CN110265999B - Highly-meshed secondary power distribution network load estimation method - Google Patents
Highly-meshed secondary power distribution network load estimation method Download PDFInfo
- Publication number
- CN110265999B CN110265999B CN201910482404.1A CN201910482404A CN110265999B CN 110265999 B CN110265999 B CN 110265999B CN 201910482404 A CN201910482404 A CN 201910482404A CN 110265999 B CN110265999 B CN 110265999B
- Authority
- CN
- China
- Prior art keywords
- power
- distribution network
- load
- value
- node
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 28
- 238000005259 measurement Methods 0.000 claims abstract description 31
- 230000005611 electricity Effects 0.000 claims abstract description 17
- 238000005206 flow analysis Methods 0.000 claims abstract description 5
- 239000011159 matrix material Substances 0.000 claims description 25
- 230000005540 biological transmission Effects 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000010606 normalization Methods 0.000 claims description 3
- 230000008901 benefit Effects 0.000 description 3
- 238000013507 mapping Methods 0.000 description 2
- 238000012937 correction Methods 0.000 description 1
- 125000004122 cyclic group Chemical group 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
Images
Classifications
-
- 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
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- 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
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Health & Medical Sciences (AREA)
- Marketing (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Entrepreneurship & Innovation (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a highly meshed secondary power distribution network load estimation method, which comprises the following specific steps: acquiring real-time information of voltage, current and power of a secondary transformer, monthly electricity bills of users, typical load curves of each building type and outdoor temperature; carrying out corresponding reduction according to the obtained topological structure of the secondary power distribution network; load prediction is carried out on the secondary power distribution network by using the transformer measurement value, the monthly electricity bill of the user and the typical load curve of the building; introducing a concept of effective temperature to improve the prediction precision, and taking the predicted value as a pseudo-measured value of the node; carrying out state estimation on the nodes by using the real measurement values and the pseudo measurement data of the secondary distribution network; and carrying out power flow analysis on the node voltage and the phase angle obtained by state estimation in the power system to obtain the active power and the reactive power of the network. The method of the invention can obtain the load of each node by network reduction and load prediction and further by adopting state estimation.
Description
Technical Field
The invention relates to the technical field of power system management and operation, in particular to a highly meshed secondary power distribution network load estimation method.
Background
With the development of national economy and rapid progress of scientific technology in China, various methods and measures are continuously adopted by a secondary power distribution network of a modern power system to accurately estimate various parameters of the secondary power distribution network, but the highly meshed secondary power distribution network becomes a potential safety hazard of operation of the power system due to the problems of unobservability and the like and brings huge economic loss.
In the existing load estimation of the power system, a data acquisition and monitoring (SCADA) system is required to provide a large amount of historical data, a corresponding estimation model is established according to the historical data, after estimation is made, corresponding decisions are made, and a necessary scheduling plan is made in time, so that the safety and stability of the operation of a power grid are maintained, the unnecessary rotating reserve capacity of a generator is reduced, a unit maintenance plan is reasonably arranged, the normal production and life of the society are guaranteed, and the economic benefit and the social benefit are improved.
Disclosure of Invention
The invention aims to provide a highly meshed secondary distribution network load estimation method.
A load estimation method for a highly meshed secondary power distribution network comprises the following specific steps:
1) acquiring real-time information of voltage, current and power of a secondary transformer, monthly electricity bills of users, typical load curves of each building type and outdoor temperature;
2) carrying out corresponding reduction according to the obtained topological structure of the secondary power distribution network;
3) load prediction is carried out on the secondary power distribution network by using the transformer measurement value, the monthly electricity bill of the user and the typical load curve of the building;
4) introducing effective temperature to improve prediction accuracy, and taking the predicted value as a pseudo measured value of a node;
5) carrying out state estimation on the nodes by using the real measurement values and the pseudo measurement data of the secondary distribution network;
6) and carrying out power flow analysis on the node voltage and the phase angle obtained by state estimation in the power system to obtain the active power and the reactive power of the network.
Preferably, the step 2) includes combining two parallel transmission lines between two bus bars; eliminating connecting buses without measuring devices and loads by using Kron's reduction method; and removing the point network from the secondary distribution network.
Preferably, the step 3) includes fitting the actually measured power value of the transformer and the outdoor temperature by using a polynomial function to obtain a power-temperature curve; dividing the power-temperature curve into four types of working days, saturdays, sundays and holidays according to datesA type; normalizing the power-temperature curve using a normalization formula
In the formula TBaseIs a reference temperature, PNormRepresents the normalized power value, P represents the power, T represents the temperature, T represents the time; the load of each node can be predicted by using a power-temperature curve and monthly electricity consumption.
Preferably, the step 4) corrects an original power-temperature curve, introduces an effective temperature, and further includes mapping an actual measurement value of the secondary transformer in a power-temperature curve; if the power actual measured value of the transformer is PAThen the effective temperature is PAThe temperature output corresponding to the power-temperature curve is TA(ii) a And predicting the load of the node by adopting the effective temperature, the power-temperature curve and the monthly electricity bill, and taking the load of the node as a pseudo-measured value of the node.
Preferably, the step 5) includes listing an objective function Min J (x) ═ z according to a state estimation principlei-hi(x)]TW-1[zi-hi(x)]Wherein J (x) is an objective function, ziFor the measured value h of the ith measuring deviceiW is a weighting matrix for the corresponding estimated value; the weighting matrix W is a diagonal matrix, and the coefficients corresponding to the actual measurement values
Coefficient corresponding to pseudo-measurement value
In the formula ofiRepresents the average of the measured data,% error represents the corresponding maximum relative error; for node voltage and phase angle initial value xkCarrying out assignment and setting an iterative threshold epsilon; calculating the estimated value h (x) of the secondary distribution networkk) And the corresponding Jacobian matrix H (x)k) (ii) a Calculating Δ x ═ HT WH)-1HT[z-h(x)]Wherein z is a measurement value of each node; updating the phase angle estimate xk+1＝xk+ Δ x; if it isStopping iteration if the updating value delta x is smaller than the set threshold epsilon, otherwise, turning to the calculation of the estimated value h (x) of the secondary distribution networkk) And the corresponding Jacobian matrix H (x)k) And (6) executing in a loop.
Has the advantages that: the method can obtain the load of each node by network reduction, load prediction and state estimation, the load estimation takes weather factors into consideration, and the load is deduced from a standard load curve by introducing an effective temperature concept, so that the load condition can be better estimated, the load estimation accuracy of the method is high, the method is different from other load prediction methods, effective prediction can be carried out only by using obtained real-time data, a large amount of historical data is not needed, and meanwhile, compared with other load estimation methods, the weighted least square estimation method ensures that the contribution rate of each building to errors is the same, and the load estimation accuracy influenced by some large/small loads is avoided.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a diagram illustrating the absolute and relative error between the true and estimated values according to the present invention.
Detailed Description
The dispatching automation and computer management level of modern secondary power distribution networks are gradually improved, and the accurate and comprehensive acquisition of real-time measurement data of the secondary power distribution networks becomes the basis and the premise for realizing various functional modules of the secondary power distribution network management system.
A highly meshed secondary distribution network load estimation method comprises the following specific steps:
1) acquiring real-time information of voltage, current and power of a secondary transformer, monthly electricity bills of users, typical load curves of each building type and outdoor temperature;
2) carrying out corresponding reduction according to the obtained topological structure of the secondary power distribution network;
3) load prediction is carried out on the secondary power distribution network by using the transformer measurement value, the monthly electricity bill of the user and the typical load curve of the building;
4) introducing effective temperature to improve prediction accuracy, and taking the predicted value as a pseudo measured value of a node;
5) carrying out state estimation on the nodes by using the real measurement values and the pseudo measurement data of the secondary distribution network;
6) and carrying out power flow analysis on the node voltage and the phase angle obtained by state estimation in the power system to obtain the active power and the reactive power of the network.
Further, step 2) includes combining two parallel transmission lines between the two buses; eliminating connecting buses without measuring devices and loads by using Kron's reduction method; and removing the point network from the secondary distribution network.
Further, step 3) comprises fitting the actually measured power value of the transformer and the outdoor temperature by adopting a polynomial function to obtain a power-temperature curve; dividing the power-temperature curve into four types of working days, saturdays, sundays and holidays according to the date; normalizing the power-temperature curve using a normalization formula
In the formula TBaseIs a reference temperature, PNormRepresents the normalized power value, P represents power, T represents temperature, and T represents time; the load of each node can be predicted by adopting a power-temperature curve and monthly power consumption.
Further, step 4) corrects the original power-temperature curve through step 2), introduces effective temperature, and also comprises mapping the actual measurement value of the secondary transformer in a power-temperature curve graph; if the power actual measured value of the transformer is PAThen the effective temperature is PAThe temperature output corresponding to the power-temperature curve is TA(ii) a And predicting the load of the node by adopting the effective temperature, the power-temperature curve and the monthly electricity bill, and taking the load of the node as a pseudo-measured value of the node.
Further, step 5) includes listing the objective function Min J (x) ═ z according to the state estimation principlei-hi(x)]TW-1[zi-hi(x)]Wherein J (x) is a targetFunction, ziMeasured value of the i-th measuring device, hiW is a weighting matrix for the corresponding estimated value; the weighting matrix W is a diagonal matrix, and the coefficients corresponding to the actual measurement values
Coefficient corresponding to pseudo-measurement value
In the formula ofiRepresents the average of the measured data,% error represents the corresponding maximum relative error; for node voltage and phase angle initial value xkCarrying out assignment and setting an iterative threshold epsilon; calculating an estimated value h (x) of the secondary distribution networkk) And the corresponding Jacobian matrix H (x)k) (ii) a Calculating Δ x ═ HT WH)-1HT[z-h(x)]Wherein z is a measurement value of each node; updating the phase angle estimate xk+1＝xk+ Δ x; stopping iteration if the updating value delta x is smaller than the set threshold epsilon, otherwise, turning to the calculation of the estimated value h (x) of the secondary distribution networkk) And the corresponding Jacobian matrix H (x)k) And (6) executing in a loop.
Examples
By acquiring real-time information of voltage, current and power of the secondary transformer, monthly electricity bills of users, typical load curves of each building type and outdoor temperature; carrying out corresponding reduction according to the obtained topological structure of the secondary power distribution network; the method comprises the steps of utilizing a transformer measurement value, a monthly electricity bill of a user and a typical load curve of a building to carry out load prediction on a secondary power distribution network, collecting and analyzing the correlation between an actual power value of a transformer in the secondary power distribution network and outdoor temperature, dividing power into 4 conditions, namely working days, saturdays, sundays and holidays, fitting the four conditions by using a third-order polynomial, and normalizing a power-temperature curve, wherein the formula is as follows:
in the formula, PNormDenotes the normalized power value, P denotes the power, TBaseSet as a reference temperature, T representsThe temperature, t represents time, the four types of data are respectively fitted with the temperature and the normalized power data by utilizing a third-order polynomial function, and the temperature is brought into a power-temperature curve to obtain a power value of the node; introducing a concept of effective temperature to improve the prediction precision, and taking the predicted value as a pseudo-measured value of the node; and performing state estimation on the node by using the real measurement value and the pseudo measurement data of the secondary distribution network, wherein the state estimation comprises the following steps of listing a target function according to the principle of state estimation: min J (x) ═ zi-hi(x)]TW-1[zi-hi(x)]Wherein J (x) is an objective function, ziIs the measured value of the ith measuring device, hiFor the corresponding estimated value, W is a weighting matrix, W is a diagonal matrix, and the coefficients corresponding to the true measured values:
the coefficients corresponding to the pseudo-measured values:
in the formula, muiRepresents the average of the measured data,% error represents the corresponding maximum relative error, voltage estimated for the network and phase angle initial value xkAssigning initial values, setting iterative threshold epsilon, and calculating estimated value h (x) of secondary distribution networkk) And the corresponding Jacobian matrix H (x)k) And updating delta x every time in the iteration of the secondary distribution network: Δ x ═ HT WH)-1HT[z-h(x)]Wherein z is a measurement value of the secondary distribution network, h (x)k) Is an estimated value of the secondary distribution network, H (x)k) Is the Jacobian matrix of the estimated value of the secondary distribution network, W is the weighting matrix, updates x to the estimated value of the phase anglek+1＝xk+ deltax, stopping iteration if the updated value deltax of the iteration is smaller than the set threshold epsilon, otherwise, the algorithm is switched to calculate the estimated value h (x) of the secondary distribution networkk) And the corresponding Jacobian matrix H (x)k) And (6) executing in a loop.
When the highly meshed secondary distribution network load estimation method works, firstly, the voltage, the current and the power of the transformer are obtainedReal-time information, monthly electricity bill of user, typical load curve of each building type and outdoor temperature, then according to two parallel transmission lines between two buses, using Kron reduction method to eliminate connecting bus without measuring device and load, removing dotted network from secondary distribution network to obtain topology structure of secondary distribution network, making corresponding reduction operation, then utilizing measured value obtained by transformer, monthly electricity bill of user and typical load curve of building to make load prediction of secondary distribution network, and the term also comprises fitting the measured power value of the transformer and the outdoor temperature by adopting a polynomial function to obtain a power-temperature curve, dividing the power-temperature curve into four types of working days, saturday, sunday and holiday and a standardized power-temperature curve according to dates, and using a standardized formula.
In the formula TBaseIs a reference temperature, PNormRepresenting the normalized power value, P represents power, T represents temperature, T represents time, a power-temperature curve and monthly power consumption are adopted to predict the load of each node, then, an effective temperature concept is introduced to improve the load prediction accuracy, the predicted value is taken as a pseudo-measured value of the node, the original power-temperature curve needs to be introduced for correction, the effective temperature concept is introduced, the actual measured value of the secondary transformer is mapped in the power-temperature curve, and if the actual measured value of the power of the transformer is PAThen the effective temperature is PAThe temperature output corresponding to the power-temperature curve is TAThe method comprises the steps of adopting effective temperature, power-temperature curves and monthly bills to predict node loads, taking the node loads as pseudo-measured values of the nodes, correcting the original power-temperature curves, and listing an objective function Min J (x) ═ z according to a state estimation principlei-hi(x)]TW-1[zi-hi(x)]Wherein J (x) is an objective function, ziMeasured value of the i-th measuring device, hiFor the corresponding estimated value, W is a weighting matrix, W is a diagonal matrix, and the real measured value corresponds toCoefficient of (2)
Coefficient corresponding to pseudo-measurement value
In the formula ofiRepresents the average of the measured data,% error represents the corresponding maximum relative error, for the node voltage and the phase angle initial value xkCarrying out assignment, setting an iterative threshold epsilon, and calculating an estimated value h (x) of the secondary distribution networkk) And the corresponding Jacobian matrix H (x)k) And calculating Δ x ═ HT WH)-1HT[z-h(x)]Wherein z is the measured value of each node, the updated phase angle estimated value xk+1＝xk+ Δ x, stopping iteration if the update value Δ x is smaller than the set threshold epsilon, otherwise, turning to the calculation of each node estimation value h (x)k) And the corresponding Jacobian matrix H (x)k) And performing cyclic execution, performing state estimation on load data by using the measurement value and the pseudo measurement value of the secondary distribution network, and finally performing power flow analysis on the obtained node voltage and phase angle estimation value to obtain active power and reactive power of the network.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (5)
1. A highly meshed secondary distribution network load estimation method is characterized by comprising the following steps: the method for estimating the load of the highly meshed secondary power distribution network comprises the following specific steps:
1) acquiring real-time information of voltage, current and power of a secondary transformer, monthly electricity bills of users, typical load curves of each building type and outdoor temperature;
2) performing corresponding reduction according to the obtained topological structure of the secondary power distribution network;
3) load prediction is carried out on the secondary power distribution network by using the transformer measurement value, the monthly electricity bill of the user and the typical load curve of the building;
4) introducing effective temperature to improve prediction accuracy, and taking a predicted value as a pseudo-measured value of a node;
5) estimating the state of the node by using the real measurement value and the pseudo measurement data of the secondary distribution network;
6) and carrying out power flow analysis on the node voltage and the phase angle obtained by state estimation in the power system to obtain the active power and the reactive power of the network.
2. The method for estimating the load of the highly meshed secondary distribution network according to claim 1, wherein: the step 2) comprises combining two parallel transmission lines between two buses; eliminating connecting buses without measuring devices and loads by using Kron's reduction method; and removing the point network from the secondary distribution network.
3. The method for estimating the load of the highly meshed secondary distribution network according to claim 1, wherein: the step 3) comprises fitting the measured power value of the transformer and the outdoor temperature by adopting a polynomial function to obtain a power-temperature curve; dividing the power-temperature curve into four types of working days, saturdays, sundays and holidays according to the date; normalizing the power-temperature curve using a normalization formula
In the formula TBaseIs a reference temperature, PNormRepresents the normalized power value, P represents the power, T represents the temperature, T represents the time; the load of each node can be predicted by using a power-temperature curve and monthly electricity consumption.
4. The method for estimating the load of the highly meshed secondary distribution network according to claim 1, wherein: the step 4) corrects the original power-temperature curve, introduces the effective temperature, and maps the actual measured value of the secondary transformerShot in a power-temperature curve chart; if the power actual measured value of the transformer is PAThen the effective temperature is PATemperature output at the power-temperature curve is TA(ii) a And predicting the load of the node by adopting the effective temperature, the power-temperature curve and the monthly electricity bill, and taking the load of the node as a pseudo-measured value of the node.
5. The method for estimating the load of the highly meshed secondary distribution network according to claim 1, wherein: said step 5) comprises listing the objective function Min J (x) ═ z according to the principle of state estimationi-hi(x)]TW-1[zi-hi(x)]Wherein J (x) is an objective function, ziMeasured value of the i-th measuring device, hiW is a weighting matrix for the corresponding estimated value; the weighting matrix W is a diagonal matrix, and the coefficients corresponding to the actual measurement values
Coefficient corresponding to pseudo-measurement value
In the formula ofiRepresents the average of the measured data,% error represents the corresponding maximum relative error; for node voltage and phase angle initial value xkCarrying out assignment and setting an iterative threshold epsilon; calculating an estimated value h (x) of the secondary distribution networkk) And the corresponding Jacobian matrix H (x)k) (ii) a Calculating Δ x ═ HT WH)-1HT[z-h(x)]Wherein z is a measurement value of each node; updating the phase angle estimate xk+1＝xk+ Δ x; stopping iteration if the updating value delta x is smaller than the set threshold epsilon, otherwise, turning to the calculation of the estimated value h (x) of the secondary distribution networkk) And the corresponding Jacobian matrix H (x)k) And (6) executing in a loop.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910482404.1A CN110265999B (en) | 2019-06-04 | 2019-06-04 | Highly-meshed secondary power distribution network load estimation method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910482404.1A CN110265999B (en) | 2019-06-04 | 2019-06-04 | Highly-meshed secondary power distribution network load estimation method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110265999A CN110265999A (en) | 2019-09-20 |
| CN110265999B true CN110265999B (en) | 2022-06-14 |
Family
ID=67916754
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910482404.1A Active CN110265999B (en) | 2019-06-04 | 2019-06-04 | Highly-meshed secondary power distribution network load estimation method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110265999B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1177226A (en) * | 1996-09-16 | 1998-03-25 | 尹顿公司 | Sequence based network protector relay with forward overcurrent protection and anti-pumping feature |
| CN103138397A (en) * | 2012-11-19 | 2013-06-05 | 江西省电力科学研究院 | Method of dynamic capacity increasing of distribution network lines based on technology of internet of things |
| CN103324847A (en) * | 2013-06-17 | 2013-09-25 | 西南交通大学 | Method for detecting and identifying dynamic bad data of electric power system |
| CN104463357A (en) * | 2014-11-27 | 2015-03-25 | 国家电网公司 | Method for evaluating random intermittent DG optimized integration based on random optimal power flow |
| CN105633956A (en) * | 2016-02-19 | 2016-06-01 | 河海大学 | Spiking neural network pseudo measurement modeling based three-phase state estimation method for power distribution network |
| CN107959308A (en) * | 2018-01-10 | 2018-04-24 | 云南电网有限责任公司电力科学研究院 | Power distribution network distributed energy accesses adaptability teaching method and device |
| CN109088407A (en) * | 2018-08-06 | 2018-12-25 | 河海大学 | The State Estimation for Distribution Network of modeling is measured based on deepness belief network puppet |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2869372C (en) * | 2013-10-31 | 2023-04-04 | Ilya Roytelman | Determining distribution system voltages from remote voltage alarm signals |
| US10061283B2 (en) * | 2015-12-07 | 2018-08-28 | Opus One Solutions Energy Corp. | Systems and methods for integrated microgrid management system in electric power systems |
-
2019
- 2019-06-04 CN CN201910482404.1A patent/CN110265999B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1177226A (en) * | 1996-09-16 | 1998-03-25 | 尹顿公司 | Sequence based network protector relay with forward overcurrent protection and anti-pumping feature |
| CN103138397A (en) * | 2012-11-19 | 2013-06-05 | 江西省电力科学研究院 | Method of dynamic capacity increasing of distribution network lines based on technology of internet of things |
| CN103324847A (en) * | 2013-06-17 | 2013-09-25 | 西南交通大学 | Method for detecting and identifying dynamic bad data of electric power system |
| CN104463357A (en) * | 2014-11-27 | 2015-03-25 | 国家电网公司 | Method for evaluating random intermittent DG optimized integration based on random optimal power flow |
| CN105633956A (en) * | 2016-02-19 | 2016-06-01 | 河海大学 | Spiking neural network pseudo measurement modeling based three-phase state estimation method for power distribution network |
| CN107959308A (en) * | 2018-01-10 | 2018-04-24 | 云南电网有限责任公司电力科学研究院 | Power distribution network distributed energy accesses adaptability teaching method and device |
| CN109088407A (en) * | 2018-08-06 | 2018-12-25 | 河海大学 | The State Estimation for Distribution Network of modeling is measured based on deepness belief network puppet |
Non-Patent Citations (2)
| Title |
|---|
| Robust Forecasting Aided Power System State Estimation Considering State Correlations;Junbo Zhao et al;《IEEE Transactions on Smart Grid》;20180731;第9卷(第4期);2658-2666 * |
| 公共建筑空调温度设定值的动态优化控制研究;马庆等;《系统工程学报》;20110831;第26卷(第4期);435-441 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110265999A (en) | 2019-09-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110471024B (en) | Intelligent electric meter online remote calibration method based on measurement data analysis | |
| CN107563550B (en) | PMU-based power distribution network real-time distributed state estimation and PMU optimal configuration method | |
| CN108448568B (en) | Power distribution network hybrid state estimation method based on multiple time period measurement data | |
| CN103066592B (en) | Power network loss on-line monitoring method | |
| CN103455716B (en) | A kind of power system voltage stabilization margin calculation method based on super short-period wind power prediction | |
| CN110299762B (en) | PMU (phasor measurement Unit) quasi-real-time data-based active power distribution network robust estimation method | |
| CN107370169B (en) | Large-scale energy storage power station peak regulation controller and method based on ANFIS short-term load prediction | |
| CN102509173A (en) | Markov chain based method for accurately forecasting power system loads | |
| CN111784030B (en) | Distributed photovoltaic power prediction method and device based on spatial correlation | |
| CN110909958A (en) | Short-term load prediction method considering photovoltaic grid-connected power | |
| CN116227637A (en) | Active power distribution network oriented refined load prediction method and system | |
| CN115481918A (en) | Active sensing and predictive analysis system for unit state based on source network load storage | |
| CN109829246B (en) | Line parameter identification method based on parameter comprehensive suspicion | |
| CN113890016B (en) | Data-driven multi-time scale voltage coordination control method for power distribution network | |
| CN110265999B (en) | Highly-meshed secondary power distribution network load estimation method | |
| Yuan et al. | Improved particle filter for non-gaussian forecasting-aided state estimation | |
| CN113131517A (en) | Distributed energy storage photovoltaic grid-connected monitoring method and system | |
| CN111756031B (en) | Power grid operation trend estimation method and system | |
| CN111708987A (en) | Method for predicting load of multiple parallel transformers of transformer substation | |
| CN110752622A (en) | Power distribution network affine state estimation method | |
| CN114912700A (en) | Factory workshop electric power energy consumption assessment method and system | |
| CN111612232B (en) | Power distribution network line re-jump probability prediction optimization method and device based on gradient descent | |
| Zhang et al. | Research on intelligent load forecast in power system dispatching automation | |
| CN111047108B (en) | Electric energy duty ratio prediction method in terminal energy consumption based on optimal combination model | |
| Hou et al. | A novel algorithm for multi-node load forecasting based on big data of 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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |