combined weights.

[0320] Preferably, the determining unit 1003 determines the weights W of the third-level indicators relative to the second-level indicators. 3 The weight W of the second-level indicator relative to the first-level indicator 2 Perform hierarchical multi-sorting to obtain the weight W of the third-level indicator relative to the first-level indicator. 1 ,include:

[0321]

[0322]

[0323]

[0324] Where i = 1, 2, 3, 4; j = 5, 6; to The weights corresponding to the residual voltage sag, sag duration, descent rate, recovery rate, sag energy, and sensitive load failure rate are as follows: and These are sets of weights for waveform characteristic indicators and energy characteristic indicators, respectively. and These are the weights of the waveform characteristic index and the energy characteristic index relative to the first-level index, respectively.

[0325] Preferably, the proximity determination unit 1004 is used to determine the proximity of each node based on the weight of the lowest level indicator in the evaluation index system relative to the first level indicator.

[0326] Preferably, the proximity determination unit 1004 determines the proximity of each node based on the weight of the lowest-level indicator relative to the first-level indicator in the evaluation index system, including:

[0327] Construct a set of solutions based on the number of nodes, F = {f1, f2, ..., f...} n}; where f i (i = 1, 2, ... n) represents the assessment of the severity of voltage sag at the i-th node based on various indicators;

[0328] Determine the ideal solution v + and negative ideal solution v - Positive and negative ideal solutions v + v - satisfy:

[0329]

[0330]

[0331] Calculate the distance between each solution and the positive and negative ideal solutions. and include:

[0332]

[0333] Calculate the relative approximation R of each solution to the positive ideal solution. i The proximity score corresponding to the i-th node includes:

[0334]

[0335] Where v1 to v6 correspond to sag residual voltage, sag duration, sag rate, recovery rate, sag energy, and sensitive load failure rate, respectively; n is the number of nodes; and m is the number of the lowest-level indicators. Let x be the weight of indicator j relative to the first-level indicators; ij Let j be the value of the index of the i-th node.

[0336] Preferably, the weak node determination unit 1005 is used to determine the weak nodes in the DC distribution network based on the proximity.

[0337] Preferably, the weak node determination unit 1005 determines weak nodes in the DC distribution network based on the proximity, including:

[0338] The node with the smallest proximity is selected as the weak node in the DC distribution network.

[0339] The voltage sag assessment system 1000 for medium and low voltage DC distribution networks in this embodiment of the invention corresponds to the voltage sag assessment method 100 for medium and low voltage DC distribution networks in another embodiment of the invention, and will not be described again here.

[0340] According to another aspect of the present invention, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the methods for evaluating voltage sags in a medium- and low-voltage DC distribution network.

[0341] According to another aspect of the present invention, the present invention provides an electronic device, comprising:

[0342] The aforementioned computer-readable storage medium; and

[0343] One or more processors for executing a program in the computer-readable storage medium.

[0344] The invention has been described with reference to a few embodiments. However, as will be known to those skilled in the art, and as defined in the appended claims, other embodiments besides those disclosed above fall equivalently within the scope of the invention.

[0345] Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the art, unless otherwise expressly defined herein. All references to “a / the / the [device, component, etc.]” ​​are openly interpreted as at least one instance of said device, component, etc., unless otherwise expressly stated. The steps of any method disclosed herein need not be performed in the exact order disclosed unless explicitly stated otherwise.

[0346] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention 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.

[0347] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. 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 illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0348] 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.

[0349] 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.

[0350] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for assessing voltage sag in medium- and low-voltage DC distribution networks, characterized in that, The method includes: Establish an evaluation index system for voltage sag assessment; The evaluation index value corresponding to each node in the DC distribution network is determined based on the voltage sag data when a voltage sag event occurs. The weight of the lowest-level indicator in the evaluation indicator system relative to the first-level indicator is determined based on the evaluation indicator value. Based on the weight of the lowest-level indicator in the evaluation index system relative to the first-level indicator, the proximity degree corresponding to each node is determined. Weak nodes in the DC distribution network are identified based on the proximity. The step of determining the weight of the lowest-level indicator in the evaluation indicator system relative to the first-level indicator based on the evaluation indicator value includes: The subjective weight W of the third-level indicator relative to the second-level indicator is obtained using the IAHP method. 3 AHP The objective weight W of the third-level indicator relative to the second-level indicator is obtained using the CRITIC method. 3 CRITIC And based on the subjective weight W 3 AHP and objective weight W 3 CRITIC The combined weight W of the third-level indicators relative to the second-level indicators is calculated using the difference coefficient method. 3 ; The weight W of the second-level indicator relative to the first-level indicator is calculated using the IAHP method. 2 ; Based on the combined weight W of the third-level indicators relative to the second-level indicators 3 The weight W of the second-level indicator relative to the first-level indicator 2 Perform hierarchical multi-sorting to obtain the weight W of the third-level indicator relative to the first-level indicator. 1 ; Wherein, the subjective weight W 3 AHP and objective weight W 3 CRITIC The combined weight W of the third-level indicators relative to the second-level indicators is calculated using the difference coefficient method. 3 ,include: β＝1-α, Where T′ is the difference coefficient; the m indicators are... Arranged in ascending order, we get [p1, p2, ... p... m ]; α and β are the coefficients of the combined weights; Among them, the combined weight W of the third-level indicator relative to the second-level indicator 3 The weight W of the second-level indicator relative to the first-level indicator 2 Perform hierarchical multi-sorting to obtain the weight W of the third-level indicator relative to the first-level indicator. 1 ,include: Where i = 1, 2, 3, 4; j = 5, 6; to The weights corresponding to the residual voltage sag, sag duration, descent rate, recovery rate, sag energy, and sensitive load failure rate are as follows: and These are sets of weights for waveform characteristic indicators and energy characteristic indicators, respectively. and These are the weights of the waveform characteristic index and the energy characteristic index relative to the first-level index, respectively.

