CN107579516A - A kind of method for improving Power system state estimation calculating speed - Google Patents
A kind of method for improving Power system state estimation calculating speed Download PDFInfo
- Publication number
- CN107579516A CN107579516A CN201710823171.8A CN201710823171A CN107579516A CN 107579516 A CN107579516 A CN 107579516A CN 201710823171 A CN201710823171 A CN 201710823171A CN 107579516 A CN107579516 A CN 107579516A
- Authority
- CN
- China
- Prior art keywords
- matrix
- row
- state estimation
- idle
- active
- 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.)
- Granted
Links
Landscapes
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a kind of method for improving Power system state estimation calculating speed, the quick calculating of active and idle Jacobian matrix, information matrix is realized using multi-threaded parallel algorithm by way of based on OpenMP shared memory programmings, during matrix multiple amount of calculation is reduced using Sparse technology by arranging zero computing, accelerate the decomposition rate of information matrix based on non-zero entry symbolic analysis and numerical value decomposition method in factorisation procedure, so as to improve the overall calculation speed of large scale electric network state estimation.
Description
Technical field
The present invention relates to a kind of method for improving Power system state estimation calculating speed, belong to power automation technology
Field.
Background technology
As intelligent scheduling technology supports system (D5000) state point to save building for scheduling system and model data center at different levels
If the and development of power network scale so that Power system state estimation calculation scale increasingly increases, at present intelligent scheduling technology branch
The state estimation that actual production is run in system is held, the state estimation run especially with model data center, it models scope from spy
High pressure 1000kV is very big to 10kV feeder line outlets, state estimation calculation scale so that its computational efficiency is difficult to meet to count in real time
The demand of point counting analysis.Therefore, improving large scale electric network state estimation computational efficiency becomes many scientific research scholars weight of interest
Point.
The calculating of information matrix and the LU of system of linear equations coefficient matrix in Power system state estimation calculating at present
Factorization occupies the state estimation most of the time, wherein for Large Scale Sparse Linear system, the Factorization calculating time accounts for
Solve equation group and calculate total time large percentage, improve the speed of information matrix and LU decomposition to whole POWER SYSTEM STATE
Acceleration plays very important effect.
The content of the invention
In order to solve the above-mentioned technical problem, the invention provides a kind of raising Power system state estimation calculating speed
Method.
In order to achieve the above object, the technical solution adopted in the present invention is：
A kind of method for improving Power system state estimation calculating speed, including,
Relationship type electric network model in Dispatching Control System is converted into hierarchical electric network model；
Read scheduling system in SCADA measurement, and with the element associated in hierarchical electric network model；
According to remote signalling and the annexation of each element, network topology is determined, i.e. node-branch model；
Measurement number based on node association carries out node sequencing；
Active Jacobian matrix H is formed based on OpenMP technologies according to node-branch modelpWith idle Jacobian matrix
Hq；
Active information matrix G is calculated based on sparse matrix technology and OpenMP technologiespWith idle information matrix Gq, GpFor
(n-1) matrix, G × (n-1) are tieed upqMatrix is tieed up for n × n, n is power network calculate node number；
To active information matrix GpLower triangle is sequentially completed from the 1st row to the (n-1)th row according to non-zero entry symbolic analysis method
The analysis of matrix L and upper triangular matrix U each row non-zero entry line number set；
To idle information matrix GqThree angular moments are sequentially completed down from the 1st row to the n-th row according to non-zero entry symbolic analysis method
The analysis of battle array L and upper triangular matrix U each row non-zero entry line number set；
To active information matrix GpAccording to Left Looking LU value decomposition calculation and analysis methods, and combine gained
The set of non-zero line number carries out Left Looking LU value decompositions from the 1st row to the (n-1)th leu time；
To idle information matrix GqAccording to Left Looking LU value decomposition calculation and analysis methods, and combine gained
The set of non-zero line number carries out Left Looking LU value decompositions from the 1st row to the n-th leu time；
Iterated in state estimation calculating process and solve equation group G Δs xk=HTR-1[z-h] and correct state
Amount, until Δ xkMeet the convergence criterion specified；Wherein, G is active information matrix GpOr idle information matrix Gq, xkFor kth
Node voltage amplitude or phase angle, i.e. quantity of state, x during secondary iterative calculationk+1=xk+Δxk, h is non-linear measurement phasor function, z
To measure vector, when G is active information matrix Gp, then R beH is Hp, when G is idle information matrix Gq, then R beH
For Hq,WithRespectively active measurement variance matrix and idle survey error covariance matrix；
As Δ xkDuring less than iteration threshold, stop iterative calculation.
