CN110247396A - State Estimation for Distribution Network and system based on adaptive robust Unscented kalman filtering - Google Patents

State Estimation for Distribution Network and system based on adaptive robust Unscented kalman filtering Download PDF

Info

Publication number
CN110247396A
CN110247396A CN201910646332.XA CN201910646332A CN110247396A CN 110247396 A CN110247396 A CN 110247396A CN 201910646332 A CN201910646332 A CN 201910646332A CN 110247396 A CN110247396 A CN 110247396A
Authority
CN
China
Prior art keywords
distribution network
state
kalman filtering
unscented kalman
robust
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.)
Pending
Application number
CN201910646332.XA
Other languages
Chinese (zh)
Inventor
张宏伟
刘磊
李保忠
张开元
梁涛
王恒杰
董江涛
刘源
袁野
邱成龙
张文
赵彤
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.)
Qingdao Power Supply Co of State Grid Shandong Electric Power Co
Original Assignee
Qingdao Power Supply Co of State Grid Shandong Electric Power Co
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 Qingdao Power Supply Co of State Grid Shandong Electric Power Co filed Critical Qingdao Power Supply Co of State Grid Shandong Electric Power Co
Priority to CN201910646332.XA priority Critical patent/CN110247396A/en
Publication of CN110247396A publication Critical patent/CN110247396A/en
Pending legal-status Critical Current

Links

Classifications

    • HELECTRICITY
    • H02GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02JCIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00Circuit arrangements for ac mains or ac distribution networks
    • H02J2003/007

Abstract

The present disclosure proposes State Estimation for Distribution Network and system based on adaptive robust Unscented kalman filtering, comprising: the dynamic model and sampling plan for determining distribution network system establish Unscented kalman filtering model, determine and measure mixed media;The Optimized model for introducing adaptive robust, using time-varying Sage-Husa Noise statistics extimators, discontinuity surface optimizes processing to distribution network system noise when each, receives most recent data;It establishes Robust filter model to handle bad data existing for system, obtaining has robust effect status predication value and covariance, to carry out the state estimation at next moment.Unscented kalman filtering model used by the disclosure obtains order of information, can satisfy requirement of the system to high non-linearity by the state distribution of setting multiple groups random symmetric point approximate simulation system.

Description

State Estimation for Distribution Network based on adaptive robust Unscented kalman filtering and System
Technical field
This disclosure relates to which distribution network status estimation technical field, is filtered more particularly to based on adaptive robust Unscented kalman The State Estimation for Distribution Network and system of wave.
Background technique
Distribution network status estimation is to ensure the important means and electric power dispatching system of power network safety operation Important component.Traditional static state estimation, can only obtain the data of current time section, filter out big error, low essence The bad data of degree establishes estimation model, supplements pseudo- measuring point to infull point is measured, current time essence is obtained after being calculated True operating status.But the ability of the limitation of algorithm discontinuity surface when it being caused to be not previously predicted next itself, with power distribution network The continuous improvement of complexity, the requirement for power distribution network discontinuity surface predictive ability when multiple also step up.Acquisition system connects The operating status of a period of time of getting off can efficiently control the power output of load, increase economic efficiency, can also find out power distribution network In will there is a situation where give warning in advance and propose corresponding processing strategie, avoid the generation of accident.Dynamic state estimator goes out Now solves the problems, such as this, the dynamic state estimator model based on Unscented kalman filtering algorithm utilizes past state estimation System real-time status is simulated with the quantity of state at current time, and by metric data system analog quantity is modified and Prediction, but the presence of noise error makes to estimate that accuracy there are also the space further promoted, needs to redesign in filtering algorithm The mathematical model of state estimation and corresponding derivation algorithm.
Existing Unscented kalman filtering method is difficult to meet well power distribution network for state estimation height both at home and abroad at present The needs of precision, are mainly manifested in:
1., due to the influence of sampling plan and state equation, can there is certain system inside Unscented kalman filtering Error is normally set up a constant and carrys out noise error inside simulation system, but general power distribution network carries out system when state estimation Noise is time-varying, and over time, the system noise for most starting setting is no longer satisfied the requirement of system accuracy, anxious The stale data for needing most recent data to be left come discontinuity surface when substituting previous, how to be filtered to system noise is also The problem to be solved such as one.
2. generally obtaining system real-time measurement data using SCADA/PMU hybrid measurement module in power grid, but catching The generation for, due to a series of influence of environmental factors such as temperature, humidity, having in data procedures and measuring rough error is obtained, how to these Bad data carries out effective and reasonable processing, and it is to be resolved to reduce influence of the bad data to system prediction precision.
Summary of the invention
The purpose of this specification embodiment is to provide the state of electric distribution network based on adaptive robust Unscented kalman filtering Estimation method has higher stability and accuracy when system being made to carry out state estimation
This specification embodiment provides the State Estimation for Distribution Network based on adaptive robust Unscented kalman filtering, It is achieved through the following technical solutions:
Include:
The dynamic model and sampling plan for determining distribution network system establish Unscented kalman filtering model, determine to measure and mix Conjunction means;
The Optimized model for introducing adaptive robust is interrupted when each using time-varying Sage-Husa Noise statistics extimators Processing is optimized in face of distribution network system noise, receives most recent data;
It establishes Robust filter model to handle bad data existing for system, obtaining has robust effect status predication Value and covariance, to carry out the state estimation at next moment.
Further technical solution, the dynamic model of distribution network system are as follows:
Wherein, xkState vector, and x are tieed up for nk=[Ukk]∈Rn, wherein UkFor the voltage magnitude of each node of system, θk For the phase angle of each node of system;ykIt is tieed up for m and measures vector, and yk=[Uk,Pk,Qk,Pi,Qi]T∈Rm, wherein PkAnd QkRespectively it is Each node trend active power and reactive power, P in unitingiAnd QiEach Branch Power Flow active power and idle function respectively in system Rate, f are state transition function;H is to measure function;qkAnd vkThe systematic error of respectively n dimension and the error in measurement of m dimension, are obeyed equal Value is zero, standard deviation is Q and the distribution of the white noise of R.
The state equation of further technical solution, distribution network system uses two exponential smoothing of Holt, by being arranged not Same smoothing parameter comes two kinds of factors in smooth original state time series, state equation are as follows:
Wherein, xkIndicate that the k moment is directed to the status predication value of power distribution network;Indicate k moment state estimation;αkAnd βkFor Smoothing parameter, between 0~1;skIndicate the smoothed out value of current state;bkIndicate the smoothed out trend of current state.
Further technical solution is corrected symmetric sampling method using ratio to the sampling plan of state of electric distribution network amount, is passed through It is sampled to being added after the different weights of the multiple sigma points impartings of state measurement, is the nonlinear system of higher-dimension for power distribution network, Obtain more accurate quantity of state random distribution information.
Further technical solution establishes Unscented kalman filtering for established state equation and sampling plan Model:
According to the state equation of system, the prior state of Unscented kalman filtering model is obtained;
After acquisition system prior state, the measurement prediction of system is calculated;
After obtaining metric data, system is filtered.
Adaptive robust mould is added to the Unscented kalman filtering algorithm based on Kalman's theory in further technical solution Type solves the state estimation of electric system.
