CN108931700A - A kind of power grid security Warning System based on WSNs - Google Patents
A kind of power grid security Warning System based on WSNs Download PDFInfo
- Publication number
- CN108931700A CN108931700A CN201810497273.XA CN201810497273A CN108931700A CN 108931700 A CN108931700 A CN 108931700A CN 201810497273 A CN201810497273 A CN 201810497273A CN 108931700 A CN108931700 A CN 108931700A
- Authority
- CN
- China
- Prior art keywords
- power grid
- security
- grid security
- network data
- situation
- 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.)
- Withdrawn
Links
- 238000012545 processing Methods 0.000 claims abstract description 12
- 230000036544 posture Effects 0.000 claims description 42
- 238000012502 risk assessment Methods 0.000 claims description 8
- 230000015572 biosynthetic process Effects 0.000 claims description 5
- 230000007123 defense Effects 0.000 claims description 5
- 238000003786 synthesis reaction Methods 0.000 claims description 5
- 238000012544 monitoring process Methods 0.000 claims description 4
- 230000001105 regulatory effect Effects 0.000 claims description 2
- 239000000155 melt Substances 0.000 claims 1
- 230000009286 beneficial effect Effects 0.000 description 7
- 238000004364 calculation method Methods 0.000 description 3
- 238000000034 method Methods 0.000 description 3
- 238000010276 construction Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
Classifications
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- Theoretical Computer Science (AREA)
- Marketing (AREA)
- Health & Medical Sciences (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Educational Administration (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The present invention provides a kind of power grid security Warning System based on WSNs, the system include:Electric network data acquisition module, including multiple sensors, for acquiring electric network data;Electric network data processing module obtains the situation information that can characterize power grid security state for handling the electric network data of acquisition;Power grid security Situation Assessment module assesses the safe condition of power grid for the situation information according to obtained power grid security state.The present invention realizes the accurate estimation to power grid security situation, is conducive to dispatcher according to estimated result and takes corresponding defensive measure, while also improving the safety and stability of power grid.
Description
Technical field
The present invention relates to power system security technical fields, and in particular to a kind of power grid security Risk-warning based on WSNs
System.
Background technique
The safe and stable operation of power grid is related to national economy and national security, the big rule that recent domestic frequently occurs
Mould power outage causes the extensive concern of people.Traditional power grid security Warning System is based on contingency set sum number
Model is learned, calculates generation automatically by system, not only the time is long, but also calculated result can not be entirely used in dispatcher.
Summary of the invention
In view of the above-mentioned problems, the present invention is intended to provide a kind of key message infrastructure security based on artificial intelligence threatens
Active Defending System Against.
The purpose of the present invention is realized using following technical scheme:
A kind of power grid security Warning System based on WSNs, the early warning system include:
Electric network data acquisition module, including multiple sensors specifically include grid equipment state for acquiring electric network data
The weather environment monitoring data of data, operation of power networks status data, network operation daily record data and power grid;
Electric network data processing module for handling the electric network data of acquisition obtains that power grid security shape can be characterized
The situation information of state;
Power grid security Situation Assessment module, for the situation information according to obtained power grid security state, to the peace of power grid
Total state is assessed;
Power grid security Risk-warning module, for determining that power grid is pacified according to the assessment result of power grid security Situation Assessment module
Full risk class, and corresponding pre-warning signal is issued according to determining power grid security risk class.
Beneficial effect:The present invention provides the power grid security Warning Systems of WSNs a kind of, by utilizing wireless sensing
Device acquires electric network data in real time, and realizes the assessment to power grid security state according to the electric network data acquired in real time, and according to commenting
Estimate result and issue corresponding pre-warning signal, the early warning system is more flexible, realizes accurate estimation to power grid security situation, has
Corresponding defensive measure is taken according to estimated result conducive to dispatcher, while also improving the safety and stability of power grid.
Detailed description of the invention
The present invention will be further described with reference to the accompanying drawings, but the embodiment in attached drawing is not constituted to any limit of the invention
System, for those of ordinary skill in the art, without creative efforts, can also obtain according to the following drawings
Other attached drawings.
Fig. 1 is the principle of the present invention figure;
Fig. 2 is the frame construction drawing of electric network data processing module 2 of the present invention;
Fig. 3 is the frame construction drawing of power grid security Situation Assessment module 3 of the present invention.
Appended drawing reference:
Electric network data acquisition module 1;Electric network data processing module 2;Power grid security Situation Assessment module 3;Power grid security wind
Dangerous warning module 4;Initiative Defense module 5;Electric network data pretreatment unit 21;Electric network data analytical unit 22;Power grid security state
Gesture understands unit 31;Power grid security Tendency Prediction unit 32;Power grid situation information fused layer 311;Power grid risk assessment layer 312;
Power grid security situation synthesis assesses layer 313.
Specific embodiment
In conjunction with following application scenarios, the invention will be further described.
Referring to Fig. 1, a kind of power grid security Warning System based on WSNs is shown, which includes:
Electric network data acquisition module 1, including multiple sensors specifically include grid equipment shape for acquiring electric network data
The weather environment monitoring data of state data, operation of power networks status data, network operation daily record data and power grid;
Electric network data processing module 2 for handling the electric network data of acquisition obtains that power grid security shape can be characterized
The situation information of state;
Power grid security Situation Assessment module 3, for the situation information according to obtained power grid security state, to the peace of power grid
Total state is assessed;
Power grid security Risk-warning module 4, for determining power grid according to the assessment result of power grid security Situation Assessment module 3
Security risk grade, and corresponding pre-warning signal is issued according to determining power grid security risk class.
Beneficial effect:The present invention provides the power grid security Warning Systems of WSNs a kind of, by utilizing wireless sensing
Device acquires electric network data in real time, and realizes the assessment to power grid security state according to the electric network data acquired in real time, and according to commenting
Estimate result and issue corresponding pre-warning signal, the early warning system is more flexible, realizes accurate estimation to power grid security situation, has
Corresponding defensive measure is taken according to estimated result conducive to dispatcher, while also improving the safety and stability of power grid.
Preferably, referring to fig. 2, electric network data processing module 2 includes electric network data pretreatment unit 21 and electric network data
Analytical unit 22;
Electric network data pretreatment unit 21, for being cleaned to the electric network data of acquisition, at de-redundant and formatting unification
Reason;
Electric network data analytical unit 22, for will treated that electric network data merges through electric network data pretreatment unit
Processing and analysis, obtain the situation information that can characterize power grid security state.
Beneficial effect:The present invention in the above-described embodiment, by be arranged electric network data processing module 2, to the electricity of acquisition
Network data is handled, and can be effectively reduced data dimension, and the situation information of description power grid security state is removed with less data,
The complexity for reducing follow-up work, improves work efficiency, and also saves time cost.
Preferably, referring to Fig. 3, power grid security Situation Assessment module 3 includes:
Power grid security situation understands unit 31, for estimating the security postures of power grid according to situation information is obtained;
Power grid security Tendency Prediction unit 32, for understanding the estimated result and history of unit 31 according to power grid security situation
Power grid security situation data, predict the future trend of power grid security state.
Beneficial effect:The present invention understands unit 31 in the above-described embodiment, by the way that power grid security situation is arranged, and assessment is worked as
The security postures of preceding power grid, and then realize the real time monitoring to network system, while power grid security predicting unit 32 is according to power grid
Security postures understand the estimated result of unit 31 and combine history power grid security situation data, analyze current electric grid system
Safe condition, and then the future trend of power grid security state is predicted, which is conducive to the system and understands power grid in time
Future time instance attack reduces the experience degree of risk of power grid, improves the stability of power grid to the threat degree of power grid
And safety.
Preferably, power grid security situation understands that unit 31 includes power grid situation information fused layer 311, power grid referring to Fig. 3
Risk assessment layer 312 and power grid security situation synthesis assess layer 313;
Power grid situation information fused layer 311 calculates separately different in equipment to be assessed for the situation information according to acquisition
The probability value that attack occurs, wherein in t moment, the probability function that single attack occurs is equipment to be assessed:
In formula, Pj(t) attack v when being t momentjThe probability value of generation, xiIt is i-th of situation information to attack
vjContribution angle value, ωiIt is i-th of situation information in attack vjShared weight when generation, n are the sums of situation information,
γ1、γ2It is regulatory factor, and meets γ1 2+γ2 2=1, vjIt is j-th of attack, J is the number of attack, κjIt is to attack
Hit behavior vjIt treats bring when assessment equipment is launched a offensive to lose, pl (vj) it is attack νjAttacking ability value.
Beneficial effect:The present invention in the above-described embodiment, when calculating the probability value that each attack occurs, not only
In view of situation information itself, while having also contemplated that the loss of assessment equipment bring is treated in attack and attack is attacked
Ability is hit, is that the probability value that the attack being calculated occurs is more accurate, can more realistically reflect attack pair
The case where equipment to be assessed is launched a offensive.
Power grid risk assessment layer 312, the probability value for being occurred according to attacks different in obtained equipment to be assessed
The threat degree that assessment equipment is treated with different attacks obtains the security postures value of equipment to be assessed;
Power grid security situation synthesis assesses layer 313, for obtaining all devices in power grid according to power grid risk assessment layer
Security postures value, and the security postures of current electric grid are assessed according to obtained security postures value.
Preferably, the power grid security Warning System further includes Initiative Defense module 5, Initiative Defense mould referring to Fig. 1
Block 5 is connected with power grid security warning module 4, the pre-warning signal for being issued according to power grid security warning module 4, takes corresponding
Defensive measure.
Preferably, probability value and different attacks for being occurred according to attacks different in obtained equipment to be assessed
The threat degree of assessment equipment is treated in behavior, obtains the security postures value of equipment to be assessed, wherein equipment to be assessed is in t moment
The calculation formula of security postures value be:
In formula,The security postures value of equipment to be assessed when being t moment, b is the truth of a matter, αjIt is attack vjTo be evaluated
Estimate the threat degree value of equipment, J is the number of attack, Pj(t) attack ν when being t momentjThe probability value of generation, χ are
The defensive strength value of equipment to be assessed itself.
Beneficial effect:In above embodiment of the present invention, when calculating the security postures value of equipment to be assessed, not only consider
The defence capability that attack itself treats the influence of assessment equipment, while having also contemplated equipment to be assessed itself, this makes
When calculating the security postures value of equipment to be assessed, it can be truly reflected out the safe condition of equipment to be assessed, after being conducive to
Continuous estimation and prediction to entire network system security postures.
Preferably, the security postures of all devices in power grid are assessed according to the power grid risk assessment layer, and
The security postures of current electric grid are assessed according to obtained assessment result and power grid history security postures information, specifically,
According to the security postures value of obtained all equipment to be assessed and power grid history security postures information, the safety of entire power grid is calculated
Situation value, wherein entirely the calculation formula of the security postures value of power grid is:
In formula,It is the security postures value of power grid,It is the security postures value of power grid in the past period,The security postures value of equipment m to be assessed when being t moment, M is the number of equipment to be assessed, ωmIt is that equipment m to be assessed exists
Importance value in entire power grid, θ is weight factor, and 0 < θ < 1, R (x, m) are equipment x to be assessed and equipment m to be assessed
Degree of correlation coefficient, and x ≠ m.
Beneficial effect:In above embodiment of the present invention, by obtaining the security postures value of all equipment to be assessed, in turn
According to the security postures value of obtained all equipment to be assessed and power grid history security postures information to the safe state of entire power grid
Gesture value is estimated that the way not only allows for the relevance between equipment to be assessed, while having also contemplated the history peace of power grid
Full influence of the situation value to current electric grid security postures value, this allows the system to the accurately security postures to current electric grid
Value is estimated, is conducive to the subsequent security postures to future time instance power grid and predicts.
Preferably, the estimated result and history power grid security situation data of unit are understood according to power grid security situation, it is right
The future trend of power grid security state is predicted, the process of the predicted value of the future time instance of power grid security state is specifically obtained
It is:
(1) safety situation evaluation unit 31 is utilized, the security postures value of the network system of different moments is calculated, constructs one
Original time series X(0)={ x(0)(1),x(0)(2),…,x(0)(g) }, wherein x(0)It (g) is safety of the network system at the g moment
Situation value;
(2) to obtained original time series X(0)It is pre-processed, obtains the first data sequence XD(0)={ xd(0)(1),
xd(0)(2),…,xd(0)(g) }, specifically, xd(0)It (c) is calculated using following formula:
In formula, xd(0)(c) when being the c moment in original time series security postures value estimated value, x(0)(k) when being original
Between sequence the k moment security postures value, t is current time;
(3) to the first obtained data sequence XD(0)Single order accumulation operations are carried out, the second data sequence XD is obtained(1)={ xd(1)(1),xd(1)(2),…,xd(1)(g) }, whereinK=1,2 ..., g;xd(1)It (k) is first
The estimated value of the security postures value of (estimated value of the security postures value including the k moment) is cumulative before the k moment of data sequence
Value;
(4) according to obtained the first data sequence and original time series, grey forecasting model is constructed, wherein the ash
The formula of color prediction model is:
In formula,It is the predicted value of the security postures of the network system at k+1 moment, xd(0)It (k) is initial time
Network system security postures value estimated value, a is development coefficient, and b is grey actuating quantity, and the calculation formula of the value of a and b
It is:
(a,b)T=(BTB)-1BTY
In formula,
Y=[xd(0)(2),xd(0)(3),…,xd(0)(g)]T
Wherein, τ is weight factor, and 0 < τ < 1;
(5) it as k > g, is obtained using the formula of grey forecasting modelValue be network system peace
The predicted value of full situation.
Above embodiment of the present invention is adopted and is predicted with the aforedescribed process power grid security situation, utilizes gray prediction
Model is to the advantages of the processing such as nonlinear data, Small Sample Database, and in the network system obtained to safety situation evaluation unit 31
When the original time series that security postures are constituted of uniting carry out subsequent processing, it is contemplated that the influence of noise, system fluctuation, to what is obtained
Original time series are pre-processed, which can exclude during actual prediction, original of the external interference factor to acquisition
The interference of beginning time series can further increase the precision of prediction of network system security postures prediction model.
Finally it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than the present invention is protected
The limitation of range is protected, although explaining in detail referring to preferred embodiment to the present invention, those skilled in the art are answered
Work as analysis, it can be with modification or equivalent replacement of the technical solution of the present invention are made, without departing from the reality of technical solution of the present invention
Matter and range.
Claims (5)
1. a kind of power grid security Warning System based on WSNs, which is characterized in that including:
Electric network data acquisition module, including multiple sensors specifically include grid equipment status number for acquiring electric network data
According to, the weather environment monitoring data of operation of power networks status data, network operation daily record data and power grid;
Electric network data processing module for handling the electric network data of acquisition obtains that power grid security state can be characterized
Situation information;
Power grid security Situation Assessment module, for the situation information according to obtained power grid security state, to the safe shape of power grid
State is assessed;
Power grid security Risk-warning module, for determining that power grid is pacified according to the assessment result of the power grid security Situation Assessment module
Full risk class, and corresponding pre-warning signal is issued according to determining power grid security risk class.
2. power grid security Warning System according to claim 1, which is characterized in that the electric network data processing module
Including electric network data pretreatment unit and electric network data analytical unit;
The electric network data pretreatment unit, for being cleaned to the electric network data of acquisition, de-redundant and formatting are uniformly processed;
The electric network data analytical unit, for will treated that electric network data melts through the electric network data pretreatment unit
It closes and analyzes, obtain the situation information that can characterize power grid security state.
3. power grid security Warning System according to claim 2, which is characterized in that the power grid security Situation Assessment
Module includes:
Power grid security situation understands unit, for estimating the security postures of power grid according to situation information is obtained;
Power grid security Tendency Prediction unit, for understanding the estimated result and history power grid of unit according to the power grid security situation
Security postures data predict the future trend of power grid security state.
4. power grid security Warning System according to claim 3, which is characterized in that the power grid security situation understands
Unit includes power grid situation information fused layer, power grid risk assessment layer and power grid security situation synthesis assessment layer;
The power grid situation information fused layer calculates separately difference in equipment to be assessed and attacks for the situation information according to acquisition
Hit the probability value of behavior generation, wherein in t moment, the probability function that single attack occurs is equipment to be assessed:
In formula, Pj(t) attack v when being t momentjThe probability value of generation, xiIt is i-th of situation information to attack vjTribute
Offer angle value, ωiIt is i-th of situation information in attack vjShared weight when generation, n are the sum of situation information, γ1、
γ2It is regulatory factor, and meets γ1 2+γ2 2=1, vjIt is j-th of attack, J is the number of attack, κjIt is attack row
For vjIt treats bring when assessment equipment is launched a offensive to lose, pl (vj) it is attack vjAttacking ability value;
The power grid risk assessment layer, for according to the probability value that difference attacks occur in obtained equipment to be assessed and not
The threat degree that assessment equipment is treated with attack, the security postures for treating assessment equipment are assessed;
The power grid security situation synthesis assesses layer, for the peace according to the power grid risk assessment layer to all devices in power grid
Full situation is assessed, and according to obtained assessment result and power grid history security postures information to the security postures of current electric grid
It is assessed.