The hierarchical electric network model deposit hierarchical data storehouse being converted into, measures and is also stored in hierarchical data storehouse, and with layer
Element associated in secondary type electric network model.
Remote signalling in measurement includes circuit-breaker status and disconnecting link state；When state estimation is calculated based on new electric network model
When, Network topology is carried out based on the whole network element；When this breaker, disconnecting link state exceed compared with last state variable number
During threshold value, Network topology is carried out based on the whole network element；When this breaker, disconnecting link state are compared with last state variable number
During not above threshold value, Local network topology analysis only is carried out by voltage class to the plant stand that remote signalling state changes.
Using the Fast decoupled state estimation algorithm based on the principle of least square by active update equation and idle amendment side
Journey decouples, the active Jacobian matrix H of parallel computationpWith idle Jacobian matrix Hq。
According to the active Jacobian matrix H of formationp, idle Jacobian matrix Hq, active measurement variance matrixWith it is idle
Survey error covariance matrixActive information matrix is calculated based on sparse matrix technology and OpenMP technologies
With idle information matrix
If coefficient matrices A=LU certain column vector is b, column vector x non-zero meta structure is obtained by solving Lx=b；
If β=i | bi≠ 0 }, χ=j | xj≠ 0 } node set of non-zero entry in b and x, wherein b are represented respectivelyiFor row
I-th row element, x in vectorial bjFor jth row element in column vector x；Wherein, i ∈ [1, n '], j ∈ [1, n '], work as state estimation
Coefficient matrices A is active information matrix G in calculatingp, n '=n-1, when coefficient matrices A is idle information in state estimation calculating
Matrix Gq, n '=n；
Assuming that the k ' -1 for having calculated L is arranged, its corresponding digraph is G (Lk′-1)；
L, U kth ' row non-zero node set is obtained by following criterion,
Wherein, lij≠ 0 represents G (Lk′-1) the middle side in the presence of from node j to node i, xiFor the i-th row member in column vector x
Element；
When solving x non-zero meta structure, ignore null value caused by numerical value counteracting, due to lij*xjCalculating cause not
By biWhether it is 0, xiNon-zero；
A and L presses row sparse storage, retrieves the line number of A row non-zero entries, and should by retrieval in row corresponding to line number from L
Row non-zero entry, x non-null set is obtained by deep search algorithm.
The beneficial effect that the present invention is reached：1st, multithreading is used by way of based on OpenMP shared memory programmings
Parallel algorithm realizes the quick calculating of active and idle Jacobian matrix, information matrix, using sparse during matrix multiple
Technology reduces amount of calculation by arranging zero computing, and non-zero entry symbolic analysis and numerical value decomposition method are based in factorisation procedure
Accelerate the decomposition rate of information matrix, so as to improve the overall calculation speed of large scale electric network state estimation；2nd, opened up based on local
The topological analysis that the technology of flutterring can substantially reduce in large scale electric network state estimation when remote signalling pre-processes takes, and effectively avoids
Remote signalling pretreatment is front and rear to carry out the topological analysis probability of occurrence that time-consuming based on the whole network model；3rd, adopted in LU decomposable processes
With symbolic analysis and the method for numerical value decomposition and separation, it is possible to prevente effectively from decomposable process neutral element calculating.
Brief description of the drawings
Fig. 1 is the flow chart of the present invention；
Fig. 2 is information matrix parallel computation flow；
Fig. 3 is non-zero entry structural analysis schematic diagram；
Fig. 4 is the schematic diagram for solving L, U jth row.
Embodiment
The invention will be further described below in conjunction with the accompanying drawings.Following examples are only used for clearly illustrating this hair
Bright technical scheme, and can not be limited the scope of the invention with this.
As shown in figure 1, a kind of method for improving Power system state estimation calculating speed, comprises the following steps：
Step 1, the relationship type electric network model in Dispatching Control System is converted into hierarchical electric network model, and is stored in layer
Secondary type database.
Because power system electric network composition has the relation of level, in order to analyze the facility of calculating, in state estimation meter
The analysis calculation method based on hierarchical data storehouse is used in calculation, first according to the power network mould of relationship type in regulation and control control system
Type is converted to the electric network model of hierarchical and electric network model is stored in into hierarchical data storehouse, and hierarchical data storehouse is using disk text
Electric network model is stored on state estimation application server hard disk by the mode of part mapping with binary file, hierarchical data
The element of relevant database is corresponded in element and Dispatching Control System in storehouse, and layer is based on when calculating analysis and calculating
Secondary type database carries out calculating analysis.
Step 2, read scheduling system in SCADA measurement, and with the element associated in hierarchical electric network model.
State estimation obtains SCADA measurement from Dispatching Control System, and wherein remote signalling includes circuit-breaker status and disconnecting link shape
State, remote measurement include that circuit is active and reactive, electric current, and transformer active, idle, electric current, gear, load are active and reactive, electric current,
Generated power, idle, electric current and busbar voltage, due to relationship type in the element and Dispatching Control System in hierarchical data storehouse
The element of database is corresponded by keyword, so being easy to the measurement of SCADA collections being associated with hierarchical data storehouse
In on corresponding element.
Step 3, according to the annexation of circuit-breaker status, disconnecting link state and each element, network topology is determined, that is, is saved
Point-branch model.
When state estimation is calculated based on new electric network model, Network topology is carried out based on the whole network element；When this
When breaker, disconnecting link state exceed threshold value compared with last state variable number, Network topology is carried out based on the whole network element.It is real
In the power network of border, many breakers, the state of disconnecting link seldom change in normal operation, therefore when this breaker, disconnecting link
State compared with last state variable number not above threshold value when, only the plant stand that remote signalling state changes is carried out by voltage class
Local network topology is analyzed.It can be substantially reduced in large scale electric network state estimation based on local topology technology and located in advance in remote signalling
Topological analysis during reason takes, and effectively prevent that remote signalling pretreatment is front and rear based on the whole network model to be carried out topological analysis time-consuming
Probability of occurrence.
Step 4, the measurement number based on node association carries out node sequencing, and OpenMP is based on according to node-branch model
(open multiprocessing) technology forms active Jacobian matrix HpWith idle Jacobian matrix Hq。
Using the Fast decoupled state estimation algorithm based on the principle of least square by active update equation and idle amendment side
Journey decouples, the active Jacobian matrix H of parallel computationpWith idle Jacobian matrix Hq。
Step 5, according to the active Jacobian matrix H of formationp, idle Jacobian matrix Hq, active measurement variance matrixWith idle survey error covariance matrixActive information matrix is calculated based on sparse matrix technology and OpenMP technologiesWith idle information matrixGpMatrix, G are tieed up for (n-1) × (n-1)qFor n × n
Matrix is tieed up, n is power network calculate node number；
G is realized using OpenMP technologiesp、GqQuick calculating, based on Sparse technology calculate Gp、GqA certain train value, specifically
Flow is as shown in Figure 2.
Step 6, to active information matrix GpIt is sequentially completed according to non-zero entry symbolic analysis method from the 1st row to the (n-1)th row
The analysis of lower triangular matrix L and upper triangular matrix U each row non-zero entry line number set；To idle information matrix GqAccording to non-zero
Metasymbol analysis method is sequentially completed lower triangular matrix L and upper triangular matrix U each row non-zero entry line number from the 1st row to the n-th row
The analysis of set.
If coefficient matrices A=LU column vector is b, column vector x non-zero meta structure is obtained by solving Lx=b；
If β=i | bi≠ 0 }, χ=j | xj≠ 0 } node set of non-zero entry in b and x, wherein b are represented respectivelyiFor row
I-th row element, x in vectorial bjFor jth row element in column vector x；Wherein, i ∈ [1, n '], j ∈ [1, n '], when state estimation meter
Coefficient matrices A is active information matrix G in calculationp, n '=n-1, when coefficient matrices A is idle information square in state estimation calculating
Battle array Gq, n '=n；
Assuming that the k ' -1 for having calculated L is arranged, its corresponding digraph is G (Lk′-1)；
L, U kth ' row non-zero node set is obtained by following criterion,
Wherein, lij≠ 0 represents G (Lk′-1) the middle side in the presence of from node j to node i, xiFor the i-th row member in column vector x
Element；
When solving x non-zero meta structure, ignore null value caused by numerical value counteracting, due to lij*xjCalculating cause not
By biWhether it is 0, xiNon-zero, non-zero entry structural analysis schematic diagram is as indicated at 3；
Because A and L presses row sparse storage, the line number of A row non-zero entries is retrieved, and by being examined in row corresponding to line number from L
The rope row non-zero entry, x non-null set is obtained by deep search algorithm.
Step 7, to active information matrix GpAccording to Left Looking (eye left) LU value decompositions calculate analysis side
Method, and the non-zero line number set for combining gained carries out Left Looking LU value decompositions from the 1st row to the (n-1)th leu time；It is right
Idle information matrix GqAccording to Left Looking LU value decomposition calculation and analysis methods, and combine the non-zero line number collection of gained
Close from the 1st row to the n-th leu time and carry out Left Looking LU value decompositions.
Left Looking LU value decompositions decompose from the 1st row to a last leu time, i.e., every time calculating one arrange to
Amount, numerical computations are carried out according to the symbolic analysis result of this row, so as to obtain the numerical value of all non-zero entries of the row.
The schematic diagrames of L, U jth row is solved as indicated at 4, if matrix-block L in Fig. 4j、L′j、UjWith vectorial aj、a′j、lj、uj、x′j
It is defined as follows；
aj=(a1j,…,a(j-1)j)T
a′j=(ajj,…,an′j)T
lj=(ljj,…,ln′j)T
uj=(u1j,…,u(j-1)j)T
x′j=(xjj,…,xn′j)T
Wherein, x 'jFor x jth row j-th to the n-th ' individual element set, ajjFor A jth row j column elements, ajFor A's
Jth arranges the 1st to -1 element set of jth, a 'jFor A jth row j-th to the n-th ' individual element set, ujThe 1st is arranged for U jth
It is individual to -1 element set of jth；
When calculating L, U jth column element, first by solving lower trigonometric equation group Ljuj=aj(Ljuj=ajSpecifically to ask
Expression formula is solved, and Lx=b above is general expression) obtain uj, then solve x 'j=a 'j-L′juj, by x 'jPivoting
Obtain pivot ujj, ujjFor selected U jth j-th of element of row, and calculate uj=x 'j, lj=x 'j/ujj, so as to the based on L
1 ..., j-1 row solve lower trigonometric equation group Ljuj=ajObtain L, U jth train value.
The method of symbolization analysis and numerical value decomposition and separation in LU decomposable processes, it is possible to prevente effectively from decomposing
The calculating of neutral element in journey.
Step 8, iterated in state estimation calculating process and solve equation group G Δs xk=HTR-1[z-h] is simultaneously corrected
Quantity of state, until Δ xkMeet the convergence criterion specified；Wherein, G is active information matrix GpOr idle information matrix Gq, xkFor
Node voltage amplitude or phase angle, i.e. quantity of state, x during kth time iterative calculationk+1=xk+Δxk, h is non-linear measurement vector letter
Number, z is measures vector, when G is active information matrix Gp, then R beH is Hp, when G is idle information matrix Gq, then R beH is Hq,WithRespectively active measurement variance matrix and idle survey error covariance matrix.
Step 9, as Δ xkDuring less than iteration threshold, stop iterative calculation.The node electricity being calculated according to state estimation
The trend value of all devices in pressure amplitude value and phase calculation power network, including the equipment such as circuit, transformer, load, generator have
Work(, without work value, capacitive reactance device without work value and performance assessment criteria etc..
The above method by way of based on OpenMP shared memory programmings using multi-threaded parallel algorithm realize it is active and
The quick calculating of idle Jacobian matrix, information matrix, dropped during matrix multiple using Sparse technology by arranging zero computing
Low amount of calculation, accelerate the decomposition of information matrix based on non-zero entry symbolic analysis and numerical value decomposition method in factorisation procedure
Speed, so as to improve the overall calculation speed of large scale electric network state estimation.
Described above is only the preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, without departing from the technical principles of the invention, some improvement and deformation can also be made, these improve and become
Shape also should be regarded as protection scope of the present invention.
Claims (6)
- A kind of 1. method for improving Power system state estimation calculating speed, it is characterised in that：Including,Relationship type electric network model in Dispatching Control System is converted into hierarchical electric network model；Read scheduling system in SCADA measurement, and with the element associated in hierarchical electric network model；According to remote signalling and the annexation of each element, network topology is determined, i.e. node-branch model；Measurement number based on node association carries out node sequencing；Active Jacobian matrix H is formed based on OpenMP technologies according to node-branch modelpWith idle Jacobian matrix Hq；Active information matrix G is calculated based on sparse matrix technology and OpenMP technologiespWith idle information matrix Gq, GpFor (n-1) × (n-1) ties up matrix, GqMatrix is tieed up for n × n, n is power network calculate node number；To active information matrix GpLower triangular matrix L is sequentially completed from the 1st row to the (n-1)th row according to non-zero entry symbolic analysis method With the analysis of upper triangular matrix U each row non-zero entry line number set；To idle information matrix GqAccording to non-zero entry symbolic analysis method from the 1st row to n-th row be sequentially completed lower triangular matrix L and The analysis of upper triangular matrix U each row non-zero entry line number set；To active information matrix GpAccording to Left Looking LU value decomposition calculation and analysis methods, and combine the non-zero row of gained Number set carries out Left Looking LU value decompositions from the 1st row to the (n-1)th leu time；To idle information matrix GqAccording to Left Looking LU value decomposition calculation and analysis methods, and combine the non-zero row of gained Number set carries out Left Looking LU value decompositions from the 1st row to the n-th leu time；Iterated in state estimation calculating process and solve equation group G Δs xk=HTR-1[z-h] and quantity of state is corrected, until ΔxkMeet the convergence criterion specified；Wherein, G is active information matrix GpOr idle information matrix Gq, xkFor kth time iteration meter Node voltage amplitude or phase angle, i.e. quantity of state, x during calculationk+1=xk+Δxk, h is non-linear measurement phasor function, and z swears to measure Amount, when G is active information matrix Gp, then R beH is Hp, when G is idle information matrix Gq, then R beH is Hq, WithRespectively active measurement variance matrix and idle survey error covariance matrix；As Δ xkDuring less than iteration threshold, stop iterative calculation.
- A kind of 2. method for improving Power system state estimation calculating speed according to claim 1, it is characterised in that：Turn The hierarchical electric network model deposit hierarchical data storehouse changed into, measures and is also stored in hierarchical data storehouse, and with hierarchical power network mould Element associated in type.
- A kind of 3. method for improving Power system state estimation calculating speed according to claim 1, it is characterised in that：Amount Remote signalling in survey includes circuit-breaker status and disconnecting link state；When state estimation is calculated based on new electric network model, based on the whole network Element carries out Network topology；When this breaker, disconnecting link state exceed threshold value compared with last state variable number, based on complete Mesh element carries out Network topology；When this breaker, disconnecting link state compared with last state variable number not above threshold value when, only Local network topology analysis is carried out by voltage class to the plant stand that remote signalling state changes.
- A kind of 4. method for improving Power system state estimation calculating speed according to claim 1, it is characterised in that：Adopt Active update equation and idle update equation are decoupled with the Fast decoupled state estimation algorithm based on the principle of least square, parallel Calculate active Jacobian matrix HpWith idle Jacobian matrix Hq。
- A kind of 5. method for improving Power system state estimation calculating speed according to claim 1, it is characterised in that：Root According to the active Jacobian matrix H of formationp, idle Jacobian matrix Hq, active measurement variance matrixWith idle survey error side Poor battle arrayActive information matrix is calculated based on sparse matrix technology and OpenMP technologiesWith it is idle Information matrix
- A kind of 6. method for improving Power system state estimation calculating speed according to claim 1, it is characterised in that：If coefficient matrices A=LU certain column vector is b, column vector x non-zero meta structure is obtained by solving Lx=b；If β=i | bi≠ 0 }, χ=j | xj≠ 0 } node set of non-zero entry in b and x, wherein b are represented respectivelyiFor column vector b In the i-th row element, xjFor jth row element in column vector x；Wherein, i ∈ [1, n '], j ∈ [1, n '], when in state estimation calculating Coefficient matrices A is active information matrix Gp, n '=n-1, when coefficient matrices A is idle information matrix G in state estimation calculatingq, n ' =n；Assuming that the k ' -1 for having calculated L is arranged, its corresponding digraph is G (Lk′-1)；L, U kth ' row non-zero node set is obtained by following criterion,Wherein, lij≠ 0 represents G (Lk′-1) the middle side in the presence of from node j to node i, xiFor the i-th row element in column vector x；When solving x non-zero meta structure, ignore null value caused by numerical value counteracting, due to lij*xjThough calculating cause biIt is No is 0, xiNon-zero；A and L presses row sparse storage, retrieves the line number of A row non-zero entries, and non-by retrieving the row in row corresponding to line number from L Null element, x non-null set is obtained by deep search algorithm.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710823171.8A CN107579516B (en) | 2017-09-13 | 2017-09-13 | Method for improving state estimation calculation speed of power system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710823171.8A CN107579516B (en) | 2017-09-13 | 2017-09-13 | Method for improving state estimation calculation speed of power system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107579516A true CN107579516A (en) | 2018-01-12 |
| CN107579516B CN107579516B (en) | 2020-08-25 |
Family
ID=61033126
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710823171.8A Active CN107579516B (en) | 2017-09-13 | 2017-09-13 | Method for improving state estimation calculation speed of power system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107579516B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108985622A (en) * | 2018-07-13 | 2018-12-11 | 清华大学 | A kind of electric system sparse matrix Parallel implementation method and system based on DAG |
| CN109274091A (en) * | 2018-10-15 | 2019-01-25 | 同济大学 | A kind of transmission & distribution integration parallel state estimation method |
| CN111062610A (en) * | 2019-12-16 | 2020-04-24 | 国电南瑞科技股份有限公司 | Power system state estimation method and system based on information matrix sparse solution |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1069517A1 (en) * | 1999-07-16 | 2001-01-17 | ABB Power Automation AG | Visual description of voltage states in an electrical switching graph |
| CN102427227A (en) * | 2011-10-18 | 2012-04-25 | 清华大学 | Quick correction decoupling power system state estimating method considering zero injection constraint |
-
2017
- 2017-09-13 CN CN201710823171.8A patent/CN107579516B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1069517A1 (en) * | 1999-07-16 | 2001-01-17 | ABB Power Automation AG | Visual description of voltage states in an electrical switching graph |
| CN102427227A (en) * | 2011-10-18 | 2012-04-25 | 清华大学 | Quick correction decoupling power system state estimating method considering zero injection constraint |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108985622A (en) * | 2018-07-13 | 2018-12-11 | 清华大学 | A kind of electric system sparse matrix Parallel implementation method and system based on DAG |
| CN109274091A (en) * | 2018-10-15 | 2019-01-25 | 同济大学 | A kind of transmission & distribution integration parallel state estimation method |
| CN111062610A (en) * | 2019-12-16 | 2020-04-24 | 国电南瑞科技股份有限公司 | Power system state estimation method and system based on information matrix sparse solution |
| CN111062610B (en) * | 2019-12-16 | 2022-07-29 | 国电南瑞科技股份有限公司 | Power system state estimation method and system based on information matrix sparse solution |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107579516B (en) | 2020-08-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112217202B (en) | Distributed new energy, energy storage and power distribution network planning method considering flexibility investment | |
| CN101976835B (en) | Parallel computation method for Newton power flow of large-scale electric power system | |
| CN103607466B (en) | A kind of wide-area multi-stage distributed parallel grid analysis method based on cloud computing | |
| CN104037776B (en) | The electric network reactive-load capacity collocation method of random inertial factor particle swarm optimization algorithm | |
| CN107330056A (en) | Wind power plant SCADA system and its operation method based on big data cloud computing platform | |
| Sun et al. | Charging load forecasting of electric vehicle charging station based on support vector regression | |
| CN107579516A (en) | A kind of method for improving Power system state estimation calculating speed | |
| CN103927693A (en) | Distribution network line loss management system | |
| CN105514997B (en) | The electric energy quality monitoring point collocation method of meter and distributed power source | |
| CN105512808A (en) | Power system transient stability assessment method based on big data | |
| CN107832876A (en) | Subregion peak load Forecasting Methodology based on MapReduce frameworks | |
| CN105160441A (en) | Real-time power load forecasting method based on integrated network of incremental transfinite vector regression machine | |
| CN115829126A (en) | Photovoltaic power generation power prediction method based on multi-view self-adaptive feature fusion | |
| CN108132423B (en) | A kind of method for rapidly positioning based on state transition probability power system monitoring data distortion | |
| CN110866633A (en) | Micro-grid ultra-short term load prediction method based on SVR support vector regression | |
| CN113991856B (en) | Micro-energy network multi-adaptability mu PMU optimal point distribution method | |
| CN114021425B (en) | Power system operation data modeling and feature selection method and device, electronic equipment and storage medium | |
| CN106372440A (en) | Method and device for estimating self-adaptive robust state of distribution network through parallel computation | |
| CN104751253B (en) | Distribution power flow Forecasting Methodology based on B- spline Basis bottom developed curve cluster | |
| CN116667369B (en) | Distributed photovoltaic voltage control method based on graph convolution neural network | |
| Cheng et al. | Short-term power load forecasting based on improved online ELM-K | |
| CN110348598A (en) | A kind of Net Frame of Electric Network planing method | |
| CN111965442A (en) | Energy internet fault diagnosis method and device under digital twin environment | |
| Wang et al. | The applications of XGBoost in fault diagnosis of power networks | |
| Chen et al. | Short-term photovoltaic power prediction based on LGBM-XGBoost |
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 |