A master-slave self-healing control method and system based on distribution network condition assessment

Abstract

This invention discloses a master-slave self-healing control method and system based on distribution network state assessment, comprising: a master station acquiring the reactive power-voltage sensitivity matrix of the distribution network; dividing the distribution network area based on the reactive power-voltage sensitivity matrix to obtain sub-regions of the distribution network; determining the central bus in each sub-region of the distribution network; performing power flow calculation using the central bus as the reference voltage to determine the current operating voltage of each node in each sub-region of the distribution network; determining the operating state of the distribution network based on the current operating voltage of each node and preset voltage thresholds for different operating states; and a slave station determining a control mode based on the operating state of the distribution network, and restoring the distribution network to normal operation based on the optimization scheme corresponding to the control mode. This invention can trigger the corresponding control mode of the slave station and start the corresponding optimization scheme under different operating states, thereby achieving the function of optimizing the operation of the distribution network by coordinating the control of the slave station and the master station.

Description

Technical Field

[0001] This invention relates to the field of power distribution network protection and control technology, and more specifically, to a master-slave self-healing control method and system based on power distribution network condition assessment. Background Technology

[0002] The power distribution network bears the heavy responsibility of supplying stable power for production and daily life. With the continuous improvement of urban development, the load density of the power distribution network has increased significantly, and the variety of load types is strong. The power quality requirements for sensitive and important loads are increasingly demanding, making the safety and reliability of the distribution network particularly important. However, the complex structure of the distribution network means its reliability is constrained by various factors such as components and the environment, making faults inevitable. Furthermore, the distribution network is typically a radial passive structure, with each node having only one path to obtain power from the generator nodes; therefore, fault tripping will inevitably cause power outages in some areas. Currently, short-term power outages are frequent in distribution networks, impacting electricity-dependent production and daily life, and causing certain economic losses. Therefore, the importance of the self-healing function of the distribution network is self-evident. Fault self-healing control has become an important feature of modern smart distribution networks, and effective self-healing control technology is a crucial guarantee for improving the safety, reliability, and economy of power supply in the distribution network. Fault self-healing control of the distribution network needs to quickly isolate faults, automatically transfer power to the out-of-power load, reduce the outage area, shorten the outage time, and maximize the continuous power supply capacity of the distribution network. Typically, after a fault occurs, rapid control of switches is required to restore power to the lost area and ensure the reliability of the distribution network. However, most existing fault self-healing control systems are based on intelligent optimization algorithms, which face challenges due to the large number of nodes, complex network topology, and ever-changing electrical parameters in the distribution network. The solution process is constrained by numerous conditions, requiring extensive iterative calculations, trial and error, and the search for the optimal solution, resulting in slow solution speed. Furthermore, the resistance of distribution network lines cannot be ignored, and active and reactive power are difficult to decouple, further increasing the computational load of iterative solutions. This leads to long calculation times for power restoration schemes, making it difficult to meet the speed requirements of distribution network fault self-healing. Therefore, it is necessary to conduct research on distribution network fault self-healing to obtain a distribution network fault self-healing scheme that meets the requirements of rapid power restoration.

[0003] With the development and improvement of distribution networks, their structures are becoming increasingly complex. At the same time, more and more distributed power sources and controllable loads are being connected to the distribution network, providing more controllable leeway for optimization and self-healing, but also increasing the computational burden on control stations in traditional centralized control systems. Summary of the Invention

[0004] This invention proposes a master-slave self-healing control method and system based on distribution network condition assessment to solve the problem of how to control the distribution network.

[0005] To address the aforementioned problems, according to one aspect of the present invention, a master-slave self-healing control method based on distribution network condition assessment is provided, the method comprising:

[0006] The main station obtains the reactive power-voltage sensitivity matrix of the distribution network, and divides the distribution network area based on the reactive power-voltage sensitivity matrix to obtain the distribution network sub-regions;

[0007] The central busbar in each sub-region of the distribution network is determined, and power flow calculation is performed using the central busbar as the reference voltage to determine the current operating voltage of each node in each sub-region of the distribution network.

[0008] The operating status of the distribution network is determined based on the current operating voltage of each node and the preset voltage thresholds for different operating states.

[0009] The slave station determines the control mode based on the operating status of the distribution network, and restores the distribution network to normal operation based on the optimization scheme corresponding to the control.

[0010] Preferably, the reactive power-voltage sensitivity matrix includes:

[0011] S=(L-MH -1 N) -1 ,

[0012] Where S is the reactive power-voltage sensitivity matrix; For ΔP with respect to δ T The partial derivative; For △P to U T The partial derivative; For △Q with respect to δ T The partial derivative; For △Q to U T The partial derivatives of ; P is the node active power; Q is the node reactive power; δ is the node voltage phase angle; U is the node voltage magnitude; T is the matrix transpose sign; ΔP is the change in P; ΔQ is the change in Q.

[0013] Preferably, the process of dividing the distribution network area based on the reactive power-voltage sensitivity matrix to obtain sub-regions of the distribution network includes:

[0014] The reactive power-voltage sensitivity matrix S is preprocessed to obtain the intermediate matrix X;

[0015] A fuzzy similarity matrix ED representing the electrical distance between nodes is established based on the intermediate matrix X;

[0016] Based on the fuzzy similarity matrix ED, the fuzzy equivalence matrix is ​​calculated using the transitive closure method;

[0017] Clustering is performed based on the fuzzy equivalence matrix and a preset membership threshold to divide the power distribution network area and obtain the power distribution network sub-regions.

[0018] Preferably, the preprocessing of the reactive power-voltage sensitivity matrix S to obtain matrix X includes:

[0019] Perform standard deviation transformation and range transformation on any element in the reactive power-voltage sensitivity matrix S in sequence to obtain matrix X;

[0020] Where any element x in matrix X ij for:

[0021]

[0022]

[0023]

[0024]

[0025] Where i and j correspond to nodes i and j in the distribution network, and n is the number of nodes; W is the mean of the j-th column elements of the reactive power-voltage sensitivity matrix S. j Let S be the standard deviation of the j-th column element of the reactive power-voltage sensitivity matrix S.

[0026] Preferably, any element ED in the fuzzy similarity matrix ED ij for:

[0027]

[0028] Among them, ED ij x represents the electrical distance between node i and node j; i and j correspond to nodes i and j in the distribution network, and n is the number of nodes; ik Let x be the element in the i-th row and k-th column of matrix X; jk Let X be the element in the j-th row and k-th column of matrix X.

[0029] Preferably, the step of calculating the fuzzy equivalence matrix using the transitive closure method based on the fuzzy similarity matrix ED includes:

[0030] Calculate the square of the fuzzy similarity matrix ED representing the electrical distance, using ED (1) This indicates that the judgment of ED (1) Is it equal to ED? If ED (1) ==ED, then ED (1) This is the fuzzy equivalence matrix; conversely, if ED (1) ≠ED, then ED (1)Squared, denoted as ED (2) Continue to determine ED (2) Is it equal to ED? (1) Until the sth time ED (s) ==ED (s-1) At that time, determine ED (s) The fuzzy equivalence matrix is ​​defined as follows;

[0031] in,

[0032]

[0033] Among them, ED ij ED represents the electrical distance between node i and node j. ik ED represents the electrical distance between node i and node k. kj Let n be the electrical distance between node k and node j; n is the number of nodes, ∨ represents taking the larger value, and ∧ represents taking the smaller value.

[0034] Preferably, the step of performing clustering based on the fuzzy equivalence matrix and a preset membership threshold to divide the power distribution network area and obtain power distribution network sub-regions includes:

[0035] For any element in the fuzzy equivalence matrix, if the value of any element is greater than or equal to a preset membership threshold, then the value of the element is updated to the first preset value; if the value of any element is less than the preset membership threshold, then the value of the element is updated to the second preset value.

[0036] In the updated fuzzy equivalence matrix, nodes corresponding to columns with the same matrix elements are grouped into one category, and the distribution network sub-region is determined based on the grouped nodes.

[0037] Preferably, determining the operating state of the distribution network based on the current operating voltage of each node and preset voltage thresholds for different operating states includes:

[0038] Based on the current operating voltage of each node and the preset voltage threshold for different operating states, the operating state of each node is determined, and the operating state with the highest priority among all nodes is determined as the operating state of the distribution network.

[0039] The priority of the operating status, from highest to lowest, is as follows: emergency status, recovery status, abnormal status, alert status, and normal status.

[0040] Wherein, if the current operating voltage of any node satisfies U S >U1 or U S If <U8, then any node is determined to be in an emergency state;

[0041] If the current operating voltage of any node satisfies U1≥US >U2 or U7>U S If ≥U8, then any node is determined to be in a recovery state;

[0042] If the current operating voltage of any node satisfies U2≥U S >U3 or U6>U S If ≥U7, then any node is determined to be in an abnormal state;

[0043] If the current operating voltage of any node satisfies U3≥U S >U4 or U5 >U S If ≥U6, then any node is determined to be in a state of alert.

[0044] If the current operating voltage of any node satisfies U1≥U S If U ≥ 5, then any node is determined to be in a normal state; U S The current operating voltage is represented by U1, U2, U3, U4, U5, U6, U7, and U8, which are respectively the first preset voltage threshold, the second preset voltage threshold, the third preset voltage threshold, the fourth preset voltage threshold, the fifth preset voltage threshold, the sixth preset voltage threshold, the seventh preset voltage threshold, and the eighth preset voltage threshold.

[0045] Preferably, the slave station determines the control mode based on the operating status of the distribution network, and restores the distribution network to normal operation based on the optimization scheme corresponding to the control mode, including:

[0046] After the master station monitors the distribution network status, the slave station determines the control mode based on the distribution network's operating status and initiates an optimization scheme according to the control mode. Within the distribution network sub-region, the distribution network is restored to normal operation by optimizing the output of distributed power sources and / or controlling load power. If the regulation within the sub-region cannot meet the distribution network's operating requirements, the master station regulates the distributed power sources in adjacent sub-regions to improve the power flow level of the distribution network, thereby restoring the distribution network to normal operation.

[0047] According to another aspect of the present invention, a master-slave self-healing control system based on power distribution network condition assessment is provided, the system comprising:

[0048] The distribution network area division unit is used to enable the master station to obtain the reactive power-voltage sensitivity matrix of the distribution network, divide the distribution network area based on the reactive power-voltage sensitivity matrix, and obtain the distribution network sub-areas.

[0049] The operating voltage determination unit is used to determine the central bus in each sub-region of the distribution network, and to perform power flow calculations based on the central bus as the reference voltage to determine the current operating voltage of each node in each sub-region of the distribution network.

[0050] The operation status determination unit is used to determine the operation status of the distribution network based on the current operating voltage of each node and the preset voltage threshold for different operation statuses.

[0051] The control unit is used by the slave station to determine the control mode according to the operating status of the distribution network, so as to restore the distribution network to the normal operating state based on the optimization scheme corresponding to the control mode.

[0052] Preferably, the reactive power-voltage sensitivity matrix, which is a unit for dividing the distribution network area, includes:

[0053] S=(L-MH -1 N) -1 ,

[0054] Where S is the reactive power-voltage sensitivity matrix; For ΔP with respect to δ T The partial derivative; For △P to U T The partial derivative; For △Q with respect to δ T The partial derivative; For △Q to U T The partial derivatives of ; P is the node active power; Q is the node reactive power; δ is the node voltage phase angle; U is the node voltage magnitude; T is the matrix transpose sign; ΔP is the change in P; ΔQ is the change in Q.

[0055] Preferably, the distribution network area division unit divides the distribution network area based on the reactive power-voltage sensitivity matrix to obtain distribution network sub-regions, including:

[0056] The reactive power-voltage sensitivity matrix S is preprocessed to obtain the intermediate matrix X;

[0057] A fuzzy similarity matrix ED representing the electrical distance between nodes is established based on the intermediate matrix X;

[0058] Based on the fuzzy similarity matrix ED, the fuzzy equivalence matrix is ​​calculated using the transitive closure method;

[0059] Clustering is performed based on the fuzzy equivalence matrix and a preset membership threshold to divide the power distribution network area and obtain the power distribution network sub-regions.

[0060] Preferably, the distribution network area division unit preprocesses the reactive power-voltage sensitivity matrix S to obtain matrix X, including:

[0061] Perform standard deviation transformation and range transformation on any element in the reactive power-voltage sensitivity matrix S in sequence to obtain matrix X;

[0062] Where any element x in matrix X ij for:

[0063]

[0064]

[0065]

[0066]

[0067] Where i and j correspond to nodes i and j in the distribution network, and n is the number of nodes; W is the mean of the j-th column elements of the reactive power-voltage sensitivity matrix S. j Let S be the standard deviation of the j-th column element of the reactive power-voltage sensitivity matrix S.

[0068] Preferably, in the distribution network area division unit, any element ED in the fuzzy similarity matrix ED ij for:

[0069]

[0070] Among them, ED ij x represents the electrical distance between node i and node j; i and j correspond to nodes i and j in the distribution network, and n is the number of nodes; ik Let x be the element in the i-th row and k-th column of matrix X; jk Let X be the element in the j-th row and k-th column of matrix X.

[0071] Preferably, the power distribution network area division unit calculates the fuzzy equivalence matrix using the transitive closure method based on the fuzzy similarity matrix ED, including:

[0072] Calculate the square of the fuzzy similarity matrix ED representing the electrical distance, using ED (1) This indicates that the judgment of ED (1) Is it equal to ED? If ED (1) ==ED, then ED (1) This is the fuzzy equivalence matrix; conversely, if ED (1) ≠ED, then ED (1) Squared, denoted as ED (2) Continue to determine ED (2) Is it equal to ED? (1) Until the sth time ED (s) ==ED (s-1) At that time, determine ED (s) The fuzzy equivalence matrix is ​​defined as follows;

[0073] in,

[0074]

[0075] Among them, ED ij ED represents the electrical distance between node i and node j. ik ED represents the electrical distance between node i and node k. kj Let n be the electrical distance between node k and node j; n is the number of nodes, ∨ represents taking the larger value, and ∧ represents taking the smaller value.

[0076] Preferably, the power distribution network area division unit performs clustering based on the fuzzy equivalence matrix and a preset membership threshold to divide the power distribution network area and obtain power distribution network sub-regions, including:

[0077] For any element in the fuzzy equivalence matrix, if the value of any element is greater than or equal to a preset membership threshold, then the value of the element is updated to the first preset value; if the value of any element is less than the preset membership threshold, then the value of the element is updated to the second preset value.

[0078] In the updated fuzzy equivalence matrix, nodes corresponding to columns with the same matrix elements are grouped into one category, and the distribution network sub-region is determined based on the grouped nodes.

[0079] Preferably, in the operating state determination unit, the operating state of the distribution network is determined based on the current operating voltage of each node and preset voltage thresholds for different operating states, including:

[0080] Based on the current operating voltage of each node and the preset voltage threshold for different operating states, the operating state of each node is determined, and the operating state with the highest priority among all nodes is determined as the operating state of the distribution network.

[0081] The priority of the operating status, from highest to lowest, is as follows: emergency status, recovery status, abnormal status, alert status, and normal status.

[0082] Wherein, if the current operating voltage of any node satisfies U S >U1 or U S If <U8, then any node is determined to be in an emergency state;

[0083] If the current operating voltage of any node satisfies U1≥U S >U2 or U7>U S If ≥U8, then any node is determined to be in a recovery state;

[0084] If the current operating voltage of any node satisfies U2≥U S >U3 or U6>U S If ≥U7, then any node is determined to be in an abnormal state;

[0085] If the current operating voltage of any node satisfies U3≥U S >U4 or U5 >U S If ≥U6, then any node is determined to be in a state of alert.

[0086] If the current operating voltage of any node satisfies U1≥U S If U ≥ 5, then any node is determined to be in a normal state; U S The current operating voltage is represented by U1, U2, U3, U4, U5, U6, U7, and U8, which are respectively the first preset voltage threshold, the second preset voltage threshold, the third preset voltage threshold, the fourth preset voltage threshold, the fifth preset voltage threshold, the sixth preset voltage threshold, the seventh preset voltage threshold, and the eighth preset voltage threshold.

[0087] Preferably, the control unit, wherein the slave station determines the control mode according to the operating status of the distribution network, and restores the distribution network to normal operation based on the optimization scheme corresponding to the control mode, includes:

[0088] After the master station monitors the distribution network status, the slave station determines the control mode based on the distribution network's operating status and initiates an optimization scheme according to the control mode. Within the distribution network sub-region, the distribution network is restored to normal operation by optimizing the output of distributed power sources and / or controlling load power. If the regulation within the sub-region cannot meet the distribution network's operating requirements, the master station regulates the distributed power sources in adjacent sub-regions to improve the power flow level of the distribution network, thereby restoring the distribution network to normal operation.

[0089] According to another invention of the present invention, a master-slave self-healing control system based on power distribution network condition assessment is provided, the system comprising: a master control device and a slave control device; wherein,

[0090] The main control equipment is used to enable the master station to acquire the reactive power-voltage sensitivity matrix of the distribution network, divide the distribution network area based on the reactive power-voltage sensitivity matrix, and acquire the distribution network sub-regions; it is used to determine the central bus in each distribution network sub-region, perform power flow calculation with the central bus as the reference voltage, and determine the current operating voltage of each node in each distribution network sub-region; it is used to determine the operating status of the distribution network based on the current operating voltage of each node and preset voltage thresholds for different operating states.

[0091] The slave control device is used to enable the slave station to determine the control mode according to the operating status of the distribution network, so as to restore the distribution network to normal operation based on the optimization scheme corresponding to the control mode.

[0092] This invention provides a master-slave self-healing control method and system based on distribution network state assessment, comprising: a master station acquiring the reactive power-voltage sensitivity matrix of the distribution network; dividing the distribution network area based on the reactive power-voltage sensitivity matrix to obtain sub-regions of the distribution network; determining the central bus in each sub-region of the distribution network; performing power flow calculation using the central bus as a reference voltage to determine the current operating voltage of each node in each sub-region of the distribution network; determining the operating state of the distribution network based on the current operating voltage of each node and preset voltage thresholds for different operating states; and a slave station determining a control mode based on the operating state of the distribution network, and corresponding to the control mode. The optimization scheme restores the distribution network to normal operation. Based on the reactive power-voltage sensitivity matrix, this invention derives the fuzzy electrical distance of the distribution network and then divides the control zones. The voltage deviation of the distribution network system nodes is used as the basis for classifying the distribution network state to realize the assessment of the distribution network operation state. Under different operation states, the corresponding control modes of the slave controller are triggered, and the corresponding optimization schemes are started. If the regulation in the region cannot meet the self-healing requirements, the master controller coordinates the adjacent regions to participate in self-healing optimization. Through the coordinated control of distributed power sources and loads in the sub-region, the distribution network is regulated to restore to normal operation. By coordinating the control of slave stations and master stations, the function of optimizing the operation of the distribution network is achieved. Attached Figure Description

[0093] Exemplary embodiments of the present invention can be more fully understood by referring to the following figures:

[0094] Figure 1 This is a flowchart of a master-slave self-healing control method 100 based on power distribution network condition assessment according to an embodiment of the present invention;

[0095] Figure 2 This is a flowchart illustrating the process of obtaining a fuzzy equivalence matrix according to an embodiment of the present invention;

[0096] Figure 3 This is a diagram of a master-slave self-healing control architecture based on power distribution network condition assessment according to an embodiment of the present invention;

[0097] Figure 4 This is a schematic diagram of the self-healing control state transition and control method according to an embodiment of the present invention;

[0098] Figure 5 This is a schematic diagram of voltage distribution and self-healing control state according to an embodiment of the present invention;

[0099] Figure 6 This is a flowchart of a self-healing control based on node voltage according to an embodiment of the present invention.

[0100] Figure 7 This is a schematic diagram of an IEEE 33-node distribution network with distributed power sources according to an embodiment of the present invention.

[0101] Figure 8 This is a schematic diagram of the fuzzy electrical distance between nodes according to an embodiment of the present invention;

[0102] Figure 9 This is a schematic diagram of the IEEE 33-node distribution network zoning results according to an embodiment of the present invention;

[0103] Figure 10 This is a schematic diagram of the structure of a master-slave self-healing control system 1000 based on power distribution network condition assessment according to an embodiment of the present invention.

[0104] Figure 11 This is a schematic diagram of the structure of a master-slave self-healing control system 1100 based on power distribution network condition assessment according to an embodiment of the present invention. Detailed Implementation

[0105] 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.

[0106] 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.

[0107] Figure 1 This is a flowchart of a master-slave self-healing control method 100 based on distribution network condition assessment according to an embodiment of the present invention. Figure 1As shown, the master-slave self-healing control method based on distribution network state assessment provided by this invention derives the fuzzy electrical distance of the distribution network based on the reactive power-voltage sensitivity matrix, and then divides the control zones; it uses the voltage deviation of the distribution network system nodes as the basis for dividing the distribution network state to realize the assessment of the distribution network operating state; it triggers the corresponding control mode of the slave controller under different operating states and starts the corresponding optimization scheme; if the regulation in the region cannot meet the self-healing requirements, the master controller coordinates the adjacent regions to participate in self-healing optimization, and adjusts the distribution network to restore it to normal operating state through the coordinated control of distributed power sources and loads in the sub-region; it achieves the function of optimizing the operation of the distribution network by coordinating the control of the slave station and the master station. The master-slave self-healing control method 100 based on distribution network state assessment provided by this invention starts from step 101. In step 101, the master station obtains the reactive power-voltage sensitivity matrix of the distribution network, divides the distribution network region based on the reactive power-voltage sensitivity matrix, and obtains the distribution network sub-regions.

[0108] Preferably, the reactive power-voltage sensitivity matrix includes:

[0109] S=(L-MH -1 N) -1 ,

[0110] Where S is the reactive power-voltage sensitivity matrix; For ΔP with respect to δ T The partial derivative; For △P to U T The partial derivative; For △Q with respect to δ T The partial derivative; For △Q to U T The partial derivatives of ; P is the node active power; Q is the node reactive power; δ is the node voltage phase angle; U is the node voltage magnitude; T is the matrix transpose sign; ΔP is the change in P; ΔQ is the change in Q.

[0111] Preferably, the process of dividing the distribution network area based on the reactive power-voltage sensitivity matrix to obtain sub-regions of the distribution network includes:

[0112] The reactive power-voltage sensitivity matrix S is preprocessed to obtain the intermediate matrix X;

[0113] A fuzzy similarity matrix ED representing the electrical distance between nodes is established based on the intermediate matrix X;

[0114] Based on the fuzzy similarity matrix ED, the fuzzy equivalence matrix is ​​calculated using the transitive closure method;

[0115] Clustering is performed based on the fuzzy equivalence matrix and a preset membership threshold to divide the power distribution network area and obtain the power distribution network sub-regions.

[0116] Preferably, the preprocessing of the reactive power-voltage sensitivity matrix S to obtain matrix X includes:

[0117] Perform standard deviation transformation and range transformation on any element in the reactive power-voltage sensitivity matrix S in sequence to obtain matrix X;

[0118] Where any element x in matrix X ij for:

[0119]

[0120]

[0121]

[0122]

[0123] Where i and j correspond to nodes i and j in the distribution network, and n is the number of nodes; W is the mean of the j-th column elements of the reactive power-voltage sensitivity matrix S. j Let S be the standard deviation of the j-th column element of the reactive power-voltage sensitivity matrix S.

[0124] Preferably, any element ED in the fuzzy similarity matrix ED ij for:

[0125]

[0126] Among them, ED ij x represents the electrical distance between node i and node j; i and j correspond to nodes i and j in the distribution network, and n is the number of nodes; ik Let x be the element in the i-th row and k-th column of matrix X; jk Let X be the element in the j-th row and k-th column of matrix X.

[0127] Preferably, the step of calculating the fuzzy equivalence matrix using the transitive closure method based on the fuzzy similarity matrix ED includes:

[0128] Calculate the square of the fuzzy similarity matrix ED representing the electrical distance, using ED (1) This indicates that the judgment of ED (1) Is it equal to ED? If ED (1) ==ED, then ED (1) This is the fuzzy equivalence matrix; conversely, if ED (1) ≠ED, then ED (1) Squared, denoted as ED (2) Continue to determine ED (2) Is it equal to ED? (1)Until the sth time ED (s) ==ED (s-1) At that time, determine ED (s) The fuzzy equivalence matrix is ​​defined as follows;

[0129] in,

[0130]

[0131] Among them, ED ij ED represents the electrical distance between node i and node j. ik ED represents the electrical distance between node i and node k. kj Let n be the electrical distance between node k and node j; n is the number of nodes, ∨ represents taking the larger value, and ∧ represents taking the smaller value.

[0132] Preferably, the step of performing clustering based on the fuzzy equivalence matrix and a preset membership threshold to divide the power distribution network area and obtain power distribution network sub-regions includes:

[0133] For any element in the fuzzy equivalence matrix, if the value of any element is greater than or equal to a preset membership threshold, then the value of the element is updated to the first preset value; if the value of any element is less than the preset membership threshold, then the value of the element is updated to the second preset value.

[0134] In the updated fuzzy equivalence matrix, nodes corresponding to columns with the same matrix elements are grouped into one category, and the distribution network sub-region is determined based on the grouped nodes.

[0135] In an embodiment of the present invention, the distribution network area is divided based on fuzzy electrical distance.

[0136] Specifically, including:

[0137] 1) Fuzzy electrical distance in distribution networks based on reactive power-voltage sensitivity

[0138] Among them, electrical distance is used to measure the degree of correlation between nodes, and the idea of ​​fuzzy clustering is applied to the distribution network partitioning, thereby transforming the distribution network partitioning problem into a node fuzzy clustering problem.

[0139] From the Jacobi equation of the system's power flow, we obtain:

[0140]

[0141] In the formula: This is the trendy Yake segment matrix; For ΔP with respect to δ T The partial derivative; For ΔP with respect to U T The partial derivative; For ΔQ with respect to δT The partial derivative; For ΔQ with respect to U T The partial derivative of .

[0142] Ignoring the effect of active power disturbances on voltage, i.e., setting ΔP = 0, we can obtain:

[0143] ΔQ=(L-MH -1 N)ΔU (2)

[0144] In the formula, S=(L-MH) -1 N) -1 This is the reactive power-voltage sensitivity matrix.

[0145] Fuzzy clustering is applied to electrical distance to derive fuzzy electrical distance, as follows:

[0146] a) Based on fuzzy clustering analysis, matrix S is first transformed by standard deviation to obtain matrix α:

[0147]

[0148] In the formula, i and j correspond to nodes i and j in the distribution network, and n is the number of nodes. Let be the mean of the elements in the j-th column of matrix S. This represents the standard deviation of the elements in the j-th column.

[0149] Matrix element α ij The physical meaning of α is the voltage influence capability of node i with reference to the average voltage influence capability of all nodes when the reactive power of node j changes. The mean of each variable in α is 0, the standard deviation is 1, and it is dimensionless, but the values ​​are obviously not necessarily in the interval [0,1].

[0150] b) To make the elements in α comparable, a range transformation is performed to obtain matrix X:

[0151]

[0152] At this time x ij ∈[0,1]. If each PQ node is considered as a one-dimensional coordinate system, then element x ij This represents the standard coordinates of the spatial point j corresponding to node j mapped to the spatial point i corresponding to node i, i.e., the per-unit value.

[0153] c) Establish a fuzzy similarity matrix, i.e., define the electrical distance. The most commonly used Euclidean distance method is chosen to represent the distance between spatial points, i.e., the electrical distance between nodes:

[0154]

[0155] In the formula, EDij This can represent the electrical distance between node i and node j, ED ij The larger the value, the tighter the electrical connection between node i and node j.

[0156] d) The distance ED between spatial points i and j ij Find the fuzzy equivalence matrix.

[0157] In this invention, the transitive closure method is used to obtain the fuzzy equivalence matrix. The formula for calculating the square of the fuzzy similarity matrix ED, representing the electrical distance, is as follows:

[0158]

[0159] Among them, ED ij ED represents the electrical distance between node i and node j. ik ED represents the electrical distance between node i and node k. kj Let n be the electrical distance between node k and node j; n is the number of nodes, ∨ represents taking the larger value, and ∧ represents taking the smaller value.

[0160] like Figure 2 As shown, first, the fuzzy similarity matrix ED, representing the electrical distance, is squared, and then... (1) This indicates that the judgment of ED (1) Is it equal to ED? If ED (1) ==ED, then ED (1) This is a fuzzy equivalent matrix, meaning that the matrix ED can be... (1) The values ​​appearing in the data are arranged from largest to smallest. Each time a value is selected, a dynamic clustering graph is formed, and then a reasonable clustering result is chosen. If ED (1) ≠ED, then ED (1) Squared, denoted as ED (2) Continue to determine ED (2) Is it equal to ED? (1) Until the i-th time, ED (i) ==ED (i-1) , take ED (i) This is a fuzzy equivalence matrix. Ultimately, the fuzzy equivalence matrix is ​​still represented by ED. Figure 1 The flowchart illustrates the process of obtaining the fuzzy equivalence matrix. From the reflexivity and symmetry of the fuzzy equivalence matrix, we know that ED... ii =1,ED ij =ED ji (i,j,k∈[1,M]).

[0161] 2) Distribution network sub-region division based on dynamic clustering

[0162] In the fuzzy equivalence matrix, each element's value corresponds to the membership degree λ. Because ED ijSince λ∈(0,1], we set the membership degree λ∈(0,1). Different membership degrees will correspond to different clustering depths, and distribution network nodes may have different affiliations.

[0163] During clustering, elements in the fuzzy similarity matrix with membership degrees greater than a preset threshold λ are assigned the first preset value of 1, and elements with membership degrees less than λ are assigned the second preset value of 0, thus transforming the fuzzy similarity matrix into a 0-1 matrix. At this point, nodes corresponding to columns with the same matrix elements are grouped into one class, forming the corresponding distribution network sub-region.

[0164] In step 102, the central bus in each sub-region of the distribution network is determined, and power flow calculation is performed using the central bus as the reference voltage to determine the current operating voltage of each node in each sub-region of the distribution network.

[0165] In step 103, the operating status of the distribution network is determined based on the current operating voltage of each node and the preset voltage threshold for different operating states.

[0166] Preferably, determining the operating state of the distribution network based on the current operating voltage of each node and preset voltage thresholds for different operating states includes:

[0167] Based on the current operating voltage of each node and the preset voltage threshold for different operating states, the operating state of each node is determined, and the operating state with the highest priority among all nodes is determined as the operating state of the distribution network.

[0168] The priority of the operating status, from highest to lowest, is as follows: emergency status, recovery status, abnormal status, alert status, and normal status.

[0169] Wherein, if the current operating voltage of any node satisfies U S >U1 or U S If <U8, then any node is determined to be in an emergency state;

[0170] If the current operating voltage of any node satisfies U1≥U S >U2 or U7>U S If ≥U8, then any node is determined to be in a recovery state;

[0171] If the current operating voltage of any node satisfies U2≥U S >U3 or U6>U S If ≥U7, then any node is determined to be in an abnormal state;

[0172] If the current operating voltage of any node satisfies U3≥U S >U4 or U5 >U S If ≥U6, then any node is determined to be in a state of alert.

[0173] If the current operating voltage of any node satisfies U1≥U S If U ≥ 5, then any node is determined to be in a normal state; U S The current operating voltage is represented by U1, U2, U3, U4, U5, U6, U7, and U8, which are respectively the first preset voltage threshold, the second preset voltage threshold, the third preset voltage threshold, the fourth preset voltage threshold, the fifth preset voltage threshold, the sixth preset voltage threshold, the seventh preset voltage threshold, and the eighth preset voltage threshold.

[0174] In step 104, the slave station determines the control mode based on the operating status of the distribution network, and restores the distribution network to normal operation based on the optimization scheme corresponding to the control mode.

[0175] Preferably, the slave station determines the control mode based on the operating status of the distribution network, and restores the distribution network to normal operation based on the optimization scheme corresponding to the control mode, including:

[0176] After the master station monitors the distribution network status, the slave station determines the control mode based on the distribution network's operating status and initiates an optimization scheme according to the control mode. Within the distribution network sub-region, the distribution network is restored to normal operation by optimizing the output of distributed power sources and / or controlling load power. If the regulation within the sub-region cannot meet the distribution network's operating requirements, the master station regulates the distributed power sources in adjacent sub-regions to improve the power flow level of the distribution network, thereby restoring the distribution network to normal operation.

[0177] In this embodiment of the invention, the distribution network adopts a master-slave optimized self-healing control architecture. Each sub-region of the distribution network is equipped with adjustable distributed power sources and controllable loads, possessing a certain degree of intra-regional regulation capability. After monitoring the distribution network status, the slave station initiates the corresponding control scheme, optimizing the output of distributed power sources and controlling load power to restore the distribution network to normal operation as much as possible. If intra-regional regulation cannot meet the distribution network's operational needs, the master station regulates distributed power sources in adjacent regions to improve the power flow level of the distribution network, ultimately restoring it to normal operation. The master-slave self-healing control architecture based on distribution network status assessment is as follows: Figure 3 As shown.

[0178] In this invention, the slave control strategy based on regional distribution network status assessment includes: Based on all collected information about the distribution network, the current operating status of the distribution network is evaluated, and its future operating status is predicted. This helps to promptly identify and adjust the operation of the distribution network, effectively monitor and identify weak links, and comprehensively ensure the safe and stable operation of the distribution network. The operating status of the distribution network is divided into: normal operation status, alert operation status, abnormal operation status, emergency operation status, and recovery operation status.

[0179] like Figure 5 As shown, the self-healing control measures corresponding to the above five states are: optimization control, preventive control, corrective control, emergency control, and recovery control. Based on the different operating states of the system, corresponding control schemes are executed to promote the evolution of the distribution network state in a positive direction, ultimately ensuring that the distribution network operates in an economical state and maintaining the safe, economical, stable, and continuous operation of the power grid.

[0180] In this invention, voltage is used as the primary reference quantity for the distribution network's operating state to classify the network's operating state, and corresponding control measures are taken according to different operating states. The voltage range and the corresponding self-healing control mode are as follows: Figure 5 As shown, U1, U2, U3, U4, U5, U6, U7, and U8 are eight voltage boundary points that decrease sequentially.

[0181] In this invention, the distribution network system frequency or node voltage is used as the basis for classifying the distribution network state, thereby enabling the assessment of the distribution network's operating state. A self-healing control process for the distribution network under different states is established, and through coordinated control of distributed power sources and loads within sub-regions, the distribution network's operating state can be switched between various states such as economic, normal, and alert.

[0182] The self-healing control process based on node voltage is as follows: Figure 6 As shown, it includes:

[0183] Step 1: Based on the distribution network zoning control, the distribution network is divided into various sub-regions using the fuzzy clustering region method.

[0184] Step 2: Select the central bus of each sub-region, and perform power flow calculation based on the voltage of the central bus to determine the current operating voltage Us of other buses (nodes) in the partition.

[0185] Step 3: Compare the current operating voltage Us with the preset voltage thresholds U1-U8 for different operating states to determine the state of each node, determine the operating state of the distribution network based on the state of each node, and select the appropriate control mode to start based on the operating state.

[0186] Specifically, if the distribution network is in an emergency state, emergency control is activated; if the distribution network is in a recovery state, recovery control is activated; if the distribution network is in an abnormal state, correction control is activated; if the distribution network is in a warning state, preventive control is activated; and if the distribution network is in a normal state, optimization control is activated.

[0187] Specifically, based on the current operating voltage of each node and preset voltage thresholds for different operating states, the operating state of each node is determined, and the highest priority operating state among all nodes is determined as the operating state of the distribution network. The priority of operating states, from highest to lowest, is: emergency state, recovery state, abnormal state, alert state, and normal state. If the current operating voltage of any node satisfies U... S >U1 or U S If U1 < U8, then any node is determined to be in an emergency state; if the current operating voltage of any node satisfies U1 ≥ U8, then the node is in an emergency state. S >U2 or U7>U S If U2 ≥ U8, then any node is determined to be in a recovery state; if the current operating voltage of any node satisfies U2 ≥ U8, then the node is determined to be in a recovery state. S >U3 or U6>U S If U3 ≥ U7, then any node is determined to be in an abnormal state; if the current operating voltage of any node satisfies U3 ≥ U7, then the node is considered to be in an abnormal state. S >U4 or U5 >U S If U1 ≥ U6, then any node is determined to be in a state of alert; if the current operating voltage of any node satisfies U1 ≥ U6, then the node is in a state of alert. S If ≥U5, then any node is determined to be in a normal state; U1, U2, U3, U4, U5, U6, U7 and U8 are the first preset voltage threshold, the second preset voltage threshold, the third preset voltage threshold, the fourth preset voltage threshold, the fifth preset voltage threshold, the sixth preset voltage threshold, the seventh preset voltage threshold and the eighth preset voltage threshold, respectively.

[0188] Furthermore, in this invention, the control parameters for voltage deviation are categorized according to different voltage levels as follows:

[0189] 1) Allowable deviation of voltage supply at the user end: The sum of the absolute values ​​of the positive and negative deviations of voltages of 35kV and above shall not exceed 10% of the rated voltage; the allowable deviation of phase voltages of 10kV and below shall be ±7% of the rated voltage.

[0190] 2) Allowable deviation of bus voltage in power plants and substations: When the 35kV-110kV bus of the substation is operating normally, the allowable voltage deviation is -3% to +7% of the rated voltage of the system; during an accident, it is ±10% of the rated voltage.

[0191] The method of this invention uses fuzzy clustering to divide the distribution network into multiple sub-regions, uses node voltage deviation as a state variable to determine the operating state of the distribution network, and triggers corresponding control modes of the slave controllers under different operating states, initiating corresponding optimization schemes. If the regulation within a region cannot meet the self-healing requirements, the master controller coordinates adjacent regions to participate in self-healing optimization, regulating the distribution network to restore it to normal operating state.

[0192] The following specific examples illustrate the embodiments of the present invention.

[0193] (1) Distribution network area delineation of IEEE 33-node network based on fuzzy electrical distance

[0194] Taking the IEEE 33-node distribution network as an example, the topology of the IEEE 33-node distribution network including distributed generation is as follows: Figure 7 As shown. The branch impedance and node load data for IEEE 33 nodes are as follows:

[0195] Table 1 IEEE 33-node parameters

[0196]

[0197]

[0198] Among them, PQ-type distributed power sources are set at nodes 4, 13, 20 and 28. The input active power of the distributed power source is 300kW and the input reactive power is 100kVar.

[0199] The power distribution network system is divided into zones. The fuzzy electrical distances between nodes are as follows: Figure 8 As shown.

[0200] The number of partitions is closely related to the selection of membership degree. The results of the region division under the three membership degrees are shown in Table 2.

[0201] Table 2 Partitioning results under different membership degrees

[0202]

[0203] Comparing the partitioning results under the three membership degrees, since the number of partitions should not be too large, and referring to the actual wiring situation, the partition with a membership degree of 0.83 is taken as the sub-region of distribution network partition control.

[0204] To ensure distributed power is available in each region, the partitioning results are adjusted. Nodes 28, 29, and 30 are merged with nodes 30, 31, 32, and 33 into a single partition. The final partitioning result is as follows: Figure 9 As shown. The IEEE 33-node system is divided into three regions: Region 1 contains nodes 1-12 and 19-27; Region 2 contains nodes 13-18; and Region 3 contains nodes 28-33.

[0205] When using the above-mentioned partitioning, each region has distributed power sources as supplementary power, which can meet the backup capacity required for self-healing control. Furthermore, the number of nodes within a partition is reduced compared to the total number of nodes in the entire distribution network, allowing for more effective control during subsequent self-healing processes. In slave control mode, the power sources within a partition prioritize meeting the self-healing needs of that partition. If slave control cannot effectively adjust the operating status of a sub-region, master control is activated to coordinate adjacent partitions to achieve optimized self-healing control.

[0206] (2) Distribution network preventive control under the alarm state based on slave control

[0207] Under normal circumstances, the per-unit voltage values ​​of all nodes in the IEEE 33-node distribution network are within the range of 0.93-1.07, and the system is in normal operation at this time.

[0208] If the load at node 18 suddenly increases, with active load increasing by 70kW and reactive load increasing by 80kVar, power flow calculations show that the per-unit voltage at node 18 exceeds the limit. Table 3 lists the voltage changes at node 18.

[0209] Table 3. Per-unit voltage changes at node 18

[0210] 18 0.9403 0.9293

[0211] As shown in Table 3, node 18 is in a state of alert operation, while the other nodes are still within the normal range and are therefore not listed. Preventive control will be activated at this time to restore the voltage of node 18 to normal.

[0212] At this point, it is necessary to adjust the backup power of the nearest electrically connected power source based on the line transmission capacity to restore the voltage of node 18 to normal as quickly as possible. The nearest electrically connected power source to node 18 is node 13. By adjusting the output of this power source, the voltage of node 18 is restored. During the simulation, the output of the distributed power source at node 13 is increased, with active power increasing by 30kW and reactive power increasing by 30kVar. According to the power flow calculation results after control, the per-unit voltage of node 18 is 0.9318, returning to the normal level.

[0213] Table 4 Comparison of voltage before and after control and adjustment of power output

[0214] 0.9403 0.9318 30kW 30kVar

[0215] Following this, the distribution network continues to monitor the node and checks whether nearby power nodes have sufficient reserve capacity to cope with the next possible alert state. If the reserve capacity is insufficient, load shifting is used to prepare for the next alert state in advance, meaning that distributed power sources in the area should have sufficient reserve power.

[0216] At this point, the prevention and control measures were completed, and the voltage level remained at a normal level after the sudden increase in load at node 18, thus the alert status was lifted.

[0217] (3) Distribution network correction control under abnormal operating conditions based on master-slave cooperation

[0218] Suppose that at a certain moment, node 18 suddenly receives an unknown load, with active power increasing by 160kW and reactive power increasing by 160kVar. At this time, power flow calculations show that nodes 14, 15, 16, and 17, which are close to node 18, are also affected, resulting in abnormal node voltages, as shown in Table 5.

[0219] Table 5 Per-unit values ​​of voltage at abnormal nodes

[0220] 14 0.9472 0.9296 15 0.9450 0.9270 16 0.9436 0.9242 17 0.9423 0.9189 18 0.9403 0.9169

[0221] The results show that the voltages at nodes 14, 15, and 16 are 7% below the rated voltage, while the voltages at nodes 17 and 18 are 8% below the rated voltage but above 9%. Based on the abnormal voltages at these nodes, it is determined that the system is in an abnormal operating state and corrective control should be initiated.

[0222] First, correction control within the partition was implemented, calling upon the standby distributed power source node 13 to increase power output, increasing active power by 100kW and reactive power by 100kVar. Power flow calculations showed that the node voltages of nodes 14, 15, and 16 recovered to above 0.93, while the voltages of nodes 17 and 18 still did not recover to above 0.93, as shown in Table 6. At this point, the correction control within the partition could not achieve its goal of restoring normal operation.

[0223] Table 6. Changes in node voltage before and after control.

[0224] 14 0.9296 0.9381 15 0.9270 0.9355 16 0.9242 0.9327 17 0.9189 0.9275 18 0.9169 0.9255

[0225] In this case, the method of disconnecting a portion of the adjacent load is used to restore the node voltage to normal. During the simulation, a portion of the load at node 17 was disconnected, resulting in a 50kW reduction in active power and a 15kVar reduction in reactive power. The changes in node voltage are shown in Table 7.

[0226] Table 7. Changes in node voltage before and after control.

[0227] 17 0.9275 0.9321 18 0.9255 0.9300

[0228] At this point, the voltage at node 17 becomes 0.9321, and the voltage at node 18 becomes 0.9300, reaching normal levels, indicating that the correction control has been effectively completed. Similarly, the distribution network should be optimized immediately, adjusting the power distribution to ensure sufficient reserve capacity within the zones to cope with any potential next abnormal operating conditions.

[0229] Figure 10 This is a schematic diagram of the structure of a master-slave self-healing control system 1000 based on power distribution network condition assessment according to an embodiment of the present invention. Figure 10 As shown, the master-slave self-healing control system 1000 based on power distribution network status assessment provided by the embodiments of the present invention includes: a power distribution network area division unit 1001, an operating voltage determination unit 1002, an operating status determination unit 1003, and a control unit 1004.

[0230] Preferably, the power distribution network area division unit 1001 is used to enable the master station to obtain the reactive power-voltage sensitivity matrix of the power distribution network, and to divide the power distribution network area based on the reactive power-voltage sensitivity matrix to obtain the power distribution network sub-region.

[0231] Preferably, in the distribution network area division unit 1001, the reactive power-voltage sensitivity matrix includes:

[0232] S=(L-MH -1 N) -1 ,

[0233] Where S is the reactive power-voltage sensitivity matrix; For ΔP with respect to δ T The partial derivative; For △P to U T The partial derivative; For △Q with respect to δ T The partial derivative; For △Q to U T The partial derivatives of ; P is the node active power; Q is the node reactive power; δ is the node voltage phase angle; U is the node voltage magnitude; T is the matrix transpose sign; ΔP is the change in P; ΔQ is the change in Q.

[0234] Preferably, the distribution network area division unit 1001 divides the distribution network area based on the reactive power-voltage sensitivity matrix to obtain distribution network sub-regions, including:

[0235] The reactive power-voltage sensitivity matrix S is preprocessed to obtain the intermediate matrix X;

[0236] A fuzzy similarity matrix ED representing the electrical distance between nodes is established based on the intermediate matrix X;

[0237] Based on the fuzzy similarity matrix ED, the fuzzy equivalence matrix is ​​calculated using the transitive closure method;

[0238] Clustering is performed based on the fuzzy equivalence matrix and a preset membership threshold to divide the power distribution network area and obtain the power distribution network sub-regions.

[0239] Preferably, the power distribution network area division unit 1001 preprocesses the reactive power-voltage sensitivity matrix S to obtain matrix X, including:

[0240] Perform standard deviation transformation and range transformation on any element in the reactive power-voltage sensitivity matrix S in sequence to obtain matrix X;

[0241] Where any element x in matrix X ij for:

[0242]

[0243]

[0244]

[0245]

[0246] Where i and j correspond to nodes i and j in the distribution network, and n is the number of nodes; W is the mean of the j-th column elements of the reactive power-voltage sensitivity matrix S. j Let S be the standard deviation of the j-th column element of the reactive power-voltage sensitivity matrix S.

[0247] Preferably, in the power distribution network area division unit 1001, any element ED in the fuzzy similarity matrix ED ij for:

[0248]

[0249] Among them, ED ij x represents the electrical distance between node i and node j; i and j correspond to nodes i and j in the distribution network, and n is the number of nodes; ik Let x be the element in the i-th row and k-th column of matrix X; jk Let X be the element in the j-th row and k-th column of matrix X.

[0250] Preferably, the power distribution network area division unit 1001 calculates the fuzzy equivalence matrix using the transitive closure method based on the fuzzy similarity matrix ED, including:

[0251] Calculate the square of the fuzzy similarity matrix ED representing the electrical distance, using ED (1) This indicates that the judgment of ED (1) Is it equal to ED? If ED (1) ==ED, then ED (1) This is the fuzzy equivalence matrix; conversely, if ED (1) ≠ED, then ED (1) Squared, denoted as ED (2) Continue to determine ED(2) Is it equal to ED? (1) Until the sth time ED (s) ==ED (s-1) At that time, determine ED (s) The fuzzy equivalence matrix is ​​defined as follows;

[0252] in,

[0253]

[0254] Among them, ED ij ED represents the electrical distance between node i and node j. ik ED represents the electrical distance between node i and node k. kj Let n be the electrical distance between node k and node j; n is the number of nodes, ∨ represents taking the larger value, and ∧ represents taking the smaller value.

[0255] Preferably, the power distribution network area division unit 1001 performs clustering based on the fuzzy equivalence matrix and a preset membership threshold to divide the power distribution network area and obtain power distribution network sub-regions, including:

[0256] For any element in the fuzzy equivalence matrix, if the value of any element is greater than or equal to a preset membership threshold, then the value of the element is updated to the first preset value; if the value of any element is less than the preset membership threshold, then the value of the element is updated to the second preset value.

[0257] In the updated fuzzy equivalence matrix, nodes corresponding to columns with the same matrix elements are grouped into one category, and the distribution network sub-region is determined based on the grouped nodes.

[0258] Preferably, the operating voltage determination unit 1002 is used to determine the central bus in each distribution network sub-region, perform power flow calculations using the central bus as the reference voltage, and determine the current operating voltage of each node in each distribution network sub-region.

[0259] Preferably, the operating status determination unit 1003 is used to determine the operating status of the distribution network based on the current operating voltage of each node and the preset voltage threshold for different operating statuses.

[0260] Preferably, the operating state determination unit 1003 determines the operating state of the distribution network based on the current operating voltage of each node and preset voltage thresholds for different operating states, including:

[0261] Based on the current operating voltage of each node and the preset voltage threshold for different operating states, the operating state of each node is determined, and the operating state with the highest priority among all nodes is determined as the operating state of the distribution network.

[0262] The priority of the operating status, from highest to lowest, is as follows: emergency status, recovery status, abnormal status, alert status, and normal status.

[0263] Wherein, if the current operating voltage of any node satisfies U S >U1 or U S If <U8, then any node is determined to be in an emergency state;

[0264] If the current operating voltage of any node satisfies U1≥U S >U2 or U7>U S If ≥U8, then any node is determined to be in a recovery state;

[0265] If the current operating voltage of any node satisfies U2≥U S >U3 or U6>U S If ≥U7, then any node is determined to be in an abnormal state;

[0266] If the current operating voltage of any node satisfies U3≥U S >U4 or U5 >U S If ≥U6, then any node is determined to be in a state of alert.

[0267] If the current operating voltage of any node satisfies U1≥U S If U ≥ 5, then any node is determined to be in a normal state; U S The current operating voltage is represented by U1, U2, U3, U4, U5, U6, U7, and U8, which are respectively the first preset voltage threshold, the second preset voltage threshold, the third preset voltage threshold, the fourth preset voltage threshold, the fifth preset voltage threshold, the sixth preset voltage threshold, the seventh preset voltage threshold, and the eighth preset voltage threshold.

[0268] Preferably, the control unit 1004 is used to determine the control mode based on the operating status of the distribution network, so as to restore the distribution network to normal operation based on the optimization scheme corresponding to the control mode.

[0269] Preferably, the control unit 1004, wherein the slave station determines the control mode according to the operating status of the distribution network, and restores the distribution network to normal operation based on the optimization scheme corresponding to the control mode, including:

[0270] After the master station monitors the distribution network status, the slave station determines the control mode based on the distribution network's operating status and initiates an optimization scheme according to the control mode. Within the distribution network sub-region, the distribution network is restored to normal operation by optimizing the output of distributed power sources and / or controlling load power. If the regulation within the sub-region cannot meet the distribution network's operating requirements, the master station regulates the distributed power sources in adjacent sub-regions to improve the power flow level of the distribution network, thereby restoring the distribution network to normal operation.

[0271] The master-slave self-healing control system 1000 based on distribution network condition assessment in an embodiment of the present invention corresponds to the master-slave self-healing control method 100 based on distribution network condition assessment in another embodiment of the present invention, and will not be described again here.

[0272] Figure 11 This is a schematic diagram of the structure of a master-slave self-healing control system 1000 based on power distribution network condition assessment according to an embodiment of the present invention. Figure 11 As shown, an embodiment of the present invention provides a master-slave self-healing control system 1100 based on power distribution network status assessment, including: master control device 1101 and slave control device 1102.

[0273] Preferably, the main control device 1101 is used to enable the main station to acquire the reactive power-voltage sensitivity matrix of the distribution network, divide the distribution network area based on the reactive power-voltage sensitivity matrix, and acquire the distribution network sub-regions; it is used to determine the central bus in each distribution network sub-region, perform power flow calculation with the central bus as the reference voltage, and determine the current operating voltage of each node in each distribution network sub-region; it is used to determine the operating state of the distribution network based on the current operating voltage of each node and the preset voltage threshold for different operating states.

[0274] Preferably, the slave control device 1102 is used to enable the slave station to determine the control mode according to the operating status of the distribution network, so as to restore the distribution network to normal operation based on the optimization scheme corresponding to the control mode.

[0275] The master-slave self-healing control system 1100 based on distribution network condition assessment in an embodiment of the present invention corresponds to the master-slave self-healing control method 100 based on distribution network condition assessment in another embodiment of the present invention, and will not be described again here.

[0276] 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.

[0277] 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.

[0278] 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.

[0279] 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.

[0280] 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.

[0281] 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.

[0282] 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 master-slave self-healing control method based on distribution network condition assessment, characterized in that, The method includes: The main station obtains the reactive power-voltage sensitivity matrix of the distribution network, and divides the distribution network area based on the reactive power-voltage sensitivity matrix to obtain the distribution network sub-regions; The central busbar in each sub-region of the distribution network is determined, and the power flow calculation is performed using the voltage of the central busbar as the reference voltage to determine the current operating voltage of each node in each sub-region of the distribution network. The operating status of the distribution network is determined based on the current operating voltage of each node and the preset voltage thresholds for different operating states. The slave station determines the control mode based on the operating status of the distribution network, and restores the distribution network to normal operation based on the optimization scheme corresponding to the control mode.

2. The method according to claim 1, characterized in that, The reactive power-voltage sensitivity matrix includes: S = (L - MH -1 N) -1 , Where S is the reactive power-voltage sensitivity matrix; for P against δ T The partial derivative; For △P to U T The partial derivative; For △Q with respect to δ T The partial derivative; For △Q to U T The partial derivatives of ; P is the node active power; Q is the node reactive power; δ is the node voltage phase angle; U is the node voltage magnitude; T is the matrix transpose sign; ΔP is the change in P; ΔQ is the change in Q.

3. The method according to claim 1, characterized in that, The process of dividing the distribution network area based on the reactive power-voltage sensitivity matrix to obtain distribution network sub-regions includes: The reactive power-voltage sensitivity matrix S is preprocessed to obtain the intermediate matrix X; A fuzzy similarity matrix ED representing the electrical distance between nodes is established based on the intermediate matrix X; Based on the fuzzy similarity matrix ED, the fuzzy equivalence matrix is ​​calculated using the transitive closure method; Clustering is performed based on the fuzzy equivalence matrix and a preset membership threshold to divide the power distribution network area and obtain the power distribution network sub-regions.

4. The method according to claim 3, characterized in that, The preprocessing of the reactive power-voltage sensitivity matrix S to obtain matrix X includes: Perform standard deviation transformation and range transformation on any element in the reactive power-voltage sensitivity matrix S in sequence to obtain matrix X; where any element x in the matrix X ij is: ， ， ， ， Where i and j correspond to nodes i and j in the distribution network, and n is the number of nodes; Let be the mean of the elements in the j-th column of the reactive power-voltage sensitivity matrix S. The standard deviation of the j-th column element of the reactive power-voltage sensitivity matrix S; matrix element α ij The physical meaning is the voltage influence capability of node i when the reactive power of node j changes, with the average voltage influence capability of all nodes as the reference value; S ij Let be the element in the i-th row and j-th column of the reactive power-voltage sensitivity matrix S.

5. The method according to claim 3, characterized in that, Any element of the fuzzy similarity matrix ED ij for: ， Among them, ED ij x represents the electrical distance between node i and node j; i and j correspond to nodes i and j in the distribution network, and n is the number of nodes; ik Let x be the element in the i-th row and k-th column of matrix X; jk Let X be the element in the j-th row and k-th column of matrix X.

6. The method according to claim 3, characterized in that, The step of calculating the fuzzy equivalence matrix using the transitive closure method based on the fuzzy similarity matrix ED includes: Calculate the square of the fuzzy similarity matrix ED representing the electrical distance, using ED (1) This indicates that the judgment of ED (1) Is it equal to ED? If ED (1) =ED, then ED (1) This is the fuzzy equivalence matrix; conversely, if ED (1) ≠ED, then ED (1) Squared, denoted as ED (2) Continue to determine ED (2) Is it equal to ED? (1) Until the sth time ED (s) =ED (s-1) At that time, determine ED (s) The fuzzy equivalence matrix is ​​defined as follows; in, ， Among them, ED ij ED represents the electrical distance between node i and node j. ik ED represents the electrical distance between node i and node k. kj Let n be the electrical distance between node k and node j; n is the number of nodes. The representative takes the larger one. The smaller value represents the smaller one.

7. The method according to claim 3, characterized in that, The step of clustering based on the fuzzy equivalence matrix and a preset membership threshold to divide the power distribution network area and obtain power distribution network sub-regions includes: For any element in the fuzzy equivalence matrix, if the value of any element is greater than or equal to a preset membership threshold, then the value of any element is updated to the first preset value; if the value of any element is less than the preset membership threshold, then the value of any element is updated to the second preset value. In the updated fuzzy equivalence matrix, nodes corresponding to columns with the same matrix elements are grouped into one category, and the distribution network sub-region is determined based on the grouped nodes.

8. The method according to claim 1, characterized in that, The process of determining the operating status of the distribution network based on the current operating voltage of each node and preset voltage thresholds for different operating states includes: Based on the current operating voltage of each node and the preset voltage threshold for different operating states, the operating state of each node is determined, and the operating state with the highest priority among all nodes is determined as the operating state of the distribution network. The priority of the operating status, from highest to lowest, is as follows: emergency status, recovery status, abnormal status, alert status, and normal status. Wherein, if the current operating voltage of any node satisfies U S >U1 or U S If <U8, then any node is determined to be in an emergency state; If the current operating voltage of any node satisfies U1≥U S >U2 or U7>U S If ≥U8, then any node is determined to be in a recovery state; If the current operating voltage of any node satisfies U2≥U S >U3 or U6>U S If ≥U7, then any node is determined to be in an abnormal state; If the current operating voltage of any node satisfies U3≥U S >U4 or U5 >U S If ≥U6, then any node is determined to be in a state of alert. If the current operating voltage of any node satisfies U1≥U S If U ≥ 5, then any node is determined to be in a normal state; U S The current operating voltage is represented by U1, U2, U3, U4, U5, U6, U7, and U8, which are respectively the first preset voltage threshold, the second preset voltage threshold, the third preset voltage threshold, the fourth preset voltage threshold, the fifth preset voltage threshold, the sixth preset voltage threshold, the seventh preset voltage threshold, and the eighth preset voltage threshold.

9. The method according to claim 1, characterized in that, The slave station determines the control mode based on the operating status of the distribution network, and restores the distribution network to normal operation based on the optimization scheme corresponding to the control mode, including: After the master station monitors the distribution network status, the slave station determines the control mode based on the distribution network's operating status and initiates an optimization scheme according to the control mode. Within the distribution network sub-region, the distribution network is restored to normal operation by optimizing the output of distributed power sources and / or controlling load power. If the regulation within the sub-region cannot meet the distribution network's operating requirements, the master station regulates the distributed power sources in adjacent sub-regions to improve the power flow level of the distribution network, thereby restoring the distribution network to normal operation.

10. A master-slave self-healing control system based on power distribution network condition assessment, characterized in that, The system includes: The distribution network area division unit is used to enable the master station to obtain the reactive power-voltage sensitivity matrix of the distribution network, divide the distribution network area based on the reactive power-voltage sensitivity matrix, and obtain the distribution network sub-areas. The operating voltage determination unit is used to determine the central bus in each distribution network sub-region, and to perform power flow calculations based on the voltage of the central bus as the reference voltage to determine the current operating voltage of each node in each distribution network sub-region. The operation status determination unit is used to determine the operation status of the distribution network based on the current operating voltage of each node and the preset voltage threshold for different operation statuses. The control unit is used by the slave station to determine the control mode according to the operating status of the distribution network, so as to restore the distribution network to the normal operating state based on the optimization scheme corresponding to the control mode.

11. The system according to claim 10, characterized in that, In the distribution network area division unit, the reactive power-voltage sensitivity matrix includes: S=(L-MH -1 N) -1 , Where S is the reactive power-voltage sensitivity matrix; for P against δ T The partial derivative; For △P to U T The partial derivative; For △Q with respect to δ T The partial derivative; For △Q to U T The partial derivatives of ; P is the node active power; Q is the node reactive power; δ is the node voltage phase angle; U is the node voltage magnitude; T is the matrix transpose sign; ΔP is the change in P; ΔQ is the change in Q.

12. The system according to claim 10, characterized in that, The distribution network area division unit divides the distribution network area based on the reactive power-voltage sensitivity matrix to obtain distribution network sub-regions, including: The reactive power-voltage sensitivity matrix S is preprocessed to obtain the intermediate matrix X; A fuzzy similarity matrix ED representing the electrical distance between nodes is established based on the intermediate matrix X; Based on the fuzzy similarity matrix ED, the fuzzy equivalence matrix is ​​calculated using the transitive closure method; Clustering is performed based on the fuzzy equivalence matrix and a preset membership threshold to divide the power distribution network area and obtain the power distribution network sub-regions.

13. The system according to claim 12, characterized in that, The power distribution network area division unit preprocesses the reactive power-voltage sensitivity matrix S to obtain matrix X, including: Perform standard deviation transformation and range transformation on any element in the reactive power-voltage sensitivity matrix S in sequence to obtain matrix X; Where any element x in matrix X ij for: ， ， ， ， Where i and j correspond to nodes i and j in the distribution network, and n is the number of nodes; Let be the mean of the elements in the j-th column of the reactive power-voltage sensitivity matrix S. The standard deviation of the j-th column element of the reactive power-voltage sensitivity matrix S; matrix element α ij The physical meaning is the voltage influence capability of node i when the reactive power of node j changes, with the average voltage influence capability of all nodes as the reference value; S ij Let be the element in the i-th row and j-th column of the reactive power-voltage sensitivity matrix S.

14. The system according to claim 12, characterized in that, In the distribution network area division unit, any element ED in the fuzzy similarity matrix ED ij for: ， Among them, ED ij x represents the electrical distance between node i and node j; i and j correspond to nodes i and j in the distribution network, and n is the number of nodes; ik Let x be the element in the i-th row and k-th column of matrix X; jk Let X be the element in the j-th row and k-th column of matrix X.

15. The system according to claim 12, characterized in that, The power distribution network area division unit calculates the fuzzy equivalence matrix using the transitive closure method based on the fuzzy similarity matrix ED, including: Calculate the square of the fuzzy similarity matrix ED representing the electrical distance, using ED (1) This indicates that the judgment of ED (1) Is it equal to ED? If ED (1) =ED, then ED (1) This is the fuzzy equivalence matrix; conversely, if ED (1) ≠ED, then ED (1) Squared, denoted as ED (2) Continue to determine ED (2) Is it equal to ED? (1) Until the sth time ED (s) =ED (s-1) At that time, determine ED (s) The fuzzy equivalence matrix is ​​defined as follows; in, ， Among them, ED ij ED represents the electrical distance between node i and node j. ik ED represents the electrical distance between node i and node k. kj Let n be the electrical distance between node k and node j; n is the number of nodes. The representative takes the larger one. The smaller value represents the smaller one.

16. The system according to claim 12, characterized in that, The power distribution network area division unit performs clustering based on the fuzzy equivalence matrix and a preset membership threshold to divide the power distribution network area and obtain power distribution network sub-regions, including: For any element in the fuzzy equivalence matrix, if the value of any element is greater than or equal to a preset membership threshold, then the value of any element is updated to the first preset value; if the value of any element is less than the preset membership threshold, then the value of any element is updated to the second preset value. In the updated fuzzy equivalence matrix, nodes corresponding to columns with the same matrix elements are grouped into one category, and the distribution network sub-region is determined based on the grouped nodes.

17. The system according to claim 10, characterized in that, In the operating status determination unit, the operating status of the distribution network is determined based on the current operating voltage of each node and preset voltage thresholds for different operating states, including: Based on the current operating voltage of each node and the preset voltage threshold for different operating states, the operating state of each node is determined, and the operating state with the highest priority among all nodes is determined as the operating state of the distribution network. The priority of the operating status, from highest to lowest, is as follows: emergency status, recovery status, abnormal status, alert status, and normal status. Wherein, if the current operating voltage of any node satisfies U S >U1 or U S If <U8, then any node is determined to be in an emergency state; If the current operating voltage of any node satisfies U1≥U S >U2 or U7>U S If ≥U8, then any node is determined to be in a recovery state; If the current operating voltage of any node satisfies U2≥U S >U3 or U6>U S If ≥U7, then any node is determined to be in an abnormal state; If the current operating voltage of any node satisfies U3≥U S >U4 or U5 >U S If ≥U6, then any node is determined to be in a state of alert. If the current operating voltage of any node satisfies U1≥U S If U ≥ 5, then any node is determined to be in a normal state; U S The current operating voltage is represented by U1, U2, U3, U4, U5, U6, U7, and U8, which are respectively the first preset voltage threshold, the second preset voltage threshold, the third preset voltage threshold, the fourth preset voltage threshold, the fifth preset voltage threshold, the sixth preset voltage threshold, the seventh preset voltage threshold, and the eighth preset voltage threshold.

18. The system according to claim 10, characterized in that, The control unit, based on the operating status of the distribution network, determines the control mode and restores the distribution network to normal operation using an optimized scheme corresponding to the control mode, including: After the master station monitors the distribution network status, the slave station determines the control mode based on the distribution network's operating status and initiates an optimization scheme according to the control mode. Within the distribution network sub-region, the distribution network is restored to normal operation by optimizing the output of distributed power sources and / or controlling load power. If the regulation within the sub-region cannot meet the distribution network's operating requirements, the master station regulates the distributed power sources in adjacent sub-regions to improve the power flow level of the distribution network, thereby restoring the distribution network to normal operation.

19. A master-slave self-healing control system based on power distribution network condition assessment, characterized in that, The system includes: a master control device and slave control devices; wherein... The main control equipment is used to enable the master station to acquire the reactive power-voltage sensitivity matrix of the distribution network, divide the distribution network area based on the reactive power-voltage sensitivity matrix, and acquire the distribution network sub-regions; it is used to determine the central bus in each distribution network sub-region, perform power flow calculation with the voltage of the central bus as the reference voltage, and determine the current operating voltage of each node in each distribution network sub-region; it is used to determine the operating status of the distribution network based on the current operating voltage of each node and preset voltage thresholds for different operating states. The slave control device is used to enable the slave station to determine the control mode according to the operating status of the distribution network, so as to restore the distribution network to normal operation based on the optimization scheme corresponding to the control mode.

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