CN117725981B - Power distribution network fault prediction method based on optimal time window mechanism - Google Patents
Power distribution network fault prediction method based on optimal time window mechanism Download PDFInfo
- Publication number
- CN117725981B CN117725981B CN202410176910.9A CN202410176910A CN117725981B CN 117725981 B CN117725981 B CN 117725981B CN 202410176910 A CN202410176910 A CN 202410176910A CN 117725981 B CN117725981 B CN 117725981B
- Authority
- CN
- China
- Prior art keywords
- hidden layer
- time window
- power distribution
- input
- fault
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 27
- 230000007246 mechanism Effects 0.000 title claims abstract description 23
- 238000003062 neural network model Methods 0.000 claims abstract description 19
- 230000006870 function Effects 0.000 claims description 15
- 210000002569 neuron Anatomy 0.000 claims description 12
- 230000008569 process Effects 0.000 claims description 7
- 230000004913 activation Effects 0.000 claims description 6
- 230000008859 change Effects 0.000 claims description 6
- 230000001186 cumulative effect Effects 0.000 claims description 6
- 238000002372 labelling Methods 0.000 claims description 4
- 238000012360 testing method Methods 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 230000001052 transient effect Effects 0.000 description 4
- 238000013528 artificial neural network Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000004020 conductor Substances 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to the technical field of power distribution network fault prediction, and discloses a power distribution network fault prediction method based on an optimal time window mechanism. The power distribution network fault prediction method based on the optimal time window mechanism is characterized in that an optimal weight is found for the established neural network model, an optimal time window mechanism is established by matching historical data, and an optimal time window width is trainedInputting real-time data as network data, predicting failure by using jagged state of output value, and repeating for several times when jagged state is intermittentWhen the pre-judging fault is about to occur, an alarm is sent out, and when the jagged state iterates for a time intervalWhen the method is used, the pre-judging fault does not occur, and an alarm is not sent out.
Description
Technical Field
The invention relates to the technical field of power distribution network fault prediction, in particular to a power distribution network fault prediction method based on an optimal time window mechanism.
Background
In areas with high forest coverage rate and many natural protection areas, when extreme weather such as windy and rainy weather occurs, the bare conductor in the power grid infrastructure is extremely easy to generate a ground fault, and how to safely and rapidly treat electric shock, mountain fire and personal casualties caused by the ground fault of the power distribution network in the mountain area is always a long-standing pain point and a long-standing difficult problem.
Disclosure of Invention
(One) solving the technical problems
Aiming at the defects of the prior art, the invention provides a power distribution network fault prediction method based on an optimal time window mechanism, which has the advantages of predicting faults in time, giving an alarm and the like, and solves the technical problems.
(II) technical scheme
In order to achieve the above purpose, the present invention provides the following technical solutions: a power distribution network fault prediction method based on an optimal time window mechanism comprises the following steps:
s1, performing fault marking on historical data of a power distribution network, and establishing a neural network model;
S2, establishing an optimal time window mechanism for judging the fault state;
s3, training the model, and predicting the faults by matching with an optimal time window mechanism.
As a preferred technical solution of the present invention, the neural network model in step S1 includes six parts, where the first part is an input layer, the second part is a first hidden layer, the third part is a second hidden layer, the fourth part is a third hidden layer, the fifth part is an output layer, and the sixth part is a recursive link.
As a preferred technical scheme of the present invention, the expression of the output result of the neural network model is as follows:
Wherein P represents the number of input elements x (t), t represents the time iteration times, I p (t) represents the network input value, P+1, N, Q respectively represent the number of input layer nodes, the number of first hidden layer nodes and the number of second hidden layer nodes, f qnp[Ip (t) ] represents the activation function of the network input value I p (t), Representing the summation of internal Q data,/>Representing the cumulative multiplication of internal N data,/>Representing summing the internal P +1 data.
As a preferred technical solution of the present invention, the overall input value I (t) = [ x 1(t),x2(t),…,xp(t),IP+1(t)]T ] of the neural network model, the network input value I p (t) includes a normal input value x p (t) and an output value y (t-1) of the last iteration, and a specific expression of the network input value I p (t) is as follows:
Where y (t-1) =0 when p=1.
