CN106159940A - PMU optimum points distributing method based on network load specificity analysis - Google Patents

PMU optimum points distributing method based on network load specificity analysis Download PDF

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CN106159940A
CN106159940A CN201610515978.0A CN201610515978A CN106159940A CN 106159940 A CN106159940 A CN 106159940A CN 201610515978 A CN201610515978 A CN 201610515978A CN 106159940 A CN106159940 A CN 106159940A
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transformer station
class
pmu
load
data
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CN106159940B (en
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徐衍会
王晨语
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North China Electric Power University
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North China Electric Power University
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    • 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
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • 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
    • 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
    • Y04S10/22—Flexible AC transmission systems [FACTS] or power factor or reactive power compensating or correcting units

Abstract

The invention belongs to power system modeling and emulation field, particularly relate to a kind of PMU optimum points distributing method based on network load specificity analysis.It is characterized in that, first each to regional power grid Load Substation carries out load composition and specificity analysis, and transformer station is clustered by the CURE algorithm in application level clustering methodology.To each packet of the transformer station after cluster, carry out the probability statistical analysis of fault data based on the basis of protection information system, choose representative and that more multiple faults and noisy data can be extracted transformer station and carry out PMU and layout.The inventive method can form and on the basis of part throttle characteristics at the load fully taking into account each transformer station, farthest reduces PMU and installs quantity, promotes economy and be applicable to production practices.

Description

PMU optimum points distributing method based on network load specificity analysis
Technical field
The invention belongs to power system modeling and emulation field, particularly to a kind of based on network load specificity analysis PMU optimum points distributing method.
Background technology
The basis that electric system simulation is Power System Planning, runs and studies, model parameter is for emulation knot accurately The credibility of fruit has material impact.For electromotor, transformator and transmission line of electricity, the model parameter phase of electric load To coarse, seriously constrain the precision of emulation.For the simulation analysis of power system, load refers under 220kV transformer station All electrical equipments, including low voltage power distribution network, reactive power compensator etc., its composition is sufficiently complex.Set up big regional power grid Load model is extremely difficult.
Power system load modeling method is divided into Component Based and Measurement-based approach.Component Based load by inquiry The load composition of transformer station, electrical equipment is constituted, and electrical equipment average characteristics obtains load model, and the method physics is general It is clear to read, but statistical data is huge, it is impossible to solves electrical equipment simultaneity factor problem, and is difficult to often carry out.Another kind of method, Measurement-based approach is by installing part throttle characteristics measurement apparatus in transformer station, it is thus achieved that dynamic load characteristic data under disturbance, by distinguishing Knowing and obtain load model parameters, the method is simple and practical, widely used in network load models.
Synchronous phasor measurement unit (Phasor Measurement Unit, PMU) is to rise nineteen nineties Measurement apparatus, reaches the purpose of synchro measure, obtains a wide range of applications in power system by accepting gps signal. At present, the transformer station of the 500kV main grid structure of each big regional power grid of China is assembled with PMU device substantially, is used for detecting electrical network electricity Tolerance, provides basis for stability analysis and control etc..Part 220kV transformer station there are also installed PMU device, and practice have shown that can be in order to Measurement-based load modeling is carried out with PMU.Consider the reason of economy, it is impossible in all of 220kV transformer station, PMU is installed and comes Carry out load modeling, so that research is for the PMU optimum points distributing method of big regional power grid measurement-based load modeling.
Summary of the invention
In order to solve the problems referred to above, the technical scheme that the present invention takes is as follows:
PMU optimum points distributing method based on network load specificity analysis, it is characterised in that the detailed process of described method is
Step 1, the part throttle characteristics of Load Substation each to regional power grid carry out investigation statistics, analyze each transformer station load special Property composition, using transformer station's load structure percentage ratio as characteristic vector；
Step 2, according to the CURE algorithm in Hierarchical Cluster, the load of transformer station is formed situation transformer station is entered Row cluster；
Step 3, to cluster after transformer station each packet, based on protect communication system logging data, obtain each power transformation The fault data stood, carries out failure probability analysis；
Step 4, the probability of malfunction of each group is sorted by size, fault rate more than 30%, voltage change ratio more than 3% time belong to More effective load measurement data can be obtained in the transformer station that disturbance is bigger, choose such and can extract more multiple faults and disturbance number According to transformer station carry out PMU and layout.
CURE algorithm according to hierarchical clustering method in described step 2, with Y ∈ Yn×mTransformer station's part throttle characteristics is described original Data matrix Y=[y1,…,yi,…,yn]T
Wherein: yi=[yi1,…,yij,…,yim], yiFor i-th transformer station, i=1 ..., n, n are the number of transformer station, yijRepresent composition j=1 of the jth type load of i-th transformer station ..., m, m are the number of Overload Class；Initially, cluster Data each of which is a class, and n part throttle characteristics data are just for n class, data n to be clustered, it is contemplated that cluster numbers be set to k；
Step 201, the distance calculated between each two class, for any one class U, u.mean represents the center of class U Point, u.rep represents the representative point of class U；Class U and data item p of class V and data item q belong to for any two, dist (p, Q) representing the distance between p and q, the computational methods of distance use Euclidean distance, manhatton distance or Lagrange distance； Calculate two cluster distance dist (U, V)=min dist between U, V (p, q)；
Two classes that distance is minimum are successively merged, forms the individual new class of n '；
Step 202, the central point of new class WWherein u, v represent step The number of contained data item in two classes U, V merging in rapid 1；V.mean represents the central point of class V；
Step 203, representative point w.rep=p+ α * (w.mean-p) of new class, wherein α takes 0.5；W.mean represents new class W Central point, w.rep represents the representative point of new class W；
If the number n ' of the new class of step 204 is more than intended cluster numbers k, return step 1, until last n '≤k.
A kind of classified according to Load Characteristic Analysis by hierarchical clustering method, using transformer station's load structure percentage ratio as Characteristic vector.The process that realizes of CURE clustering procedure is divided into following steps:
(1) from source data object, a random sample S is extracted.
(2) sample S is divided into one group of division.
(3) to each cluster dividing local.
(4) isolated point is rejected by randomization.If a class increases too slow, just remove it.
(5) class of local is clustered.Fall the representative point at each newly formed apoplexy due to endogenous wind according to user-defined one Contraction factor shrinks Huo Xianglei center and moves.These points represent and capture the shape of class.
(6) flag data is carried out with corresponding class label.
Beneficial effect
The PMU optimum points distributing method based on network load actual measurement modeling and Load Characteristic Analysis of the present invention can be fully Load in view of each transformer station forms and on the basis of part throttle characteristics, farthest reduces PMU and installs quantity, promotes warp Ji property and be applicable to production practices.
Accompanying drawing explanation
Fig. 1 is the flow chart of PMU optimum points distributing method based on network load specificity analysis.
Detailed description of the invention
Below in conjunction with the accompanying drawings, the present invention is elaborated.Fig. 1 is PMU optimum cloth based on network load specificity analysis The flow chart of point methods.
As a example by the load characteristics investigation statistical result of 101 220KV Substation Stations of somewhere electrical network, compare accompanying drawing pair The present invention is further described.
(1) Load Substation each to regional power grid carries out load composition and specificity analysis, clusters according to analysis result；
CURE algorithm according to hierarchical clustering method, here with Y ∈ Yn×mTransformer station's part throttle characteristics initial data square is described Battle array:
Y=[y1,…,yi,…,yn]T
Wherein, yi=[yi1,…,yij,…,yim], yiFor i-th transformer station, i=1 ..., n；N=111；yilRepresent i-th The composition of the i-th type load of individual transformer station, l=1 ..., m；M=5；Represent respectively industry, agricultural, business, resident, other Deng 5 type load compositions.Initially, the data each of which of cluster is a class, and therefore 101 part throttle characteristics data are just 101 Class.Data the most to be clustered 101, and intended cluster numbers is set to 4 i.e. k=4.The first step calculate each two cluster between away from From, (p, q), by minimum the successively merging of distance to calculate dist therein with Euclidean distance.The central point of new class presses formula (1) institute The formula shown calculates, the number of contained data item during wherein u represents this cluster u.
w . m e a n = | u | u . m e a n + | v | v . m e a n | u | + | v | - - - ( 1 )
The representative point calculating method of new class is shown in formula (2), and wherein α takes 0.5, represents some rep and represents.
W.rep=p+ α * (w.mean-p) (2)
After iterative computation, when the number of class is more than the number of intended class, need the between class distance continuing to calculate each class And merge, till finally 111 class iteration being merged into 4 classes.Calculating through many experiments, cluster result is as shown in table 1.
The cluster result of table 1CURE clustering algorithm
(2) transformer station each to regional power grid carries out probability statistical analysis
Protection information management and trouble analysis system can obtain online power system operating mode information and relay protected operation Point information, considers comprehensively and neatly and formulates definite value assessment and the various rules of on-line tuning, it is achieved protection performance of relay setting Online evaluation and on-line tuning, there is abundant standardization result output function, help relay protection personnel, dispatching of power netwoks people Member, method of operation personnel grasp the running status of relay protection in time, and the reliability and the system that improve relay protected operation are run Safety.Through the development of more than ten years, the utilization rate protecting communication system in China in 220kV and above transformer station reaches 90%, interior communication stipulations and protection communication protocol are unified and standard, are a set of to have tended to perfect system, with it The applied research carried out by platform possesses stability and practicality, it also avoid repeated construction.Protect communication system and can gather electricity Secondary device action message during net fault, can collect again the self-inspection of the secondary device such as protective relaying device, fault oscillograph Warning information.Protecting communication system to the basic application of substation relay protection device enforcement repair based on condition of component is: collects data, analyze instead Reflecting, wherein collecting data with inquiry data reflection is the basis that the work of whole repair based on condition of component is implemented.
By the cluster analysis of step (1), transformer station is divided into 4 classes, logging data based on guarantor's communication system, obtain each The fault data of transformer station and probability of malfunction.The fault rate of each group is ranked up analyze, fault rate more than 30%, change in voltage The rate transformer station more than 3% can obtain more effective load measurement data.
(3) carry out PMU optimum according to Load Characteristic Analysis and substation fault probability analysis result to layout；
The economy problems layouted in view of PMU, PMU layout needs select the most representational transformer station install, to reach To the highest efficiency.In each class transformer station, choose the transformer station that fault rate is higher and disturbance is bigger can get more effectively Load measurement data, are applied to the engineering practice aspects such as load modeling.Select in 4 class transformer stations, the transformer station that fault rate is higher, The transformer station that four class substation fault rates are the highest is respectively y9, y55, y30 and y26, according to practical situation need carry out PMU cloth Point.On the premise of ensureing economy, the prioritization scheme of layouting of this PMU both can guarantee that this part throttle characteristics layouted was representative Can guarantee that again and be obtained in that enough market demands are in the middle of production practices.

Claims (2)

1. PMU optimum points distributing method based on network load specificity analysis, it is characterised in that the detailed process of described method is
Step 1, the part throttle characteristics of transformer station each to regional power grid carry out investigation statistics, analyze each transformer station part throttle characteristics and constitute ratio Example, using transformer station's load structure percentage ratio as characteristic vector；
Step 2, according to the CURE algorithm in Hierarchical Cluster, the load of transformer station is formed situation and cluster；
Step 3, for cluster after transformer station each packet, based on protection information system logging data, obtain each become The fault data in power station, carries out failure probability analysis；
Step 4, the probability of malfunction of each group is sorted by size, fault rate more than 30%, voltage change ratio more than 3% time belong to and disturb Dynamic bigger transformer station, chooses such transformer station that can extract more multiple faults and noisy data and carries out PMU and layout.
PMU optimum points distributing method based on network load specificity analysis the most according to claim 1, it is characterised in that institute State the CURE algorithm according to hierarchical clustering method in step 2, with Y ∈ Yn×mTransformer station part throttle characteristics raw data matrix Y=is described [y1,…,yi,…,yn]T
Wherein: yi=[yi1,…,yij,…,yim], yiFor i-th transformer station, i=1 ..., n, n are the number of transformer station, yijTable Show the composition of the jth type load of i-th transformer station, j=1 ..., m, m are the number of Overload Class；Initially, the number of cluster Being a class according to each of which, n part throttle characteristics data are just for n class, data n to be clustered, it is contemplated that cluster numbers be set to k；
Step 201, the distance calculated between each two class, for any one class U, u.mean represents the central point of class U, U.rep represents the representative point of class U；Class U and data item p of class V and data item q, dist (p, q) table are belonged to for any two Showing the distance between p and q, the computational methods of distance use Euclidean distance, manhatton distance or Lagrange distance；Calculate Two distances clustered between U, V
Dist (U, V)=min dist (p, q)；
Two classes that distance is minimum are successively merged, forms the individual new class of n '；
Step 202, the central point of new class WWherein u, v represent step 201 The number of contained data item in two classes U, V of middle merging；V.mean represents the central point of class V；
Step 203, representative point w.rep=p+ α * (w.mean-p) of new class, wherein setting value α takes 0.5；W.mean represents new class The central point of W, w.rep represents the representative point of new class W；
If the number n ' of the new class of step 204 is more than intended cluster numbers k, return step 201, until last n '≤k.
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CN110165657A (en) * 2018-08-30 2019-08-23 中国南方电网有限责任公司 Consider substation's load characteristics clustering analysis method of user's industry attribute
CN110676940A (en) * 2019-10-21 2020-01-10 国网上海市电力公司 Power distribution network PMU configuration method and system for improving parameter identification precision
WO2020232716A1 (en) * 2019-05-23 2020-11-26 西门子股份公司 Method and device for assessing state of health of transformer, and storage medium

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Cited By (4)

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Publication number Priority date Publication date Assignee Title
CN110165657A (en) * 2018-08-30 2019-08-23 中国南方电网有限责任公司 Consider substation's load characteristics clustering analysis method of user's industry attribute
WO2020232716A1 (en) * 2019-05-23 2020-11-26 西门子股份公司 Method and device for assessing state of health of transformer, and storage medium
CN110676940A (en) * 2019-10-21 2020-01-10 国网上海市电力公司 Power distribution network PMU configuration method and system for improving parameter identification precision
CN110676940B (en) * 2019-10-21 2021-02-02 国网上海市电力公司 Power distribution network PMU configuration method and system for improving parameter identification precision

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