CN114915035B - Power distribution network monitoring method, device and system - Google Patents

Power distribution network monitoring method, device and system Download PDF

Info

Publication number
CN114915035B
CN114915035B CN202210846447.5A CN202210846447A CN114915035B CN 114915035 B CN114915035 B CN 114915035B CN 202210846447 A CN202210846447 A CN 202210846447A CN 114915035 B CN114915035 B CN 114915035B
Authority
CN
China
Prior art keywords
state
order
power distribution
distribution network
monitoring
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
Application number
CN202210846447.5A
Other languages
Chinese (zh)
Other versions
CN114915035A (en
Inventor
霍超
苑佳楠
尹志斌
白晖峰
郑利斌
甄岩
张港红
高建
杨双双
谢凡
罗安琴
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Smartchip Microelectronics Technology Co Ltd
Original Assignee
Beijing Smartchip Microelectronics Technology Co Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Beijing Smartchip Microelectronics Technology Co Ltd filed Critical Beijing Smartchip Microelectronics Technology Co Ltd
Priority to CN202210846447.5A priority Critical patent/CN114915035B/en
Publication of CN114915035A publication Critical patent/CN114915035A/en
Application granted granted Critical
Publication of CN114915035B publication Critical patent/CN114915035B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/08—Locating faults in cables, transmission lines, or networks
    • G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
    • G01R31/086—Locating faults in cables, transmission lines, or networks according to type of conductors in power transmission or distribution networks, i.e. with interconnected conductors
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/049—Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y40/00—IoT characterised by the purpose of the information processing
    • G16Y40/10—Detection; Monitoring
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00001—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
    • 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
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector

Abstract

The embodiment of the invention provides a monitoring method, a device and a system for a power distribution network, which relate to the field of power distribution and Internet of things, and the monitoring method comprises the following steps: acquiring state information in the power distribution network in real time, wherein the state information is at least one of a state video vector, a state image vector and a state text vector; taking the state information as particles to be brought into a PSO particle swarm algorithm to determine optimal parameters of the particles; bringing the optimal parameters of the particles into a BilSTM model to obtain a key feature set; determining attention degrees of different features in the key feature set by combining an AM algorithm and the BilSTM model; monitoring for faults in the power distribution network based on the attentiveness of the characteristic. The monitoring method improves the accuracy and convergence time of power distribution network monitoring fault positioning, realizes the flow control of the power distribution terminal equipment fault, and reduces the operation risk of the terminal equipment.