2. The method according to claim 1, characterized in that, The evaluation index system includes: the first-level index includes: single voltage sag time assessment; the second-level index includes: waveform characteristics and energy characteristics; the third-level index includes: sag residual voltage, sag duration, sag voltage drop rate and sag voltage recovery rate, which belong to the waveform characteristic index, and sag energy and sensitive load failure rate, which belong to the energy characteristic index.

3. The method according to claim 1, characterized in that, The determination of the evaluation index value for each node in the DC distribution network based on the voltage sag data at the time of the voltage sag event includes: The residual voltage of the voltage sag is determined by the minimum node voltage during the duration of the voltage sag event. The duration of a voltage sag is determined based on the time between the start and end of the event duration in the voltage sag event. The rate of decrease δ of the voltage sag is determined based on the rate of change of the node voltage from a preset percentage of the rated voltage to the residual voltage during a voltage sag event. fall ,include: The recovery rate δ of the voltage sag is determined based on the rate of change of the node voltage from the residual voltage to a preset percentage of the rated voltage during a voltage sag event. rise ,include: The transient energy E is determined using the following method. vs ,include: The sensitive load failure rate P is determined using the following methods: Among them, U 额定 U is the rated voltage of the node; res The residual voltage; T1 is the time point at which the node voltage drops from a preset percentage of the rated voltage to the residual voltage; T2 is the time point at which the node voltage equals U. res The corresponding time point is T3, which is the time point when the node voltage is about to recover from the residual voltage to the preset percentage of the rated voltage; U(t) is the expression for the change of node voltage with time; T duration For voltage sag duration events; T min and T max These represent the minimum and maximum voltage sag durations of the node's overall voltage withstand curve, respectively; U max and U min These represent the maximum and minimum residual voltage sags of the node's integrated voltage withstand curve; A, B, and C are the uncertain regions, and D is the equipment fault region.

4. The method according to claim 1, characterized in that, The determination of the proximity degree for each node based on the weight of the lowest-level indicator relative to the first-level indicator in the evaluation index system includes: Construct a set of solutions based on the number of nodes, F = {f1, f2, ..., f...} n }; where f i (i = 1, 2, ... n) represents the assessment of the severity of voltage sag at the i-th node based on various indicators; Determine the ideal solution v + and negative ideal solution v - Positive and negative ideal solutions v + v - satisfy: Calculate the distance between each solution and the positive and negative ideal solutions. and include: Calculate the relative approximation R of each solution to the positive ideal solution. i The proximity score corresponding to the i-th node includes: Where v1 to v6 correspond to sag residual voltage, sag duration, sag rate, recovery rate, sag energy, and sensitive load failure rate, respectively; n is the number of nodes; and m is the number of the lowest-level indicators. Let x be the weight of indicator j relative to the first-level indicators; ij Let j be the value of the index of the i-th node.

5. The method according to claim 1, characterized in that, The process of identifying weak nodes in the DC distribution network based on the proximity includes: The node with the smallest proximity is selected as the weak node in the DC distribution network.

6. A voltage sag assessment system for medium- and low-voltage DC distribution networks, characterized in that, The system includes: The evaluation index system determination unit is used to determine the evaluation index system for voltage sag assessment. The evaluation index value determination unit is used to determine the evaluation index value corresponding to each node in the DC distribution network based on the voltage sag data when a voltage sag event occurs. The weight determination unit is used to determine the weight of the lowest-level indicator in the evaluation indicator system relative to the first-level indicator based on the evaluation indicator value. The proximity determination unit is used to determine the proximity of each node based on the weight of the lowest-level indicator in the evaluation index system relative to the first-level indicator. A weak node determination unit is used to determine weak nodes in the DC distribution network based on the proximity. The weight determination unit determines the weight of the lowest-level indicator in the evaluation indicator system relative to the first-level indicator based on the evaluation indicator value, including: The subjective weight W of the third-level indicator relative to the second-level indicator is obtained using the IAHP method. 3 AHP The objective weight W of the third-level indicator relative to the second-level indicator is obtained using the CRITIC method. 3 CRITIC And based on the subjective weight W 3 AHP and objective weight W 3 CRITIC The combined weight W of the third-level indicators relative to the second-level indicators is calculated using the difference coefficient method. 3 ; Calculate the weight W of the second-level indicator relative to the first-level indicator using the IAHP system. 2 ; Based on the combined weight W of the third-level indicators relative to the second-level indicators 3 The weight W of the second-level indicator relative to the first-level indicator 2 Perform hierarchical multi-sorting to obtain the weight W of the third-level indicator relative to the first-level indicator. 1 ; The weight determination unit, based on the subjective weight W, 3 AHP and objective weight W 3 CRITIC The combined weight W of the third-level indicators relative to the second-level indicators is calculated using the difference coefficient method. 3 ,include: β＝1-α, Where T′ is the difference coefficient; the m indicators are... Arranged in ascending order, we get [p1, p2, ... p... m ]; α and β are the coefficients of the combined weights; The weight determination unit determines the weight based on the combined weight W of the third-level indicator relative to the second-level indicator. 3 The weight W of the second-level indicator relative to the first-level indicator 2 Perform hierarchical multi-sorting to obtain the weight W of the third-level indicator relative to the first-level indicator. 1 ,include: Where i = 1, 2, 3, 4; j = 5, 6; to The weights corresponding to the residual voltage sag, sag duration, descent rate, recovery rate, sag energy, and sensitive load failure rate are as follows: and These are sets of weights for waveform characteristic indicators and energy characteristic indicators, respectively. and These are the weights of the waveform characteristic index and the energy characteristic index relative to the first-level index, respectively.

7. The system according to claim 6, characterized in that, The evaluation index system includes: the first-level index includes: single voltage sag time assessment; the second-level index includes: waveform characteristics and energy characteristics; the third-level index includes: sag residual voltage, sag duration, sag voltage drop rate and sag voltage recovery rate, which belong to the waveform characteristic index, and sag energy and sensitive load failure rate, which belong to the energy characteristic index.

8. The system according to claim 6, characterized in that, The evaluation index value determination unit determines the evaluation index value corresponding to each node in the DC distribution network based on the voltage sag data when a voltage sag event occurs, including: The residual voltage of the voltage sag is determined by the minimum node voltage during the duration of the voltage sag event. The duration of a voltage sag is determined based on the time between the start and end of the event duration in the voltage sag event. The rate of decrease δ of the voltage sag is determined based on the rate of change of the node voltage from a preset percentage of the rated voltage to the residual voltage during a voltage sag event. fall ,include: The recovery rate δ of the voltage sag is determined based on the rate of change of the node voltage from the residual voltage to a preset percentage of the rated voltage during a voltage sag event. rise ,include: The transient energy E is determined using the following method. vs ,include: The sensitive load failure rate P is determined using the following methods: Among them, U 额定 U is the rated voltage of the node; res The residual voltage; T1 is the time point at which the node voltage drops from a preset percentage of the rated voltage to the residual voltage; T2 is the time point at which the node voltage equals U. res The corresponding time point is T3, which is the time point when the node voltage is about to recover from the residual voltage to the preset percentage of the rated voltage; U(t) is the expression for the change of node voltage with time; T duration For voltage sag duration events; T min and T max These represent the minimum and maximum voltage sag durations of the node's overall voltage withstand curve, respectively; U max and U min These represent the maximum and minimum residual voltage sags of the node's integrated voltage withstand curve; A, B, and C are the uncertain regions, and D is the equipment fault region.

9. The system according to claim 6, characterized in that, The proximity determination unit determines the proximity of each node based on the weight of the lowest-level indicator relative to the first-level indicator in the evaluation index system, including: Construct a set of solutions based on the number of nodes, F = {f1, f2, ..., f...} n }; where f i (i = 1, 2, ... n) represents the assessment of the severity of voltage sag at the i-th node based on various indicators; Determine the ideal solution v + and negative ideal solution v - Positive and negative ideal solutions v + v - satisfy: Calculate the distance between each solution and the positive and negative ideal solutions. and include: Calculate the relative approximation R of each solution to the positive ideal solution. i The proximity score corresponding to the i-th node includes: Where v1 to v6 correspond to sag residual voltage, sag duration, sag rate, recovery rate, sag energy, and sensitive load failure rate, respectively; n is the number of nodes; and m is the number of the lowest-level indicators. Let x be the weight of indicator j relative to the first-level indicators; ij Let j be the value of the index of the i-th node.

10. The system according to claim 6, characterized in that, The weak node determination unit determines weak nodes in the DC distribution network based on the proximity, including: The node with the smallest proximity is selected as the weak node in the DC distribution network.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method as described in any one of claims 1-5.

12. An electronic device, characterized in that, include: The computer-readable storage medium as described in claim 11; as well as One or more processors for executing a program in the computer-readable storage medium.

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