CN107730061A - A kind of power distribution network overload methods of risk assessment and device - Google Patents
A kind of power distribution network overload methods of risk assessment and device Download PDFInfo
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- CN107730061A CN107730061A CN201610659867.7A CN201610659867A CN107730061A CN 107730061 A CN107730061 A CN 107730061A CN 201610659867 A CN201610659867 A CN 201610659867A CN 107730061 A CN107730061 A CN 107730061A
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- 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
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- 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
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Abstract
The embodiment of the invention discloses a kind of power distribution network overload methods of risk assessment and device.This method includes：Obtain the historical variations scope of the operational factor of target primary equipment and ambient parameter in the power distribution network；Obtain the service data of the target primary equipment and the environmental data；According to service data and the environmental data of the historical variations scope and the target primary equipment of the operational factor of the target primary equipment and the ambient parameter, judge whether the power distribution network overloads.The historical variations scope structure assessment models of operational factor and ambient parameter of the embodiment of the present invention based on power distribution network target primary equipment, power distribution network is assessed with the service data and the environmental data of the target primary equipment gathered based on assessment models, in real time, compared with prior art, having reduces modeling complexity, improves the advantages of assessing efficiency.
Description
Technical field
The present embodiments relate to electric power network technique field, and in particular to a kind of power distribution network overload methods of risk assessment and dress
Put.
Background technology
Photovoltaic generation is as most a kind of forms of electricity generation of economic development prospect in new energy, gradually by the weight of various countries
Depending on and widely developed and utilized.Power network, its randomness, intermittence and perturbation etc. is accessed however as large-scale photovoltaic
Characteristic brings very big challenge to safe operation of power system, usually causes the generation of the accidents such as circuit overload, voltage out-of-limit.To line
The purpose for reducing system risk can be reached by passing by the progress risk control such as load, stable to power system security with running with non-
Often important realistic meaning.
And during the embodiment of the present invention is realized, inventor has found that traditional circuit overload risk assessment needs complexity
Load flow calculation process, and then cause assess limited efficacy.
The content of the invention
One purpose of the embodiment of the present invention is that solution traditional circuit overload risk assessment scheme is complex, assesses efficiency
The problem of relatively low.
The embodiment of the present invention proposes a kind of power distribution network overload methods of risk assessment, including：
Obtain the historical variations scope of the operational factor of target primary equipment and ambient parameter in the power distribution network；
Obtain the service data of the target primary equipment and the environmental data；
According to the historical variations scope and the mesh of the operational factor of the target primary equipment and the ambient parameter
The service data of primary equipment and the environmental data are marked, judges whether the power distribution network overloads.
Preferably, the operational factor of target primary equipment and the historical variations model of ambient parameter in the power distribution network is obtained
Before the step of enclosing, this method also includes：
Obtain the operational factor of the target primary equipment and the historical data of ambient parameter；
The historical data is screened, with obtain the operational factor of the target primary equipment under normal operating conditions and
The historical data of ambient parameter；
According to the historical data after screening, the history of the operational factor and ambient parameter that obtain the target primary equipment becomes
Change scope.
Preferably, it is described according to the operational factor of the target primary equipment and the historical variations model of the ambient parameter
Enclose and the service data of the target primary equipment and the environmental data, the step of whether power distribution network overloads judged
Specifically include：
According to corresponding to obtaining the historical variations scope of the operational factor of the target primary equipment and the ambient parameter
Line electricity flow valuve；
Using the historical variations scope of the operational factor of the target primary equipment and the ambient parameter as input variable,
The line electricity flow valuve builds supporting vector machine model as output vector；
Assessed according to the supporting vector machine model and the service data of the target primary equipment and the environmental data
Whether the power distribution network overloads.
Preferably, the service data and the ring according to the supporting vector machine model and the target primary equipment
The step of whether power distribution network overloads described in the data assessment of border specifically includes：
The service data of the target primary equipment and the environmental data are inputted to the support as input vector
Vector machine model, obtain the line electricity flow valuve of the power distribution network；
The line electricity flow valuve of the current point in time is contrasted with load current value, to judge whether know power distribution network
Overload.