Description

Monitoring method, device and system for power distribution network
Technical Field
The invention relates to the field of power distribution and Internet of things, in particular to a method, a device and a system for monitoring a power distribution network.
Background
With the development of the power distribution internet of things, the platform area intelligent terminal is used as an edge computing node of the low-voltage power distribution internet of things and is mainly deployed near a low-voltage power distribution transformer, core capabilities of network, computing, storage and application are integrated, edge intelligent service is provided nearby, and value flow of low-voltage platform area data, modeling of heterogeneous data and business application of the heterogeneous data and cooperation of all links of a cloud edge end are achieved.
Towards lean fortune and comprehensive energy management, novel fortune dimension devices such as camera, intelligence inspection robot use in a large number, contain abundant power equipment health status in the video image, nevertheless because intelligent technical level limits, data such as a large amount of video image that produce in the operation process do not carry out the degree of depth and excavate, do not realize yet with the degree of depth integration of distribution thing networking monitoring system and merge, the urgent need artificial intelligence technique supports.
Disclosure of Invention
The embodiment of the invention aims to provide a monitoring method, a monitoring device and a monitoring system for a power distribution network.
In order to achieve the above object, an embodiment of the present invention provides a method for monitoring a power distribution network, where the method for monitoring the power distribution network includes: acquiring state information in the power distribution network in real time, wherein the state information is at least one of a state video vector, a state image vector and a state text vector; taking the state information as particles to be brought into a PSO particle swarm algorithm to determine optimal parameters of the particles; bringing the optimal parameters of the particles into a BilSTM model to obtain a key feature set; determining attention degrees of different features in the key feature set by combining an AM algorithm and the BilSTM model; monitoring for faults in the power distribution network based on the attentiveness of the characteristic.
Optionally, the PSO particle swarm algorithm is to dynamically adjust a learning factor:
Figure 323871DEST_PATH_IMAGE001
wherein the content of the first and second substances,
Figure 540088DEST_PATH_IMAGE002
for the distance between each particle and the current global optimum,
Figure DEST_PATH_IMAGE003
and
Figure 953752DEST_PATH_IMAGE004
in order to improve the learning factor of the learning factor,
Figure DEST_PATH_IMAGE005
as a learning factor
Figure 962028DEST_PATH_IMAGE006
The maximum value of (a) is,
Figure DEST_PATH_IMAGE007
as a learning factor
Figure 435735DEST_PATH_IMAGE006
The minimum value of (a) is determined,
Figure 14615DEST_PATH_IMAGE008
is the maximum value of the distance of the particle from the global optimum,
Figure DEST_PATH_IMAGE009
in order to be able to perform the number of iterations,
Figure 231970DEST_PATH_IMAGE010
in order to be the maximum number of iterations,
Figure DEST_PATH_IMAGE011
is the position of the particle after t iterations, j is the dimension of the search space,
Figure 88893DEST_PATH_IMAGE012
is the j-dimensional value of the global optimal position.
Optionally, the bringing the optimal parameters of the particles into the BiLSTM model to obtain a key feature set includes: taking the optimal parameters of the particles as input vectors to be brought into a BilSTM model to obtain a key feature set, wherein the BilSTM model comprises the following steps:
Figure DEST_PATH_IMAGE013
wherein the content of the first and second substances,
Figure 733501DEST_PATH_IMAGE014
in order to forget to leave the door,
Figure DEST_PATH_IMAGE015
in order to memorize the door, the door is provided with a memory,
Figure 65256DEST_PATH_IMAGE016
in order to be the current memory state,
Figure DEST_PATH_IMAGE017
in order to remember the memory state at the last moment,
Figure 820723DEST_PATH_IMAGE018
in order to temporarily memorize the state of the memory,
Figure DEST_PATH_IMAGE019
in order to activate the function(s),
Figure 803591DEST_PATH_IMAGE020
in order to be a weight matrix, the weight matrix,
Figure DEST_PATH_IMAGE021
for the input vector at this moment, m is the dimension of the input vectorN is the number of the hidden layer units,
Figure 353521DEST_PATH_IMAGE022
is a hidden state at the last moment,
Figure DEST_PATH_IMAGE023
in order to output the output gate, the output gate is provided with a gate,
Figure 172572DEST_PATH_IMAGE024
in order to be a vector of the offset,
Figure DEST_PATH_IMAGE025
and the current hidden state is a set of the hidden states as a key feature set.
Optionally, the BiLSTM model simplifies the current memory state through the memory gate.
Optionally, the determining the attention degree of different features in the key feature set by combining the AM algorithm and the BiLSTM model includes: determining the degree of attention of different features in the set of key features by the formula:
Figure 731730DEST_PATH_IMAGE026
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE027
the attention weight is expressed for the feature,
Figure 569105DEST_PATH_IMAGE028
is a hidden state output by the BilSTM model,
Figure DEST_PATH_IMAGE029
in order to be a probability vector, the probability vector,
Figure 821094DEST_PATH_IMAGE030
the weighting value is a degree of attention to different features in the set of key features.
Optionally, the monitoring faults in the power distribution network according to the attention degree of the features includes: when the attention degree of the characteristics is smaller than the fault threshold range, the power distribution network stops monitoring the state information corresponding to the characteristics; and when the attention degree of the characteristics is not less than the fault threshold range, the power distribution network starts monitoring the state information corresponding to the characteristics and starts fault alarm.
In another aspect, the present invention provides a monitoring device for a power distribution network, the monitoring device comprising: the acquiring unit is used for acquiring state information in the power distribution network, wherein the state information is at least one of a state video vector, a state image vector and a state text vector; the optimization unit takes the state information as particles and brings the state information into a PSO particle swarm algorithm to determine optimal parameters of the particles; the first processing unit is used for bringing the optimal parameters of the particles into a BilSTM model to obtain a key feature set; a second processing unit, configured to combine the AM algorithm and the BiLSTM model to determine attention degrees of different features in the key feature set; and the management unit is used for monitoring faults in the power distribution network according to the attention degree.