Time-varying noise statistics valuation mould is added in further technical solution after Unscented kalman filtering model filtering Type, the system noise data of discontinuity surface recently when receiving each forget the stale data for influencing system accuracy.
Further technical solution, the measurement covariance P of Unscented kalman filtering modelyA judgment basis is added before Determine the presence or absence of bad data, if it does not exist bad data, then μk=1, otherwise introduce robust model;
After substituting into new measurement covariance, being obtained using Unscented kalman filtering model has robust effect status predication value And covariance, to carry out the state estimation at next moment.
This specification embodiment provides the distribution network status estimation system based on adaptive robust Unscented kalman filtering, It is achieved through the following technical solutions:
Include:
Model building module determines the dynamic model and sampling plan of distribution network system, establishes Unscented kalman filtering mould Type determines and measures mixed media;
Optimization processing module introduces the Optimized model of adaptive robust, utilizes time-varying Sage-Husa noise statistics valuation Device, discontinuity surface optimizes processing to distribution network system noise when each, receives most recent data;
State estimation module is established Robust filter model and is handled bad data existing for system, obtains with anti- Poor effect status predication value and covariance, to carry out the state estimation at next moment.
Compared with prior art, the beneficial effect of the disclosure is:
State equation and sampling plan used by the disclosure have multiple adjustment parameters, and the selection of multiple parameters is so that be System model has very strong adaptability and flexibility and status predication can be kept more accurate with smooth state amount.
Unscented kalman filtering model used by the disclosure passes through setting multiple groups random symmetric point approximate simulation system State distribution, obtains order of information, can satisfy requirement of the system to high non-linearity.
Adaptive robust model used by the disclosure concludes error caused by system modelling the simulation system to time-varying In system noise, and its influence to system stability can be effectively resisted there are bad data, anti-locking system hair It dissipates.
Detailed description of the invention
The Figure of description for constituting a part of this disclosure is used to provide further understanding of the disclosure, and the disclosure is shown Meaning property embodiment and its explanation do not constitute the improper restriction to the disclosure for explaining the disclosure.
Fig. 1 is the overall design drawing of the adaptive robust Unscented kalman filtering model of embodiment of the present disclosure;
Fig. 2 is the Unscented kalman filtering flow chart of embodiment of the present disclosure;
Fig. 3 is the adaptive noise statistical estimation device model schematic of embodiment of the present disclosure;
Fig. 4 is the robust model schematic of embodiment of the present disclosure.
Specific embodiment
It is noted that following detailed description is all illustrative, it is intended to provide further instruction to the disclosure.Unless another It indicates, all technical and scientific terms used herein has usual with disclosure person of an ordinary skill in the technical field The identical meanings of understanding.
It should be noted that term used herein above is merely to describe specific embodiment, and be not intended to restricted root According to the illustrative embodiments of the disclosure.As used herein, unless the context clearly indicates otherwise, otherwise singular Also it is intended to include plural form, additionally, it should be understood that, when in the present specification using term "comprising" and/or " packet Include " when, indicate existing characteristics, step, operation, device, component and/or their combination.
Examples of implementation one
This embodiment disclose the State Estimation for Distribution Network based on adaptive robust Unscented kalman filtering, referring to attached Shown in Fig. 1, it is first determined the dynamic model and sampling plan of system establish Unscented kalman filtering model, determine and measure mixing Means.The Optimized model of adaptive robust is subsequently introduced, using time-varying Sage-Husa Noise statistics extimators, in each time Section optimizes processing to system noise, receives most recent data, improves the accuracy of system model.Establish Robust filter again afterwards Model is adjusted and improves to bad data existing for system, it is ensured that distribution network status estimation system model stable operation.
Specifically, establishing the dynamic model of system:
Wherein, xkState vector, and x are tieed up for nk=[Ukk]∈Rn, wherein UkFor the voltage magnitude of each node of system, θk For the phase angle of each node of system;ykIt is tieed up for m and measures vector, and yk=[Uk,Pk,Qk,Pi,Qi]T∈Rm, wherein PkAnd QkRespectively it is Each node trend active power and reactive power, P in unitingiAnd QiEach Branch Power Flow active power and idle function respectively in system Rate.F is state transition function;H is to measure function;qkAnd vkThe systematic error of respectively n dimension and the error in measurement of m dimension, are obeyed equal Value is zero, standard deviation is Q and the distribution of the white noise of R.
State equation uses two exponential smoothing of Holt, by the way that different smoothing parameters is arranged, carrys out the smooth original state time Two kinds of factors in ordered series of numbers.State equation are as follows:
Wherein, xkIndicate that the k moment is directed to the status predication value of power distribution network;Indicate k moment state estimation;αkAnd βkFor Smoothing parameter, between 0~1;(skIndicate the smoothed out value of current state;bkIndicate current state it is smoothed out become Gesture).
Symmetric sampling method is corrected using ratio to the sampling plan of distribution quantity of state, by measuring multiple sigma to state It is added and is sampled after the different weights of point imparting, be the nonlinear system of higher-dimension for power distribution network, can obtain more accurately Quantity of state random distribution information generates higher order term error when avoiding sampling, and accurately captures high level matrix information:
λ=α2(n+κ)-n (5)
Wherein,It is the system state estimation value at k-1 moment;P is status predication covariance;λ is a correction factor, Error is generated in sampling process for reducing;Wherein α is ratio modifying factor, by the adjustment to α can control Sigma point with The distance of mean value, makes high order effects become smaller;κ is positive semidefinite free parameter;β is a non-negative influence coefficient, should by introducing Its influence factor can be included by coefficient when containing higher-order function in systems;Wi mAnd Wi cRespectively Unscented transform when Weighting weight needed for calculating mean value and covariance.
In the examples of implementation, for established state equation and sampling plan, Unscented kalman filtering mould is established Type.
Flow chart is as shown in Figure 2.Specific steps include:
(1) according to the state equation of system, the prior state of Unscented kalman filtering model is obtained:
Wherein,It is Sigma sampling point set,It is that each point is calculated using Holt two-parameter exponential exponential smoothing Prior state estimation,It is that the prior state about the k moment obtained by weight number combining is estimated,It is that system noise is added The covariance of prior estimate error is calculated in sound error battle array.
(2) after obtaining system prior state, the measurement prediction of system is calculated:
Wherein,It is that resulting Sigma point set is calculated according to prior state stage the data obtained;It is by calculating The measurement about each point arrived is predicted;ykIt is to be obtained measuring about the state at k moment by weight number combining;PyIt is to measure prediction Covariance;PxyIt is to estimate about prior stateWith system measurements predicted value ykCovariance.
(3) after obtaining metric data, system is filtered:
Wherein, KkIt is that kalman gain is calculated by covariance;ZkIt is real-time system measuring value;ykIt is system measurements Predicted value;Pass through ZkAnd ykTo the state estimation at k momentAnd covarianceIt is updated and corrects, obtain state variable It updates.
In the examples of implementation, the Unscented kalman filtering algorithm based on Kalman's theory is improved, is added adaptive Robust model is answered, to improve the filtering accuracy and system stability of algorithm, is used for the algorithm to solve Power system state estimation Optimization problem.
(1) adaptive Sage-Husa time-varying noise statistics Valuation Modelling is as shown in figure 3, be added time-varying after filtering Noise statistics Valuation Modelling, the system noise data of discontinuity surface recently when receiving each forget the outmoded number for influencing system accuracy According to.Concrete model is as follows:
dk-1=(1-b)/(1-bk) (16)
Wherein, b is forgetting factor, VkNewly to cease update equation and Vk=Zk-yk
(2) Robust filter model flow figure is as shown in figure 4, measuring covariance PyA judgment basis is added before to determine The presence or absence of bad data, if it does not exist bad data, then μk=1, otherwise introduce robust model:
Wherein, μkIt is robust enhancement factor, by reducing kalman gain, the amendment of system in Lai Youhua renewal process Amount is minimized measurement rough error to systematic influence;Tr () is the mark of matrix;Py' for there are measurement association sides when bad data Difference.
Substitute into new measurement covariance Py' after, by formula (12)~(14) obtain have robust effect status predication value and Covariance, to carry out the state estimation at next moment.
Examples of implementation two
This specification embodiment provides the distribution network status estimation system based on adaptive robust Unscented kalman filtering, It is achieved through the following technical solutions:
Include:
Model building module determines the dynamic model and sampling plan of distribution network system, establishes Unscented kalman filtering mould Type determines and measures mixed media;
Optimization processing module introduces the Optimized model of adaptive robust, utilizes time-varying Sage-Husa noise statistics valuation Device, discontinuity surface optimizes processing to distribution network system noise when each, receives most recent data;
State estimation module is established Robust filter model and is handled bad data existing for system, obtains with anti- Poor effect status predication value and covariance, to carry out the state estimation at next moment.
The specific implementation of module in the examples of implementation is referring to the specific computation model in examples of implementation one, herein not It is described in detail again.
Examples of implementation three
This specification embodiment provides a kind of computer equipment, including memory, processor and storage are on a memory And the computer program that can be run on a processor, which is characterized in that the processor realizes embodiment when executing described program Son one in the State Estimation for Distribution Network based on adaptive robust Unscented kalman filtering the step of.
Examples of implementation four
This specification embodiment provides a kind of computer readable storage medium, is stored thereon with computer program, special Sign is, the matching based on adaptive robust Unscented kalman filtering in examples of implementation one is realized when which is executed by processor The step of Power Network Status Estimation method.
It is understood that in the description of this specification, reference term " embodiment ", " another embodiment ", " other The description of embodiment " or " first embodiment~N embodiment " etc. means specific spy described in conjunction with this embodiment or example Sign, structure, material or feature are included at least one embodiment or example of the invention.In the present specification, to above-mentioned The schematic representation of term may not refer to the same embodiment or example.Moreover, the specific features of description, structure, material Person's feature can be combined in any suitable manner in any one or more of the embodiments or examples.
The foregoing is merely preferred embodiment of the present disclosure, are not limited to the disclosure, for the skill of this field For art personnel, the disclosure can have various modifications and variations.It is all within the spirit and principle of the disclosure, it is made any to repair Change, equivalent replacement, improvement etc., should be included within the protection scope of the disclosure.

