CN107621594A - A kind of electric network failure diagnosis method based on fault recorder data and Bayesian network - Google Patents
A kind of electric network failure diagnosis method based on fault recorder data and Bayesian network Download PDFInfo
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Abstract
The invention discloses a kind of electric network failure diagnosis method based on fault recorder data and Bayesian network, comprise the following steps：S1：Fault zone is determined by protection device action message, breaker information and the Fault Recorder Information in SCADA system information；S2：Bayesian network diagnostic model is established according to fault zone, obtains the probability of malfunction G1 of each element；S3：Using fault recorder data, by fault diagnosis principle, the probability of malfunction G2 of each element is obtained；S4：Assign G1 the weights different with G2 according to an expert view, the probability of malfunction G3 of each element is obtained by weighted average method.Compared with prior art, the diagnosis to failure is more accurate by the present invention, and the weight of two methods can be adjusted according to actual conditions, has very high practicality.
Description
Technical Field
The invention relates to the field of power grid fault diagnosis, in particular to a power grid fault diagnosis method based on fault recording data and a Bayesian network.
Background
An SCADA (supervisory control and data acquisition) system is indispensable equipment in a dispatching center, can assist a dispatcher in monitoring a power system when a power grid operates in a steady state, and can also provide fault information for the dispatcher when a fault occurs. When a fault occurs, the SCADA system can provide switching information and protected outlet action information for workers, but this cannot meet the information requirement for fault diagnosis. In addition, the existing dispatching automation system can only collect data when the system fails, and the information is directly provided to a dispatcher without screening and processing, so that the dispatcher cannot judge and process the information in time. When a complex fault occurs, the diagnostic system cannot give a satisfactory result if only the data reported by the SCADA system is used. In order to ensure the accuracy of diagnosis, new diagnostic information sources and diagnostic methods need to be found.
Disclosure of Invention
The invention overcomes the technical defects of the existing fault diagnosis method and provides a power grid fault diagnosis method based on fault recording data and a Bayesian network. According to the method, a current direction matrix of each element during fault is acquired through fault recording data, breaker action information and electronic element fault information, and a suspicious element set is formed; and meanwhile, power grid fault diagnosis is carried out through a Bayesian network to form a suspicious element set, and diagnosis is carried out by using a weighted average model by using weight distribution given by a special method.
In order to solve the technical problems, the technical scheme of the invention is as follows:
a power grid fault diagnosis method based on fault recording data and a Bayesian network comprises the following steps:
s1: determining a fault area through protection device action information, breaker information and fault recording information in the SCADA system information;
s2: establishing a Bayesian network diagnosis model according to the fault area to obtain the fault probability G1 of each element;
s3: obtaining the fault probability G2 of each element by utilizing fault recording data and according to a fault diagnosis principle;
s4: the G1 and G2 are given different weights according to expert opinions, and the failure probability G3 of each element is obtained through a weighted average method.
In a preferred embodiment, the S2 includes the following steps:
s2.1: establishing a corresponding Bayesian network diagnosis model for nodes in a fault area; the node comprises a bus, a line, an electronic element, a circuit breaker and a protection device;
s2.2: solving the fault prior probability of each node for the Bayesian network diagnosis model of each electronic element;
s2.3: and correlating the fault prior probability with the posterior probability to obtain the fault probability G1 of the element.
In a preferred embodiment, the failure prior probability of each node in S2.2 includes the following calculation procedures:
s2.2.1: calculating the fault prior probability of the electronic element through the annual fault frequency of the equipment, namely calculating the fault probability after the equipment continuously operates for a period of time; prior probability of failure P of electronic component 1 The following formula is used for solving the problem:
P 1 ＝P{0<t}＝1-e wt
in the formula, w is the annual failure rate of the element, and t is the number of times of overhaul of the element.
S2.2.2: fault prior probability P for circuit breaker and protection device linkage 2 ，P 2 The following formula is used for solving the problem:
in the formula, parent (X) i ) Representing a parent node set, i.e., an electronic component node.
In a preferred embodiment, the failure probability G1 of the component is determined by the following formula:
wherein P (Y) represents a failure prior probability of the electronic component, i.e., P (Y) = P 1 (ii) a P (X) represents the prior probability of a fault in which the circuit breaker node is linked to the protection device, i.e. P (X) = P 2 (ii) a P (Y | X) represents the probability of a failure of an electronic component given the known breaker and protection device action; p (X | Y) represents the probability of a fault with the circuit breaker in linkage with the protection device under known electronic component fault conditions.
In a preferred embodiment, said S3 comprises the following steps:
s3.1: for node x in fault recording data 1 ，x 2 ，…，x n-1 ，x n Inserting sampling points for processing by a Lagrange interpolation method to obtain a polynomial equation y = f (x) passing through n nodes;
s3.2: carrying out fault judgment on a sampling point of a Lagrange interpolation method in fault recording data by adopting a mutation algorithm;
s3.3: extracting fault components of the fault node to obtain positive/negative sequence voltage and current component phases;
s3.4: and judging the current direction through the positive/negative sequence voltage and current component phase to form an element current direction association matrix, and obtaining the fault probability G2 of each element according to the current direction matrix.
In a preferred embodiment, the S3.1 adopts a quadratic lagrange interpolation method for processing, and the specific content is as follows:
for node x in fault recording data 1 ，x 2 ，…，x n-1 ，x n Setting an interpolation point x and three corresponding points x i-1 ,x i ,x i+1 The i is obtained by the following formula:
according to x, x i-1 ,x i ,x i+1 Obtaining a quadratic lagrange interpolation polynomial y = f (x), where y = f (x) is obtained by the following formula:
in a preferred embodiment, a four-sample-value mutation algorithm is adopted in S3.2, and the content of the four-sample-value mutation algorithm is as follows:
Δi(k)＝[i(k)-i(k-N)]-[i(k-N)+i(k-2N)]
in the formula, Δ i (k) is a current variation, i (k) is a current sampling value of the kth sampling point in the period, i (k-N) is a current sampling value of the kth sampling point in the last period, and i (k-2N) is a current sampling value of the kth sampling point in the last period;
fault starting discrimination formula:
Δi(k)＞I set
in the formula I set Is a preset value. If Δ I (k) > I set And judging the node as a fault node.
In a preferred embodiment, I is set The following formula is used for solving the following problem:
I set ＝k 1 Δi(k)+k 2 I N
in the formula, k 1 And k 2 Is a preset value, I N Is the rated current value before the corresponding electronic component fails.
In a preferred embodiment, the S3.3 includes the following calculation procedures:
s3.3.1: extracting a fault component i g (t) said i g (t) is obtained by the following formula:
i g (t)＝i(t)-i(t-nN)
wherein i (t) is a current sampling value during fault, i (t-nN) is a current sampling value before fault, i (t-nN) and i (t) are separated by N periods, and N is the number of sampling points per period;
s3.3.2: in the fault component i g (t) to obtain the corresponding positive/negative sequence voltage current component phase.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that:
according to the method, a current direction matrix of each element during fault is acquired through fault recording data, breaker action information and electronic element fault information, and a suspicious element set is formed; and meanwhile, power grid fault diagnosis is carried out through a Bayesian network to form a suspicious element set, and diagnosis is carried out by using a weighted average model by using weight distribution given by a special method. Compared with the prior art, the method has more accurate diagnosis of the fault, can adjust the weights of the two methods according to actual conditions, and has high practicability.
Drawings
Fig. 1 is a flowchart of the present embodiment.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent;
it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
As shown in fig. 1, a power grid fault diagnosis method based on fault recording data and a bayesian network includes the following steps:
s1, determining a fault area through SCADA system information;
and determining a fault area through the action information of the protection device, the breaker information and the fault recording information in the SCADA system information.
S2, establishing a corresponding Bayesian network diagnosis model for the nodes in the fault area;
the node comprises a bus, a line, an electronic component, a circuit breaker and a protection device.
S3, solving the prior probability of the fault of each node;
calculating the fault prior probability of the electronic element through the annual fault frequency of the equipment, namely calculating the fault probability after the equipment continuously operates for a period of time; prior probability of failure P of electronic component 1 The following formula is used for solving the problem:
P 1 ＝P{0<t}＝1-e wt
in the formula, w is the annual failure rate of the element, t is the number of times of overhaul of the element, and in this embodiment, t =2;
fault prior probability P for circuit breaker and protection device linkage 2 ，P 2 The following formula is used for solving the problem:
in the formula, parent (X) i ) Representing a parent node set, i.e., an electronic component node.
S4, correlating the fault prior probability with the posterior probability to obtain the fault probability G1 of the element;
the failure probability G1 of a component is determined by the following formula:
wherein P (Y) represents a failure prior probability of the electronic component, i.e., P (Y) = P 1 (ii) a P (X) represents the prior probability of a fault of the circuit breaker node in linkage with the protection device, i.e. P (X) = P 2 (ii) a P (Y | X) represents the probability of a failure of an electronic component given the known breaker and protection device action; p (X | Y) represents the probability of a fault with the circuit breaker in linkage with the protection device under known electronic component fault conditions.
S5, using a Lagrange interpolation method for nodes in the fault recording data;
for node x in fault recording data 1 ，x 2 ，…，x n-1 ，x n Setting an interpolation point x and corresponding three points x i-1 ,x i ,x i+1 The i is obtained by the following formula:
according to x, x i-1 ,x i ,x i+1 Obtaining a quadratic lagrange interpolation polynomial y = f (x), where y = f (x) is obtained by the following formula:
s6, judging the fault by adopting a four-sampling-value mutation algorithm for sampling points in the fault recording data;
the four-sample-value mutation algorithm content is as follows:
Δi(k)＝[i(k)-i(k-N)]-[i(k-N)+i(k-2N)]
in the formula, Δ i (k) is a current variation, i (k) is a current sampling value of the kth sampling point in the period, i (k-N) is a current sampling value of the kth sampling point in the last period, and i (k-2N) is a current sampling value of the kth sampling point in the last period;
fault starting discrimination formula:
Δi(k)＞I set
I set ＝k 1 Δi(k)+k 2 I N
in the formula, k 1 And k 2 Is a preset value, I N Is the rated current value before the corresponding electronic component fails. If Δ I (k) > I set And judging the node as a fault node.
S7, extracting fault components of the fault nodes to obtain positive/negative sequence voltage and current component phases;
extracting fault component i from fault node g (t)，i g (t) is obtained by the following formula:
i g (t)＝i(t)-i(t-nN)
wherein i (t) is a current sampling value during fault, i (t-nN) is a current sampling value before fault, i (t-nN) and i (t) are separated by N periods, and N is the number of sampling points per period;
in the fault component i g (t) to obtain the corresponding positive/negative sequence voltage current component phase.
S8, obtaining the fault probability G2 of each element through the positive/negative sequence voltage and current component phase;
and judging the current direction through the positive/negative sequence voltage and current component phase to form an element current direction association matrix, and obtaining the fault probability G2 of each element according to the current direction matrix.
S9, obtaining the fault probability G3 of each element by a weighted average method;
in this example, G1 is given a weight of 40% and G2 is given a weight of 60%.
The same or similar reference numerals correspond to the same or similar parts;
the terms describing positional relationships in the drawings are for illustrative purposes only and are not to be construed as limiting the patent;
it should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (9)
1. A power grid fault diagnosis method based on fault recording data and a Bayesian network is characterized by comprising the following steps:
s1: determining a fault area through protection device action information, breaker information and fault recording information in the SCADA system information;
s2: establishing a Bayesian network diagnosis model according to the fault area to obtain the fault probability G1 of each element;
s3: obtaining the fault probability G2 of each element by utilizing fault recording data and according to a fault diagnosis principle;
s4: the G1 and G2 are given different weights according to expert opinions, and the failure probability G3 of each element is obtained through a weighted average method.
2. The grid fault diagnosis method according to claim 1, wherein S2 comprises the following steps:
s2.1: establishing a corresponding Bayesian network diagnosis model for nodes in a fault area; the node comprises a bus, a line, an electronic element, a circuit breaker and a protection device;
s2.2: solving the fault prior probability of each node for the Bayesian network diagnosis model of each electronic element;
s2.3: and correlating the fault prior probability with the posterior probability to obtain the fault probability G1 of the element.
3. The grid fault diagnosis method according to claim 2, wherein the fault prior probability of each node in S2.2 comprises the following calculation procedures:
s2.2.1: calculating the fault prior probability of the electronic element through the annual fault frequency of the equipment, namely calculating the fault probability after the equipment continuously operates for a period of time; prior probability of failure P of electronic component 1 The following formula is used for solving the following problem:
P 1 ＝P{0<t}＝1-e wt
in the formula, w is the annual failure rate of the element, and t is the number of times of overhaul of the element.
S2.2.2: fault prior probability P for circuit breaker and protection device linkage 2 ，P 2 By passing throughThe following formula is used for solving:
in the formula, parent (X) i ) Representing a parent node set, i.e., an electronic component node.
4. The grid fault diagnosis method according to claim 3, wherein the fault probability G1 of the element is obtained by the following formula:
wherein P (Y) represents a failure prior probability of the electronic component, i.e., P (Y) = P 1 (ii) a P (X) represents the prior probability of a fault of the circuit breaker node in linkage with the protection device, i.e. P (X) = P 2 (ii) a P (Y | X) represents the probability of a failure of an electronic component given the known breaker and protection device action; p (X | Y) represents the probability of a fault with the circuit breaker in linkage with the protection device under known electronic component fault conditions.
5. The grid fault diagnosis method according to any one of claims 1 to 4, wherein the step S3 comprises the following steps:
s3.1: for node x in fault recording data 1 ，x 2 ，…，x n-1 ，x n Inserting sampling points by a Lagrange interpolation method for processing to obtain a polynomial equation y = f (x) passing through n nodes;
s3.2: carrying out fault judgment on a sampling point of a Lagrange interpolation method in fault recording data by adopting a mutation algorithm;
s3.3: extracting fault components of the fault node to obtain positive/negative sequence voltage and current component phases;
s3.4: and judging the current direction through the positive/negative sequence voltage and current component phase to form an element current direction association matrix, and obtaining the fault probability G2 of each element according to the current direction matrix.
6. The power grid fault diagnosis method according to claim 5, wherein a quadratic Lagrangian interpolation method is adopted in S3.1, and the specific content is as follows:
for node x in fault recording data 1 ，x 2 ，…，x n-1 ，x n Setting an interpolation point x and corresponding three points x i-1 ,x i ,x i+1 The i is obtained by the following formula:
according to x, x i-1 ,x i ,x i+1 Obtaining a quadratic lagrange interpolation polynomial y = f (x), wherein the y = f (x) is obtained by the following formula:
7. the grid fault diagnosis method according to claim 6, wherein a four-sample-value-mutation algorithm is adopted in S3.2, and the contents of the four-sample-value-mutation algorithm are as follows:
Δi(k)＝[i(k)-i(k-N)]-[i(k-N)+i(k-2N)]
in the formula, Δ i (k) is a current variation, i (k) is a current sampling value of a kth sampling point in the period, i (k-N) is a current sampling value of a kth sampling point in the previous period, and i (k-2N) is a current sampling value of a kth sampling point in the previous period;
fault starting discrimination formula:
Δi(k)＞I set
in the formula I set Is a predetermined value, if Δ I (k) > I set And judging the node as a fault node.
8. The grid fault diagnosis method according to claim 7, wherein I is set The following formula is used for solving the problem:
I set ＝k 1 Δi(k)+k 2 I N
in the formula, k 1 And k 2 Is a preset value, I N Is the rated current value before the corresponding electronic component fails.
9. The grid fault diagnosis method according to claim 7 or 8, wherein S3.3 comprises the following calculation procedures:
s3.3.1: extracting a fault component i g (t) said i g (t) is obtained by the following formula:
i g (t)＝i(t)-i(t-nN)
wherein i (t) is a current sampling value during fault, i (t-nN) is a current sampling value before fault, i (t-nN) and i (t) are separated by N periods, and N is the number of sampling points per period;
s3.3.2: in the fault component i g (t) to obtain the corresponding positive/negative sequence voltage current component phase.
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