As a preferred technical solution of the present invention, the expression of the weight vector W 0 between the second hidden layer and the third hidden layer and the expression of the weight vector W n between the first hidden layer and the input layer are as follows:
W0＝[w01,w02,…,w0Q]
Wn＝[wn1,wn2,…,wnP,wn(P+1)]
Wherein Q is the number of nodes of the second hidden layer, w 0Q represents the weight vector of the corresponding node, n represents the number of nodes of the first hidden layer, w n(P+1) represents the weight vector of the corresponding node, and the variable epsilon (t) of the input layer corresponds to the output epsilon n (t) of the first hidden layer as follows:
Wherein, Representing summing the internal p+1 data, W n is a weight vector between the first hidden layer and the input layer, W np is a weight vector of the corresponding node, I p (t) is a network input value, and the connection weight between the first hidden layer and the second hidden layer is fixed to 1.
As a preferred technical solution of the present invention, the connection manner between the first hidden layer and the second hidden layer includes full connection and partial connection, when the connection manner between the first hidden layer and the second hidden layer is full connection, the number of connection combinations of neurons is 2 N, when the connection manner between the first hidden layer and the second hidden layer is partial connection, the number of connection combinations of neurons is less than 2 N, and when the connection manner between the first hidden layer and the second hidden layer is partial connection, the network connection combination expression between the first hidden layer and the second hidden layer is as follows:
Wherein A q is the set of the q-th neuron in the second hidden layer connected to the whole first hidden layer, B n is the set of the n-th neuron in the first hidden layer connected to the whole second hidden layer, For the number of elements in A q,/>For the number of elements in B n, the output δ q (t) of the second hidden layer is expressed as follows:
Wherein ε i represents the i-th output value of the first hidden layer, W iIi (t) represents the conversion expression of the variable of the input layer corresponding to the output of the first hidden layer, Representing the cumulative multiplication of the internal data.
As a preferred embodiment of the present invention, the output y (t) of the third hidden layer is expressed as follows:
where f represents the activation function, W 0q represents the elements in the set W 0, delta (t) represents the total output of the second hidden layer, Representing summing the internal data.
As a preferred technical solution of the present invention, the neural network model training set and the error function E (w) are expressed as follows:
Wherein, The training sample set is represented, I j (t) represents a corresponding input set, O j (t) represents a corresponding output set, J represents the total number of elements of the sample set, J represents a J-th element, and the value is iterated when training is carried out each time, wherein the specific iteration expression is as follows:
Wherein, Representing the weight change of the error function to W 0 at the kth iteration, W 0 represents the weight vector between the second hidden layer and the third hidden layer,/>Representing the weight change of the error function to W n at the kth iteration, W n represents the weight vector W n,/>, between the first hidden layer and the input layerRepresenting E (W) to perform bias calculation on W 0、Wn, and eta represents the learning rate of the training process.
As a preferable technical scheme of the invention, the specific process of the step S2 is as follows:
S2.1, reading historical fault information;
S2.2, searching an optimal time window, searching a serrated time interval upwards according to the historical fault information, and continuously comparing with the historical data until determining an optimal time window width T *;
S2.3, comparing the actual saw-tooth time width with the optimal time window width T *.
As a preferable technical scheme of the invention, the process of the S3 is as follows:
s3.1, training a neural network model;
S3.2, taking the test set data as network input, and comparing the output value with the fault labeling value;
S3.3, inputting real-time data as network data, and predicting faults by using a state that the output value is saw-toothed:
when the serrated time width delta T is more than or equal to T *, pre-judging that the fault is about to occur, and sending out an alarm;
when the sawtooth-shaped time width delta T is smaller than T *, the pre-judging fault cannot occur, and no alarm is given.
Compared with the prior art, the invention provides a power distribution network fault prediction method based on an optimal time window mechanism, which has the following beneficial effects:
According to the invention, the optimal weight is found out for the established neural network model, the optimal time window mechanism is established by matching with historical data, the optimal time window width T * is trained, then real-time data is used as network data to be input, the output value is used for carrying out fault prediction in a zigzag state, when the iteration time interval delta T of the zigzag state is more than or equal to T *, the fault is predicted to be happened, an alarm is sent out, and when the iteration time interval delta T of the zigzag state is less than T *, the fault is predicted not to happen, and the alarm is not sent out. Therefore, the effects of predicting the transient faults and eliminating hidden danger are achieved.