5. power grid security Warning System according to claim 4, which is characterized in that it further include Initiative Defense module,
The Initiative Defense module is connected with the power grid security warning module, for what is issued according to the power grid security warning module
Pre-warning signal takes corresponding defensive measure.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201810497273.XA CN108931700A (en) | 2018-05-22 | 2018-05-22 | A kind of power grid security Warning System based on WSNs |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201810497273.XA CN108931700A (en) | 2018-05-22 | 2018-05-22 | A kind of power grid security Warning System based on WSNs |
Publications (1)
Publication Number | Publication Date |
---|---|
CN108931700A true CN108931700A (en) | 2018-12-04 |
Family
ID=64449211
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201810497273.XA Withdrawn CN108931700A (en) | 2018-05-22 | 2018-05-22 | A kind of power grid security Warning System based on WSNs |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN108931700A (en) |
Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109660561A (en) * | 2019-01-24 | 2019-04-19 | 西安电子科技大学 | A kind of network security system quantitative estimation method, network security assessment platform |
CN109992086A (en) * | 2019-04-14 | 2019-07-09 | 北京中大科慧科技发展有限公司 | A kind of the state assessment method and state assessment device of data center's dynamical system |
CN111654321A (en) * | 2020-06-01 | 2020-09-11 | 清华大学 | Satellite network management method and device and electronic equipment |
US11693763B2 (en) | 2019-07-30 | 2023-07-04 | General Electric Company | Resilient estimation for grid situational awareness |
-
2018
- 2018-05-22 CN CN201810497273.XA patent/CN108931700A/en not_active Withdrawn
Cited By (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109660561A (en) * | 2019-01-24 | 2019-04-19 | 西安电子科技大学 | A kind of network security system quantitative estimation method, network security assessment platform |
CN109992086A (en) * | 2019-04-14 | 2019-07-09 | 北京中大科慧科技发展有限公司 | A kind of the state assessment method and state assessment device of data center's dynamical system |
CN109992086B (en) * | 2019-04-14 | 2020-10-20 | 北京中大科慧科技发展有限公司 | State evaluation method and state evaluation device for data center power system |
US11693763B2 (en) | 2019-07-30 | 2023-07-04 | General Electric Company | Resilient estimation for grid situational awareness |
CN111654321A (en) * | 2020-06-01 | 2020-09-11 | 清华大学 | Satellite network management method and device and electronic equipment |
CN111654321B (en) * | 2020-06-01 | 2021-04-27 | 清华大学 | Satellite network management method and device and electronic equipment |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN108418841B (en) | Next-generation key message infrastructure network Security Situation Awareness Systems based on AI | |
Kurt et al. | Online cyber-attack detection in smart grid: A reinforcement learning approach | |
CN108931700A (en) | A kind of power grid security Warning System based on WSNs | |
Huang et al. | Bad data injection in smart grid: attack and defense mechanisms | |
Wan et al. | Direct interval forecasting of wind power | |
CN110365647B (en) | False data injection attack detection method based on PCA and BP neural network | |
CN105868629B (en) | Security threat situation assessment method suitable for electric power information physical system | |
CN108494802A (en) | Key message infrastructure security based on artificial intelligence threatens Active Defending System Against | |
CN107786369A (en) | Based on the perception of IRT step analyses and LSTM powerline network security postures and Forecasting Methodology | |
CN104486141A (en) | Misdeclaration self-adapting network safety situation predication method | |
CN109767352A (en) | A kind of power information physics emerging system safety situation evaluation method | |
Alamaniotis et al. | Regression to fuzziness method for estimation of remaining useful life in power plant components | |
CN102148820A (en) | System and method for estimating network security situation based on index logarithm analysis | |
CN115469627B (en) | Intelligent factory operation management system based on Internet of things | |
CN108809706A (en) | A kind of network risks monitoring system of substation | |
Reda et al. | Data-driven approach for state prediction and detection of false data injection attacks in smart grid | |
Hu et al. | Deep reinforcement learning based valve scheduling for pollution isolation in water distribution network | |
CN113065218B (en) | Electric power system reliability evaluation method, device and system considering LR attack | |
Sheng et al. | Water quality prediction method based on preferred classification | |
He et al. | Detection of false data injection attacks leading to line congestions using Neural networks | |
CN113191485B (en) | Power information network security detection system and method based on NARX neural network | |
Wang et al. | Stealthy attack detection method based on Multi-feature long short-term memory prediction model | |
Song et al. | On credibility of adversarial examples against learning-based grid voltage stability assessment | |
CN117763555A (en) | Power distribution network data safety protection and evaluation method based on block chain | |
CN104091047A (en) | Traffic flow missing data estimation system and method based on traffic time-spatial information |
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 | ||
WW01 | Invention patent application withdrawn after publication | ||
WW01 | Invention patent application withdrawn after publication |
Application publication date: 20181204 |