Preferably, the operational factor of the target primary equipment includes：Distributed power source DG capacity, DG on-positions and negative
Lotus changes；The ambient parameter includes：Intensity of illumination.
The invention also provides a kind of power distribution network overload risk assessment device, including：
First acquisition module, for obtaining going through for the operational factor of target primary equipment and ambient parameter in the power distribution network
History excursion；
Second acquisition module, for the service data for obtaining the target primary equipment and the environmental data；
Evaluation module, for the operational factor and the historical variations model of the ambient parameter according to the target primary equipment
Enclose and the service data of the target primary equipment and the environmental data, judge whether the power distribution network overloads.
Preferably, the device also includes：Processing module；
The processing module, for the operational factor of target primary equipment in the power distribution network is obtained and ambient parameter
Before historical variations scope, the operational factor of the target primary equipment and the historical data of ambient parameter are obtained；Gone through to described
History data are screened, to obtain the history number of the operational factor of the target primary equipment under normal operating conditions and ambient parameter
According to；According to the historical data after screening, the operational factor of the target primary equipment and the historical variations model of ambient parameter are obtained
Enclose.
Preferably, the evaluation module, specifically for the operational factor according to the target primary equipment and the environment
Line electricity flow valuve corresponding to the historical variations scope acquisition of parameter；By the operational factor of the target primary equipment and the environment
The historical variations scope of parameter builds supporting vector machine model as input variable, the line electricity flow valuve as output vector；
The distribution is assessed according to the supporting vector machine model and the service data of the target primary equipment and the environmental data
Whether net overloads.
Preferably, the evaluation module, specifically for by the service data of the target primary equipment and the environment number
The line electricity flow valuve for the supporting vector machine model, obtaining the power distribution network according to being inputted as input vector；Will be described current
Line electricity flow valuve and the load current value at time point are contrasted, to judge to know whether power distribution network overloads.
Preferably, the operational factor of the target primary equipment includes：Distributed power source DG capacity, DG on-positions and negative
Lotus changes；The ambient parameter includes：Intensity of illumination.
As shown from the above technical solution, the power distribution network overload methods of risk assessment and device that the embodiment of the present invention proposes are based on
The operational factor of power distribution network target primary equipment and the historical variations scope structure assessment models of ambient parameter, with based on assessment mould
Type, the service data of the target primary equipment gathered in real time and the environmental data are assessed power distribution network, with prior art
Compare, having reduces modeling complexity, improves the advantages of assessing efficiency.
Brief description of the drawings
The features and advantages of the present invention can be more clearly understood by reference to accompanying drawing, accompanying drawing is schematically without that should manage
Solve to carry out any restrictions to the present invention, in the accompanying drawings：
Fig. 1 shows the schematic flow sheet for the power distribution network overload methods of risk assessment that one embodiment of the invention provides；
Fig. 2 shows the support vector network in the power distribution network overload methods of risk assessment that one embodiment of the invention provides
Figure；
Fig. 3 shows the schematic flow sheet for the power distribution network overload methods of risk assessment that another embodiment of the present invention provides；
Fig. 4 is to show the IEEE 33- in the power distribution network overload methods of risk assessment that further embodiment of this invention provides
The structural representation of bus systems；
Fig. 5 is to show photovoltaic system power in the power distribution network overload methods of risk assessment that further embodiment of this invention provides
Output characteristic curve；
Fig. 6 shows the structural representation for the power distribution network overload risk assessment device that one embodiment of the invention provides.
Embodiment
To make the purpose, technical scheme and advantage of the embodiment of the present invention clearer, below in conjunction with the embodiment of the present invention
In accompanying drawing, the technical scheme in the embodiment of the present invention is clearly and completely described, it is clear that described embodiment is
The part of the embodiment of the present invention, rather than whole embodiments.Based on the embodiment in the present invention, ordinary skill people
The every other embodiment that member is obtained on the premise of creative work is not made, belongs to the scope of protection of the invention.
Fig. 1 shows the schematic flow sheet for the power distribution network overload methods of risk assessment that one embodiment of the invention provides, referring to
Fig. 1, power distribution network overload methods of risk assessment, including：
110th, the operational factor of target primary equipment and the historical variations scope of ambient parameter in the power distribution network are obtained；
It should be noted that in power distribution network primary equipment be directly used in production and using electric energy, compare control loop
The high electrical equipment of voltage, including：Generator (motor), transformer, breaker, disconnecting switch, automatic switch, contactor,
Knife-like switch etc..
During distribution network operation, the operational factor of primary equipment may keep constant, or over time, it is outside
The change of instruction and change；The data or delta data of operational factor will be saved to memory；
In addition, the ambient parameter of power distribution network includes：Temperature, humidity etc., but for the power distribution network containing photo-voltaic power supply, with
It is intensity of illumination to assess the larger ambient parameter of the degree of correlation.
It is understandable to be, the historical data of operational factor and ambient parameter based on target primary equipment, you can obtain
Both historical variations data.
Wherein, target primary equipment is pre-selected, and may not for different power distribution network target primary equipments
Together.
120th, the service data of the target primary equipment and the environmental data are obtained；
It will be appreciated that processor can extract the service data of target primary equipment directly from memory, or it is logical
Cross the data acquisition devices such as sensor and gather the service data of target primary equipment and the environmental data of power distribution network in real time.
130th, according to the historical variations scope of the operational factor of the target primary equipment and the ambient parameter, Yi Jisuo
The service data of target primary equipment and the environmental data are stated, judges whether the power distribution network overloads.
It should be noted that historical variations scope of the processor based on two kinds of parameters builds model, and it is complete in model construction
Cheng Hou, using the real time data of two kinds of parameters as in input vector typing model, to be assessed, and export assessment result.
In addition, for the power distribution network containing photo-voltaic power supply, the operational factor of target primary equipment includes in the present embodiment：Distribution
Formula power supply DG capacity, DG on-positions and load variations；The ambient parameter includes：Intensity of illumination.
The historical variations scope of operational factor and ambient parameter of the embodiment of the present invention based on power distribution network target primary equipment
Assessment models are built, with the service data of target primary equipment gathered based on assessment models, in real time and the environmental data pair
Power distribution network is assessed, and compared with prior art, having reduces modeling complexity, improves the advantages of assessing efficiency.
The present embodiment is described in detail below：
Before step 110, processor is additionally operable to handle historical data to obtain the historical variations of two kinds of parameters
Scope, it is specific as follows：
Obtain the operational factor of the target primary equipment and the historical data of ambient parameter；
The historical data is screened, with obtain the operational factor of the target primary equipment under normal operating conditions and
The historical data of ambient parameter；
According to the historical data after screening, with reference to expertise, the operational factor and ring of the target primary equipment are obtained
The historical variations scope of border parameter.
It can be seen that in the present embodiment, processor obtains historical data from memory, and then historical data is sieved
Choosing, invalid data is screened out from historical data, and is based on valid data, and with reference to expertise, two kinds of parameters of setting are gone through
History excursion.
Step 130 specifically includes：
According to corresponding to obtaining the historical variations scope of the operational factor of the target primary equipment and the ambient parameter
Line electricity flow valuve；
Using the historical variations scope of the operational factor of the target primary equipment and the ambient parameter as input variable,
The line electricity flow valuve builds supporting vector machine model as output vector；
The service data of the target primary equipment and the environmental data are inputted to the support as input vector
Vector machine model, obtain the line electricity flow valuve of the power distribution network；
The line electricity flow valuve of the current point in time is contrasted with load current value, to judge whether know power distribution network
Overload.
The present embodiment is based on SVMs the Theory Construction supporting vector machine model, and thus, the present embodiment has following excellent
Point：1st, SVMs is specific to finite sample situation, its target be obtain optimal solution under existing information and not only
Only it is optimal value when sample number tends to infinity.2nd, it is final to be converted into as a quadratic form optimization problem, in theory,
What is obtained will be globe optimum, solve the unavoidable local extremum problem in neural net method.3rd, will actually ask
Topic is transformed into the feature space of higher-dimension by nonlinear transformation, and linear discriminant function is constructed in higher dimensional space to replace former space
In Nonlinear Discriminant Function, this special property can guarantee that machine has preferably extensive ability, while it is dexterously solved
Determine problem of dimension so that its algorithm complex is unrelated with sample dimension.
Fig. 2 shows the support vector network in the power distribution network overload methods of risk assessment that one embodiment of the invention provides
Figure, the principle of SVMs is described in detail referring to Fig. 2：
As seen from Figure 2, the regression function that SVMs is tried to achieve is similar to a neutral net in form, and it is exported
It is the linear combination of some middle layer nodes, and each middle layer node corresponds to the interior of input sample and supporting vector
Product, therefore be also referred to as support vector network.
It is assumed that collecting sample point set is generated according to certain probability distribution p (x, y)：X={ (xi,yi) | i=1 ... l }, wherein
xi∈Rd, yi∈ R, x and y existence function dependences：F=f | f:Rd→R}.First consider with linear regression function f (x)=
The problem of wx+b fitting data collection X.If all training datas can (also known as insensitive parameter, be referred to by user in precision ε
Linear function fit is free from errors used under calmly), i.e.,
Optimization aim is minimumLikewise, in view of situation existing for permission error of fitting, non-negative pine is introduced
Relaxation variable ξiWithThen above formula is rewritten into
Optimization object function is changed into minimizing
In formula, constant C ＞ 0 are specified by user, and it controls the punishment degree to the sample beyond error ε.In order to solve
This constrained optimization problem, its dual problem can be obtained by introducing Lagrange functions, in constraints
Under, to Lagrange factor alphasiWithMaximize object function.
Obtaining regression function is
HereOnly fraction is not 0, and sample point corresponding to them is exactly supporting vector, usually in function
Sample on the more violent position of change.And also simply it is related to inner product operation here, as long as with kernel function K (xi,xj) generation
Nonlinear function approximation is realized for the inner product operation can in formula (5) and formula (6).
Several conventional kernel functions are listed below.
(1) Polynomial kernel function
K(x,xi)=[(xxi)+1]q
The SVMs now obtained is a q rank multinomial grader.Wherein q is the parameter determined by user.
(2) Gauss kernel functions
Obtained SVMs is a kind of RBF (RBF) grader.Wherein, δ is that the core determined by user is wide
Degree.The basic distinction of it and conventional radial basis function method is that the center of each basic function here corresponds to a support
Vector, they and output weights are all automatically determined by algorithm.
(3) Sigmoid kernel functions
K(x,xi)=tanh (v (xxi)+c)
What then SVMs was realized is exactly two layers of perceptron neural network, simply the not only power of network herein
Value, and the hidden layer node number of network is also to be automatically determined by algorithm, and also algorithm is not present and perplexs very smart network side
Local minimum point's problem of method.
Fig. 3 shows the schematic flow sheet for the power distribution network overload methods of risk assessment that another embodiment of the present invention provides, under
Principle of the face based on SVMs corresponding to Fig. 2, referring to Fig. 3, to the original of the present invention by taking the power distribution network containing photo-voltaic power supply as an example
Reason is described in detail：
S1, consider that photo-voltaic power supply causes the major influence factors of circuit overload, i.e., intensity of illumination, DG capacity, load variations,
DG on-positions, as the input vector of network.
S2, the change by testing each influence factor of the determination such as regional historical statistical data, the topological structure of power network, electrical network parameter
Change scope, carry out scene composition further according to the actual capabilities situation in test area, total sample is formed, as input vector X.
S3, different scenes combination under, carry out Power flow simulation calculating, obtain corresponding to line electricity flow valuve as desired output
Y.And gross sample is originally divided into training sample and test sample two parts.
S4, provide one group of input sample xi, i=1,2 ..., l and its corresponding desired output yi.Wherein, l is training sample
This sum.
S5, the suitable kernel function K (x of selectioni, x) and=Φ (xi) Φ (x) and there is related parameter.
S6, regression function is solved under constraints, obtain distribution network line overload risk evaluation model.
S7, test sample is inputted to distribution network line overload risk evaluation model, if exceeding safe range, send risk
Early warning.
Below based on example, the present invention is described in detail：
The structural representation of IEEE 33-bus power distribution networks shown in reference picture 4, its course of work are as follows：DG installations to be selected
Node serial number is 1~32, totally 32 nodes, and DG maximum access quantities mesh is 2.Specific step is as follows：
1), photo-voltaic power supply is accessed simultaneously in node 15,31 or 17,32.
2), single DG accesses active 0.2~1.0MW of range of capacity.DG capacity is pressed into 0.2MW, is equidistantly divided into multigroup appearance
Value.
3), 0~1100W/m of intensity of illumination excursion2.Illumination intensity value is pressed into 100W/m2, equidistantly it is divided into multigroup light
According to intensity level.
4), change draws a hour peak load with reference to the hour load model of certain peak load system each seasonal work day and weekend
The percentage excursion for accounting for day peak load is 56%~100%.Load percentage is pressed 10%, is equidistantly divided into multigroup load hundred
Divide ratio.
5), this four influence factors are combined, obtain total sample.
6), under each combination, Power flow simulation calculating is carried out to system.Input sample and its corresponding desired output are obtained,
Solved with support vector machine method.
Wherein, photovoltaic power generation system output power size and intensity of illumination are closely related.
Referring to the photovoltaic system power out-put characteristic curve shown in Fig. 5 to photovoltaic power generation system output power size with
Relation between intensity of illumination illustrates：
In formula, P (It) it is photovoltaic system power output；IsFor standard intensity of illumination, I is takens=1000W/m2；KcTurn for photoelectricity
Intensity of illumination when efficiency reaches maximum is changed, takes Kc=150W/m2；PrFor designed photovoltaic generating system rated capacity.
In summary, the present invention constructs the Nonlinear Mapping between input feature vector amount and line current based on SVMs
Relation, and the circuit overload risk evaluation model based on SVMs is established, the line that the input of any new samples is trained
Pass by load risk evaluation model, you can obtain line electricity flow valuve.Reduce in-circuit emulation calculating process, so as to realize circuit overload
The quick early warning of risk.
For method embodiment, in order to be briefly described, therefore it is all expressed as to a series of combination of actions, but ability
Field technique personnel should know that embodiment of the present invention is not limited by described sequence of movement, because according to the present invention
Embodiment, some steps can use other orders or carry out simultaneously.Secondly, those skilled in the art should also know,
Embodiment described in this description belongs to preferred embodiment, involved action embodiment party not necessarily of the present invention
Necessary to formula.
Fig. 6 shows the structural representation for the power distribution network overload risk assessment device that one embodiment of the invention provides, referring to
Fig. 6, power distribution network overload risk assessment device, including：First acquisition module 61, the second acquisition module 62 and evaluation module 63,
Wherein；
First acquisition module 61, for obtaining the operational factor of target primary equipment and ambient parameter in the power distribution network
Historical variations scope；
Second acquisition module 62, for the service data for obtaining the target primary equipment and the environmental data；
Evaluation module 63, for the operational factor and the historical variations of the ambient parameter according to the target primary equipment
The service data and the environmental data of scope and the target primary equipment, judge whether the power distribution network overloads.
Wherein, the operational factor of the target primary equipment includes：Distributed power source DG capacity, DF on-positions and load
Change；The ambient parameter includes：Intensity of illumination.
The historical variations scope of operational factor and ambient parameter of the embodiment of the present invention based on power distribution network target primary equipment
Assessment models are built, with the service data of target primary equipment gathered based on assessment models, in real time and the environmental data pair
Power distribution network is assessed, and compared with prior art, having reduces modeling complexity, improves the advantages of assessing efficiency.
In the present embodiment, the device also includes：Processing module 64；
The processing module 63, operational factor and ambient parameter for the target primary equipment in the power distribution network is obtained
Historical variations scope before, obtain the operational factor of the target primary equipment and the historical data of ambient parameter；To described
Historical data is screened, to obtain the operational factor of target primary equipment and the history of ambient parameter under normal operating conditions
Data；According to the historical data after screening, with reference to expertise, the operational factor and environment for obtaining the target primary equipment are joined
Several historical variations scopes.
In the present embodiment, evaluation module 63, specifically for the operational factor according to the target primary equipment and the ring
Line electricity flow valuve corresponding to the historical variations scope acquisition of border parameter；By the operational factor of the target primary equipment and the ring
The historical variations scope of border parameter builds SVMs mould as input variable, the line electricity flow valuve as output vector
Type；The service data of the target primary equipment and the environmental data are inputted to the SVMs as input vector
Model, obtain the line electricity flow valuve of the power distribution network；The line electricity flow valuve of the current point in time and load current value are carried out
Contrast, to judge to know whether power distribution network overloads.
For device embodiments, because it is substantially similar to method embodiment, so description is fairly simple,
Related part illustrates referring to the part of method embodiment.
It should be noted that in all parts of the device of the present invention, according to the function that it to be realized to therein
Part has carried out logical partitioning, and still, the present invention is not only restricted to this, all parts can be repartitioned as needed or
Person combines.
The all parts embodiment of the present invention can be realized with hardware, or to be transported on one or more processor
Capable software module is realized, or is realized with combinations thereof.In the present apparatus, PC is by realizing internet to equipment or device
Remote control, the step of accurately control device or device each operate.The present invention is also implemented as being used to perform here
The some or all equipment or program of device of described method are (for example, computer program and computer program production
Product).Being achieved in that the program of the present invention can store on a computer-readable medium, and file or document caused by program has
Having can be statistical, produces data report and cpk reports etc., and batch testing can be carried out to power amplifier and is counted.On it should be noted that
Stating embodiment, the present invention will be described rather than limits the invention, and those skilled in the art are not departing from
Replacement embodiment can be designed in the case of the scope of attached claim.In the claims, should not will be between bracket
Any reference symbol be configured to limitations on claims.Word "comprising" does not exclude the presence of member not listed in the claims
Part or step.Word "a" or "an" before element does not exclude the presence of multiple such elements.The present invention can borrow
The hardware that helps to include some different elements and realized by means of properly programmed computer.If listing equipment for drying
Unit claim in, several in these devices can be embodied by same hardware branch.Word first,
Second and third use do not indicate that any order.These words can be construed to title.
Although being described in conjunction with the accompanying embodiments of the present invention, those skilled in the art can not depart from this hair
Various modifications and variations are made in the case of bright spirit and scope, such modifications and variations are each fallen within by appended claims
Within limited range.
Claims (10)
1. a kind of power distribution network overloads methods of risk assessment, it is characterised in that including：
Obtain the historical variations scope of the operational factor of target primary equipment and ambient parameter in the power distribution network；
Obtain the service data of the target primary equipment and the environmental data；
According to the operational factor of the target primary equipment and the historical variations scope and the target one of the ambient parameter
The service data of secondary device and the environmental data, judge whether the power distribution network overloads.
2. according to the method for claim 1, it is characterised in that the operation of target primary equipment in the power distribution network is obtained
Before the step of historical variations scope of parameter and ambient parameter, this method also includes：
Obtain the operational factor of the target primary equipment and the historical data of ambient parameter；
The historical data is screened, to obtain the operational factor and environment of the target primary equipment under normal operating conditions
The historical data of parameter；
According to the historical data after screening, the operational factor of the target primary equipment and the historical variations model of ambient parameter are obtained
Enclose.
3. according to the method for claim 1, it is characterised in that the operational factor according to the target primary equipment and
The service data and the environmental data of the historical variations scope of the ambient parameter and the target primary equipment, judge
The step of whether power distribution network overloads specifically includes：
The circuit according to corresponding to obtaining the historical variations scope of the operational factor of the target primary equipment and the ambient parameter
Current value；
It is described using the historical variations scope of the operational factor of the target primary equipment and the ambient parameter as input variable
Line electricity flow valuve builds supporting vector machine model as output vector；
According to being assessed the supporting vector machine model and the service data of the target primary equipment and the environmental data
Whether power distribution network overloads.
4. according to the method for claim 3, it is characterised in that described according to the supporting vector machine model and the target
The service data of primary equipment and the environmental data assess that the step of whether power distribution network overloads specifically includes：
The service data of the target primary equipment and the environmental data are inputted to the supporting vector as input vector
Machine model, obtain the line electricity flow valuve of the power distribution network；
The line electricity flow valuve of the current point in time is contrasted with load current value, with judge know power distribution network whether mistake
Carry.
5. according to the method described in claim any one of 1-4, it is characterised in that the operational factor bag of the target primary equipment
Include：Distributed power source DG capacity, DG on-positions and load variations；The ambient parameter includes：Intensity of illumination.
6. a kind of power distribution network overloads risk assessment device, it is characterised in that including：
First acquisition module, become for obtaining the history of the operational factor of target primary equipment and ambient parameter in the power distribution network
Change scope；
Second acquisition module, for the service data for obtaining the target primary equipment and the environmental data；
Evaluation module, for the operational factor according to the target primary equipment and the historical variations scope of the ambient parameter,
And service data and the environmental data of the target primary equipment, judge whether the power distribution network overloads.
7. device according to claim 6, it is characterised in that the device also includes：Processing module；
The processing module, for the operational factor of target primary equipment and the history of ambient parameter in the power distribution network is obtained
Before excursion, the operational factor of the target primary equipment and the historical data of ambient parameter are obtained；To the history number
According to being screened, to obtain the operational factor of target primary equipment and the historical data of ambient parameter under normal operating conditions；
According to the historical data after screening, the operational factor of the target primary equipment and the historical variations scope of ambient parameter are obtained.
8. device according to claim 6, it is characterised in that the evaluation module, specifically for according to the target one
Line electricity flow valuve corresponding to the acquisition of the historical variations scope of the operational factor of secondary device and the ambient parameter；By the target one
The historical variations scope of the operational factor of secondary device and the ambient parameter is as input variable, and the line electricity flow valuve is as defeated
Outgoing vector, build supporting vector machine model；According to the service data of the supporting vector machine model and the target primary equipment
Assess whether the power distribution network overloads with the environmental data.
9. device according to claim 8, it is characterised in that the evaluation module, specifically for by the target once
The service data of equipment and the environmental data are inputted to the supporting vector machine model as input vector, obtain the distribution
The line electricity flow valuve of net；The line electricity flow valuve of the current point in time is contrasted with load current value, matched somebody with somebody with judging to know
Whether power network overloads.
10. according to the device described in claim any one of 6-9, it is characterised in that the operational factor of the target primary equipment
Including：Distributed power source DG capacity, DG on-positions and load variations；The ambient parameter includes：Intensity of illumination.
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| CN201610659867.7A CN107730061A (en) | 2016-08-11 | 2016-08-11 | A kind of power distribution network overload methods of risk assessment and device |
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| CN201610659867.7A CN107730061A (en) | 2016-08-11 | 2016-08-11 | A kind of power distribution network overload methods of risk assessment and device |
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