Optionally, the PSO particle swarm algorithm is to dynamically adjust a learning factor:
Figure DEST_PATH_IMAGE031
wherein the content of the first and second substances,
Figure 596283DEST_PATH_IMAGE032
for the distance between each particle and the current global optimum,
Figure DEST_PATH_IMAGE033
and
Figure 959132DEST_PATH_IMAGE034
in order to improve the learning factor,
Figure DEST_PATH_IMAGE035
as a learning factor
Figure 916592DEST_PATH_IMAGE036
The maximum value of (a) is,
Figure DEST_PATH_IMAGE037
as a learning factor
Figure 73904DEST_PATH_IMAGE036
The minimum value of (a) is determined,
Figure 336389DEST_PATH_IMAGE038
is the maximum value of the distance of the particle from the global optimum,
Figure DEST_PATH_IMAGE039
in order to be able to perform the number of iterations,
Figure 971770DEST_PATH_IMAGE040
in order to be the maximum number of iterations,
Figure DEST_PATH_IMAGE041
is the position of the particle after t iterations, j is the dimension of the search space,
Figure 659103DEST_PATH_IMAGE042
is the j-dimensional value of the global optimal position.
Optionally, the bringing the optimal parameters of the particles into the BiLSTM model to obtain a key feature set includes: taking the optimal parameters of the particles as input vectors to be brought into a BilSTM model to obtain a key feature set, wherein the BilSTM model comprises the following steps:
Figure DEST_PATH_IMAGE043
wherein the content of the first and second substances,
Figure 377530DEST_PATH_IMAGE044
in order to forget to leave the door,
Figure DEST_PATH_IMAGE045
in order to memorize the door, the door is provided with a memory,
Figure 392890DEST_PATH_IMAGE046
in order to obtain the current memory state,
Figure DEST_PATH_IMAGE047
in order to remember the memory state at the last moment,
Figure 566382DEST_PATH_IMAGE048
in order to temporarily memorize the state of the memory,
Figure DEST_PATH_IMAGE049
in order to activate the function(s),
Figure 108222DEST_PATH_IMAGE050
in order to be a weight matrix, the weight matrix,
Figure DEST_PATH_IMAGE051
is the input vector at this moment, m is the dimension of the input vector, n is the number of hidden layer units,
Figure 731970DEST_PATH_IMAGE052
is a hidden state at the last moment,
Figure DEST_PATH_IMAGE053
in order to output the output gate, the output gate is provided with a gate,
Figure 359261DEST_PATH_IMAGE054
in order to be a vector of the offset,
Figure DEST_PATH_IMAGE055
and the current hidden state is a set of the hidden states as a key feature set.
Optionally, the BiLSTM model simplifies the current memory state through the memory gate.
Optionally, the determining the attention degree of different features in the key feature set by combining the AM algorithm and the BiLSTM model includes: determining the degree of attention of different features in the set of key features by the following formula:
Figure 477390DEST_PATH_IMAGE056
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE057
the attention weight is expressed for a feature,
Figure 873736DEST_PATH_IMAGE058
for the hidden state output by the BiLSTM model,
Figure DEST_PATH_IMAGE059
in order to be a probability vector, the probability vector,
Figure 668385DEST_PATH_IMAGE060
the weighting value is a degree of attention to different features in the set of key features.
Optionally, when the attention degree of the feature is smaller than the fault threshold range, the power distribution network closes monitoring on the state information corresponding to the feature; and when the attention degree of the characteristics is not less than the fault threshold range, the power distribution network starts monitoring the state information corresponding to the characteristics and starts fault alarm.
On the other hand, the invention also provides a monitoring system of the power distribution network, which comprises the monitoring device of the power distribution network and at least one terminal, wherein the monitoring device is used for monitoring the terminal.
The monitoring method of the power distribution network comprises the following steps: acquiring state information in the power distribution network in real time, wherein the state information is at least one of a state video vector, a state image vector and a state text vector; taking the state information as particles into a PSO particle swarm algorithm for determining optimal parameters of the particles; substituting the particle optimal parameters into a BilSTM model to obtain a key feature set; combining an AM algorithm and the BilSTM model for determining attention degrees of different features in the key feature set; monitoring for faults in the power distribution network based on the attentiveness of the characteristic. The monitoring method improves the accuracy and convergence time of power distribution network monitoring fault positioning, realizes the flow control of the power distribution terminal equipment fault, and reduces the operation risk of the terminal equipment.
Additional features and advantages of embodiments of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the embodiments of the invention without limiting the embodiments of the invention. In the drawings:
fig. 1 is a schematic flow chart of a monitoring method for a power distribution network according to the present invention;
FIG. 2 is a schematic flow diagram of the IPSO-IBiLSTM-AM architecture of the present invention;
FIG. 3 is a hierarchical schematic of the IBiLSTM-AM algorithm of the present invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating embodiments of the invention, are given by way of illustration and explanation only, not limitation.
Fig. 1 is a schematic flow chart of a monitoring method for a power distribution network according to the present invention, and as shown in fig. 1, step S101 is to obtain state information in the power distribution network in real time, where the state information is at least one of a state video vector, a state image vector, and a state text vector. Specifically, the state information is obtained by performing vectorization conversion on a state video image and/or a text to obtain a vector expression of the vector information, where the vector expression includes a speed attribute and a position attribute of the state information.
And step S102, taking the state information as particles into a PSO particle swarm algorithm for determining optimal parameters of the particles. The PSO Particle Swarm Optimization (PSO) is a random search Algorithm based on group cooperation, the principle of which is derived from the research on the predation behavior of a bird group, and the basic idea is to find the optimal solution through cooperation and information sharing among individuals in the group. The PSO algorithm is generated by the influence of the behavior of the bird swarm to search for food, and there are 2 factors that influence the bird search for food in the bird swarm: the speed at which the bird flies, the distance between the bird and the food. Likewise, the birds in the group of birds are considered as particles in the group of particles, and the process of searching for food is considered as the process of searching for the optimal solution by the particles. The particle optimal parameter is the optimal solution of the particle search.
Each particle has 2 attributes of speed and position, in each iterative calculation, each particle searches for an optimal solution at a certain speed and position in a K-dimensional space, and the position of the searched optimal solution is marked as an individual extreme value.
The PSO particle swarm algorithm comprises the following basic processes: randomly initializing each particle; evaluating each particle and obtaining global optimum; judging whether the global optimum meets an end condition, if so, ending, and if not, updating the speed and the position of each particle; evaluating a function fitness value for each particle; updating the historical optimal position of each particle; and updating the global optimal position of the population.
Specifically, the standard particle swarm algorithm has the following flow:
1) setting parameters such as population size, variable range, inertial weight, learning factors and the like, and randomly initializing a group of particles (including speed and position information) uniformly distributed in a given optimization space;
2) calculating the fitness value (namely a function value) of each particle in the population, setting the fitness value of the ith particle as the current individual extreme value of the ith particle, and setting the best particle in all the particles as the overall extreme value of the population;
3) updating the velocity and position of each particle according to equations 1) and 2);
4) comparing the current function value of all the particles with the best position found previously, if the current position is better, setting the individual optimal position as the position of the particle, and then updating the global extreme value of the group;
5) judging whether a given termination 1 condition is met, if so, stopping searching and outputting a required result; otherwise, return to 3) to continue searching.
The correlation formula is as follows: assuming an objective function of
Figure DEST_PATH_IMAGE061
，
Figure 782972DEST_PATH_IMAGE062
For the ith particle position, the ith particle individual extremum is expressed as:
Figure 439212DEST_PATH_IMAGE063
assuming that the particle swarm contains N particles, the iteration number is t, and the global optimal position is
Figure 424486DEST_PATH_IMAGE064
The solving process is as follows:
Figure 999824DEST_PATH_IMAGE065
if it is
Figure 336127DEST_PATH_IMAGE066
Then there is
Figure 779747DEST_PATH_IMAGE067
，
Figure 885106DEST_PATH_IMAGE068
And the fitness function corresponding to the global optimal position.
Each particle adjusts its position and velocity by a global optimum position. Velocity of the particles
Figure 631345DEST_PATH_IMAGE069
And position
Figure 454945DEST_PATH_IMAGE070
The calculation formula is as follows:
Figure 187408DEST_PATH_IMAGE071
wherein: i is the number of particles in the particle group, i is 1, 2, … N; j is the dimension of the search space, j is 1, 2, … K;
Figure 147274DEST_PATH_IMAGE072
the velocity of the particles after t iterations;
Figure 329994DEST_PATH_IMAGE073
the position of the particle after t iterations;
Figure 375310DEST_PATH_IMAGE074
j-dimensional value of the individual extreme value of the ith particle;
Figure 166592DEST_PATH_IMAGE075
a j-dimensional value of the global optimal position;
Figure 246543DEST_PATH_IMAGE076
and
Figure 334585DEST_PATH_IMAGE077
is [0, 1 ]]A random number above;
Figure 867198DEST_PATH_IMAGE078
and
Figure 207043DEST_PATH_IMAGE079
is a learning factor;
Figure 141501DEST_PATH_IMAGE080
for inertial weight, by adjustment
Figure 400444DEST_PATH_IMAGE081
The global optimization performance and the local optimization performance can be adjusted, and particularly,
Figure 420353DEST_PATH_IMAGE078
and
Figure 547578DEST_PATH_IMAGE079
also referred to as the acceleration constant, of the,
Figure 336542DEST_PATH_IMAGE082
for each particle an individual learning factor is used,
Figure 31966DEST_PATH_IMAGE083
social learning factor for each particle, in general
Figure 539170DEST_PATH_IMAGE078
And
Figure 955239DEST_PATH_IMAGE079
is a constant of 0 to 4.
Obtaining the fitness value of the particle according to the following formula, and determining the global optimal position of the particle according to the fitness value:
Figure 864289DEST_PATH_IMAGE084
wherein:
Figure 465035DEST_PATH_IMAGE085
is a value of the fitness of the particle,
Figure 459536DEST_PATH_IMAGE086
is the true value of the particle or particles,
Figure 928563DEST_PATH_IMAGE087
is the predicted value of the particle, and n is the sample number of the particle.
The invention introduces the distance between each particle and the current global optimal position in the t iteration
Figure 692120DEST_PATH_IMAGE088
By using
Figure 729346DEST_PATH_IMAGE088
The invention has the characteristics of larger learning factor in the early period of optimization and smaller learning factor in the later period of optimization
Figure 945564DEST_PATH_IMAGE078
And
Figure 969015DEST_PATH_IMAGE079
the improvement is carried out, and the learning factor is dynamically adjusted, wherein:
Figure 587078DEST_PATH_IMAGE089
wherein the content of the first and second substances,
Figure 795205DEST_PATH_IMAGE090
as a learning factor
Figure 498719DEST_PATH_IMAGE091
The maximum value of (a) is,
Figure 309549DEST_PATH_IMAGE092
as a learning factor
Figure 47698DEST_PATH_IMAGE091
The minimum value of (a) is determined,
Figure 426727DEST_PATH_IMAGE093
is the maximum value of the distance of the particle from the global optimum,
Figure 617537DEST_PATH_IMAGE094
is the maximum number of iterations. By pairs
Figure 982790DEST_PATH_IMAGE078
And
Figure 309866DEST_PATH_IMAGE079
a non-linear dynamic adjustment is carried out so that
Figure 859796DEST_PATH_IMAGE095
Individual whose value determines the particleThe ability of the user to recognize the position of the user,
Figure 537902DEST_PATH_IMAGE096
the social cognitive ability of the particles is determined, and the convergence speed and precision of the PSO algorithm are improved.
The hyper-parameter Optimization part of the invention automatically finds the hyper-parameter combination which enables the fault location effect to be optimal by utilizing an Improved PSO Algorithm (also called IPSO Algorithm: Improved Particle Swarm Optimization Algorithm), and the IPSO Algorithm introduces the distance between each Particle and the current global optimal position on the basis of a standard PSO Algorithm to optimize the learning factor and realize dynamic adjustment.
Step S103, substituting the optimal parameters of the particles into a BilSTM (Bi-directional Long Short-Term Memory model) to obtain a key feature set; the BilSTM model is formed by combining a forward LSTM (Long Short-Term Memory model) and a backward LSTM. Both are often used to model context information in natural language processing tasks, LSTM is an improved model of RNN (current Neural Network Recurrent Neural Network), where the standard BiLSTM algorithm Neural Network is: let m be the dimension of the input vector, n be the number of hidden layer units, according to the hidden state of the last moment
Figure 956114DEST_PATH_IMAGE097
And input at this moment
Figure 403276DEST_PATH_IMAGE098
Get the forget door
Figure 858528DEST_PATH_IMAGE099
Memory door
Figure 23930DEST_PATH_IMAGE100
And output gate
Figure 730986DEST_PATH_IMAGE101
From
Figure 298234DEST_PATH_IMAGE102
And memory state at last moment
Figure 924387DEST_PATH_IMAGE103
Obtaining the current memory state
Figure 577085DEST_PATH_IMAGE104
From
Figure 337100DEST_PATH_IMAGE105
And
Figure 493275DEST_PATH_IMAGE106
obtaining the hidden state at the current moment
Figure 555909DEST_PATH_IMAGE107
. Forgetting, memorizing and outputting of the memory unit are achieved, important information is reserved, information with low importance degree is ignored, key feature identification of input service flow can be achieved, and a key feature set is output. The specific formula is as follows:
Figure 430324DEST_PATH_IMAGE108
wherein, the first and the second end of the pipe are connected with each other,
Figure 744761DEST_PATH_IMAGE109
in order to activate the function(s),
Figure 21022DEST_PATH_IMAGE110
、
Figure 254557DEST_PATH_IMAGE111
、
Figure 616268DEST_PATH_IMAGE112
in order to be a weight matrix, the weight matrix,
Figure 718086DEST_PATH_IMAGE113
、
Figure 848853DEST_PATH_IMAGE114
in order to be a vector of the offset,
Figure 253289DEST_PATH_IMAGE115
in order to temporarily memorize the state of the memory,
Figure 102296DEST_PATH_IMAGE116
is the input vector at this moment.
The BilSTM is composed of forward and reverse LSTMs, and the hidden state is
Figure 492958DEST_PATH_IMAGE117
Said hidden state
Figure 743810DEST_PATH_IMAGE117
Is a key feature set:
Figure 319148DEST_PATH_IMAGE118
wherein, the first and the second end of the pipe are connected with each other,
Figure 655452DEST_PATH_IMAGE119
is in the forward direction
Figure 99071DEST_PATH_IMAGE120
，
Figure 204431DEST_PATH_IMAGE121
Is in the reverse direction
Figure 950670DEST_PATH_IMAGE122
。
The invention provides an Improved BilSTM algorithm neural network, namely IBiLSTM (Improved Bi-directional Long Short-Term Memory), which specifically comprises the steps of simplifying the gate structure and parameters of standard LSTM, and using the gate structure and parameters
Figure 508690DEST_PATH_IMAGE123
Simplifying the current memory state and removing the input weight
Figure 506733DEST_PATH_IMAGE124
And a bias matrix
Figure 466599DEST_PATH_IMAGE125
The LSTM algorithm is improved, and the training time of the network is shortened on the premise of not losing the precision. And taking the optimal parameters of the particles as input vectors to be introduced into an improved BilSTM model, wherein the improved BilSTM model has the following specific formula:
Figure 383739DEST_PATH_IMAGE126
wherein, the first and the second end of the pipe are connected with each other,
Figure 694635DEST_PATH_IMAGE044
in order to forget to leave the door,
Figure 497635DEST_PATH_IMAGE045
in order to memorize the door, the door is provided with a memory,
Figure 312007DEST_PATH_IMAGE046
in order to be the current memory state,
Figure 665628DEST_PATH_IMAGE047
in order to remember the memory state at the last moment,
Figure 198241DEST_PATH_IMAGE048
in order to temporarily memorize the state of the memory,
Figure 538086DEST_PATH_IMAGE049
in order to activate the function(s),
Figure 206965DEST_PATH_IMAGE050
in the form of a matrix of weights,
Figure 731487DEST_PATH_IMAGE127
is the input vector at this moment, m is the dimension of the input vector, n is the number of hidden layer units,
Figure 751396DEST_PATH_IMAGE052
is a hidden state at the last moment,
Figure 878621DEST_PATH_IMAGE053
in order to output the output gate, the output gate is provided with a gate,
Figure 667585DEST_PATH_IMAGE054
in order to be a vector of the offset,
Figure 363009DEST_PATH_IMAGE055
and the current hidden state is a set of the hidden states as a key feature set.
The difference between the improved BilSTM algorithm designed by the invention and the control signal of the standard neural network is to forget the gate
Figure 604634DEST_PATH_IMAGE128
Instead, by memory gates
Figure 286283DEST_PATH_IMAGE129
And an output gate
Figure 195333DEST_PATH_IMAGE130
Two gate structures are formed so that the network gate structure signal is output by a t-1 moment output signal
Figure 796078DEST_PATH_IMAGE131
The recursive weight matrix and the bias matrix, and the calculation complexity is reduced.
Step S104 is to combine the AM algorithm and the BiLSTM model for determining attention degrees of different features in the key feature set.
The AM algorithm (attention model) is an attention mechanism algorithm, a BilSTM-AM (Bi-directional Long Short-Term Memory and attention model) identifies an attention mechanism, the attention mechanism is introduced into the BilSTM model, calculation with probability distribution is obtained through calculation of attention probability distribution, attention degrees of different characteristics in fault description are highlighted, and the purpose of improving classification accuracy is achieved.
Hidden states output from a BilSTM model
Figure 790579DEST_PATH_IMAGE132
Formula for feature expression attention weight
Figure 259607DEST_PATH_IMAGE133
Comprises the following steps:
Figure 23163DEST_PATH_IMAGE134
probability calculation is carried out by utilizing a softmax function to obtain a probability vector, and the hidden state is multiplied by the corresponding probability vector to obtain a weighted value formula:
Figure 794810DEST_PATH_IMAGE135
wherein the content of the first and second substances,
Figure 276607DEST_PATH_IMAGE136
the attention weight is expressed for the feature,
Figure 300058DEST_PATH_IMAGE137
for the hidden state output by the BiLSTM model,
Figure 918121DEST_PATH_IMAGE138
in order to be a probability vector, the probability vector,
Figure 126248DEST_PATH_IMAGE139
the weighting value is a degree of attention to different features in the set of key features.
Step S105 is monitoring a fault in the power distribution network according to the attention degree of the feature, including: when the attention degree of the characteristic is smaller than the fault threshold range, the power distribution network stops monitoring the state information corresponding to the characteristic; and when the attention degree of the characteristics is not less than the fault threshold range, the power distribution network starts monitoring the state information corresponding to the characteristics and starts fault alarm. The fault threshold range is determined according to methods such as experience and big data test.
FIG. 2 is a schematic flow diagram of the IPSO-IBiLSTM-AM construction of the present invention, and as shown in FIG. 2, the present invention fuses the proposed hyper-parameter optimization part and the fault localization part to construct an IPSO-IBiLSTM-AM algorithm, the construction process is as follows: configuring a fault location part by using the initial hyper-parameter provided by the hyper-parameter optimization part; the hyper-parameter optimization part optimizes the hyper-parameters according to the output result of the iterative operation of the prediction part.
The specific training steps of the IPSO-IBiLSTM-AM are as follows:
step 1: dividing a data set into a training set and a test set according to a proportion, and carrying out vectorization processing;
step 2: initializing parameters such as the number of units, the speed, the position, the inertia weight, the acceleration factor, the iteration times and the like of a BilSTM layer;
and step 3: and (3) training the model part by using a training set, and solving the individual optimal position and the global position according to the fitness value of the particles, wherein the fitness calculation formula is as follows:
Figure 829762DEST_PATH_IMAGE140
wherein:
Figure 640592DEST_PATH_IMAGE141
in order to be the true value of the value,
Figure 378741DEST_PATH_IMAGE142
is a predicted value, n is the number of samples;
and 4, step 4: according to
Figure 492191DEST_PATH_IMAGE143
And
Figure 683001DEST_PATH_IMAGE144
dynamically adjusting the learning factor by a formula, and starting iteration;
and 5: and (4) judging whether the termination condition is met, if so, outputting the optimal position, and otherwise, returning to the step (3). The termination condition is that the particle fitness is smaller than a set value or the maximum iteration number is reached;
step 6: after the operation is finished, distributing the result obtained by IPSO to an IBiLSTM-AM neural network as an optimal hyper-parameter combination, and training the combination by using a training set;
and 7: inputting the characteristic data of the test set into the trained model, and outputting a fault positioning result.
FIG. 3 is a schematic diagram of an IBiLSTM-AM (attribute model and Improved Bi-directional Short-Term Memory) algorithm layer, as shown in FIG. 3, the hyper-parameter optimization part combines an attention mechanism with a neural network model, and comprises a series of different layers, namely an input layer, an IBiLSTM layer, an AM layer and a full connection layer. The layers function as follows:
an input layer: the feature data at each moment is used as the input of the model, and the state (including fault state) video image and text are required to be subjected to vectorization conversion to obtain the vector expression of the state information.
IBiLSTM layer: and (3) performing feature extraction on the fault description word vector by using two simplified LSTM neural networks in opposite directions to obtain semantic features of fault description.
An AM layer: and the attention mechanism layer is used for carrying out weight distribution on the semantic features, improving the weight of important information in the semantic features and obtaining a final description feature vector of the fault.
Full connection layer: the full-connection layer is used for reducing dimensionality operation and facilitating classification and identification, and specifically, a sigmoid (S-shaped growth curve) activation function is selected as a gating mechanism to classify the output of the attention layer to obtain a fault type result.
The invention also provides a monitoring device of the power distribution network, which comprises: the acquiring unit is used for acquiring state information in the power distribution network, wherein the state information is at least one of a state video vector, a state image vector and a state text vector; the optimization unit takes the state information as particles into a PSO particle swarm algorithm and is used for determining optimal parameters of the particles; the first processing unit is used for bringing the optimal parameters of the particles into a BilSTM model to obtain a key feature set; a second processing unit for combining an AM algorithm with the BilSTM model to determine attention degrees of different features in the key feature set; and the management unit is used for monitoring faults in the power distribution network according to the attention degree.
On the other hand, the invention also provides a monitoring system of the power distribution network, which comprises the monitoring device of the power distribution network and at least one terminal, wherein the monitoring device is used for monitoring the terminal.
The monitoring method of the power distribution network comprises the following steps: acquiring state information in the power distribution network in real time, wherein the state information is at least one of a state video vector, a state image vector and a state text vector; taking the state information as particles into a PSO particle swarm algorithm for determining optimal parameters of the particles; bringing the optimal parameters of the particles into a BilSTM model to obtain a key feature set; combining an AM algorithm and the BilSTM model to determine attention degrees of different features in the key feature set; monitoring for faults in the power distribution network based on the attentiveness of the characteristic. The monitoring method not only improves the door structure and parameters and simplifies the standard BilSTM model, but also dynamically adjusts the learning factors in the PSO particle swarm algorithm, improves the accuracy and convergence time of the monitoring fault positioning of the power distribution network, realizes the flow management and control of the faults of the power distribution terminal equipment, and reduces the operation risk of the terminal equipment.
Although the embodiments of the present invention have been described in detail with reference to the accompanying drawings, the embodiments of the present invention are not limited to the details of the above embodiments, and various simple modifications can be made to the technical solutions of the embodiments of the present invention within the technical idea of the embodiments of the present invention, and the simple modifications all belong to the protection scope of the embodiments of the present invention.
It should be noted that the various features described in the foregoing embodiments may be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the embodiments of the present invention do not describe every possible combination.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement or the like made within the spirit and principle of the present application shall be included in the scope of the claims of the present application.

Claims (9)

1. A monitoring method for a power distribution network is characterized by comprising the following steps:
acquiring state information in the power distribution network in real time, wherein the state information is at least one of a state video vector, a state image vector and a state text vector;
taking the state information as particles to be brought into a PSO particle swarm algorithm to determine optimal parameters of the particles;
bringing the optimal parameters of the particles into a BilSTM model to obtain a key feature set;
determining attention degrees of different features in the key feature set by combining an AM algorithm and the BilSTM model;
monitoring faults in the power distribution network according to the attention degree of the characteristics;
the PSO particle swarm algorithm is a dynamic regulation learning factor:
Figure 44617DEST_PATH_IMAGE001
wherein the content of the first and second substances,
Figure 387874DEST_PATH_IMAGE002
for the distance between each particle and the current global optimum,
Figure 754176DEST_PATH_IMAGE003
and
Figure 436961DEST_PATH_IMAGE004
in order to improve the learning factor of the learning factor,
Figure 388606DEST_PATH_IMAGE005
as a learning factor
Figure 422421DEST_PATH_IMAGE006
The maximum value of (a) is,
Figure 55396DEST_PATH_IMAGE007
as a learning factor
Figure 655005DEST_PATH_IMAGE006
The minimum value of (a) is determined,
Figure 465966DEST_PATH_IMAGE008
is the maximum value of the distance of the particle from the global optimum,
Figure 236345DEST_PATH_IMAGE009
in order to be the number of iterations,
Figure 220481DEST_PATH_IMAGE010
in order to be the maximum number of iterations,
Figure 127126DEST_PATH_IMAGE011
is the position of the particle after t iterations, j is the dimension of the search space,
Figure 171306DEST_PATH_IMAGE012
a j-dimensional value of the global optimal position; the monitoring for faults in the power distribution network according to the degree of attention of the feature comprises:
when the attention degree of the characteristics is smaller than the fault threshold range, the power distribution network stops monitoring the state information corresponding to the characteristics;
and when the attention degree of the characteristics is not less than the fault threshold range, the power distribution network starts monitoring the state information corresponding to the characteristics and starts fault alarm.
2. The monitoring method of claim 1, wherein the bringing the particle optimal parameters into a BilSTM model to obtain a set of key features comprises:
taking the optimal parameters of the particles as input vectors to be brought into a BilSTM model to obtain a key feature set, wherein the BilSTM model comprises the following steps:
Figure 914134DEST_PATH_IMAGE013
Figure 154491DEST_PATH_IMAGE014
wherein the content of the first and second substances,
Figure 728692DEST_PATH_IMAGE015
in order to forget to leave the door,
Figure 130723DEST_PATH_IMAGE016
in order to memorize the door, the door is provided with a memory,
Figure 157585DEST_PATH_IMAGE017
in order to obtain the current memory state,
Figure 952366DEST_PATH_IMAGE018
in order to remember the memory state at the last moment,
Figure 568024DEST_PATH_IMAGE019
in order to temporarily memorize the state of the memory,
Figure 954006DEST_PATH_IMAGE020
in order to activate the function(s),
Figure 938272DEST_PATH_IMAGE021
in order to be a weight matrix, the weight matrix,
Figure 67902DEST_PATH_IMAGE022
for the input vector at this moment in time,
m is the dimension of the input vector, n is the number of hidden layer units,
Figure 288799DEST_PATH_IMAGE023
is a hidden state at the last moment,
Figure 360529DEST_PATH_IMAGE024
in order to output the gate, the gate is provided with a gate,
Figure 299666DEST_PATH_IMAGE025
in order to be a vector of the offset,
Figure 232987DEST_PATH_IMAGE026
setting a set of hidden states as a key feature set for the current hidden state;
Figure 823237DEST_PATH_IMAGE027
is in the forward direction
Figure 754284DEST_PATH_IMAGE028
，
Figure 492302DEST_PATH_IMAGE029
Is in a reverse direction
Figure 901417DEST_PATH_IMAGE030
。
3. The monitoring method according to claim 2,
and the BilSTM model simplifies the current memory state through the memory gate.
4. The method of monitoring of claim 1, wherein said determining a degree of attention for different features in said set of key features in combination with an AM algorithm and said BiLSTM model comprises:
determining the degree of attention of different features in the set of key features by the following formula:
Figure 159223DEST_PATH_IMAGE031
wherein the content of the first and second substances,
Figure 510439DEST_PATH_IMAGE032
the attention weight is expressed for the feature,
Figure 220906DEST_PATH_IMAGE033
for the hidden state output by the BiLSTM model,
Figure 682981DEST_PATH_IMAGE034
in order to be a probability vector, the probability vector,
Figure 60872DEST_PATH_IMAGE035
the weighting value, which is the degree of attention of the different features in the key feature set,
softmax is a probabilistic calculation function.
5. A monitoring device for an electrical distribution network, the monitoring device comprising:
the acquiring unit is used for acquiring state information in the power distribution network, wherein the state information is at least one of a state video vector, a state image vector and a state text vector;
the optimization unit takes the state information as particles to be brought into a PSO particle swarm algorithm so as to determine optimal parameters of the particles;
the first processing unit is used for bringing the optimal parameters of the particles into a BilSTM model to obtain a key feature set;
a second processing unit, configured to combine the AM algorithm and the BiLSTM model to determine attention degrees of different features in the key feature set;
the management unit is used for monitoring faults in the power distribution network according to the attention degree;
the PSO particle swarm algorithm is a dynamic regulation learning factor:
Figure 68143DEST_PATH_IMAGE036
wherein the content of the first and second substances,
Figure 780752DEST_PATH_IMAGE037
for the distance between each particle and the current global optimum,
Figure 797250DEST_PATH_IMAGE038
and
Figure 216599DEST_PATH_IMAGE039
in order to improve the learning factor of the learning factor,
Figure 457087DEST_PATH_IMAGE040
as a learning factor
Figure 345409DEST_PATH_IMAGE041
The maximum value of (a) is,
Figure 217462DEST_PATH_IMAGE042
as a learning factor
Figure 242050DEST_PATH_IMAGE041
The minimum value of (a) is calculated,
Figure 919019DEST_PATH_IMAGE043
is the maximum value of the distance of the particle from the global optimum,
Figure 278325DEST_PATH_IMAGE044
in order to be able to perform the number of iterations,
Figure 433363DEST_PATH_IMAGE045
is the maximum number of iterations in the sequence,
Figure 827304DEST_PATH_IMAGE046
is the position of the particle after t iterations, j is the dimension of the search space,
Figure 612857DEST_PATH_IMAGE047
a j-dimensional value of the global optimal position;
the monitoring for faults in the power distribution network according to the degree of attention of the feature comprises:
when the attention degree of the characteristic is smaller than the fault threshold range, the power distribution network stops monitoring the state information corresponding to the characteristic;
and when the attention degree of the characteristics is not less than the fault threshold range, the power distribution network starts monitoring the state information corresponding to the characteristics and starts fault alarm.
6. The monitoring device of claim 5, wherein the substituting the particle optimal parameters into the BilSTM model to obtain a set of key features comprises:
taking the optimal parameters of the particles as input vectors to be brought into a BilSTM model to obtain a key feature set, wherein the BilSTM model comprises the following steps:
Figure 272509DEST_PATH_IMAGE048
Figure 418188DEST_PATH_IMAGE049
wherein the content of the first and second substances,
Figure 479685DEST_PATH_IMAGE050
in order to forget to leave the door,
Figure 950987DEST_PATH_IMAGE051
in order to memorize the door, the door is provided with a memory,
Figure 35617DEST_PATH_IMAGE052
in order to be the current memory state,
Figure 532458DEST_PATH_IMAGE053
in order to remember the memory state at the last moment,
Figure 635412DEST_PATH_IMAGE054
in order to temporarily memorize the state of the memory,
Figure 825085DEST_PATH_IMAGE055
in order to activate the function(s),
Figure 646279DEST_PATH_IMAGE056
in the form of a matrix of weights,
Figure 946810DEST_PATH_IMAGE057
for the input vector at this moment in time,
m is the dimension of the input vector, n is the number of hidden layer units,
Figure 920582DEST_PATH_IMAGE058
is a hidden state at the last moment,
Figure 468107DEST_PATH_IMAGE059
in order to output the gate, the gate is provided with a gate,
Figure 589647DEST_PATH_IMAGE060
in order to be a vector of the offset,
Figure 874961DEST_PATH_IMAGE061
is the current hidden state, the set of hidden states is a set of key feature sets,
Figure 703239DEST_PATH_IMAGE062
is in the forward direction
Figure 234715DEST_PATH_IMAGE028
，
Figure 764922DEST_PATH_IMAGE029
Is in a reverse direction
Figure 672835DEST_PATH_IMAGE030
。
7. The monitoring device of claim 6,
and the BilSTM model simplifies the current memory state through the memory gate.
8. The monitoring device of claim 5, wherein said determining the degree of attention of different features in said set of key features in combination with an AM algorithm and said BilStm model comprises:
determining the degree of attention of different features in the set of key features by the following formula:
Figure 870467DEST_PATH_IMAGE063
wherein the content of the first and second substances,
Figure 510527DEST_PATH_IMAGE032
the attention weight is expressed for the feature,
Figure 341080DEST_PATH_IMAGE033
for the hidden state output by the BiLSTM model,
Figure 974056DEST_PATH_IMAGE034
in the form of a probability vector, is,
Figure 776926DEST_PATH_IMAGE035
the weighted value is the attention degree of different characteristics in the key characteristic set;
softmax is a probabilistic calculation function.
9. A monitoring system for an electric distribution network, characterized in that the monitoring system comprises a monitoring device for an electric distribution network according to any of claims 5-8 and at least one terminal, said monitoring device being adapted to monitor said terminal.
CN202210846447.5A 2022-07-19 2022-07-19 Power distribution network monitoring method, device and system Active CN114915035B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210846447.5A CN114915035B (en) 2022-07-19 2022-07-19 Power distribution network monitoring method, device and system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210846447.5A CN114915035B (en) 2022-07-19 2022-07-19 Power distribution network monitoring method, device and system

Publications (2)

Publication Number Publication Date
CN114915035A CN114915035A (en) 2022-08-16
CN114915035B true CN114915035B (en) 2022-09-13

Family

ID=82772913

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210846447.5A Active CN114915035B (en) 2022-07-19 2022-07-19 Power distribution network monitoring method, device and system

Country Status (1)

Country Link
CN (1) CN114915035B (en)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111429034A (en) * 2020-04-21 2020-07-17 国网信通亿力科技有限责任公司 Method for predicting power distribution network fault
AU2020101683A4 (en) * 2020-08-05 2020-09-10 Abu-Siada, Ahmed ASSOC PROF Fault detection, location, and prediction within an electricity power transmission and distribution networks
CN113327033A (en) * 2021-05-28 2021-08-31 广西电网有限责任公司电力科学研究院 Power distribution network fault diagnosis method and system
CN113484669A (en) * 2021-06-23 2021-10-08 国网江苏省电力有限公司淮安供电分公司 Bidirectional LSTM-based power distribution network low-voltage reason positioning method
CN113964885A (en) * 2021-08-31 2022-01-21 国网山东省电力公司东营供电公司 Reactive active prediction and control technology of power grid based on situation awareness

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111429034A (en) * 2020-04-21 2020-07-17 国网信通亿力科技有限责任公司 Method for predicting power distribution network fault
AU2020101683A4 (en) * 2020-08-05 2020-09-10 Abu-Siada, Ahmed ASSOC PROF Fault detection, location, and prediction within an electricity power transmission and distribution networks
CN113327033A (en) * 2021-05-28 2021-08-31 广西电网有限责任公司电力科学研究院 Power distribution network fault diagnosis method and system
CN113484669A (en) * 2021-06-23 2021-10-08 国网江苏省电力有限公司淮安供电分公司 Bidirectional LSTM-based power distribution network low-voltage reason positioning method
CN113964885A (en) * 2021-08-31 2022-01-21 国网山东省电力公司东营供电公司 Reactive active prediction and control technology of power grid based on situation awareness

Also Published As

Publication number Publication date
CN114915035A (en) 2022-08-16

Similar Documents

Publication Publication Date Title
Qolomany et al. Parameters optimization of deep learning models using particle swarm optimization
Khajavi et al. Predicting the carbon dioxide emission caused by road transport using a Random Forest (RF) model combined by Meta-Heuristic Algorithms
CN111260124A (en) Chaos time sequence prediction method based on attention mechanism deep learning
CN113489654B (en) Routing method, device, electronic equipment and storage medium
Nagahamulla et al. An ensemble of artificial neural networks in rainfall forecasting
CN111416797A (en) Intrusion detection method for optimizing regularization extreme learning machine by improving longicorn herd algorithm
CN115511177A (en) Ultra-short-term wind speed prediction method based on INGO-SWGMN hybrid model
CN113722980A (en) Ocean wave height prediction method, system, computer equipment, storage medium and terminal
Froelich et al. Predictive capabilities of adaptive and evolutionary fuzzy cognitive maps-a comparative study
Culclasure Using neural networks to provide local weather forecasts
CN115168720A (en) Content interaction prediction method and related equipment
CN112770256B (en) Node track prediction method in unmanned aerial vehicle self-organizing network
CN113825165B (en) 5G slice network congestion early warning method and device based on time diagram network
CN114915035B (en) Power distribution network monitoring method, device and system
Chen et al. A Spark-based Ant Lion algorithm for parameters optimization of random forest in credit classification
Shi et al. A modified particle swarm optimization and radial basis function neural network hybrid algorithm model and its application
Li et al. Prediction of short-term traffic flow based on PSO-optimized chaotic BP neural network
Lima et al. Cognitive smart cities and deep learning: A classification framework
Iizaka et al. A novel daily peak load forecasting method using analyzable structured neural network
CN114548297A (en) Data classification method, device, equipment and medium based on domain self-adaption
CN114372418A (en) Wind power space-time situation description model establishing method
Bin et al. Simple and effective fault diagnosis method of power lithium-ion battery based on GWA-DBN
Liu et al. Wind power prediction based on LSTM-CNN optimization
Li et al. A novel RBF neural network design based on immune algorithm system
Anh et al. Wind speed intervals prediction using meta-cognitive approach

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