Drawings
FIG. 1 is a schematic diagram of a neural network according to the present invention;
FIG. 2 is a diagram illustrating a mechanism of a jagged optimal time window according to the present invention;
FIG. 3 is a flow chart of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-3, the present invention provides the following technical solutions: a power distribution network fault prediction method based on an optimal time window mechanism comprises the following steps:
s1, performing fault marking on historical data of a power distribution network, and establishing a neural network model;
The neural network model in step S1 includes six parts, where the first part is an input layer, the second part is a first hidden layer, the third part is a second hidden layer, the fourth part is a third hidden layer, the fifth part is an output layer, and the sixth part is a recursive link (shown as a dotted line in fig. 1).
The expression of the output result of the neural network model is as follows:
Wherein P represents the number of input elements x (t), t represents the time iteration times, I p (t) represents the network input value, P+1, N, Q respectively represent the number of input layer nodes, the number of first hidden layer nodes and the number of second hidden layer nodes, f qnp[Ip (t) ] represents the activation function of the network input value I p (t), Representing the summation of internal Q data,/>Representing the cumulative multiplication of internal N data,/>Representing summing the internal P +1 data.
The overall input value I (t) = [ x 1(t),x2(t),…,xp(t),IP+1(t)]T, the network input value I p (t) includes a normal input value x p (t) and an output value y (t-1) of the last iteration, and the specific expression of the network input value I p (t) is as follows:
Where when p=1, y (t-1) =0, and during the first iteration, the input element of the network contains only x p (t). From the second iteration, the recursive element y (t-1) is added to I p (t).
The weight vector W 0 between the second hidden layer and the third hidden layer, and the weight vector W n between the first hidden layer and the input layer are expressed as follows:
W0＝[w01,w02,…,w0Q]
Wn＝[wn1,wn2,…,wnP,wn(P+1)]
Wherein Q is the number of nodes of the second hidden layer, w 0Q represents the weight vector of the corresponding node, n represents the number of nodes of the first hidden layer, w n(P+1) represents the weight vector of the corresponding node, and the variable epsilon (t) = { epsilon 1(t),…,εN (t) } of the input layer corresponds to the output epsilon n (t) of the first hidden layer as follows:
Wherein, Representing summing the internal p+1 data, W n is a weight vector between the first hidden layer and the input layer, W np is a weight vector of the corresponding node, I p (t) is a network input value, and the connection weight between the first hidden layer and the second hidden layer is fixed to 1.
The connection mode between the first hidden layer and the second hidden layer comprises full connection and partial connection, when the connection mode between the first hidden layer and the second hidden layer is full connection, the connection combination number of the neurons is 2 N, when the connection mode between the first hidden layer and the second hidden layer is partial connection, the connection combination number of the neurons is less than 2 N, and the network connection combination expression between the first hidden layer and the second hidden layer is as follows:
Wherein A q (1.ltoreq.q.ltoreq.Q) is the whole first hidden layer connected with the set of the Q-th neuron in the second hidden layer, B n (1.ltoreq.n.ltoreq.N) is the whole second hidden layer connected with the set of the N-th neuron in the first hidden layer, For the number of elements in A q,/>For the number of elements in B n, the output δ q (t) expression of the second hidden layer is as follows:
Wherein ε i represents the i-th output value of the first hidden layer, W iIi (t) represents the conversion expression of the variable of the input layer corresponding to the output of the first hidden layer, Representing the cumulative multiplication of the internal data.
The output y (t) of the third hidden layer is expressed as follows:
where f represents the activation function, W 0q represents the elements in the set W 0, delta (t) represents the total output of the second hidden layer, Representing summing the internal data.
The neural network model training set and the error function E (w) are expressed as follows:
Wherein, The training sample set is represented, I j (t) represents a corresponding input set, O j (t) represents a corresponding output set, J represents the total number of elements of the sample set, J represents a J-th element, and the value is iterated when training is carried out each time, wherein the specific iteration expression is as follows:
Wherein, Representing the weight change of the error function to W 0 at the kth iteration, W 0 represents the weight vector between the second hidden layer and the third hidden layer,/>Representing the weight change of the error function to W n at the kth iteration, W n represents the weight vector W n,/>, between the first hidden layer and the input layerRepresenting E (W) to perform partial derivative calculation on W 0、Wn, wherein eta represents the learning rate of the training process and is usually a fixed value;
s2, an optimal time window mechanism is established, and faults occurring in the power distribution network are mainly classified into permanent faults and transient faults. For permanent faults, historical fault data is used as network input, and an optimal time window is searched: finding out a sawtooth-shaped output state value, namely, the last moment of output value is a fault state, the current moment of output value is a normal state, the next moment of output value is a fault state, and the like, training an optimal time window width T *, and taking the sawtooth-shaped time width (namely, the sawtooth-shaped state iteration time interval) as a basis for judging the transient fault so as to predict the transient fault and eliminate hidden danger as long as the sawtooth-shaped time width is more than or equal to the optimal time window width;
s3, training the model, and predicting faults by matching with an optimal time window mechanism;
Model training, namely dividing historical fault information data into a training set and a testing set by using the thought of supervised learning, and training the neural network by using the training set. The historical data is subjected to fault labeling ('0' is represented as a fault state, '1' is represented as a normal state), then the historical data is used as different network inputs, the neural network model is trained, and the optimal weight is found out on the basis of meeting certain errors.
And secondly, verifying the model, namely taking the test set data as network input, and comparing the output value with the fault labeling value, so as to verify the accuracy and timeliness of the model.
Finally, the fault prediction is carried out by taking real-time data as network data to be input and using the state that the output value is in a saw tooth shape to carry out the fault prediction:
when the serrated time width delta T is more than or equal to T *, pre-judging that the fault is about to occur, and sending out an alarm;
when the sawtooth-shaped time width delta T is smaller than T *, the pre-judging fault cannot occur, and no alarm is given.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (5)
1. A power distribution network fault prediction method based on an optimal time window mechanism is characterized by comprising the following steps of: the method comprises the following steps:
s1, performing fault marking on historical data of a power distribution network, and establishing a neural network model;
The neural network model in the step S1 includes six parts, wherein the first part is an input layer, the second part is a first hidden layer, the third part is a second hidden layer, the fourth part is a third hidden layer, the fifth part is an output layer, and the sixth part is a recursive link;
the expression of the output result of the neural network model is as follows:
Wherein P represents the number of input elements x (t), t represents the time iteration times, I p (t) represents the network input value, P+1, N, Q respectively represent the number of input layer nodes, the number of first hidden layer nodes and the number of second hidden layer nodes, f qnp[Ip (t) ] represents the activation function of the network input value I p (t), Representing the summation of internal Q data,/>Representing the cumulative multiplication of internal N data,/>Representing summing the internal p+1 data;
The overall input value I (t) = [ x 1(t),x2(t),...,xp(t),IP+1(t)]T ] of the neural network model, the network input value I p (t) includes a normal input value x p (t) and an output value y (t-1) of the last iteration, and the specific expression of the network input value I p (t) is as follows:
Wherein when p=1, y (t-1) =0
S2, establishing an optimal time window mechanism for judging the fault state;
the specific process of the step S2 is as follows:
S2.1, reading historical fault information;
S2.2, searching an optimal time window, searching a serrated time interval upwards according to the historical fault information, and continuously comparing with the historical data until determining an optimal time window width T *, wherein the optimal time window is as follows: the method comprises the steps of presenting a sawtooth-shaped output state value, namely, the output value at the previous moment is in a fault state, the current moment is in a normal state, and the next moment is in a fault state;
S2.3 comparing the actual saw-tooth time width with the optimal time window width T *
S3, training the model, and predicting faults by matching with an optimal time window mechanism;
the process of S3 is as follows:
s3.1, training a neural network model;
S3.2, taking the test set data as network input, and comparing the output value with the fault labeling value;
S3.3, inputting real-time data as network data, and predicting faults by using a state that the output value is saw-toothed:
when the serrated time width delta T is more than or equal to T *, pre-judging that the fault is about to occur, and sending out an alarm;
when the sawtooth-shaped time width delta T is smaller than T *, the pre-judging fault cannot occur, and no alarm is given.
2. The power distribution network fault prediction method based on the optimal time window mechanism according to claim 1, wherein the power distribution network fault prediction method is characterized by comprising the following steps of: the weight vector W 0 between the second hidden layer and the third hidden layer, and the weight vector W n between the first hidden layer and the input layer are expressed as follows:
W0＝[w01,w02,...,w0Q]
Wn＝[wn1,wn2,…,wnP,wn(P+1)]
Wherein Q is the number of nodes of the second hidden layer, w 0Q represents the weight vector of the corresponding node, n represents the number of nodes of the first hidden layer, w n(P+1) represents the weight vector of the corresponding node, and the variable epsilon (t) of the input layer corresponds to the output epsilon n (t) of the first hidden layer as follows:
Wherein, Representing summing the internal p+1 data, W n is a weight vector between the first hidden layer and the input layer, W np is a weight vector of the corresponding node, I p (t) is a network input value, and the connection weight between the first hidden layer and the second hidden layer is fixed to 1.
3. The power distribution network fault prediction method based on the optimal time window mechanism according to claim 2, wherein the power distribution network fault prediction method is characterized by: the connection mode between the first hidden layer and the second hidden layer comprises full connection and partial connection, when the connection mode between the first hidden layer and the second hidden layer is full connection, the connection combination number of the neurons is 2 N, when the connection mode between the first hidden layer and the second hidden layer is partial connection, the connection combination number of the neurons is less than 2 N, and the network connection combination expression between the first hidden layer and the second hidden layer is as follows:
Wherein A q is the set of the q-th neuron in the second hidden layer connected to the whole first hidden layer, B n is the set of the n-th neuron in the first hidden layer connected to the whole second hidden layer, For the number of elements in A q,/>For the number of elements in B n, the output δ q (t) of the second hidden layer is expressed as follows:
Wherein ε i represents the i-th output value of the first hidden layer, W iIi (t) represents the conversion expression of the variable of the input layer corresponding to the output of the first hidden layer, Representing the cumulative multiplication of the internal data.
4. A power distribution network fault prediction method based on an optimal time window mechanism according to claim 3, wherein: the output y (t) expression of the third hidden layer is as follows:
where f represents the activation function, W 0q represents the elements in the set W 0, delta (t) represents the total output of the second hidden layer, Representing summing the internal data.
5. The power distribution network fault prediction method based on the optimal time window mechanism according to claim 1, wherein the power distribution network fault prediction method is characterized by comprising the following steps of: the neural network model training set and the error function E (w) are expressed as follows:
Wherein, The training sample set is represented, I j (t) represents a corresponding input set, O j (t) represents a corresponding output set, J represents the total number of elements of the sample set, J represents a J-th element, and the value is iterated when training is carried out each time, wherein the specific iteration expression is as follows:
Wherein, Representing the weight change of the error function to W 0 at the kth iteration, W 0 represents the weight vector between the second hidden layer and the third hidden layer,/>Representing the weight change of the error function to W n at the kth iteration, W n represents the weight vector W n,/>, between the first hidden layer and the input layerRepresenting E (W) to perform bias calculation on W 0、Wn, and eta represents the learning rate of the training process.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410176910.9A CN117725981B (en) | 2024-02-08 | 2024-02-08 | Power distribution network fault prediction method based on optimal time window mechanism |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410176910.9A CN117725981B (en) | 2024-02-08 | 2024-02-08 | Power distribution network fault prediction method based on optimal time window mechanism |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117725981A CN117725981A (en) | 2024-03-19 |
| CN117725981B true CN117725981B (en) | 2024-04-30 |
Family
ID=90202007
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410176910.9A Active CN117725981B (en) | 2024-02-08 | 2024-02-08 | Power distribution network fault prediction method based on optimal time window mechanism |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117725981B (en) |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102062831A (en) * | 2010-10-29 | 2011-05-18 | 昆明理工大学 | Single-phase permanent fault recognition method for extra-high voltage AC transmission line |
| CN112051481A (en) * | 2020-08-12 | 2020-12-08 | 华中科技大学 | Alternating current-direct current hybrid power grid fault area diagnosis method and system based on LSTM |
| CN112230100A (en) * | 2020-09-29 | 2021-01-15 | 山东大学 | Slow-development permanent fault early warning method and system |
| CN112542823A (en) * | 2020-11-05 | 2021-03-23 | 上海合凯电气科技有限公司 | Reclosing control method and system and reclosing control equipment |
| CN113589098A (en) * | 2021-07-12 | 2021-11-02 | 国网河南省电力公司灵宝市供电公司 | Power grid fault prediction and diagnosis method based on big data drive |
| CN114156831A (en) * | 2021-11-22 | 2022-03-08 | 昆明理工大学 | Photoelectric combined instantaneous fault discrimination method |
| CN114429248A (en) * | 2022-03-31 | 2022-05-03 | 山东德佑电气股份有限公司 | Transformer apparent power prediction method |
| CN115221769A (en) * | 2021-04-15 | 2022-10-21 | 广州中国科学院先进技术研究所 | Fault prediction method, system, electronic equipment and storage medium |
| CN116361624A (en) * | 2023-03-31 | 2023-06-30 | 成都理工大学 | Error feedback-based large-range ground subsidence prediction method and system |
| CN116400172A (en) * | 2023-05-22 | 2023-07-07 | 合肥工业大学 | Cloud-edge cooperative power distribution network fault detection method and system based on random matrix |
| CN116592993A (en) * | 2023-04-11 | 2023-08-15 | 辽宁科技大学 | Mechanical vibration fault diagnosis method based on deep learning |
| CN117118856A (en) * | 2023-08-23 | 2023-11-24 | 中国电信股份有限公司技术创新中心 | Knowledge graph completion-based network fault reasoning method and related equipment |
| CN117434384A (en) * | 2023-11-07 | 2024-01-23 | 南方电网科学研究院有限责任公司 | Power distribution network insulation fault identification method and related device |
| CN117517876A (en) * | 2024-01-04 | 2024-02-06 | 昆明理工大学 | Fault positioning method, fault positioning equipment and storage medium for direct current transmission line |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7730364B2 (en) * | 2007-04-05 | 2010-06-01 | International Business Machines Corporation | Systems and methods for predictive failure management |
| EP3208904B1 (en) * | 2016-02-19 | 2019-01-23 | General Electric Technology GmbH | Apparatus for determination of a ground fault and associated method |
| US20220221852A1 (en) * | 2021-01-14 | 2022-07-14 | University Of Louisiana At Lafayette | Method and architecture for embryonic hardware fault prediction and self-healing |
-
2024
- 2024-02-08 CN CN202410176910.9A patent/CN117725981B/en active Active
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102062831A (en) * | 2010-10-29 | 2011-05-18 | 昆明理工大学 | Single-phase permanent fault recognition method for extra-high voltage AC transmission line |
| CN112051481A (en) * | 2020-08-12 | 2020-12-08 | 华中科技大学 | Alternating current-direct current hybrid power grid fault area diagnosis method and system based on LSTM |
| WO2022068074A1 (en) * | 2020-09-29 | 2022-04-07 | 山东大学 | Early warning method and system for slowly developing permanent fault |
| CN112230100A (en) * | 2020-09-29 | 2021-01-15 | 山东大学 | Slow-development permanent fault early warning method and system |
| CN112542823A (en) * | 2020-11-05 | 2021-03-23 | 上海合凯电气科技有限公司 | Reclosing control method and system and reclosing control equipment |
| CN115221769A (en) * | 2021-04-15 | 2022-10-21 | 广州中国科学院先进技术研究所 | Fault prediction method, system, electronic equipment and storage medium |
| CN113589098A (en) * | 2021-07-12 | 2021-11-02 | 国网河南省电力公司灵宝市供电公司 | Power grid fault prediction and diagnosis method based on big data drive |
| CN114156831A (en) * | 2021-11-22 | 2022-03-08 | 昆明理工大学 | Photoelectric combined instantaneous fault discrimination method |
| CN114429248A (en) * | 2022-03-31 | 2022-05-03 | 山东德佑电气股份有限公司 | Transformer apparent power prediction method |
| CN116361624A (en) * | 2023-03-31 | 2023-06-30 | 成都理工大学 | Error feedback-based large-range ground subsidence prediction method and system |
| CN116592993A (en) * | 2023-04-11 | 2023-08-15 | 辽宁科技大学 | Mechanical vibration fault diagnosis method based on deep learning |
| CN116400172A (en) * | 2023-05-22 | 2023-07-07 | 合肥工业大学 | Cloud-edge cooperative power distribution network fault detection method and system based on random matrix |
| CN117118856A (en) * | 2023-08-23 | 2023-11-24 | 中国电信股份有限公司技术创新中心 | Knowledge graph completion-based network fault reasoning method and related equipment |
| CN117434384A (en) * | 2023-11-07 | 2024-01-23 | 南方电网科学研究院有限责任公司 | Power distribution network insulation fault identification method and related device |
| CN117517876A (en) * | 2024-01-04 | 2024-02-06 | 昆明理工大学 | Fault positioning method, fault positioning equipment and storage medium for direct current transmission line |
Non-Patent Citations (8)
| Title |
|---|
| Higher Order Neural Network and Its Applications: A Comprehensive Survey;VIT-AP University, Amravati等;Analytics and Networking;20180131;695-709 * |
| Nature of fault determination on transmission lines for single phase autoreclosing applications;Iman Nikoofekr等;The Institution of Engineering and Technology;20180115;第12卷(第4期);903-911 * |
| Predicting Physical Time Series Using Dynamic Ridge Polynomial Neural Networks;Dhiya Al-Jumeily等;PLOS ONE;20140826;第9卷(第8期);1-15 * |
| The novel characteristics for training Ridge Polynomial neural network based on Lagrange multiplier;Fei Deng等;Alexandria Engineering Journal;20230315;第67卷;93-103 * |
| 一种基于链表法和时间延时的配电网故障定位方法;邓飞 等;昆明理工大学学报(自然科学版);20190415;第44卷(第2期);63-68 * |
| 基于瞬时性故障时频分析的配网绝缘状态监测;严秋问;江修波;蔡金锭;;电气开关;20160415(第02期);39-43，46 * |
| 基于瞬时故障分析的配电网在线绝缘监测;俞嘉 等;电气自动化;20220530;第44卷(第3期);103-106 * |
| 带并联电抗器的超高压输电线路单相故障识别;梁林;江亚群;黄纯;;电力系统及其自动化学报;20160815(第08期);32-37 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117725981A (en) | 2024-03-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112098714B (en) | Electricity stealing detection method and system based on ResNet-LSTM | |
| Qu et al. | A temporal distributed hybrid deep learning model for day-ahead distributed PV power forecasting | |
| CN110070226A (en) | Photovoltaic power prediction technique and system based on convolutional neural networks and meta learning | |
| CN111310968A (en) | LSTM neural network circulation hydrological forecasting method based on mutual information | |
| CN111582551B (en) | Wind power plant short-term wind speed prediction method and system and electronic equipment | |
| CN106649479A (en) | Probability graph-based transformer state association rule mining method | |
| Paoli et al. | Solar radiation forecasting using ad-hoc time series preprocessing and neural networks | |
| CN114221790A (en) | BGP (Border gateway protocol) anomaly detection method and system based on graph attention network | |
| Mustafa et al. | Fault identification for photovoltaic systems using a multi-output deep learning approach | |
| CN112085621A (en) | Distributed photovoltaic power station fault early warning algorithm based on K-Means-HMM model | |
| CN112183877A (en) | Photovoltaic power station fault intelligent diagnosis method based on transfer learning | |
| CN115829145A (en) | Photovoltaic power generation capacity prediction system and method | |
| CN116383658A (en) | BP neural network-based solar panel fault diagnosis method and device | |
| CN113469457B (en) | Power transmission line fault probability prediction method integrating attention mechanism | |
| CN113361737A (en) | Abnormity early warning method and system for photovoltaic module | |
| CN117725981B (en) | Power distribution network fault prediction method based on optimal time window mechanism | |
| CN109993368A (en) | Power forecasting method based on unusual spectral factorization and shot and long term memory network | |
| CN113111592A (en) | Short-term wind power prediction method based on EMD-LSTM | |
| Liang et al. | Transmission line fault-cause identification method for large-scale new energy grid connection scenarios | |
| Ferreira et al. | Investigating the use of reservoir computing for forecasting the hourly wind speed in short-term | |
| CN115795360A (en) | Cable fault detection method based on artificial neural network | |
| CN115730526A (en) | Intelligent monitoring and predicting method and system for airport electric load | |
| CN115952901A (en) | Power load prediction method based on ensemble learning | |
| CN115759185A (en) | Prediction method of transformer oil chromatographic characteristic gas based on Elman neural network | |
| Huang et al. | Probabilistic prediction intervals of wind speed based on explainable neural network |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |