CN115940120A - Master-slave self-healing control method and system based on power distribution network state evaluation - Google Patents
Master-slave self-healing control method and system based on power distribution network state evaluation Download PDFInfo
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Abstract
The invention discloses a master-slave self-healing control method and a system based on power distribution network state evaluation, which comprises the following steps: the method comprises the steps that a main station obtains a reactive power-voltage sensitivity matrix of the power distribution network, and the power distribution network is divided according to the reactive power-voltage sensitivity matrix to obtain sub-regions of the power distribution network; determining a central bus in each power distribution network subregion, performing load flow calculation by taking the central bus as a reference voltage, and determining the current operating voltage of each node in each power distribution network subregion; determining the operation state of the power distribution network according to the current operation voltage of each node and the preset voltage threshold of different operation states; and the slave station determines a control mode according to the running state of the power distribution network, and enables the power distribution network to be recovered to a normal running state based on an optimization scheme corresponding to the control mode. The invention can trigger the corresponding control mode of the slave control under different operation states, start the corresponding optimization scheme, and achieve the function of optimizing the operation of the power distribution network by coordinating the control of the slave station and the master station.
Description
Technical Field
The invention relates to the technical field of power distribution network protection and control, in particular to a master-slave self-healing control method and system based on power distribution network state evaluation.
Background
The distribution network carries the burden of delivering stable electrical energy for production and life. Along with the continuous improvement of urban development, the power supply load density of a power distribution network is remarkably increased, and the variety diversity of loads is strong; sensitive load and important load electric energy quality requirement are increasing day by day, and distribution network security and reliability are especially important. However, the structure of the distribution network is complex, the reliability of the distribution network is limited by elements, environment and other factors, and faults are difficult to avoid. And the distribution network is usually a radial passive structure, each node only has a unique path for acquiring the transmission power of the generator node, and the fault trip will inevitably cause the loss of power in a part of areas. At present, the short-time power failure phenomenon of the power distribution network is frequent, the influence is brought to the production and the life depending on the electric power, and certain economic loss is caused, so that the importance of the self-healing function of the power distribution network is self-evident. The fault self-healing control becomes an important characteristic of the current intelligent power distribution network, and an effective self-healing control technology is an important guarantee for improving the power supply safety, reliability and economy of the power distribution network. The fault self-healing control of the power distribution network needs to quickly remove faults, the power-losing load of the power distribution network is automatically transferred, the power failure range is reduced, the power failure time is shortened, and the continuous power supply capacity of the power distribution network is guaranteed to the maximum extent. Under the normal condition, after a fault occurs, the action of a switch needs to be quickly controlled, the power supply of a power-losing area is recovered, and the power supply reliability of a power distribution network is ensured. However, most of the existing fault self-healing control is based on an intelligent optimization algorithm, and the existing fault self-healing control is limited by numerous constraint conditions due to the characteristics of numerous nodes of a power distribution network, complex network topology structure, and various electrical parameters, and needs to rely on a large amount of iterative calculations, try and error and find an optimal solution, so that the solving speed is low; in addition, the resistance of the power distribution network line cannot be ignored, active power and reactive power are difficult to decouple, the calculation amount of iterative solution is further increased, the calculation time of a power supply recovery scheme is long, and the requirement on rapidity of power distribution network fault self-healing recovery is difficult to meet. Therefore, research needs to be conducted on the power distribution network fault self-healing recovery so as to obtain a power distribution network fault self-healing scheme capable of meeting the requirement of rapidity of power supply recovery.
With the development and perfection of power distribution networks, the structure of the power distribution network is increasingly complex. Meanwhile, more and more distributed power supplies and controllable loads are connected to the power distribution network, more control room is provided for optimizing and self-healing of the power distribution network, and the operation burden of the control station in the traditional centralized control process is increased.
Disclosure of Invention
The invention provides a master-slave self-healing control method and system based on power distribution network state evaluation, and aims to solve the problem of how to control a power distribution network.
In order to solve the above problem, according to an aspect of the present invention, there is provided a master-slave self-healing control method based on power distribution network state evaluation, the method including:
the method comprises the steps that a main station obtains a reactive power-voltage sensitivity matrix of the power distribution network, and the power distribution network is divided according to the reactive power-voltage sensitivity matrix to obtain sub-regions of the power distribution network;
determining a central bus in each power distribution network subregion, performing load flow calculation by taking the central bus as a reference voltage, and determining the current operating voltage of each node in each power distribution network subregion;
determining the operation state of the power distribution network according to the current operation voltage of each node and the preset voltage threshold of different operation states;
and the slave station determines a control mode according to the running state of the power distribution network so as to enable the power distribution network to be recovered to a normal running state based on an optimization scheme corresponding to the control.
Preferably, wherein the reactive-voltage sensitivity matrix comprises:
S＝(L-MH -1 N) -1 ，
wherein S is a reactive-voltage sensitivity matrix;
is Δ P to δ T Partial derivatives of (a); />
Is DeltaP to U T Partial derivatives of (a); />
Is DeltaQ to delta T The partial derivatives of (1); />
Is Delta Q to U T The partial derivatives of (1); p is node active power; q is node reactive power; delta is the node voltage phase angle; u is a node voltage amplitude; t is a matrix transposition symbol; Δ P is the amount of change in P; Δ Q is the amount of change in Q.
Preferably, the dividing of the distribution network region based on the reactive-voltage sensitivity matrix to obtain the distribution network sub-region includes:
preprocessing the reactive-voltage sensitivity matrix S to obtain an intermediate matrix X;
establishing a fuzzy similar matrix ED representing the electrical distance between the nodes according to the intermediate matrix X;
calculating a fuzzy equivalent matrix by using a transfer closure method according to the fuzzy similar matrix ED;
and clustering according to the fuzzy equivalent matrix and a preset membership threshold so as to divide the distribution network region and obtain the distribution network sub-region.
Preferably, the preprocessing the reactive-voltage sensitivity matrix S to obtain a matrix X includes:
sequentially carrying out standard deviation transformation and range transformation on any element in the reactive-voltage sensitivity matrix S to obtain a matrix X;
wherein any element X in the matrix X ij Comprises the following steps:
wherein, i and j correspond to nodes i and j in the power distribution network, and n is the number of the nodes;
is the mean value, W, of the jth column element of the reactive-voltage sensitivity matrix S j Is the standard deviation of the jth column element of the reactive-voltage sensitivity matrix S.
Preferably, wherein any element ED in said fuzzy similarity matrix ED ij Comprises the following steps:
wherein ED ij Is the electrical distance between node i and node j; i and j correspond to nodes i and j in the power distribution network, and n is the number of the nodes; x is the number of ik Is the element of the ith row and the kth column in the matrix X; x is the number of jk Is the element in the jth row and kth column of the matrix X.
Preferably, the calculating a fuzzy equivalent matrix by using a transfer closed-packet method according to the fuzzy similar matrix ED includes:
calculating the square of a fuzzy similarity matrix ED representing the electrical distance by ED (1) Indicating, judging ED (1) Whether or not equal to ED, if ED (1) = ED, then ED (1) Is a fuzzy equivalence matrix; otherwise, if ED (1) Not equal to ED, then ED (1) Squared, denoted as ED (2) Continuously judging ED (2) Whether or not equal to ED (1) Until the s-th time ED (s) ＝＝ED (s-1) When ED is determined (s) Is the fuzzy equivalence matrix;
wherein the content of the first and second substances,
wherein ED ij Is the electrical distance between node i and node j; ED (electronic device) ik Is the electrical distance between node i and node k; ED (electronic device) kj Is the electrical distance between node k and node j; n is the number of nodes, V is taken as large as representative, and A is taken as small as representative.
Preferably, the clustering according to the fuzzy equivalent matrix and a preset membership threshold to divide the power distribution network region and obtain a power distribution network subregion includes:
for any element in the fuzzy equivalent matrix, if the value of the element is greater than or equal to a preset membership threshold, updating the value of the element to be considered as a first preset value, and if the value of the element is less than the preset membership threshold, updating the value of the element to be considered as a second preset value;
and (4) merging the nodes corresponding to the columns with the same matrix elements in the updated fuzzy equivalent matrix into a class, and determining the sub-area of the power distribution network according to the classified nodes.
Preferably, the determining the operation state of the power distribution network according to the current operation voltage of each node and the preset voltage threshold of different operation states includes:
determining the operation state of each node according to the current operation voltage of each node and the preset voltage threshold of different operation states, and determining the operation state with the highest priority in all the nodes as the operation state of the power distribution network;
the priority of the running state is from high to low: an emergency state, a recovery state, an abnormal state, a warning state and a normal state;
wherein, if the current operating voltage of any node satisfies U S ＞U 1 Or U S ＜U 8 Determining that any node is in an emergency state;
if the current operating voltage of any node meets U 1 ≥U S ＞U 2 Or U 7 ＞U S ≥U 8 Then determining that any node is in a recovery state;
if the current operating voltage of any node meets U 2 ≥U S ＞U 3 Or U 6 ＞U S ≥U 7 If yes, determining that any node is in an abnormal state;
if the current operating voltage of any node meets U 3 ≥U S ＞U 4 Or U 5 ＞U S ≥U 6 If yes, determining that any node is in an alert state;
if the current operating voltage of any node meets U 1 ≥U S ≥U 5 If yes, determining that any node is in a normal state; u shape S Is the current operating voltage; u shape 1 、U 2 、U 3 、U 4 、U 5 、U 6 、U 7 And U 8 Respectively a first preset voltage threshold and a second preset voltageThe voltage threshold value is a quantity threshold value, a third preset voltage threshold value, a fourth preset voltage threshold value, a fifth preset voltage threshold value, a sixth preset voltage threshold value, a seventh preset voltage threshold value and an eighth preset voltage threshold value.
Preferably, the determining, by the slave station, a control mode according to the operation state of the power distribution network, so as to restore the power distribution network to a normal operation state based on an optimization scheme corresponding to the control mode includes:
after the master station monitors the state of the power distribution network, the slave station determines a control mode according to the running state of the power distribution network, starts an optimization scheme according to the control mode, and enables the power distribution network to be recovered to a normal running state by means of optimizing the output of the distributed power supply and/or controlling the load power in the sub-area of the power distribution network; if the adjustment in the sub-areas cannot meet the operation requirement of the power distribution network, the distributed power supplies in the adjacent sub-areas are regulated and controlled through the main station, and the power flow level of the power distribution network is improved, so that the power distribution network is recovered to a normal operation state.
According to another aspect of the invention, a master-slave self-healing control system based on power distribution network state evaluation is provided, which comprises:
the distribution network region division unit is used for enabling the master station to obtain a reactive-voltage sensitivity matrix of the distribution network, and dividing the distribution network region based on the reactive-voltage sensitivity matrix to obtain sub-regions of the distribution network;
the operation voltage determining unit is used for determining a central bus in each power distribution network subregion, performing load flow calculation by taking the central bus as reference voltage, and determining the current operation voltage of each node in each power distribution network subregion;
the operation state determining unit is used for determining the operation state of the power distribution network according to the current operation voltage of each node and the preset voltage threshold of different operation states;
and the control unit is used for determining a control mode from the station according to the running state of the power distribution network and enabling the power distribution network to be recovered to a normal running state based on an optimization scheme corresponding to the control mode.
Preferably, wherein in the distribution network area division unit, a reactive-voltage sensitivity matrix comprises:
S＝(L-MH -1 N) -1 ，
wherein S is a reactive-voltage sensitivity matrix;
is Δ P to δ T Partial derivatives of (a); />
Is DeltaP to U T Partial derivatives of (a); />
Is Δ Q to δ T Partial derivatives of (a); />
Is Delta Q to U T Partial derivatives of (a); p is the active power of the node; q is node reactive power; delta is the node voltage phase angle; u is a node voltage amplitude; t is a matrix transposition symbol; Δ P is the amount of change in P; Δ Q is the amount of change in Q.
Preferably, the power distribution network region division unit divides the power distribution network region based on the reactive-voltage sensitivity matrix to obtain sub-regions of the power distribution network, and includes:
preprocessing the reactive-voltage sensitivity matrix S to obtain an intermediate matrix X;
establishing a fuzzy similar matrix ED representing the electrical distance between the nodes according to the intermediate matrix X;
calculating a fuzzy equivalent matrix by using a transfer closure method according to the fuzzy similar matrix ED;
and clustering according to the fuzzy equivalent matrix and a preset membership threshold so as to divide the distribution network region and obtain the distribution network sub-region.
Preferably, the power distribution network region division unit preprocesses the reactive-voltage sensitivity matrix S to obtain a matrix X, and includes:
sequentially carrying out standard deviation transformation and range transformation on any element in the reactive-voltage sensitivity matrix S to obtain a matrix X;
wherein any element X in the matrix X ij Comprises the following steps:
wherein, i and j correspond to nodes i and j in the power distribution network, and n is the number of the nodes;
is the mean value, W, of the jth column element of the reactive-voltage sensitivity matrix S j Is the standard deviation of the jth column element of the reactive-voltage sensitivity matrix S.
Preferably, any element ED in the fuzzy similarity matrix ED is divided into units in the distribution network area ij Comprises the following steps:
wherein ED ij Is the electrical distance between node i and node j; i and j correspond to nodes i and j in the power distribution network, and n is the number of the nodes; x is the number of ik Is the element of the ith row and the kth column in the matrix X; x is the number of jk Is the element in the jth row and kth column of the matrix X.
Preferably, the calculating, by the distribution network region dividing unit, a fuzzy equivalent matrix by using a transfer closed-packet method according to the fuzzy similar matrix ED includes:
calculating the square of a fuzzy similarity matrix ED representing the electrical distance, using ED (1) Indicating, judging ED (1) Whether or not equal to ED, if ED (1) = ED, then ED (1) Is a fuzzy equivalence matrix; otherwise, if ED (1) Not equal to ED, then ED (1) Squared, denoted as ED (2) Continuously judging ED (2) Whether or not equal to ED (1) Until the s-th time ED (s) ＝＝ED (s-1) Determining ED (s) Is the fuzzy equivalence matrix;
wherein the content of the first and second substances,
wherein ED ij Is the electrical distance between node i and node j; ED (electronic device) ik Is the electrical distance between node i and node k; ED (electronic device) kj Is the electrical distance between node k and node j; n is the number of nodes, V is taken as large as representative, and A is taken as small as representative.
Preferably, the power distribution network region division unit performs clustering according to the fuzzy equivalent matrix and a preset membership threshold to divide the power distribution network region and obtain the power distribution network sub-region, including:
for any element in the fuzzy equivalent matrix, if the value of the element is greater than or equal to a preset membership threshold, updating the value of the element to be considered as a first preset value, and if the value of the element is less than the preset membership threshold, updating the value of the element to be considered as a second preset value;
and (4) merging the nodes corresponding to the columns with the same matrix elements in the updated fuzzy equivalent matrix into a class, and determining the sub-area of the power distribution network according to the classified nodes.
Preferably, the determining, at the operation state determining unit, an operation state of the power distribution network according to a current operation voltage of each node and a preset voltage threshold of a different operation state includes:
determining the operation state of each node according to the current operation voltage of each node and the preset voltage threshold of different operation states, and determining the operation state with the highest priority in all the nodes as the operation state of the power distribution network;
the priority of the running state is from high to low: an emergency state, a recovery state, an abnormal state, a warning state and a normal state;
wherein, if the current operating voltage of any node satisfies U S ＞U 1 Or U S ＜U 8 Determining that any node is in an emergency state;
if the current operating voltage of any node meets U 1 ≥U S ＞U 2 Or U 7 ＞U S ≥U 8 Then determining that any node is in a recovery state;
if the current operating voltage of any node meets U 2 ≥U S ＞U 3 Or U 6 ＞U S ≥U 7 If yes, determining that any node is in an abnormal state;
if the current operating voltage of any node meets U 3 ≥U S ＞U 4 Or U 5 ＞U S ≥U 6 If yes, determining that any node is in an alert state;
if the current operating voltage of any node meets U 1 ≥U S ≥U 5 If yes, determining that any node is in a normal state; u shape S The current operating voltage; u shape 1 、U 2 、U 3 、U 4 、U 5 、U 6 、U 7 And U 8 Respectively, a first preset voltage threshold, a second preset voltage threshold, a third preset voltage threshold, a fourth preset voltage threshold, a fifth preset voltage threshold, a sixth preset voltage threshold, a seventh preset voltage threshold and an eighth preset voltage threshold.
Preferably, the determining, by the control unit, a control mode according to the operating state of the power distribution network by the slave station, so as to restore the power distribution network to a normal operating state based on an optimization scheme corresponding to the control mode includes:
after the master station monitors the state of the power distribution network, the slave station determines a control mode according to the running state of the power distribution network, starts an optimization scheme according to the control mode, and enables the power distribution network to be recovered to a normal running state by means of optimizing the output of the distributed power supply and/or controlling the load power in the sub-area of the power distribution network; if the adjustment in the sub-areas cannot meet the operation requirement of the power distribution network, the distributed power supplies in the adjacent sub-areas are regulated and controlled through the main station, and the power flow level of the power distribution network is improved, so that the power distribution network is recovered to a normal operation state.
According to another aspect of the present invention, there is provided a master-slave self-healing control system based on power distribution network state assessment, the system including: the system comprises a master control device and a slave control device; wherein the content of the first and second substances,
the main control equipment is used for enabling the main station to obtain a reactive-voltage sensitivity matrix of the power distribution network, dividing the power distribution network region based on the reactive-voltage sensitivity matrix and obtaining a power distribution network subregion; the central control system is used for determining a central bus in each power distribution network subregion, performing load flow calculation by taking the central bus as a reference voltage, and determining the current operating voltage of each node in each power distribution network subregion; the system comprises a power distribution network, a node, a power supply and a control unit, wherein the power distribution network is used for determining the running state of the power distribution network according to the current running voltage of each node and the preset voltage threshold of different running states;
and the slave control equipment is used for enabling the slave station to determine a control mode according to the running state of the power distribution network so as to enable the power distribution network to recover to a normal running state based on an optimization scheme corresponding to the control mode.
The invention provides a master-slave self-healing control method and a system based on power distribution network state evaluation, which comprises the following steps: the method comprises the steps that a main station obtains a reactive power-voltage sensitivity matrix of the power distribution network, and the power distribution network is divided according to the reactive power-voltage sensitivity matrix to obtain sub-regions of the power distribution network; determining a central bus in each power distribution network subregion, performing load flow calculation by taking the central bus as a reference voltage, and determining the current operating voltage of each node in each power distribution network subregion; determining the operation state of the power distribution network according to the current operation voltage of each node and the preset voltage threshold of different operation states; the slave station determines a control mode according to the running state of the power distribution network, and enables the power distribution network to be recovered to a normal running state based on an optimization scheme corresponding to the control mode; according to the method, the fuzzy electrical distance of the power distribution network is derived based on the reactive-voltage sensitivity matrix, and then the control subareas are divided; the method comprises the following steps of taking the voltage deviation of a system node of the power distribution network as a state division basis of the power distribution network to realize the evaluation of the running state of the power distribution network; triggering corresponding control modes of slave control under different running states, and starting a corresponding optimization scheme; if the adjustment in the region cannot meet the self-healing requirement, the main control coordinates adjacent regions to participate in self-healing optimization, and adjusts the power distribution network to recover to a normal operation state through coordination control of a distributed power supply and a load in a subregion; the function of optimizing the operation of the power distribution network is achieved by coordinating the control of the slave station and the master station.
Drawings
A more complete understanding of exemplary embodiments of the present invention may be had by reference to the following drawings in which:
fig. 1 is a flowchart of a master-slave self-healing control method 100 based on power distribution network state evaluation according to an embodiment of the present invention;
FIG. 2 is a flow chart of solving a fuzzy equivalence matrix according to an embodiment of the invention;
fig. 3 is a diagram of a master-slave self-healing control architecture based on power distribution network state evaluation according to an embodiment of the present invention;
fig. 4 is a schematic diagram illustrating a self-healing control state transition and control manner according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a voltage distribution and self-healing control state according to an embodiment of the present invention;
fig. 6 is a flowchart of self-healing control based on node voltage according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of an IEEE33 node power distribution network including distributed power sources, according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of ambiguous electrical distances between nodes in accordance with an embodiment of the present invention;
fig. 9 is a schematic diagram of IEEE33 node distribution network partitioning results according to an embodiment of the present invention;
fig. 10 is a schematic structural diagram of a master-slave self-healing control system 1000 based on power distribution network state evaluation according to an embodiment of the present invention;
fig. 11 is a schematic structural diagram of a master-slave self-healing control system 1100 based on power distribution network state evaluation according to an embodiment of the present invention.
Detailed Description
The exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, however, the present invention may be embodied in many different forms and is not limited to the embodiments described herein, which are provided for complete and complete disclosure of the present invention and to fully convey the scope of the present invention to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the accompanying drawings is not intended to be limiting of the invention. In the drawings, the same unit/element is denoted by the same reference numeral.
Unless otherwise defined, terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Further, it will be understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense.
Fig. 1 is a flowchart of a master-slave self-healing control method 100 based on power distribution network state evaluation according to an embodiment of the present invention. As shown in fig. 1, in the master-slave self-healing control method based on power distribution network state evaluation provided by the embodiment of the invention, based on a reactive-voltage sensitivity matrix, a fuzzy electrical distance of a power distribution network is derived, and then control partitions are divided; the voltage deviation of the system nodes of the power distribution network is used as a power distribution network state division basis, so that the running state of the power distribution network is evaluated; triggering corresponding control modes of slave control under different running states, and starting a corresponding optimization scheme; if the adjustment in the region cannot meet the self-healing requirement, the main control coordinates adjacent regions to participate in self-healing optimization, and the distribution network is adjusted to be recovered to a normal operation state through coordination control of the distributed power supplies and loads in the sub-regions; the function of optimizing the operation of the power distribution network is achieved by coordinating the control of the slave stations and the master station. According to the master-slave self-healing control method 100 based on power distribution network state evaluation provided by the embodiment of the invention, starting from step 101, a master station acquires a reactive power-voltage sensitivity matrix of a power distribution network in step 101, and divides the power distribution network area based on the reactive power-voltage sensitivity matrix to acquire sub-areas of the power distribution network.
Preferably, wherein the reactive-voltage sensitivity matrix comprises:
S＝(L-MH -1 N) -1 ，
wherein S is a reactive-voltage sensitivity matrix;
is Δ P to δ T Partial derivatives of (a); />
Is DeltaP to U T The partial derivatives of (1); />
Is DeltaQ to delta T The partial derivatives of (1); />
Is a pair of delta Q to U T Partial derivatives of (a); p is node active power; q is node reactive power; delta is the node voltage phase angle; u is a node voltage amplitude; t is a matrix transposition symbol; Δ P is the amount of change in P; Δ Q is the amount of change in Q.
Preferably, the dividing of the distribution network region based on the reactive-voltage sensitivity matrix to obtain the distribution network sub-region includes:
preprocessing the reactive-voltage sensitivity matrix S to obtain an intermediate matrix X;
establishing a fuzzy similar matrix ED representing the electrical distance between the nodes according to the intermediate matrix X;
calculating a fuzzy equivalent matrix by using a transfer closure method according to the fuzzy similar matrix ED;
and clustering according to the fuzzy equivalent matrix and a preset membership threshold so as to divide the distribution network region and obtain the distribution network sub-region.
Preferably, the preprocessing the reactive-voltage sensitivity matrix S to obtain a matrix X includes:
sequentially carrying out standard deviation transformation and range transformation on any element in the reactive-voltage sensitivity matrix S to obtain a matrix X;
wherein any element X in the matrix X ij Comprises the following steps:
wherein, i and j correspond to nodes i and j in the power distribution network, and n is the number of the nodes;
is the mean value, W, of the jth column element of the reactive-voltage sensitivity matrix S j Is the standard deviation of the jth column element of the reactive-voltage sensitivity matrix S.
Preferably, wherein any element ED in said fuzzy similarity matrix ED ij Comprises the following steps:
wherein ED ij Is the electrical distance between node i and node j; i and j correspond to nodes i and j in the power distribution network, and n is the number of the nodes; x is the number of ik Is the ith row in the matrix XThe element of the kth column; x is the number of jk Is the element in the jth row and kth column of the matrix X.
Preferably, the calculating a fuzzy equivalent matrix by using a transfer closed-packet method according to the fuzzy similar matrix ED includes:
calculating the square of a fuzzy similarity matrix ED representing the electrical distance, using ED (1) Indicating, judging ED (1) Whether or not equal to ED, if ED (1) = ED, then ED (1) Is a fuzzy equivalence matrix; otherwise, if ED (1) Not equal to ED, then ED (1) Squared, denoted as ED (2) Continuously judging ED (2) Whether or not equal to ED (1) Until the s-th time ED (s) ＝＝ED (s-1) When ED is determined (s) Is the fuzzy equivalence matrix;
wherein the content of the first and second substances,
wherein ED ij Is the electrical distance between node i and node j; ED (electronic device) ik Is the electrical distance between node i and node k; ED (electronic device) kj Is the electrical distance between node k and node j; n is the number of nodes, V is taken as large as representative, and A is taken as small as representative.
Preferably, the clustering according to the fuzzy equivalent matrix and a preset membership threshold to divide the distribution network region and obtain the distribution network sub-region includes:
for any element in the fuzzy equivalent matrix, if the value of the element is greater than or equal to a preset membership threshold, updating the value of the element to be considered as a first preset value, and if the value of the element is less than the preset membership threshold, updating the value of the element to be considered as a second preset value;
and (4) merging the nodes corresponding to the columns with the same matrix elements in the updated fuzzy equivalent matrix into a class, and determining the sub-area of the power distribution network according to the classified nodes.
In an embodiment of the invention, the partitioning of the distribution network area is performed on the basis of the fuzzy electrical distance.
Specifically, the method comprises the following steps:
1) Power distribution network fuzzy electrical distance based on reactive-voltage sensitivity
The method comprises the steps of measuring the association degree between nodes by adopting the electrical distance, and applying the idea of fuzzy clustering to power distribution network partitions, so that the problem of power distribution network partition is converted into the problem of node fuzzy clustering.
Obtained from the system tide Jacobian:
in the formula:
namely a tidal current jacobi block matrix; />
Is Δ P to δ T Partial derivatives of (a);
is DeltaP to U T The partial derivatives of (1); />
Is Δ Q to δ T Partial derivatives of (a); />
Is Delta Q to U T The partial derivatives of (1).
Neglecting the effect of active power disturbances on the voltage, i.e. let Δ P =0, one can obtain:
ΔQ＝(L-MH -1 N)ΔU (2)
wherein, S = (L-MH) -1 N) -1 I.e. a reactive-voltage sensitivity matrix.
Applying fuzzy clustering to the electrical distance to deduce the fuzzy electrical distance, which is as follows:
a) According to a fuzzy clustering analysis method, firstly, standard deviation transformation is carried out on a matrix S to obtain a matrix alpha:
in the formula, i and j correspond to nodes i and j in the power distribution network, n is the number of the nodes,
is the mean value of the jth column element of the matrix S, < >>
Representing the standard deviation of the elements of column j.
Matrix element alpha ij The physical meaning of (1) is that when the reactive power of the node j changes, the node i has the voltage influence ability which takes the average value of all the node voltage influence abilities as a reference value. The mean value of each variable in alpha is 0, the standard deviation is 1, and there is no dimension, but the values are obviously not necessarily in the interval [0,1 ]]The above.
b) To make the elements in α comparable, they are subjected to range transformation to obtain a matrix X:
at this time x ij ∈[0,1]. If each PQ node is considered as one dimension in spatial coordinates, then element x ij It represents the standard coordinate, i.e. the per unit value, of the spatial point j corresponding to the node j mapped to the spatial point i corresponding to the node i.
c) And establishing a fuzzy similarity matrix, namely defining the electrical distance. The most commonly adopted Euclidean distance method is adopted to represent the distance of space points, namely the electrical distance between nodes:
in the formula, ED ij I.e. representing the electricity between node i and node jAir distance, ED ij A larger size indicates a tighter electrical connection between node i and node j.
d) Distance ED through spatial points i and j ij And solving a fuzzy equivalent matrix.
In the invention, a fuzzy equivalence matrix is obtained by using a transmission closed-packet method. Wherein, the formula for calculating the square of the fuzzy similarity matrix ED representing the electrical distance is:
wherein ED ij Is the electrical distance between node i and node j; ED (electronic device) ik Is the electrical distance between node i and node k; ED (electronic device) kj Is the electrical distance between node k and node j; n is the number of nodes, V is taken as large as representative, and A is taken as small as representative.
As shown in FIG. 2, the fuzzy similarity matrix ED representing electrical distances is first squared, using ED (1) Indicating, judging ED (1) Whether or not equal to ED, if ED (1) = ED, then ED (1) It is just a fuzzy equivalence matrix, i.e. the matrix ED can be aligned (1) The numerical values appearing in the middle are arranged from large to small, a dynamic clustering chart is formed by taking the numerical values once, and then a reasonable clustering result is selected. If ED (1) Not equal to ED, then ED (1) Squared, denoted as ED (2) Continuously judging ED (2) Whether or not equal to ED (1) Until the ith time ED (i) ＝＝ED (i-1) Get ED (i) Is a fuzzy equivalence matrix. Finally, the fuzzy equivalence matrix is still represented by ED. FIG. 1 is a flow chart of the fuzzy equivalence matrix calculation. ED is known from the reflexivity and symmetry of the fuzzy equivalence matrix ii ＝1,ED ij ＝ED ji (i,j,k∈[1,M])。
2) Power distribution network subregion partitioning based on dynamic clustering
The element values in the fuzzy equivalent matrix correspond to the values of the membership degree lambda. Due to ED ij ∈(0,1]Therefore, the membership degree lambda epsilon (0, 1) is set, different membership degrees correspond to different clustering depths, and nodes of the power distribution network can appearDifferent attributions.
And during clustering, the value of the element which is larger than a preset membership threshold lambda in the fuzzy similarity matrix is a first preset value 1, and the value of the element which is smaller than the lambda is a second preset value 0, so that the fuzzy similarity matrix is converted into a 0-1 matrix. At the moment, nodes corresponding to columns with the same matrix elements are merged into one type to form a corresponding power distribution network sub-area.
In step 102, a central bus in each power distribution network subregion is determined, load flow calculation is performed by taking the central bus as a reference voltage, and the current operating voltage of each node in each power distribution network subregion is determined.
In step 103, the operation state of the power distribution network is determined according to the current operation voltage of each node and the preset voltage threshold of different operation states.
Preferably, the determining the operation state of the power distribution network according to the current operation voltage of each node and the preset voltage threshold of different operation states includes:
determining the operation state of each node according to the current operation voltage of each node and the preset voltage threshold of different operation states, and determining the operation state with the highest priority in all the nodes as the operation state of the power distribution network;
the priority of the running state is from high to low: an emergency state, a recovery state, an abnormal state, a warning state and a normal state;
wherein, if the current operating voltage of any node satisfies U S ＞U 1 Or U S ＜U 8 Determining that any node is in an emergency state;
if the current operating voltage of any node meets U 1 ≥U S ＞U 2 Or U 7 ＞U S ≥U 8 Then determining that any node is in a recovery state;
if the current operating voltage of any node meets U 2 ≥U S ＞U 3 Or U 6 ＞U S ≥U 7 If yes, determining that any node is in an abnormal state;
if any node is currently operatingPressure satisfies U 3 ≥U S ＞U 4 Or U 5 ＞U S ≥U 6 If yes, determining that any node is in an alert state;
if the current operating voltage of any node meets U 1 ≥U S ≥U 5 If yes, determining that any node is in a normal state; u shape S The current operating voltage; u shape 1 、U 2 、U 3 、U 4 、U 5 、U 6 、U 7 And U 8 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 are respectively set.
In step 104, the slave station determines a control mode according to the operation state of the power distribution network, and enables the power distribution network to be recovered to a normal operation state based on an optimization scheme corresponding to the control mode.
Preferably, the determining, by the slave station, a control mode according to the operation state of the power distribution network, so as to restore the power distribution network to a normal operation state based on an optimization scheme corresponding to the control mode includes:
after the master station monitors the state of the power distribution network, the slave station determines a control mode according to the running state of the power distribution network, starts an optimization scheme according to the control mode, and enables the power distribution network to be recovered to a normal running state by means of optimizing the output of the distributed power supply and/or controlling the load power in the sub-area of the power distribution network; if the adjustment in the sub-areas cannot meet the operation requirement of the power distribution network, the distributed power supplies in the adjacent sub-areas are regulated and controlled through the main station, and the power flow level of the power distribution network is improved, so that the power distribution network is recovered to a normal operation state.
In the embodiment of the invention, the power distribution network adopts a master-slave optimization self-healing control architecture. Each subarea of the power distribution network is internally provided with an adjustable distributed power supply and a controllable load, and the subarea internal adjusting capacity is certain. After the slave station monitors the state of the power distribution network, a control scheme of the corresponding state is started, and the power distribution network is recovered to a normal operation state as far as possible by means of optimizing the output of the distributed power supply, controlling the load power and the like. If the regulation in the region cannot meet the operation requirement of the power distribution network, the distributed power supplies in the adjacent regions are regulated and controlled by the master station, the power flow level of the power distribution network is improved, and finally the power distribution network is restored to a normal operation state. Fig. 3 shows a master-slave self-healing control architecture based on power distribution network state evaluation.
In the invention, the slave control strategy based on the regional distribution network state evaluation comprises the following steps: the power distribution network state evaluation is based on all collected information of the power distribution network, the current operation state of the power distribution network is evaluated, the future operation state is predicted, the power distribution network operation condition can be found and adjusted in time, effective monitoring and identification of weak links are achieved, and safe and stable operation of the power distribution network is guaranteed comprehensively. Wherein, the running state of distribution network divide into: normal running state, warning running state, abnormal running state, emergency running state and recovery running state.
As shown in fig. 5, the self-healing control measures corresponding to the above 5 states are: optimization control, prevention control, correction control, emergency control and recovery control. And executing corresponding control schemes according to different running states of the system, so that the state of the power distribution network is promoted to evolve towards a good direction, and finally the power distribution network runs in an economic state, so that the safe, economic, stable and continuous running of the power grid is maintained.
In the invention, the voltage is taken as the main reference quantity of the running state of the power distribution network to divide the running state of the power distribution network, corresponding control measures are taken according to different running states, the interval of the voltage and the corresponding self-healing control mode are shown in figure 5, wherein U 1 、U 2 、U 3 、U 4 、U 5 、U 6 、U 7 、U 8 Respectively 8 voltage demarcation points which are decreased in sequence.
In the invention, the system frequency or the node voltage of the power distribution network is used as the state division basis of the power distribution network, so that the evaluation of the running state of the power distribution network is realized. The self-healing control processes of the power distribution network under different states are formulated, and the conversion of the running state of the power distribution network under various states such as economy, normal state, warning and the like is realized through the coordination control of the distributed power sources and loads in the sub-regions.
As shown in fig. 6, the self-healing control process based on the node voltage includes:
step 1: based on the partition control of the power distribution network, the power distribution network is divided into sub-regions by adopting a fuzzy clustering region method.
And 2, step: selecting a central bus of each subregion, carrying out load flow calculation by taking the voltage of the central bus as a reference, and determining the current operating voltage amount Us of other buses (nodes) in the subregion.
And step 3: according to the current operating voltage Us and the preset voltage threshold U of different operating states 1 -U 8 And comparing, determining the state of each node, determining the running state of the power distribution network according to the state of each node, and selecting and starting a corresponding control mode according to the running state.
If the running state of the power distribution network is an emergency state, starting emergency control; if the running state of the power distribution network is a recovery state, starting recovery control; if the running state of the power distribution network is an abnormal state, starting correction control; if the running state of the power distribution network is an alert state, starting prevention control; and if the running state of the power distribution network is a normal state, starting optimization control.
The method comprises the steps that the operation state of each node is determined according to the current operation voltage of each node and preset voltage threshold values of different operation states, and the operation state with the highest priority in all the nodes is determined to be the operation state of a power distribution network; the priority of the running state is from high to low: an emergency state, a recovery state, an abnormal state, a warning state and a normal state; wherein, if the current operation voltage of any node satisfies U S ＞U 1 Or U S ＜U 8 Determining that any node is in an emergency state; if the current operating voltage of any node meets U 1 ≥U S ＞U 2 Or U 7 ＞U S ≥U 8 Then determining that any node is in a recovery state; if the current operating voltage of any node meets U 2 ≥U S ＞U 3 Or U 6 ＞U S ≥U 7 Then determining the any nodeIs in an abnormal state; if the current operating voltage of any node meets U 3 ≥U S ＞U 4 Or U 5 ＞U S ≥U 6 If yes, determining that any node is in an alert state; if the current operating voltage of any node meets U 1 ≥U S ≥U 5 If yes, determining that any node is in a normal state; u shape 1 、U 2 、U 3 、U 4 、U 5 、U 6 、U 7 And U 8 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 are respectively set.
Further, in the present invention, according to different voltage levels, the control parameters of the voltage deviation are divided into:
1) And the allowable deviation of the pressure supply of the user side: rated voltage with the sum of absolute values of positive and negative deviations of voltage of 35kV or above not more than 10%; rated voltage with allowable deviation of +/-7% for phase voltage of 10kV and below.
2) Allowable deviation values of bus voltages of power plants and substations: when a 35kV-110kV bus of a transformer substation normally runs, the voltage allowable deviation is-3% -7% of the rated voltage of the system; the rated voltage is +/-10% when in accident.
The method adopts fuzzy clustering to divide the power distribution network into a plurality of sub-regions, takes the node voltage deviation as the state quantity to judge the operation state of the power distribution network, triggers corresponding control modes of slave control under different operation states, and starts a corresponding optimization scheme. If the adjustment in the area can not meet the self-healing requirement, the main control coordinates adjacent areas to participate in self-healing optimization, and adjusts the power distribution network to recover to a normal operation state.
The following specifically exemplifies embodiments of the present invention
(1) IEEE33 node power distribution network region division based on fuzzy electrical distance
Taking an IEEE33 node distribution network as an example, the topology of the IEEE33 node distribution network including distributed power sources is shown in fig. 7. The branch impedance and node load data for the IEEE33 node are as follows:
TABLE 1 IEEE33 node parameters
The nodes 4, 13, 20 and 28 are provided with PQ-type distributed power supplies, and the distributed power supplies have an input active power of 300kW and an input reactive power of 100kVar.
And partitioning the power distribution network system. The fuzzy electrical distances between the nodes are shown in fig. 8.
The number of the partitions is closely related to the selection of the membership degree, and the result of the partition of the area under 3 membership degrees is shown in table 2.
TABLE 2 results of zoning under different membership
And comparing the partition results under the three membership degrees, and taking the partition with the membership degree of 0.83 as a partition control sub-area of the power distribution network by referring to the actual wiring condition because the number of the partitions is not too large.
And adjusting the partitioning result in order to ensure that each region has the distributed power supply. The nodes 28, 29, 30 and the nodes 30, 31, 32, 33 are merged into one partition, and the final partition result is shown in fig. 9. The IEEE33 node system is divided into three areas, one area containing nodes: 1-12, 19-27; region two contains nodes 13-18; region three contains nodes 28-33.
When the partitions are adopted, a distributed power supply is used as power supply supplement in each area, and the spare capacity required by self-healing control can be met. And the number of nodes in the subarea is reduced relative to the number of nodes of the whole power distribution network, so that the control can be more effective in the subsequent self-healing control process. And during slave control, the power supply in the subarea preferentially meets the self-healing requirement in the subarea. And if the slave control is difficult to realize effective regulation of the operation state of the sub-area, the master control is started, and adjacent sub-areas are coordinated to realize self-healing optimization control together.
(2) Power distribution network prevention control in alert state based on slave control
Under normal conditions, the voltage per unit value of all nodes in the IEEE33 node power distribution network is within 0.93-1.07, and the system is in a normal operation state at the moment.
If the load at node 18 suddenly increases, wherein the active load increases by 70kW and the reactive load increases by 80kVar. The voltage per unit value of the node 18 exceeds the limit value as can be known from the load flow calculation result, and table 3 lists the voltage variation of the node 18.
TABLE 3 node 18 Voltage per unit value variation
| Node numbering | Voltage per unit value before load increase | Voltage per unit value before load increase |
| 18 | 0.9403 | 0.9293 |
As can be seen from table 3, node 18 is in the armed operating state while the remaining nodes are still within normal range and are therefore not listed. At this point, the precautionary control will be initiated to restore the node 18 voltage to normal.
At this time, the backup power of the power supply electrically closest to the line transmission capacity needs to be changed so that the voltage at the node 18 is restored to normal as soon as possible. The power supply node electrically closest to node 18 is node 13, and the voltage at node 18 is restored by adjusting the power supply output. During simulation, the distributed power output of the node 13 is increased, the active power is increased by 30kW, the reactive power is increased by 30kVar, and according to the calculated result of the controlled system power flow, the voltage per unit value of the node 18 is 0.9318 and is restored to a normal level.
TABLE 4 comparison of voltage before and after control and regulation of power output
| | Post-control node | 18 per | Node | 13 adds | Node | 13 added reactive power |
| 0.9403 | 0.9318 | 30kW | 30kVar |
After this, the distribution network keeps monitoring the node, while it is checked whether there is enough spare capacity left for a possible next alert state for a nearby power node. If the spare capacity is insufficient, the next alert state is prepared in advance by means of load transfer, namely the distributed power supply in the region should have enough spare power.
At this time, the preventive control is completed, the voltage level is maintained at the normal level even after the node 18 suddenly increases the load, and the armed state is released.
(3) Power distribution network correction control under abnormal operation state based on master-slave cooperation
Suppose at some point, node 18 suddenly increases the unknown load, the active power increases by 160kW, and the reactive power increases by 160kVar. At this time, it is found from the load flow calculation that the nodes 14, 15, 16, and 17 close to the node 18 are similarly affected, and the node voltage is abnormal, as shown in table 5.
TABLE 5 abnormal node Voltage per unit value
| Node numbering | Voltage per unit value in normal time | Voltage per unit value in |
| 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 |
It is known from the results that the voltages at nodes 14, 15, 16 are below-7% of the rated voltage, and the voltages at nodes 17 and 18 are above-9% below-8% of the rated voltage. And judging that the system is in an abnormal operation state at the moment according to the abnormal voltage of the node, and starting correction control.
Firstly, correction control in a subarea is carried out, the spare capacity distributed power supply node 13 is called to increase power output, active power is increased by 100kW, reactive power is increased by 100kVar, and the power flow calculation result shows that the node voltages of the nodes 14, 15 and 16 are recovered to be more than 0.93, but the voltages of the node 17 and the node 18 are not recovered to be more than 0.93, and the result is shown in table 6. At this time, the correction control in the sub-area cannot achieve the purpose of restoring the sub-area to normal.
TABLE 6 control of front and rear node Voltage variations
| Node numbering | Controlling the front voltage | Controlling the post-voltage |
| 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 |
At the moment, the node voltage is recovered to be normal by cutting off a part of adjacent load. During simulation, a part of load of the node 17 is cut off, the active power is reduced by 50kW, the reactive power is reduced by 15kVar, and the change condition of the node voltage is shown in Table 7.
TABLE 7 control of front and rear node Voltage variations
| Node numbering | Controlling the front voltage | Controlling the post-voltage |
| 17 | 0.9275 | 0.9321 |
| 18 | 0.9255 | 0.9300 |
At this time, the voltage value at the node 17 becomes 0.9321, the voltage at the node 18 becomes 0.9300, and the normal level is reached, and the correction control is effectively completed. Also, the distribution grid should be immediately optimized to adjust the power distribution so that there is sufficient spare capacity in the partition to handle the next possible abnormal operating condition.
Fig. 10 is a schematic structural diagram of a master-slave self-healing control system 1000 based on power distribution network state evaluation according to an embodiment of the present invention. As shown in fig. 10, a master-slave self-healing control system 1000 based on power distribution network state evaluation according to an embodiment of the present invention includes: distribution network area dividing unit 1001, operating voltage determining unit 1002, operating state determining unit 1003, and control unit 1004.
Preferably, the power distribution network region dividing unit 1001 is configured to enable the master station to acquire a reactive-voltage sensitivity matrix of the power distribution network, divide the power distribution network region based on the reactive-voltage sensitivity matrix, and acquire sub-regions of the power distribution network.
Preferably, in the distribution network area division unit 1001, the reactive-voltage sensitivity matrix includes:
S＝(L-MH -1 N) -1 ，
wherein S is a reactive-voltage sensitivity matrix;
is Δ P to δ T Partial derivatives of (a); />
Is DeltaP to U T Partial derivatives of (a); />
Is Δ Q to δ T Partial derivatives of (a); />
Is a pair of delta Q to U T Partial derivatives of (a); p is the active power of the node; q is node reactive power; delta is the node voltage phase angle; u is a node voltage amplitude; t is a matrix transposition symbol; Δ P is the amount of change in P; Δ Q is the amount of change in Q.
Preferably, the power distribution network region division unit 1001 divides the power distribution network region based on the reactive-voltage sensitivity matrix, and acquires a power distribution network subregion, including:
preprocessing the reactive-voltage sensitivity matrix S to obtain an intermediate matrix X;
establishing a fuzzy similar matrix ED representing the electrical distance between the nodes according to the intermediate matrix X;
calculating a fuzzy equivalent matrix by using a transfer closure method according to the fuzzy similar matrix ED;
and clustering according to the fuzzy equivalent matrix and a preset membership threshold so as to divide the distribution network region and obtain the distribution network sub-region.
Preferably, the power distribution network region dividing unit 1001 preprocesses the reactive-voltage sensitivity matrix S to obtain a matrix X, and includes:
sequentially carrying out standard deviation transformation and range transformation on any element in the reactive-voltage sensitivity matrix S to obtain a matrix X;
wherein any element X in the matrix X ij Comprises the following steps:
wherein, i and j correspond to nodes i and j in the power distribution network, and n is the number of the nodes;
is the mean value, W, of the jth column element of the reactive-voltage sensitivity matrix S j Is the standard deviation of the jth column element of the reactive-voltage sensitivity matrix S.
Preferably, in the distribution network area division unit 1001, any element ED in the fuzzy similarity matrix ED ij Comprises the following steps:
wherein ED ij Is the electrical distance between node i and node j; i and j correspond to nodes i and j in the power distribution network, and n is the number of the nodes; x is a radical of a fluorine atom ik Is the element of the ith row and the kth column in the matrix X; x is the number of jk Is the element in the jth row and kth column of the matrix X.
Preferably, the power distribution network region dividing unit 1001 calculates a fuzzy equivalent matrix by using a transfer close-packet method according to the fuzzy similar matrix ED, and includes:
calculating the square of a fuzzy similarity matrix ED representing the electrical distance by ED (1) Indicating, judging ED (1) Whether or not equal to ED, if ED (1) = ED, then ED (1) Is a fuzzy equivalence matrix; otherwise, if ED (1) Not equal to ED, then ED (1) Squared, denoted as ED (2) Continuously judging ED (2) Whether or not equal to ED (1) Until the s-th time ED (s) ＝＝ED (s-1) When ED is determined (s) Is the fuzzy equivalence matrix;
wherein the content of the first and second substances,
wherein ED ij Is the electrical distance between node i and node j; ED (electronic device) ik Is the electrical distance between node i and node k; ED (electronic device) kj Is the electrical distance between node k and node j; n is the number of nodes, V is taken as large as representative, and A is taken as small as representative.
Preferably, the power distribution network region dividing unit 1001 performs clustering according to the fuzzy equivalent matrix and a preset membership threshold to divide the power distribution network region, so as to obtain the power distribution network sub-region, including:
for any element in the fuzzy equivalent matrix, if the value of the element is greater than or equal to a preset membership threshold, updating the value of the element to be considered as a first preset value, and if the value of the element is less than the preset membership threshold, updating the value of the element to be considered as a second preset value;
and (4) merging the nodes corresponding to the columns with the same matrix elements in the updated fuzzy equivalent matrix into a class, and determining the sub-area of the power distribution network according to the classified nodes.
Preferably, the operating voltage determining unit 1002 is configured to determine a central bus in each power distribution network sub-area, perform power flow calculation with the central bus as a reference voltage, and determine a current operating voltage of each node in each power distribution network sub-area.
Preferably, the operation state determining unit 1003 is configured to determine an operation state of the power distribution network according to a current operation voltage of each node and a preset voltage threshold of different operation states.
Preferably, in the operation state determining unit 1003, determining the operation state of the power distribution network according to the current operation voltage of each node and a preset voltage threshold of different operation states includes:
determining the operation state of each node according to the current operation voltage of each node and the preset voltage threshold of different operation states, and determining the operation state with the highest priority in all the nodes as the operation state of the power distribution network;
the priority of the running state is from high to low: an emergency state, a recovery state, an abnormal state, a warning state and a normal state;
wherein, if the current operating voltage of any node satisfies U S ＞U 1 Or U S ＜U 8 Determining that any node is in an emergency state;
if the current operating voltage of any node meets U 1 ≥U S ＞U 2 Or U 7 ＞U S ≥U 8 Then determining that any node is in a recovery state;
if the current operating voltage of any node meets U 2 ≥U S ＞U 3 Or U 6 ＞U S ≥U 7 If yes, determining that any node is in an abnormal state;
if the current operating voltage of any node meets U 3 ≥U S ＞U 4 Or U 5 ＞U S ≥U 6 Determining that any node is in an alert state;
if the current operating voltage of any node meets U 1 ≥U S ≥U 5 If yes, determining that any node is in a normal state; u shape S The current operating voltage; u shape 1 、U 2 、U 3 、U 4 、U 5 、U 6 、U 7 And U 8 Respectively, a first preset voltage threshold, a second preset voltage threshold, a third preset voltage threshold, a fourth preset voltage threshold, a fifth preset voltage threshold, a sixth preset voltage threshold, a seventh preset voltage threshold and an eighth preset voltage threshold.
Preferably, the control unit 1004 is configured to determine a control mode from the station according to the operation state of the power distribution network, so as to restore the power distribution network to a normal operation state based on an optimization scheme corresponding to the control mode.
Preferably, the determining, by the control unit 1004, a control mode from the slave station according to the operation state of the power distribution network to restore the power distribution network to the normal operation state based on the optimization scheme corresponding to the control mode includes:
after the master station monitors the state of the power distribution network, the slave station determines a control mode according to the running state of the power distribution network, starts an optimization scheme according to the control mode, and enables the power distribution network to be recovered to a normal running state by means of optimizing the output of the distributed power supply and/or controlling the load power in the sub-area of the power distribution network; if the adjustment in the sub-area cannot meet the operation requirement of the power distribution network, the distributed power supplies in the adjacent sub-area are regulated and controlled by the master station, and the power flow level of the power distribution network is improved, so that the power distribution network is restored to a normal operation state.
The master-slave self-healing control system 1000 based on power distribution network state evaluation according to the embodiment of the present invention corresponds to the master-slave self-healing control method 100 based on power distribution network state evaluation according to another embodiment of the present invention, and details thereof are not repeated herein.
Fig. 11 is a schematic structural diagram of a master-slave self-healing control system 1000 based on power distribution network state evaluation according to an embodiment of the present invention. As shown in fig. 11, an embodiment of the present invention provides a master-slave self-healing control system 1100 based on power distribution network state evaluation, including: a master device 1101 and a slave device 1102.
Preferably, the master control device 1101 is configured to enable a master station to obtain a reactive-voltage sensitivity matrix of a power distribution network, and divide a power distribution network region based on the reactive-voltage sensitivity matrix to obtain a power distribution network sub-region; the central control system is used for determining a central bus in each power distribution network subregion, performing load flow calculation by taking the central bus as a reference voltage, and determining the current operating voltage of each node in each power distribution network subregion; and the method is used for determining the operation state of the power distribution network according to the current operation voltage of each node and the preset voltage threshold of different operation states.
Preferably, the slave control device 1102 is configured to enable the slave station to determine a control mode according to the operation state of the power distribution network, so as to enable the power distribution network to recover to a normal operation state based on an optimization scheme corresponding to the control mode.
The master-slave self-healing control system 1100 based on power distribution network state evaluation according to the embodiment of the present invention corresponds to the master-slave self-healing control method 100 based on power distribution network state evaluation according to another embodiment of the present invention, and details thereof are not repeated herein.
The invention has been described with reference to a few embodiments. However, other embodiments of the invention than the ones disclosed above are equally possible within the scope of these appended patent claims, as these are known to those skilled in the art.
Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the technical field, unless explicitly defined otherwise herein. All references to "a/an/the [ device, component, etc ]" are to be interpreted openly as referring to at least one instance of said device, component, etc., unless explicitly stated otherwise. The steps of any method disclosed herein do not have to be performed in the exact order disclosed, unless explicitly stated.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (19)
1. A master-slave self-healing control method based on power distribution network state evaluation is characterized by comprising the following steps:
the method comprises the steps that a main station obtains a reactive-voltage sensitivity matrix of a power distribution network, and divides the power distribution network area based on the reactive-voltage sensitivity matrix to obtain sub-areas of the power distribution network;
determining a central bus in each power distribution network subregion, performing load flow calculation by taking the central bus as a reference voltage, and determining the current operating voltage of each node in each power distribution network subregion;
determining the operation state of the power distribution network according to the current operation voltage of each node and the preset voltage threshold of different operation states;
and the slave station determines a control mode according to the running state of the power distribution network, and enables the power distribution network to be recovered to a normal running state based on an optimization scheme corresponding to the control mode.
2. The method of claim 1, wherein the reactive-voltage sensitivity matrix comprises:
S＝(L-MH -1 N) -1 ，
wherein S is a reactive-voltage sensitivity matrix;
is Δ P to δ T Partial derivatives of (a); />
Is DeltaP to U T Partial derivatives of (a); />
Is Δ Q to δ T Partial derivatives of (a); />
Is a pair of delta Q to U T Partial derivatives of (a); p is the active power of the node; q is node reactive power; delta is the node voltage phase angle; u is a node voltage amplitude; t is a matrix transposition symbol; Δ P is the amount of change in P; Δ Q is the amount of change in Q.
3. The method according to claim 1, wherein the dividing of the distribution network region based on the reactive-voltage sensitivity matrix to obtain the distribution network sub-region comprises:
preprocessing the reactive-voltage sensitivity matrix S to obtain an intermediate matrix X;
establishing a fuzzy similar matrix ED representing the electrical distance between the nodes according to the intermediate matrix X;
calculating a fuzzy equivalent matrix by using a transfer closure method according to the fuzzy similar matrix ED;
and clustering according to the fuzzy equivalent matrix and a preset membership threshold so as to divide the distribution network region and obtain the distribution network sub-region.
4. The method of claim 3, wherein the preprocessing the reactive-voltage sensitivity matrix S to obtain a matrix X comprises:
sequentially carrying out standard deviation transformation and range transformation on any element in the reactive-voltage sensitivity matrix S to obtain a matrix X;
wherein any element X in the matrix X ij Comprises the following steps:
wherein, i and j correspond to nodes i and j in the power distribution network, and n is the number of the nodes;
is the mean value, W, of the jth column element of the reactive-voltage sensitivity matrix S j Is the standard deviation of the jth column element of the reactive-voltage sensitivity matrix S.
5. The method according to claim 3, characterized in that any element ED in the fuzzy similarity matrix ED ij Comprises the following steps:
wherein ED ij Is the electrical distance between node i and node j; i and j correspond to nodes i and j in the power distribution network, and n is the number of the nodes; x is the number of ik Is the element of the ith row and the kth column in the matrix X; x is the number of jk For the j-th row and the k-th column of the matrix XAnd (4) elements.
6. The method according to claim 3, wherein said calculating a fuzzy equivalence matrix using a transitive closure method according to said fuzzy similarity matrix ED comprises:
calculating the square of a fuzzy similarity matrix ED representing the electrical distance, using ED (1) Indicating, judging ED (1) Whether or not equal to ED, if ED (1) = ED, then ED (1) A fuzzy equivalence matrix is formed; otherwise, if ED (1) Not equal to ED, then ED (1) Squared, denoted as ED (2) Continuously judging ED (2) Whether or not equal to ED (1) Until the s-th time ED (s) ＝＝ED (s-1) Determining ED (s) Is the fuzzy equivalence matrix;
wherein the content of the first and second substances,
wherein ED ij Is the electrical distance between node i and node j; ED (electronic device) ik Is the electrical distance between node i and node k; ED (electronic device) kj Is the electrical distance between node k and node j; n is the number of nodes, V represents large, and A represents small.
7. The method of claim 3, wherein the clustering according to the fuzzy equivalent matrix and a preset membership threshold to divide the distribution network region to obtain the distribution network sub-region comprises:
for any element in the fuzzy equivalent matrix, if the value of the element is greater than or equal to a preset membership threshold, updating the value of the element to be considered as a first preset value, and if the value of the element is less than the preset membership threshold, updating the value of the element to be considered as a second preset value;
and (4) merging the nodes corresponding to the columns with the same matrix elements in the updated fuzzy equivalent matrix into a class, and determining the sub-area of the power distribution network according to the classified nodes.
8. The method of claim 1, wherein determining the operating state of the power distribution network according to the current operating voltage of each node and a preset voltage amount threshold for different operating states comprises:
determining the operation state of each node according to the current operation voltage of each node and the preset voltage threshold of different operation states, and determining the operation state with the highest priority in all the nodes as the operation state of the power distribution network;
the priority of the running state is from high to low: an emergency state, a recovery state, an abnormal state, a warning state and a normal state;
wherein, if the current operating voltage of any node satisfies U S ＞U 1 Or U S ＜U 8 Determining that any node is in an emergency state;
if the current operating voltage of any node meets U 1 ≥U S ＞U 2 Or U 7 ＞U S ≥U 8 Then determining that any node is in a recovery state;
if the current operating voltage of any node meets U 2 ≥U S ＞U 3 Or U 6 ＞U S ≥U 7 If so, determining that any node is in an abnormal state;
if the current operating voltage of any node meets U 3 ≥U S ＞U 4 Or U 5 ＞U S ≥U 6 Determining that any node is in an alert state;
if the current operating voltage of any node meets U 1 ≥U S ≥U 5 If yes, determining that any node is in a normal state; u shape S Is the current operating voltage; u shape 1 、U 2 、U 3 、U 4 、U 5 、U 6 、U 7 And U 8 Respectively being a first preset voltage threshold, a second preset voltage threshold, a third preset voltage threshold, a fourth preset voltage threshold, a fifth preset voltage threshold, a fourth preset voltage thresholdA sixth preset voltage threshold, a seventh preset voltage threshold, and an eighth preset voltage threshold.
9. The method of claim 1, wherein the slave station determines a control mode according to the operation state of the power distribution network, and restores the power distribution network to a normal operation state based on an optimization scheme corresponding to the control mode, and the method comprises the following steps:
after the master station monitors the state of the power distribution network, the slave station determines a control mode according to the running state of the power distribution network, starts an optimization scheme according to the control mode, and enables the power distribution network to be recovered to a normal running state by means of optimizing the output of the distributed power supply and/or controlling the load power in the sub-area of the power distribution network; if the adjustment in the sub-areas cannot meet the operation requirement of the power distribution network, the distributed power supplies in the adjacent sub-areas are regulated and controlled through the main station, and the power flow level of the power distribution network is improved, so that the power distribution network is recovered to a normal operation state.
10. A master-slave self-healing control system based on power distribution network state assessment is characterized in that the system comprises:
the distribution network region division unit is used for enabling the master station to obtain a reactive-voltage sensitivity matrix of the distribution network, and dividing the distribution network region based on the reactive-voltage sensitivity matrix to obtain sub-regions of the distribution network;
the operation voltage determining unit is used for determining a central bus in each power distribution network subregion, performing load flow calculation by taking the central bus as reference voltage, and determining the current operation voltage of each node in each power distribution network subregion;
the operation state determining unit is used for determining the operation state of the power distribution network according to the current operation voltage of each node and the preset voltage threshold of different operation states;
and the control unit is used for determining a control mode from the station according to the running state of the power distribution network and enabling the power distribution network to be recovered to a normal running state based on an optimization scheme corresponding to the control mode.
11. The system of claim 10, wherein in the distribution grid area partitioning unit, a reactive-voltage sensitivity matrix comprises:
S＝(L-MH -1 N) -1 ，
wherein S is a reactive-voltage sensitivity matrix;
is Δ P to δ T Partial derivatives of (a); />
Is DeltaP to U T Partial derivatives of (a); />
Is Δ Q to δ T Partial derivatives of (a); />
Is a pair of delta Q to U T Partial derivatives of (a); p is the active power of the node; q is node reactive power; delta is the node voltage phase angle; u is a node voltage amplitude; t is a matrix transposition symbol; Δ P is the amount of change in P; Δ Q is the amount of change in Q.
12. The system of claim 10, wherein the distribution network region division unit performs distribution network region division based on the reactive-voltage sensitivity matrix to obtain distribution network sub-regions, and the distribution network sub-regions comprise:
preprocessing the reactive-voltage sensitivity matrix S to obtain an intermediate matrix X;
establishing a fuzzy similar matrix ED representing the electrical distance between the nodes according to the intermediate matrix X;
calculating a fuzzy equivalent matrix by using a transfer closure method according to the fuzzy similar matrix ED;
and clustering according to the fuzzy equivalent matrix and a preset membership threshold so as to divide the distribution network region and obtain the distribution network sub-region.
13. The system of claim 12, wherein the power distribution network region partitioning unit preprocesses the reactive-voltage sensitivity matrix S to obtain a matrix X, comprising:
sequentially carrying out standard deviation transformation and range transformation on any element in the reactive-voltage sensitivity matrix S to obtain a matrix X;
wherein any element X in the matrix X ij Comprises the following steps:
wherein, i and j correspond to nodes i and j in the power distribution network, and n is the number of the nodes;
is the mean value, W, of the jth column element of the reactive-voltage sensitivity matrix S j Is the standard deviation of the jth column element of the reactive-voltage sensitivity matrix S.
14. The system according to claim 12, wherein in the distribution network area division unit, any element ED in a fuzzy similarity matrix ED is identified ij Comprises the following steps:
wherein ED ij Is the electrical distance between node i and node j; i and j correspond to nodes i and j in the power distribution network, and n is the number of the nodes; x is the number of ik Is the element of the ith row and the kth column in the matrix X; x is a radical of a fluorine atom jk Is the element in the jth row and kth column of the matrix X.
15. The system according to claim 12, wherein the distribution network region dividing unit calculates a fuzzy equivalent matrix according to the fuzzy similar matrix ED by using a transfer closed-packet method, including:
calculating the square of a fuzzy similarity matrix ED representing the electrical distance, using ED (1) Indicating, judging ED (1) Whether or not equal to ED, if ED (1) = ED, then ED (1) Is a fuzzy equivalence matrix; otherwise, if ED (1) Not equal to ED, then ED (1) Squared, denoted as ED (2) Continuously judging ED (2) Whether or not equal to ED (1) Until the s-th time ED (s) ＝＝ED (s-1) When ED is determined (s) Is the fuzzy equivalence matrix;
wherein the content of the first and second substances,
wherein ED ij Is the electrical distance between node i and node j; ED (electronic device) ik Is the electrical distance between node i and node k; ED (electronic device) kj Is the electrical distance between node k and node j; n is the number of nodes, V is taken as large as representative, and A is taken as small as representative.
16. The system of claim 12, wherein the power distribution network region partitioning unit performs clustering according to the fuzzy equivalent matrix and a preset membership threshold to partition the power distribution network region and obtain the power distribution network sub-region, and includes:
for any element in the fuzzy equivalent matrix, if the value of the element is greater than or equal to a preset membership threshold, updating the value of the element to be considered as a first preset value, and if the value of the element is less than the preset membership threshold, updating the value of the element to be considered as a second preset value;
and (4) merging the nodes corresponding to the columns with the same matrix elements in the updated fuzzy equivalent matrix into a class, and determining the sub-area of the power distribution network according to the classified nodes.
17. The system of claim 10, wherein the determining, at the operation state determining unit, the operation state of the power distribution network according to the current operation voltage of each node and the preset voltage amount threshold of the different operation states comprises:
determining the operation state of each node according to the current operation voltage of each node and the preset voltage threshold of different operation states, and determining the operation state with the highest priority in all the nodes as the operation state of the power distribution network;
the priority of the running state is from high to low: an emergency state, a recovery state, an abnormal state, a warning state and a normal state;
wherein, if the current operating voltage of any node satisfies U S ＞U 1 Or U S ＜U 8 Determining that any node is in an emergency state;
if the current operating voltage of any node meets U 1 ≥U S ＞U 2 Or U 7 ＞U S ≥U 8 Then determining that any node is in a recovery state;
if the current operating voltage of any node meets U 2 ≥U S ＞U 3 Or U 6 ＞U S ≥U 7 If yes, determining that any node is in an abnormal state;
if the current operating voltage of any node meets U 3 ≥U S ＞U 4 Or U 5 ＞U S ≥U 6 Determining that any node is in an alert state;
if any node is full of current operating voltageFoot U 1 ≥U S ≥U 5 If yes, determining that any node is in a normal state; u shape S The current operating voltage; u shape 1 、U 2 、U 3 、U 4 、U 5 、U 6 、U 7 And U 8 Respectively, a first preset voltage threshold, a second preset voltage threshold, a third preset voltage threshold, a fourth preset voltage threshold, a fifth preset voltage threshold, a sixth preset voltage threshold, a seventh preset voltage threshold and an eighth preset voltage threshold.
18. The system of claim 10, wherein the control unit determines the control mode from the station according to the operation state of the power distribution network, and restores the power distribution network to a normal operation state based on an optimization scheme corresponding to the control mode, and the control unit comprises:
after the master station monitors the state of the power distribution network, the slave station determines a control mode according to the running state of the power distribution network, starts an optimization scheme according to the control mode, and enables the power distribution network to be recovered to a normal running state by means of optimizing the output of the distributed power supply and/or controlling the load power in the sub-area of the power distribution network; if the adjustment in the sub-areas cannot meet the operation requirement of the power distribution network, the distributed power supplies in the adjacent sub-areas are regulated and controlled through the main station, and the power flow level of the power distribution network is improved, so that the power distribution network is recovered to a normal operation state.
19. A master-slave self-healing control system based on power distribution network state assessment is characterized in that the system comprises: the system comprises a master control device and a slave control device; wherein the content of the first and second substances,
the main control equipment is used for enabling the main station to obtain a reactive power-voltage sensitivity matrix of the power distribution network, dividing the power distribution network area based on the reactive power-voltage sensitivity matrix and obtaining a power distribution network sub-area; the system comprises a central control unit, a load flow calculation unit and a power distribution network sub-area, wherein the central control unit is used for determining a central control bus in each power distribution network sub-area, performing load flow calculation by taking the central control bus as reference voltage, and determining the current operating voltage of each node in each power distribution network sub-area; the system comprises a power distribution network, a node, a power supply and a control unit, wherein the power distribution network is used for determining the running state of the power distribution network according to the current running voltage of each node and the preset voltage threshold of different running states;
and the slave control equipment is used for enabling the slave station to determine a control mode according to the running state of the power distribution network so as to enable the power distribution network to be recovered to a normal running state based on an optimization scheme corresponding to the control mode.
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