Claims (10)

1. the State Estimation for Distribution Network based on adaptive robust Unscented kalman filtering, characterized in that
Include:
The dynamic model and sampling plan for determining distribution network system establish Unscented kalman filtering model, determine and measure mixing hand Section;
The Optimized model for introducing adaptive robust, using time-varying Sage-Husa Noise statistics extimators, discontinuity surface pair when each Distribution network system noise optimizes processing, receives most recent data;
Robust filter model is established to handle bad data existing for system, obtain have robust effect status predication value and Covariance, to carry out the state estimation at next moment.
2. the State Estimation for Distribution Network as described in claim 1 based on adaptive robust Unscented kalman filtering, special Sign is the dynamic model of distribution network system are as follows:
Wherein, xkState vector, and x are tieed up for nk=[Ukk]∈Rn, wherein UkFor the voltage magnitude of each node of system, θkFor system The phase angle of each node;ykIt is tieed up for m and measures vector, and yk=[Uk,Pk,Qk,Pi,Qi]T∈Rm, wherein PkAnd QkIt is each respectively in system Node trend active power and reactive power, PiAnd QiEach Branch Power Flow active power and reactive power, f are respectively in system State transition function;H is to measure function;qkAnd vkThe systematic error of respectively n dimension and the error in measurement of m dimension, obeying mean value is Zero, the white noise that standard deviation is Q and R is distributed.
3. the State Estimation for Distribution Network as claimed in claim 2 based on adaptive robust Unscented kalman filtering, special Sign is that the state equation of distribution network system uses two exponential smoothing of Holt, by the way that different smoothing parameters is arranged, is come smooth former Two kinds of factors in state for time ordered series of numbers, state equation are as follows:
Wherein, xkIndicate that the k moment is directed to the status predication value of power distribution network;Indicate k moment state estimation;αkAnd βkIt is smooth Parameter, between 0~1;skIndicate the smoothed out value of current state;bkIndicate the smoothed out trend of current state.
4. the State Estimation for Distribution Network as claimed in claim 3 based on adaptive robust Unscented kalman filtering, special Sign is symmetric sampling method to be corrected using ratio to the sampling plan of state of electric distribution network amount, by measuring multiple sigma points to state It assigns being added after different weights and be sampled, be the nonlinear system of higher-dimension for power distribution network, obtain more accurate quantity of state Random distribution information.
5. the State Estimation for Distribution Network as described in claim 1 based on adaptive robust Unscented kalman filtering, special Sign is, for established state equation and sampling plan, to establish Unscented kalman filtering model:
According to the state equation of system, the prior state of Unscented kalman filtering model is obtained;
After acquisition system prior state, the measurement prediction of system is calculated;
After obtaining metric data, system is filtered.
6. the State Estimation for Distribution Network as claimed in claim 5 based on adaptive robust Unscented kalman filtering, special Sign is that adaptive robust model is added to the Unscented kalman filtering algorithm based on Kalman's theory, solves the shape of electric system State estimation.
7. the State Estimation for Distribution Network as claimed in claim 5 based on adaptive robust Unscented kalman filtering, special Sign is that time-varying noise statistics Valuation Modelling, discontinuity surface when receiving each are added after Unscented kalman filtering model filtering Recently system noise data forget the stale data for influencing system accuracy;
The measurement covariance P of Unscented kalman filtering modelyBe added before a judgment basis determine the presence of bad data with It is no, bad data if it does not exist, then μk=1, otherwise introduce robust model;
After substituting into new measurement covariance, being obtained using Unscented kalman filtering model has robust effect status predication value and association Variance, to carry out the state estimation at next moment.
8. the distribution network status estimation system based on adaptive robust Unscented kalman filtering, characterized in that include:
Model building module determines the dynamic model and sampling plan of distribution network system, establishes Unscented kalman filtering model, really Quantitatively survey mixed media;
Optimization processing module introduces the Optimized model of adaptive robust, using time-varying Sage-Husa Noise statistics extimators, Discontinuity surface optimizes processing to distribution network system noise when each, receives most recent data;
State estimation module is established Robust filter model and is handled bad data existing for system, and obtaining has robust effect Fruit status predication value and covariance, to carry out the state estimation at next moment.
9. a kind of computer equipment including memory, processor and stores the meter that can be run on a memory and on a processor Calculation machine program, which is characterized in that the processor is realized as claimed in claim 1 to 7 based on adaptive when executing described program The step of answering the State Estimation for Distribution Network of robust Unscented kalman filtering.
10. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the program is by processor The distribution network status estimation side as claimed in claim 1 to 7 based on adaptive robust Unscented kalman filtering is realized when execution The step of method.
CN201910646332.XA 2019-07-17 2019-07-17 State Estimation for Distribution Network and system based on adaptive robust Unscented kalman filtering Pending CN110247396A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910646332.XA CN110247396A (en) 2019-07-17 2019-07-17 State Estimation for Distribution Network and system based on adaptive robust Unscented kalman filtering

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910646332.XA CN110247396A (en) 2019-07-17 2019-07-17 State Estimation for Distribution Network and system based on adaptive robust Unscented kalman filtering

Publications (1)

Publication Number Publication Date
CN110247396A true CN110247396A (en) 2019-09-17

Family

ID=67892591

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910646332.XA Pending CN110247396A (en) 2019-07-17 2019-07-17 State Estimation for Distribution Network and system based on adaptive robust Unscented kalman filtering

Country Status (1)

Country Link
CN (1) CN110247396A (en)

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101615794A (en) * 2009-08-05 2009-12-30 河海大学 Electrical Power System Dynamic method for estimating state based on no mark transformation Kalman filter
CN105334462A (en) * 2014-08-07 2016-02-17 华为技术有限公司 Online estimation method for battery capacity loss
CN107565553A (en) * 2017-09-19 2018-01-09 贵州大学 A kind of power distribution network robust dynamic state estimator method based on UKF
CN109376910A (en) * 2018-09-28 2019-02-22 河海大学 A kind of power distribution network dynamic state estimator method based on historical data driving

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101615794A (en) * 2009-08-05 2009-12-30 河海大学 Electrical Power System Dynamic method for estimating state based on no mark transformation Kalman filter
CN105334462A (en) * 2014-08-07 2016-02-17 华为技术有限公司 Online estimation method for battery capacity loss
CN107565553A (en) * 2017-09-19 2018-01-09 贵州大学 A kind of power distribution network robust dynamic state estimator method based on UKF
CN109376910A (en) * 2018-09-28 2019-02-22 河海大学 A kind of power distribution network dynamic state estimator method based on historical data driving

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
孙江山等: "基于自适应无迹卡尔曼滤波的配电网状态估计", 《电力系统保护与控制》 *
张宏伟等: "基于无迹卡尔曼滤波的配网状态估计", 《电子质量》 *
赵洪山等: "基于自适应无迹卡尔曼滤波的电力系统动态状态估计", 《电网技术》 *

Similar Documents

Publication Publication Date Title
CN106772101B (en) Modification method, correcting device and the battery SOH evaluation method of battery SOC
Caines et al. Mean Field Games.
Ke et al. A novel probabilistic optimal power flow model with uncertain wind power generation described by customized Gaussian mixture model
Mathieu et al. State estimation and control of heterogeneous thermostatically controlled loads for load following
US10049373B2 (en) System, method and computer program for energy consumption management
Džafić et al. Real time estimation of loads in radial and unsymmetrical three-phase distribution networks
CN105093122B (en) Emergency light battery SOC method of estimation based on the adaptive SQKF of strong tracking
Wang et al. Secondary forecasting based on deviation analysis for short-term load forecasting
Le Floch et al. Optimal charging of electric vehicles for load shaping: A dual-splitting framework with explicit convergence bounds
Liu et al. Online voltage stability assessment for load areas based on the holomorphic embedding method
Yang et al. Interpolation of missing wind data based on ANFIS
Stott et al. Security analysis and optimization
Rohjans et al. mosaik-A modular platform for the evaluation of agent-based Smart Grid control
CN106443285B (en) Multiple-harmonic-source harmonic responsibility quantitative analysis method based on total least square method
CN107590317B (en) Generator dynamic estimation method considering model parameter uncertainty
CN103326353A (en) Environmental economic power generation dispatching calculation method based on improved multi-objective particle swarm optimization algorithm
CN104134999A (en) Power-distribution-network measurement effectiveness analysis practical calculation method based on multiple data sources
CN108107372A (en) Accumulator health status quantization method and system based on the estimation of SOC subregions
CN102540096A (en) Self-correction method for remaining capacity estimation of lithium iron phosphate power battery
CN103279639B (en) Receiving end Network Voltage Stability overall process Situation Assessment based on response and preventing control method
Zavala A multiobjective optimization perspective on the stability of economic MPC
CN105978016B (en) A kind of Multi-end flexible direct current transmission system optimal control method based on optimal load flow
US9639642B2 (en) Time series forecasting ensemble
CN103337001B (en) Consider the wind farm energy storage capacity optimization method of optimal desired output and state-of-charge
CN104318482A (en) Comprehensive assessment system and method of smart distribution network

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination